Wearable Device For Diabetes; Emerging Trends Ai Integration And Multi Biomarker Approaches
Authors: Krishnendu Ghosh, Soumyajit Roy, Debayan Bag
Abstract: The integration of artificial intelligence (AI) with wearable sensing technology has emerged as a transformative approach in modern healthcare, particularly for the management of chronic diseases such as diabetes mellitus. AI-enabled wearable devices utilize advanced sensing mechanisms—including electrochemical, optical, microneedle, and sweat-based sensors—to enable continuous, real-time monitoring of physiological parameters. These systems provide significant advantages over traditional invasive techniques by offering non-invasive or minimally invasive solutions, improving patient comfort, compliance, and data accuracy. In diabetes management, AI-driven wearable technologies such as continuous glucose monitoring (CGM) systems, smart insulin pumps, and closed-loop artificial pancreas systems have revolutionized glycemic control. Machine learning and deep learning algorithms analyze large volumes of real-time data to predict glucose trends, detect anomalies, and provide personalized treatment recommendations. Additionally, emerging non-invasive technologies, including smart contact lenses and smartphone-based photoplethysmography, offer promising alternatives for early detection and monitoring. The application of AI in wearable devices also supports remote patient monitoring, enabling timely intervention and reducing the risk of complications. Despite significant advancements, challenges such as sensor accuracy, data interpretation, scalability, and integration into clinical practice remain. Overall, AI-based wearable sensing technology holds immense potential to enhance personalized healthcare, improve clinical outcomes, and reduce the global burden of diabetes.
DOI: http://doi.org/10.5281/zenodo.22763785
Leadership Agility And Organizational Resilience In The Digital Era
Authors: Dr. Rajidi Rammohan Reddy
Abstract: The digital age has brought about new challenges to companies in terms of volatility, uncertainty, complexity, and ambiguity (VUCA), where leadership agility and organizational resilience have emerged as strategic priorities. This research paper explores the connections between leadership agility and organizational resilience and the role digital transformation plays in them as catalysts and contextual variables. Through the review of existing literature and development of a mixed-methodological approach for research, it is revealed what dimensions of leadership agility – adaptive thinking, decentralized decision making, emotional intelligence, transparent communication, and learning – have a significant effect on the organizational resilience factors in a causal way. The results show the impact of digital leadership orientation on organizational resilience through mediation by strategic foresight, agility, and flexibility. Comparative analysis shows how companies with leadership agility have 34 percent more capacity for organizational resilience than companies with lower leadership agility.
DOI: http://doi.org/10.5281/zenodo.22768793
Fintech & Future of Finance Transforming the Financial Sector in the Digital Era
Authors: Harleen kour, Associate Professor Dr. Navneet Seth
Abstract: This research paper studies the role of FinTech in the financial sector and its impact on the future of finance. The study focuses on the development of FinTech, its major applications, benefits and challenges. Financial Technology, commonly referred to as FinTech, represents the integration of technology with financial services to improve the way financial activities are delivered, managed and accessed. The rapid development of digital technologies has transformed traditional financial systems from branch-based and manually operated services into increasingly digital, automated and data-driven financial ecosystems.This research examines the role of FinTech in finance with particular emphasis on digital payments, digital banking, online lending, artificial intelligence, blockchain, financial inclusion and data-driven financial services.The study identifies that FinTech has become an important component of modern finance, while challenges related to cybersecurity, data privacy, digital fraud, regulatory compliance, technological dependence and digital literacy remain significant. The research further discusses the future potential of artificial intelligence, blockchain, open banking, embedded finance and other emerging technologies.The study concludes that FinTech is not simply replacing traditional finance; rather, it is contributing to the development of a more digital, accessible, efficient and technology-driven financial ecosystem.
ActLeak: Action-Channel Memory Leaks in Tool-Using LLM Agents
Authors: Monish Kanungo, Seemant Kaushal
Abstract: Classical software memory leaks are allocations that outlive their intended lifetime. Tool-using large language model (LLM) agents exhibit an analogous failure that current “forgetting” mechanisms do not measure. A user or policy issues a forget request for a memory m*; the underlying store correctly drops the record, and content probes no longer reproduce the fact; yet the agent’s tool-selection behavior on the next turn continues to favor the same tool that m* had taught it to prefer. We call this an action-channel memory leak. Formally, let π(t∣q,M) denote an agent’s distribution over tools t in a catalog T given a query q and memory state M, let F(M,m*) denote a declared forget operator, and let M★ denote an oracle store from which m* and all of its derived traces have been removed. The leak is the total-variation distance between π under the forgotten store MF and π under M★. This paper contributes a three-layer leak taxonomy (store, shadow, action), an interventional audit built on masking, upweighting, and swapping retrieved memories, and Forget-Consistent Coupling (FCC) a reference monitor that withholds confirmation of a successful forget until the measured action-channel leak falls under a budget ε. We further specify ActLeak-Bench, an evaluation protocol addressing tool selection after forgetting, as distinct from tool-parameter deflection while a memory remains live. We situate this contribution relative to closely adjacent work on memory-driven tool-selection bias and behavioral unlearning verification, present the protocol, evaluation conditions, and pre-registered hypotheses, and discuss what is required to execute the protocol and what a small-scale pilot would look like.
Preparation and Characterization of Nanocomposite Cotton Fabrics (NCCFs) With in Situ Generated Silver Nanoparticles
Authors: Associate Professor N.Chandanaa, Assistant Professor Vijaya Ushasreeb
Abstract: Nanocomposite cotton fabrics (NCCFs) embedded with in situ generated silver nanoparticles (AgNPs) were successfully prepared using an eco‑friendly green synthesis approach. Lemon leaf extract was employed as a natural reducing and stabilizing agent to functionalize cotton fabrics and facilitate the formation of AgNPs within the cotton matrix. The incorporation of phytochemicals from the extract enabled effective reduction of silver ions and strong adhesion of nanoparticles onto the cellulose fibers. The synthesized NCCFs were characterized using X‑ray diffraction (XRD), scanning electron microscopy (SEM) coupled with energy dispersive X‑ray analysis (EDX), Fourier transform infrared spectroscopy (FTIR), and thermogravimetric analysis (TGA). XRD results confirmed the presence of crystalline silver nanoparticles along with cellulose structure, while SEM analysis revealed uniformly distributed spherical AgNPs with sizes increasing with higher precursor concentration. EDX analysis verified the elemental composition of silver within the fabric. FTIR studies indicated that the fundamental structure of cotton remained intact, with interactions between AgNPs and functional groups occurring primarily through electrostatic forces. TGA results showed a slight reduction in thermal stability of NCCFs compared to the untreated cotton matrix. The antibacterial activity of NCCFs was evaluated against Gram-positive (Staphylococcus aureus, Bacillus subtilis) and Gram-negative (Escherichia coli, Pseudomonas aeruginosa) bacteria using the agar diffusion method. The prepared nanocomposites exhibited significant antibacterial performance, with inhibition zones ranging from 11 to 25 mm, whereas untreated cotton fabrics showed no antibacterial activity.
Recovery of Crude Oil Losses to Effluent Water Disposal System in the Production Platform Using Sand Filter
Authors: Ephraim Anthony Jacob, Jackson.G. Akpa, Fidelis. O. Wopara
Abstract: The work entails research on recovery of crude oil losses to effluent water disposal system in the production platform using sand as the filtration media. In this dissertation a glass column filled with sand is used as a low-cost filtration media for the removal of crude oil from synthetic oil produced water (SOPW) and real produce water PW from oil and gas wells. The objective of this work is to examine the filtration efficiency of sand grains tilled in a glass column as sand filter in recovering crude oil from produced water. This work investigates the influence of process parameters such as physical characteristics of sand, column height, types of effluent (oilfield produced water and synthetic oil produced water), and oil concentration on oil removal efficiency. The sand fractions used for this work was selected using a set of Tyler sieves. After the different grain sizes were selected, the sand was washed several times to remove impurities. The sand samples were oven dried at 1000C for 24h. The gravel was washed and also dried at 1000C, for l2h. Oil was removed from laboratory-produced water in a batch process at standard atmospheric conditions. The effect of contact time, physical characteristics of sand, column height, pH as well as temperature on the removal efficiency of oil was investigated. The optimum parameters for oil removal were: pH 9.5, contact time 40.0 minutes and temperature 55.00C. The results showed that the oil concentration decreased significantly after the filtration process. The oil removal efficiency was influenced by the sand particle size and bed height, reaching 90% using a 30 cm high sand bed composed by mixed grain sizes.
A Refined Framework for Incorporating Facial Stimulation into Hybrid SSVEP-P300 Brain-Computer Interfaces
Authors: Research Scholar Ms. Monali Khune, Dr. Amol Y. Deshmukh
Abstract: Background: Brain–computer interface (BCI) systems frequently utilize P300 and steady-state visual evoked potential (SSVEP) paradigms. Because neither approach yields universal success across users, recent research has explored hybrid BCI designs that merge multiple techniques to expand user compatibility. Although hybrid P300/SSVEP systems are a relatively recent innovation with limited performance optimization studies to date, they represent a promising avenue for improving system accessibility. New method: In this research, we contrast a conventional hybrid P300/SSVEP BCI framework with an innovative approach. Specifically, shape alterations are utilized instead of color modulations to evoke the P300 wave, aiming to minimize any adverse impact on SSVEP signal strength. Result: The new hybrid paradigm presented in this paper yields much better performance than the traditional hybrid paradigm. Comparison with existing method: The novel hybrid paradigm yields an SSVEP classification improvement of close to 20% over the standard approach. Furthermore, all tested paradigms—excluding the conventional hybrid model—achieve a perfect 100% accuracy rate in P300 classification. Conclusions: The innovative hybrid P300/SSVEP brain-computer interface paradigm replaces traditional color-shifting stimuli with shape-altering visual elements, matching the classification accuracy of conventional SSVEP and P300 setups. Furthermore, the author explored how presenting multiple visual stimuli at once triggers overlapping brain responses and evaluated the resulting interference on signal detection.
The Dynamics of Bank Credit and Industry Credit in India: An Empirical Study of the MSME Sector
Authors: Rajpurohit Priya Bhavani Singh, Assistant Professor Ms Manisha Kalra
Abstract: Micro, Small and Medium Enterprises (MSMEs) are widely described as the backbone of the Indian economy, contributing close to 30 per cent of national income, a little over a third of manufacturing output, and roughly 45 per cent of the country’s merchandise exports, while sustaining employment for more than 110 million people. Despite this scale, the sector has historically operated under a severe financing constraint, with credible estimates of the gap between demand and formally supplied credit ranging from about ₹20 lakh crore to ₹30 lakh crore in recent years. This paper examines the evolving relationship between bank credit and industrial credit in India, with specific reference to the MSME sector, using secondary data drawn from the Reserve Bank of India (RBI), the Ministry of Micro, Small and Medium Enterprises, Parliamentary disclosures, and other government and industry publications. Employing a Pearson correlation framework on annual data for scheduled commercial bank (SCB) credit to MSMEs and to the industrial sector as a whole, the study finds a very strong, statistically significant positive association between the two series (r ≈ 0.98, p < 0.001), leading to rejection of the null hypothesis of no relationship. The paper further documents that credit to MSMEs grew faster than credit to any other major sector in 2024-25, even as overall bank credit growth dece Fund lerated, reflecting the cumulative effect of policy interventions such as the Credit Guarantee Trust for Micro and Small Enterprises (CGTMSE), the Emergency Credit Line Guarantee Scheme (ECLGS), and priority sector lending norms. At the same time, the study finds that formal credit penetration remains low — by some estimates only about one in seven MSMEs has access to institutional finance — and that collateral requirements, asset-quality caution among lenders, and disparities across enterprise size and gender continue to constrain the sector. The paper concludes with a set of policy-relevant observations on deepening formal credit flow to MSMEs.
Overcoming Interfacial Bottlenecks in Commercial Hard Carbon Anodes: SEI Dynamics, Pre-sodiation Strategies, and Safety Protocols for High-Energy Sodium-Ion Batteries
Authors: Daniel Karikari Frempong, Hannah Owusu Ansah, Gabriel Oduro Asirifi
Abstract: One of the most promising anode materials for high-energy sodium-ion batteries (SIBs) is hard carbon (HC); however, low initial Coulombic efficiency (ICE), unstable solid electrolyte interphase (SEI) formation, irreversible sodium loss, and sodium-plating risks at low potentials continue to limit its commercial implementation. The interfacial mechanisms controlling HC performance are critically examined in this paper, which also assesses methods for getting beyond these restrictions in realistic full-cell systems. The effects of surface imperfections, specific surface area, and open microporosity are highlighted in the link between HC microstructure, electrolyte breakdown, SEI composition, and sodium-ion transport. In addition, surface and artificial interphase engineering through atomic layer deposition, chemical vapour deposition, thermal-pyrolysis carbon coatings, and conductive protective layers is discussed for suppressing parasitic reactions and stabilising the interface; the mechanisms and operational triggers of sodium plating, such as high-rate charging, low temperature, and mass-transfer polarisation, are also examined; and finally, operando characterisation, scalable manufacturing, thermal management, and techno-economic considerations are identified as critical paths for converting HC-based SIBs into safe, long-lasting, and commercially viable high-energy cells
Nanotechnology At The Frontiers Of Societal Transformation: Opportunities, Impacts, And Future Directions
Authors: Hannah Owusu Ansah, Daniel Karikari Frempong, Gabriel Oduro Asirifi
Abstract: Nanotechnology has emerged as a transformative field with the potential to reshape multiple dimensions of society through the manipulation and application of materials at the nanoscale. The distinctive optical, electrical, mechanical, chemical, and biological properties of nanomaterials have enabled advances across healthcare, environmental sustainability, agriculture, energy, electronics, transportation, and consumer products. This review provides an overview of major nanomaterial classes, their synthesis, fabrication, and characterization, and examines their growing contributions to societal transformation. Particular attention is given to nanomedicine, targeted drug delivery, medical diagnostics, cancer therapy, nano-enabled agriculture, water purification, environmental remediation, nanoelectronics, energy generation and storage, and lightweight automotive materials. Beyond technological applications, the review discusses the broader socioeconomic and societal implications of nanotechnology, including employment opportunities, public acceptance, privacy, security, and environmental risks. Although nanotechnology offers considerable opportunities for improving human health, resource efficiency, environmental sustainability, energy performance, and economic development, its widespread implementation is accompanied by challenges related to nanomaterial toxicity, environmental fate and bioaccumulation, high production costs, technological scalability, uncertainty, public awareness, privacy, and security. Addressing these challenges requires interdisciplinary research, improved risk assessment and characterization methods, responsible innovation, effective regulation, transparent communication, and stronger consideration of the entire nanomaterial life cycle. The responsible development and commercialization of nanotechnology can contribute substantially to societal transformation while ensuring that technological progress is aligned with human health, environmental protection, security, and sustainable development
DOI: http://doi.org/10.5281/zenodo.22827579
Produced Water Treatment Using Bacteria And Microalgae Consortia
Authors: Okorinama E, Nmegbu CGJ
Abstract: Due to the many processes involved in oil and gas production, Produced Water is usually given off as a major by-product. This wastewater constitutes of contaminants like dissolved salts, hydrocarbons, organic matter and other toxins which pose significant risks to the environment if discharged without the necessary treatments. This study analyzed the treatment potential of hydrocarbon-degrading bacteria and microalgae species, utilizing Produced water gotten from the Obaji Oil field, Rivers State, Nigeria. A total of five microalgae species -Chlorella vulgaris, Scenedesmus obliquus, Arthrospira platensis, Monoraphidium sp. and Neochloris oleoabundans -were screened and acclimatized, while bacterial isolates were obtained from rhizosphere soil and identified as Pseudomonas aeruginosa, Bacillus subtilis, Acinetobacter calcoaceticus, Rhodococcus erythropolis and Sphingomonas paucimobilis. The treatments were carried out in batch reactor systems at 50%, 60%, 75% and 100% produced water concentrations for seven days. Notable changes in temperature, pH, Total Dissolved Solids (TDS), Biological Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Total Hydrocarbon Content (THC) and Dissolved Oxygen (DO) were observed. Microalgae treatment reduced the major pollutants and produced biomass, while bacterial treatment gave noticeable reductions in TDS, BOD and THC. At 100% produced water concentration, bacterial treatment achieved 12.7%, 16.8%, 23.7% and 14.6% reductions in TDS, COD, BOD and THC respectively. At the same concentration, microalgal treatment reduced TDS by 11.9%, COD by 24.7%, BOD by 31.4% and THC by 22.8%. Comparing the given results with Nigerian Upstream Petroleum Regulatory Commission (NUPRC) Onshore Discharge standards showed that the treated water still surpassed the limits for major parameters. The study indicated that biological treatment alone, of the produced water sample, was inadequate for direct discharge into the environment. The findings from this study demonstrated the viability of microbial treatment and aids further development of combined bacteria-microalgae systems for produced water remediation.
DOI: http://doi.org/10.5281/zenodo.22828486
Communication Skills
Authors: Amit Jalindar Sawant
Abstract: Learning English language’ is essential for career growth. There are different ways of learning English language. Using different ways of learning English language is really helpful to be comfortable and fluent. If we don’t practice enough to be habitual then definitely it does not work. We often learn different things knowingly-unknowingly because of being practiced or habitual after revising many more times. Similar thing works for learning English language. So many tactics are there to learn, generally we notice, how people are there, who they use differente words, even sentences but they don’t know, which word they should use in their mother tongue, and that is the practice and habitual process. Surrounding really well important to become a part of learning English language. It says, what people are there in our surrounding, so we become one of them, likewise, if there are English language speakers, we tend to speak English language without any hesitation. Actually, there are different expectation, demands, requirements of English language users, average, proficient, expert etc. And definitely no one is born perfectionist, we become educated through process, different ways, ‘Learning English language’ is a part of ways of learning.
Intelligent Phishing Website Detection System Using Hybrid Machine Learning and Deep Learning Approaches
Authors: Rajesh Chauhan, Akshay Bhardwaj, Shubham Rana
Abstract: Phishing websites are still one of the most common infection vectors for stealing credentials, financial information and other sensitive data from users of digital platforms, and there is a growing demand for accurate and deployable automated phishing-detection systems. However, models trained on a set of phishing URLs may experience a large performance drop when transferred to another feature scheme or a different collection window outside the scope of their training. In this work, we compare four classical machine-learning models, and five deep-learning and hybrid architectures for within-domain and cross-domain phishing-website detection. We evaluate our approach over five publicly available benchmark datasets, which contain more than 445k labeled instances in total, including UCI Phishing Websites Dataset, PhiUSIIL Phishing URL Dataset, Web-Page Phishing Detection Dataset, Mendeley Web-Page Phishing Dataset and Phishing Dataset based on Machine Learning. The models are evaluated in terms of classification accuracy, F1-score, transfer of cross domain performance, computational cost, data needs and interpretability. The Hybrid CNN-BiLSTM with attention model achieved the highest accuracy of 98.1% on PhiUSIIL and 85.6% on the transfer task from the PhiUSIIL to the Mendeley Web-Page dataset, outperforming the Linear SVM model by 13.1 percentage points. However, classical models still work well in resource-limited and interpretability-sensitive settings, and a simple BiLSTM with additive attention offers a balanced compromise. The results suggest that model selection for phishing detection should not only focus on predictive performance, but also consider operational deployment constraints.
Artificial Intelligence in Digital Marketing: Connecting Personalization, Consumer Trust, Engagement, and Purchase Intention
Authors: Sagar Shivaji Thakare
Abstract: Artificial intelligence (AI) is becoming an important part of digital marketing. Marketers now use AI for personalized recommendations, customer service chatbots, targeted advertising, content creation, customer analysis, and other activities across the digital customer journey. These applications can make marketing communication faster and more relevant, but they also raise questions about privacy, transparency, trust, authenticity, and the appropriate role of human interaction. This conceptual paper reviews and connects research on AI and marketing to explain how AI-enabled marketing activities may influence consumer engagement and purchase intention. The paper uses a structured literature-based approach and does not use primary survey data. The review suggests that AI capability alone does not determine consumer response. Instead, consumer perceptions of relevance, usefulness, convenience, transparency, privacy, and trust play an important role in shaping outcomes. Based on the literature, the paper develops a conceptual framework and six propositions linking AI-enabled digital marketing with consumer engagement, trust, and purchase intention. The paper also discusses practical implications for marketers and identifies directions for future empirical research.
Microbiological Profile and Phenotypic Antimicrobial Susceptibility Patterns in Chronic Suppurative Otitis Media Patients at A Tertiary Care Hospital
Authors: Pawandeep Kaur, Jasleen Kaur, Priya Bhat, Upasana Bhumbla
Abstract: Objective: To characterize the aerobic bacterial and fungal isolates recovered from patients with chronic suppurative otitis media (CSOM) and to evaluate their antimicrobial susceptibility profiles along with selected phenotypic resistance mechanisms. Methods: A prospective cross-sectional study was conducted over six months at Adesh Institute of Medical Sciences & Research, Bathinda. A total of 178 ear discharge specimens obtained from patients clinically suspected of having CSOM were processed for isolation and identification of aerobic bacteria and fungi. Organisms were identified by conventional microbiological methods and, where applicable, confirmed using the VITEK® 2 Compact system. Antimicrobial susceptibility was assessed by the Kirby–Bauer disk diffusion method in accordance with CLSI M100 (2024). Phenotypic testing included detection of extended-spectrum β-lactamase (ESBL) production among selected Enterobacterales, metallo-β-lactamase (MBL) production among Gram-negative isolates, and methicillin resistance in Staphylococcus aureus. Colistin susceptibility among multidrug-resistant Gram-negative isolates was determined by broth microdilution following EUCAST recommendations. Results: Of the 178 specimens examined, 132 (74.16%) yielded microbial growth, whereas 46 (25.84%) showed no growth. All culture-positive specimens demonstrated monomicrobial growth. Culture-positive cases were predominantly male (88/132, 66.67%), and the 41–50-year age group accounted for the largest proportion (50/132, 37.88%). Tubotympanic disease constituted 66.67% of cases, while atticoantral disease accounted for 33.33%. Pseudomonas aeruginosa was the predominant bacterial isolate (60/132, 45.45%), followed by Staphylococcus aureus (28/132, 21.21%). Among fungal isolates, Candida albicans was more frequently recovered (5/132, 3.79%) than Aspergillus niger (3/132, 2.27%). S. aureus exhibited complete susceptibility to vancomycin, linezolid, and teicoplanin. Conclusion: The study demonstrated a predominance of Gram-negative organisms, particularly P. aeruginosa, in CSOM, along with notable antimicrobial resistance among several bacterial isolates and the presence of phenotypic resistance mechanisms. These findings emphasize the value of periodic local antimicrobial resistance surveillance and microbiologically guided treatment for CSOM. Such surveillance may assist in optimizing antimicrobial selection and strengthening antimicrobial stewardship.
DOI: http://doi.org/10.5281/zenodo.22845104
Analysis Engineering Material Feasibility of RAP & RAS Modified Hot Mix Asphalt for Bahadurganj–Araria Section of NH-327E
Authors: Dhananjay Kumar Singh, Assistant Professor Dr. Amit Kumar Ahirwar
Abstract: The use of reclaimed asphalt pavement (RAP) and recycled asphalt shingles (RAS) can support material circularity in highway construction by returning aged aggregate and binder to productive use. This study evaluates the resource-substitution potential and engineering feasibility of a dense-graded hot mix asphalt containing a combined 31.53% RAP and tear-off RAS. The mixture incorporated 19.89% virgin 13.2 mm aggregate, 43.66% virgin 6 mm aggregate, 1.94% hydrated lime, 2.48% VG-30 virgin bitumen, and 0.50% rejuvenator. A material-flow assessment was combined with gradation compliance and Marshall design results. Recycled binder supplied 3.33 percentage points of the 5.80% total binder content, corresponding to 57.4% of the binder system; RAS alone supplied 1.88 percentage points. All sieve results remained within the adopted Ministry of Road Transport and Highways limits. At the selected 5.8% binder content, the mixture achieved 1232.70 kg Marshall stability, 3.27 mm flow, bulk specific gravity of 2.443, 3.99% air voids, 14.19% VMA, and 71.89% VFB. These results demonstrate that substantial recycled-material and recycled-binder contributions can be achieved without losing basic Marshall and volumetric compliance. The design offers a technically credible pathway for reducing dependence on virgin pavement materials. However, project-specific cost, energy, emissions, leaching, durability, and field-performance data are required before claiming quantified environmental or economic benefits.
Marshall Mix Design and Performance Evaluation of RAP RAS Modified Hot Mix Asphalt with Rejuvenator
Authors: Dhananjay Kumar Singh, Assistant Professor Dr. Amit Kumar Ahirwar
Abstract: The stiff aged binder contained in reclaimed asphalt pavement (RAP) and recycled asphalt shingles (RAS) can reduce the demand for virgin binder, but it may also limit workability and cracking tolerance. This study develops a dense-graded hot mix asphalt containing a combined 31.53% RAP and tear-off RAS, 0.50% rejuvenator, 1.94% hydrated lime, and VG-30 virgin bitumen. The aggregate blend was checked against the applicable Ministry of Road Transport and Highways grading envelope, and Marshall specimens were evaluated at total binder contents of 5.4, 5.6, 5.8, 6.0, and 6.2%. The measured responses included bulk and maximum specific gravity, air voids, voids in mineral aggregate, voids filled with bitumen, stability, flow, and Marshall quotient. All combined gradation values remained within the specified limits. Marshall stability increased from 902.12 kg at 5.4% binder to a maximum of 1232.70 kg at 5.8%, then decreased to 905.33 kg at 6.2%. At 5.8% binder, the mixture achieved a bulk specific gravity of 2.443, maximum specific gravity of 2.545, 3.99% air voids, 14.19% VMA, 71.89% VFB, 3.27 mm flow, and a Marshall quotient of 3.77 kN/mm-equivalent as reported from the adopted calculation basis. The convergence of maximum stability, maximum bulk density, and approximately 4% air voids established 5.8% as the optimum binder content. The results show that a properly graded RAP-RAS blend with a modest rejuvenator dosage can satisfy Marshall volumetric and strength requirements. Direct wheel-tracking, fatigue, moisture-susceptibility, and field validation are recommended before project-scale implementation.
Herbal Contraceptives: Phytochemistry, Mechanisms of Action, Therapeutic Potential, Safety, and Future Perspectives
Authors: Sambhaji Borgude, Vinayak Kanchan, Pranav Ghumare, Sohel Shaikh, Tanmay Gangawane, Bhagyshree Gaikwad
Abstract: The rising worldwide need for safe, cost-effective, and reversible contraception has sparked new interest in using medicinal plants as alternative fertility-controlling solutions. Conventional medical systems have utilized various plants for contraception by impacting ovulation, spermatogenesis, fertilization, implantation, and uterine functions. Recent pharmacological studies have discovered various phytochemicals such as alkaloids, flavonoids, saponins, terpenoids, coumarins, lignans, and phytoestrogens that influence reproductive endocrine pathways and gamete activity. This review thoroughly outlines existing evidence related to phytochemistry, action mechanisms, pharmacological effects, and safety profiles of herbal contraceptives. Key medicinal plants like Azadirachta indica, Carica papaya, Ruta graveolens, Juglans regia, Ricinus communis, Daucus carota, and other commonly utilized species are thoroughly examined with a focus on their antifertility properties, active compounds, and empirical support. Despite considerable in vitro and animal research supplementing contraceptive effectiveness via anti-ovulatory, spermicidal, anti-implantation, endocrine-modulating, and uterotonic processes, strong clinical evidence continues to be scarce. Moreover, issues related to toxicity, reproductive safety, teratogenic effects, dose consistency, and long-term reversibility still impede clinical translation. Future studies must focus on standardized phytochemical profiling, mechanistic molecular investigations, GLP-compliant toxicity assessments, and rigorously structured randomized clinical trials. Herbal contraceptives thus serve as encouraging options for creating new contraceptive methods; however, significant scientific confirmation is necessary before their regular clinical use.
AI-Based Coordinated Traffic Load Scheduling for Rail-Road Freight Corridors
Authors: Krishna Kumar Singh, Professor Vinay W. Deulkar, Professor Piyush Mahajan
Abstract: The rapid growth of freight transportation has increased the need for efficient traffic load scheduling in integrated rail-road freight corridors. Conventional scheduling methods often suffer from poor coordination, traffic congestion, delivery delays, and inefficient resource utilization. This research proposes an Artificial Intelligence (AI)-based Coordinated Traffic Load Scheduling Framework using an Artificial Neural Network (ANN) to predict traffic conditions, freight demand, travel time, and scheduling performance. Historical and simulated transportation data are used to train and validate the ANN model, and the predicted outputs are integrated into a traffic scheduling simulation for intelligent freight allocation and route optimization. The performance of the proposed framework is evaluated using key indicators such as transportation cost, travel time, delivery delay, vehicle utilization, rail utilization, fuel consumption, carbon emissions, and scheduling efficiency. The results demonstrate that the ANN-based approach improves prediction accuracy, optimizes multimodal freight scheduling, reduces operational costs and congestion, and enhances the overall efficiency of rail-road freight transportation. The proposed framework provides an effective and intelligent solution for sustainable freight corridor management and future smart logistics systems.
Development of Ultra-High-Performance Concrete (UHPC) for High-Traffic Highway Pavements
Authors: Girish Prasad, Professor Vinay Deulkar, Assistant Professor Piyush Mahajan
Abstract: Ultra-High-Performance Concrete (UHPC) is increasingly recognized as an advanced construction material for high-traffic highway pavements because of its outstanding mechanical performance, durability, and extended service life. This review provides a comprehensive assessment of UHPC, covering its development, constituent materials, mix-design principles, mechanical characteristics, durability performance, and recent developments in pavement applications. Particular attention is given to its superior compressive and flexural strength, very low permeability, high abrasion resistance, and enhanced resistance to freeze–thaw cycles and chemical deterioration. These properties make UHPC particularly suitable for pavements subjected to heavy traffic and demanding environmental conditions. Despite these benefits, its wider adoption is constrained by factors such as relatively high initial cost, limited availability of specialized materials, and challenges associated with large-scale construction and implementation. Overall, the reviewed research indicates that UHPC offers considerable potential for improving pavement performance, extending service life, reducing maintenance requirements, and supporting the development of more durable and sustainable highway infrastructure.
Review on AI-Based Durability Prediction of RCC Buildings with Floating Columns Using STAAD.Pro Analysis
Authors: Vishal Sahu, Sandeep Choudhary
Abstract: Reinforced Cement Concrete (RCC) buildings with floating columns are increasingly adopted in modern urban construction to achieve architectural flexibility, open parking spaces, and large column-free areas. However, the discontinuity introduced by floating columns significantly alters the load transfer mechanism and may adversely affect the structural durability and long-term performance of buildings, particularly under seismic and environmental loading conditions. Recent advances in Artificial Intelligence (AI) provide an opportunity to predict the durability and service life of such complex structural systems more accurately than conventional empirical methods. This review paper presents a comprehensive analysis of AI-based durability prediction techniques for RCC buildings incorporating floating columns, with structural behavior evaluated using STAAD.Pro. The review summarizes the influence of floating column configurations on stress distribution, deflection, drift, and load-carrying capacity, while examining AI approaches such as Artificial Neural Networks (ANN), Machine Learning (ML), Deep Learning (DL), Support Vector Machines (SVM), Random Forest (RF), and ensemble models for predicting durability indicators including crack development, corrosion potential, service life, and structural degradation. The paper also discusses the integration of finite element analysis results obtained from STAAD.Pro with AI algorithms to improve prediction accuracy and facilitate intelligent structural health assessment. Furthermore, existing research gaps, challenges, and future opportunities in AI-assisted durability evaluation are highlighted. The review concludes that the combination of STAAD.Pro-based structural analysis and AI-driven predictive models offers a reliable and efficient framework for enhancing the safety, durability, maintenance planning, and sustainable design of RCC buildings with floating columns. This integrated approach supports data-driven decision-making and contributes to the development of resilient and smart infrastructure.
The Role of Training and Development in Employee Retention
Authors: Mansi Mishra
Abstract: Employee retention has become an important concern for organisations because employee turnover can increase recruitment costs, disrupt organisational continuity, and lead to the loss of knowledge and experience. Training and development can influence employees’ perceptions of organisational support by providing opportunities to acquire skills, improve performance, and develop their careers. This research paper examines the role of training and development in employee retention, with particular attention to the mediating role of employee satisfaction. The proposed study will examine whether access to relevant training, career development opportunities, learning support, and development programmes is associated with employee satisfaction and employees’ intention to remain with their organisations. A quantitative research design is proposed, using a structured questionnaire administered to employees from different organisations. The data may be analysed using descriptive statistics, reliability analysis, correlation, regression, and mediation analysis. The study is expected to contribute to human resource management literature by clarifying the relationship between development opportunities, employee satisfaction, and retention. It may also provide practical guidance to organisations seeking to design training and development practices that support employee retention.
A Behavioural And Quantitative Analysis Of FinTech Adoption And Financial Mathematics Among Young Investors
Authors: Mataprasad Chaurasia
Abstract: The past five years have produced one of the fastest demographic shifts in the history of retail investing: tens of millions of people under 30 have opened their first brokerage or demat account, most of them through a mobile app rather than a bank branch or broker’s office. This paper examines that shift at the intersection of three forces — investor behaviour, financial technology, and financial mathematics — drawing on primary market data (NSE, RBI, SEBI), international survey research (the FINRA Investor Education Foundation, CFA Institute, TIAA Institute-GFLEC, Gemini, YouGov, IPX1031), and peer-reviewed academic literature. Using secondary-data analysis, the study documents how young investors are entering markets earlier than any prior generation, disproportionately through low-cost, app-based fintech platforms, while simultaneously scoring lowest of any generation on standardised tests of financial literacy. A worked compound-interest illustration then quantifies what this “literacy-behaviour gap” is worth in practice. The paper concludes that fintech has solved the problem of market access far faster than anyone — fintech included — has solved the problem of financial education, and it proposes concrete, evidence-based measures for investors, platforms, and policymakers to close that gap.
DOI: http://doi.org/10.5281/zenodo.22895860
A Behavioural And Quantitative Analysis Of FinTech Adoption And Financial Mathematics Among Young Investors
Authors: Mataprasad Chaurasia
Abstract: The past five years have produced one of the fastest demographic shifts in the history of retail investing: tens of millions of people under 30 have opened their first brokerage or demat account, most of them through a mobile app rather than a bank branch or broker’s office. This paper examines that shift at the intersection of three forces — investor behaviour, financial technology, and financial mathematics — drawing on primary market data (NSE, RBI, SEBI), international survey research (the FINRA Investor Education Foundation, CFA Institute, TIAA Institute-GFLEC, Gemini, YouGov, IPX1031), and peer-reviewed academic literature. Using secondary-data analysis, the study documents how young investors are entering markets earlier than any prior generation, disproportionately through low-cost, app-based fintech platforms, while simultaneously scoring lowest of any generation on standardised tests of financial literacy. A worked compound-interest illustration then quantifies what this “literacy-behaviour gap” is worth in practice. The paper concludes that fintech has solved the problem of market access far faster than anyone — fintech included — has solved the problem of financial education, and it proposes concrete, evidence-based measures for investors, platforms, and policymakers to close that gap.
DOI: http://doi.org/10.5281/zenodo.22895860
AI Adoption Among Gen Z Learners: A Critical Examination Of Cognitive Engagement And Critical Thinking
Authors: Aarya Moossaddee
Abstract: The swift adoption of artificial intelligence (AI) tools in higher education has greatly impacted the academic activities and study approaches of a large number of students, as well as their thinking styles. This research is concerned with the investigation of the parties’ AI employment extent in multiple academic work, as well as exploring how patterns of AI use relate to level of critical thinking. Based on primary data, the study makes use of a structured questionnaire to assess the frequency and purpose of AI-aided learning, as well as self-reported measures of critical thinking, among undergraduate and graduate students. This descriptive–correlational study can reveal patterns of use and it can explore potential relationships. Descriptive statistics, reliability tests as well as correlation analyses will be used to analyse the data to investigate the relationship between the degree of AI implementation in academic work and students’ cognitive engagement. The results will likely add substance to continuing conversations surrounding the potential educational fallout from AI and also offer actionable recommendations for educators attempting to right the scale between tech adoption and essential skills development.
DOI: http://doi.org/10.5281/zenodo.22912929
Lexical-Semantic Features of French Diplomatic Texts and Methods of Their Translation
Authors: Khasanova Shakhnoza
Abstract: This article investigates the lexical-semantic organization of French diplomatic discourse and the principal methods used to translate it into English. Diplomatic documents combine terminological precision with political caution; consequently, their meaning is produced not only by dictionary definitions but also by institutional convention, modality, collocation, politeness, and controlled ambiguity. The study applies descriptive, contextual, contrastive, and functional analysis to representative units drawn from recurrent diplomatic genres, including notes verbales, communiqués, declarations, agreements, and official statements. The analysis identifies eight central features: standardized terminology, formulaic expressions, polysemy, nominalization, modality, euphemistic mitigation, constructive ambiguity, and institutional collocation. It demonstrates that established equivalence is the safest method for conventional legal-diplomatic terms, whereas transposition, modulation, functional equivalence, explicitation, borrowing, calque, and cautious adaptation are required when structural or pragmatic asymmetry occurs. Particular attention is paid to the preservation of legal force and degrees of commitment. The article proposes a controlled translation workflow that integrates genre identification, terminology verification, pragmatic analysis, drafting, and bilingual quality assurance. It concludes that high-quality diplomatic translation is a form of disciplined intercultural mediation in which semantic fidelity, institutional consistency, and pragmatic equivalence must be evaluated together.
DOI: http://doi.org/10.5281/zenodo.22913354
AI-Powered Observability in Cloud-Native DevOps: LSTM-Based Anomaly Detection for Kubernetes Microservices
Authors: Parav Sharma, Rajesh Chauhan, Akshay Bhardwaj
Abstract: In today’s complex cloud-native microservice architectures, the traditional rule-based monitoring approaches are insufficient for system reliability. The solution to this is the integration of Artificial Intelligence (AI) into DevOps, often known as AIOps. This paper introduces an AI-powered observability framework that combines the LSTM (Long Short-Term Memory) autoencoder with the Prometheus-Grafana observability stack for anomaly detection in Kubernetes-based microservice environments. The system collects real-time CPU utilization metrics from different microservices, trains an LSTM model on metric records, and detects anomalies using reconstruction error thresholding. Under controlled CPU stress injection, the characteristic scale of reconstruction error, measured as the mean plus two standard deviations of each condition’s own reconstruction errors, is approximately 27 times higher than under baseline conditions, indicating that the model’s reconstruction error responds strongly to abnormal workload patterns. Because a single training run may not be representative, the model is retrained ten times on a 6,407-timestep dataset; the reconstruction-error threshold is 0.000676 +/- 0.000038 and the anomaly rate 4.38% +/- 0.26% (mean +/- SD), a coefficient of variation of 5.6%, indicating that the result is reproducible rather than an artifact of one random initialization. The trained model is then deployed as a continuous detector that scores live metrics every 60 seconds; in a controlled test it flagged all 17 readings taken while an injected workload was active and returned to normal after its removal. The LSTM-based model detects anomalies without relying on fixed alert boundaries, offering a more adaptive alternative to static threshold approaches and enabling proactive detection of system abnormalities in cloud-native DevOps environments.
Climate Resilient Horticulture Infrastructure and Market Connectivity in Kangra District
Authors: Assistant Professor Hakam Chand
Abstract: The paper analyses infrastructure and market connectivity as joint determinants of climate-resilient horticulture in Kangra district, Himachal Pradesh. It uses a secondary-data case-study approach and the latest consistent state horticulture series available in the supplied publications, covering 2011-12 to 2024-25. Although horticulture output expanded during the period, climate variability, fragmented production, seasonal irrigation, post-harvest losses and costly transport threaten the stability of smallholder returns. Kangra’s traditional kuhl systems, road network, urban centres and crop diversity provide a strong base, but infrastructure must function as an integrated service chain. The study develops an Infrastructure Readiness Matrix covering water, planting material, extension, protection, aggregation, grading, cold chain, processing, roads, digital information and finance. It also proposes indicators for reliability, utilisation and inclusion. The analysis suggests that water harvesting and micro-irrigation should be combined with crop planning; pack houses should be located only where verified throughput exists; and producer aggregation should precede capital-intensive cold-chain investment. Climate resilience requires diversified crops and seasons, quality planting material, local weather and pest advisories, and multiple market channels. A phased investment and monitoring framework is recommended for Kangra.
Hybrid InceptionV3-LSTM Framework for Real-Time Deepfake Video Detection
Authors: Professor Vikram Singh, Research Scholar Naresh Kumar
Abstract: The rapid generation of deepfake videos poses significant challenges to real-time detection systems, which often encounter a wide range of quality variations and novel manipulation techniques. In order to deal with the challenges faced we propose a hybrid InceptionV3-LSTM network that encapsulates spatial feature extraction and action sequence over time. This architecture employs InceptionV3 pretrained on ImageNet for modeling frame-level artifacts, then stacks a bidirectional LSTM to jointly learn the temporal problem from multiple sequences. The model displays robustness under high compression and when tested on low compression, it shows a decline of just 2.5% in accuracy which implies that the model is sensitive. According to temporal analysis, LSTM proves capable of detecting manipulations as illustrated due to reconstruction loss variance being higher by 3.2× of low-fidelity deepfakes. The deepfake inference data under tool-based evaluation shows this detection. The InceptionV3-LSTM’s full-fledged version achieves 150 ms latency and 96% accuracy while real-time performance granted 20 ms/frame latency using MobileNet-LSTM with no accuracy loss (96%). The solution presented in this study can be deployed, balancing detection accuracy and computational efficiency. However, limitations still exist for novel manipulation techniques and multi-modal integration could be an avenue for future work. The objective of this experimental study is to achieve the enhanced detection accuracy using separate training and testing datasets of deepfake videos which can help get a better security performance and robustness. By integrating these advances, the application allows users to check the authenticity of people and deep fake videos. A framework for real-time detection of visual deepfake videos analyzes pixel-level artifacts including blending boundaries, double edges, eyes, ears, nose, head pose inconsistencies and other possible parameters sub-pixel noise patterns unique to manipulation videos.
Controlling The Pitch Angle Of A Variable Wind Speed On A Wind Turbine Using Fuzzy-Logic
Authors: Hosea, J. O., Ogbonna, B. O.
Abstract: One of the problems associated with wind energy is fluctuations in wind speed, which cause unstable and inefficient operation of variable-speed wind turbines, especially above the rated operating range where blade pitch control is necessary to adjust aerodynamic power and maintain generator speed within the rated range. This study investigates the effect of a Fuzzy Logic Controller (FLC) on pitch-angle regulation compared with the dynamic performance of a proportional-integral (PI) controller. The same wind turbine model, generator reference speed, wind-speed profile, and operating conditions were used for both controllers. The proposed Mamdani-type FLC is based on generator-speed error and its rate of change, using scaling, saturation, fuzzification, rule-based inference, and defuzzification to derive an appropriate pitch-angle command. The FLC adjusts the pitch command according to changing turbine operating conditions, enabling control of the aerodynamic power coefficient, aerodynamic torque, generator speed, and output power under wind-speed disturbances. Controller performance was evaluated using rise time, settling time, percentage overshoot, steady-state error, RMSE, IAE, ISE, ITAE, and ITSE. The FLC increased rise time from 1.7677 s to 3.2920 s, while reducing settling time from 17.2793 s to 5.5018 s and overshoot from 7.662% to 0.880%. Furthermore, the FLC reduced steady-state error, IAE, ITAE, and ITSE by 19.20%, 20.76%, 71.56%, and 61.97%, respectively. The PI controller produced lower RMSE and ISE. Overall, the results showed that FLC-based pitch-angle control provides better damping, disturbance rejection, steady-state accuracy, and long-term tracking performance under variable wind operation, although its initial response is slower than conventional PI control.
Green Computing: Approaches, Techniques And Its Implementation
Authors: Vishal Sankhyan, Dr.Rajesh Chauhan, Dr. Akshay Bhardwaj
Abstract: Green computing refers to the environmentally responsible design, manufacture, operation, and disposal of computing systems and information technology (IT) infrastructure. The rapid growth of data centers, cloud computing, artificial intelligence, Internet of Things (IoT) devices, and other digital technologies has increased energy consumption and electronic waste. Green computing seeks to reduce the environmental impact of IT while maintaining performance, reliability, security, and economic efficiency. This research paper examines major approaches, techniques, and implementation strategies for green computing. The study discusses energy-efficient hardware, virtualization, server consolidation, cloud computing, power management, green software development, efficient data centers, sustainable networking, renewable energy, and electronic-waste management. It also presents an implementation framework for measuring and reducing the energy consumption of computing infrastructure. The paper identifies important challenges, including initial investment, compatibility, performance requirements, lack of awareness, measurement difficulties, and e-waste management. Finally, it discusses future directions such as artificial intelligence-based energy optimization, edge computing, sustainable data centers, and carbon-aware computing. The study concludes that combining hardware, software, infrastructure, and organizational strategies can significantly improve the sustainability of modern information technology.
Adaptive Trust-Based Continuous Authentication Framework Using Risk-Aware Zero Trust Architecture
Authors: Atharv Sharma
Abstract: In this paper, we propose Adaptive Trust-Based Continuous Authentication Framework (ATCAF), a lightweight architecture to extend Zero Trust principles with continuous risk aware authentication. The proposed framework does not make a single authentication decision at login but continuously calculates a dynamic trust score by combining multiple contextual security indicators such as device trust, behavioral consistency, location confidence, network reputation and historical user reputation. Access decisions are made in real-time based on the continuously evolving trust score, enabling adaptive responses such as seamless access, step-up multi-factor authentication, privilege reduction or session termination. The paper describes the system architecture, mathematical trust model, continuous evaluation workflow, implementation methodology, public evaluation datasets, security analysis and experimental design to evaluate authentication accuracy, False Acceptance Rate (FAR), False Rejection Rate (FRR), Equal Error Rate (EER), trust score stability and response latency . The framework we propose aims at improving identity security, while maintaining usability and reducing unnecessary authentication interruptions.
DOI: https://zenodo.org/records/22976298
A Safety-Bounded Multi-Agent Medical Assistant: Retrieval-Augmented Generation, Medical Vision, And Human Validation
Authors: Dr. K. Himabindu, Faruk Ahmed, Pankaj Kumar Bara, Krishna Kumar Yadav, Soham Parmar
Abstract: Healthcare chatbots are gradually moving beyond simple question-and-answer systems by bringing together large language models, information retrieval, multimodal analysis, and human oversight. This paper explores the open-source Multi-Agent Medical Assistant as a safety-focused tool for providing medical information. The system includes separate components for input screening, conversation, retrieval-augmented generation (RAG), web-based evidence retrieval, medical image processing, response generation, and human validation. Rather than attempting to provide autonomous diagnoses, the system emphasizes evidence-based responses, transparent communication of uncertainty, and human review.
Silent Data Corruption And The Case For Algorithmic Fault Tolerance
Authors: Faaiq Mushtaq
Abstract: Silent data corruptions or SDCs are computational errors that produce a wrong but plausible result with no crash, no exception and no flag of any kind. For decades the working assumption in systems design was that a tested processor either computes correctly or fails loudly. Recent measurement studies from Meta, Google and Alibaba have overturned that assumption: mercurial cores that occasionally miscompute at rates far above what fault injection studies predicted are now a documented, recurring phenomenon in hyperscale fleets. This paper argues that the drivers behind SDCs, transistor density, lower operating voltages and sheer deployment scale, are structural rather than transient and that the problem will intensify over the next decade as workloads move toward quantized, low precision AI training and toward radiation exposed space computing. Because full hardware correction is prohibitively expensive and the space of possible fault sites is too large to fully screen, this paper surveys algorithmic fault tolerance, particularly checksum based approaches descended from classical ABFT, as a lightweight, mathematically grounded complement to hardware mitigation. It closes by proposing a concrete thesis direction: characterizing when a modest, bounded performance cost is worth paying to catch a corrupted core before its errors propagate through a distributed system or a multi week training run.
An Analytical Study Of The Indian Securities Market: Regulatory Evolution, Retail Participation, And Emerging Technological Trends
Authors: Pratik Agarwal
Abstract: The securities market forms the backbone of capital formation and resource allocation in a modern economy, channelling household savings into productive investment while creating avenues for wealth generation. In India, this market has undergone considerable structural and regulatory transformation over the past three decades under the oversight of the Securities and Exchange Board of India (SEBI). This paper presents an analytical study of the Indian securities market, examining its institutional structure, its evolving regulatory architecture — including the proposed Securities Markets Code, 2025 — and the unprecedented surge in retail investor participation, with demat accounts rising from roughly 3.6 crore in 2019 to over 19 crore by 2025. The study further examines the regulatory response to algorithmic and technology-driven trading, with particular reference to SEBI’s 2025 framework governing retail participation in algorithmic trading. Drawing on regulatory publications and secondary market data, the paper identifies key challenges relating to investor protection, market integrity and technological risk, and concludes with observations on the likely future trajectory of India’s capital markets.
DOI: http://doi.org/10.5281/zenodo.23008007
Radiometric Evaluation Of Oml 30 Oil Fields And Host Community Soils In Delta State Using Resrad-Biota Codes
Authors: Stephen Ovie Ewagba, Orute Destiny, Ezekiel O. Agbalagba, Alex Orduvwe Teghware, Erienu Obruche Kennedy
Abstract: This study evaluated the radiometric characteristics of soils from selected OML 30 oil fields and host-community environments in Delta State using natural radionuclide measurements and the RESRAD-BIOTA code. The activity concentrations of ^40K, ^226Ra and ^232Th were determined for the investigated soil samples, while radium equivalent activity (Raeq) was used to provide an integrated measure of the concentrations of the radionuclides. The results showed considerable spatial variation across the studied oil fields. ^40K concentrations ranged from 219.25 to 553.65 Bq kg⁻¹, ^226Ra from 26.20 to 55.30 Bq kg⁻¹, and ^232Th from 17.35 to 38.10 Bq kg⁻¹. Kokori recorded the highest concentrations of all three radionuclides, whereas Otorogu generally recorded the lowest values. The calculated Raeq values ranged from 69.60 to 152.40 Bq kg⁻¹ and were below the reported recommended value of 370 Bq kg⁻¹. RESRAD-BIOTA version 1.8 was subsequently applied to estimate external, internal and total radiation doses to terrestrial plants and animals. The modeled results demonstrated spatial differences in biota dose, with Kokori recording among the highest total doses. Overall, the findings provide evidence of spatially variable natural radioactivity in the investigated oil-field soils and establish a radiometric baseline for continued environmental monitoring.
DOI: http://doi.org/10.5281/zenodo.23056985
Mechanical Properties and Elastic Modulus of Ambient-Cured Fly Ash-GGBS Geopolymer Concrete
Authors: Dasari Vinay, Assistant Professor A. Ram Kumar
Abstract: Nowadays concrete plays a major role in the human life. It is the prominent material to the construction industry and also usage of concrete became second only to water around the world. In this, cement is the most commonly used as a binding material in the concrete industry. But usage of concrete is enormously increased to produce structural elements. In this, cement is the most promising binding material to produce conventional concrete. But there are many environmental issues are associated with producing cement. Portland cement (PC) production has resulted in high amount of CO2 emissions into the atmosphere and PC is one of the major energy-intensive material and it requires huge amount of natural resources i.e; limestone. In the present scenario emission of greenhouse gas contributes about 1.5 billion tons annually or about 7% of the total greenhouse gas emissions to the earth’s atmosphere due to PC production. However, many efforts have been started in the construction industry to overcome this by utilizing industrial by products and developing substitute binders in concrete. To preventing this there must be study needs to focus to develop alternative and sustainable material towards cement free concrete. Which can be replaced by supplementary binder materials in the form of silica fume, metkaolin, rice-husk ash, GGBS and fly ash? Geopolymer concrete is an inorganic polymer aluminum silicon Acid material and it was introduced by the French chemist J. Davidovits in the last century in the late 1970. Geopolymer generated at the same time as excited by alkaline substances, while consuming one of these various industrial wastes have the characteristics of low energy consumption and low resource consumption. Geopolymer has received much attention in recent years as an environmentally friendly material. Geopolymer materials need a broad range of naturally available industrial bi-products. Geopolymer concrete has advantages like; simple process, Inexpensive, low energy consumption, high mechanical performance and durability, etc. The broad application development prospect has always been an internationally active research material.
Consumption Puzzle theory in Economics
Authors: Shad Ali Khan, Dr. Shikhar Kumar
Abstract: This research paper examines the concept of consumption Puzzle in the long-term context. An eye caching and an important problem in macroeconomics theory which economists have tried to solve for decades. The mirage of a given predicted relationship between income and consumption is yet to be supported by theories over the long term. The absolute theory of consumption is an important traditional theory. The theory says changing income will lead to change in consumption but only at a diminishing rate. This concept leads to formation of another important concept that is average propensity to consume (APC) will gradually decrease when the economy grows. According to this reasoning the long run economic growth will be achieved by raising the savings in the economy. Historical economic data shows a distinct trend when examining national income in the long term. As economics grew over many years, both income and spending increased together in almost the same. In simple words, when people earn more over the long term they also spend more and divide their income for future expenditures that make their consumption and income stable in the long run. But according to old theories if the income rises then people should save more of their income and the spending share will change. To solve the consumption puzzle many economists said people do not make spendings by only present income. In fact they think more broadly when their income is increased. The Permanent income hypothesis says that people spend their income on the basis of not only current income but also care about future income. Example: if someone’s income increases for a short time or a one-time increment then they may save most of it because they know it is a temporary income change. But when the income changes permanently then they increase their consumption. The Life Cycle Hypothesis also provides a similar idea. It says people plan their spending over their whole life, they may save during the working year, make consumption and save for retirement and sustain their consumption even after retirement. However their low income doesn’t affect their consumption. People not only save for retirement but also have saving habits from generation for emergence funds, education and major household works.
Mechanical and Durability Performance of Polypropylene Fibre Reinforced Fly Ash-Based Geopolymer Concrete
Authors: K. Sathish Kumar, Assistant Professor A. Ram Kumar
Abstract: This research investigates the development and structural performance of fly ash-based Geopolymer Concrete (GPC) reinforced with polypropylene fibers as a sustainable alternative to conventional Ordinary Portland Cement (OPC) concrete. The study addresses the growing environmental concerns associated with cement production, particularly the high emission of carbon dioxide and the disposal of industrial waste materials. By utilizing Class F fly ash as the primary alumina-silicate and activating it with sodium hydroxide and sodium silicate solutions, a cement-free geopolymer binder was produced to promote environmentally friendly construction practices. The experimental program focused on evaluating the influence of polypropylene fibers on the mechanical and micro structural properties of geopolymer concrete. Geopolymer mixtures were prepared using locally available fly ash, fine and coarse aggregates, and alkaline activators with different molar concentrations. Heat curing was adopted to accelerate the geopolymerization process and to achieve rapid strength development. The study examined important engineering properties including compressive strength, split tensile strength, flexural strength, modulus of elasticity, ultrasonic pulse velocity, and durability characteristics to assess the suitability of GPC for structural applications. In addition to mechanical evaluation, the research investigated the nano- and micro structural behaviour of polypropylene fibre-reinforced geopolymer concrete through characterization of pore volume, crystalline structure, morphology, surface area, and surface-active phases. These analyses provided insight into the formation of a dense alumina-silicate gel matrix and the role of fibers in controlling micro-crack propagation and improving the internal structure of the composite. The interaction between geopolymer binder and polypropylene fibers was found to enhance the integrity of the matrix while reducing brittleness. The results demonstrate that the incorporation of polypropylene fibers significantly improves tensile and flexural performance, crack resistance, and overall durability without compromising the environmental advantages of geopolymer concrete. Heat-cured fly ash geopolymer concrete exhibited excellent early-age strength, superior resistance to aggressive environments, and the potential to replace OPC concrete in sustainable infrastructure. The study concludes that polypropylene fibre-reinforced geopolymer concrete is a high-performance, eco-friendly construction material capable of contributing to low-carbon and durable structural development.
Experimental Investigation on the Performance and Durability of Self-Curing Self-Compacting Concrete Using Polyethylene Glycol and Liquid Paraffin Wax
Authors: U. Srikanth, Assistant Professor A. Ram Kumar
Abstract: Variations in concrete performance are frequently associated with inadequate compaction and insufficient curing, particularly in structural members containing congested reinforcement. Self-compacting concrete (SCC) is capable of flowing and consolidating under its own weight, whereas self-curing concrete is intended to maintain the internal moisture required for continued cement hydration without depending entirely on external curing. The present experimental investigation combines these two concepts to evaluate the performance and durability of self-curing self-compacting concrete using polyethylene glycol of two molecular weights, PEG 4000 and PEG 200, together with light and heavy grades of liquid paraffin wax as self-curing agents. The agents were incorporated at dosages ranging from 0.1% to 1.0% by mass of cement in three SCC mixes, designated A, B and C, having different water-cement ratios. Fresh-state performance was assessed through slump-flow, T50, V-funnel, J-ring and L-box tests in accordance with the workability requirements adopted for SCC. Hardened concrete performance was investigated through compressive strength, acid-attack resistance and sorptivity tests. The experimental results were compared with conventional SCC and conventionally cured specimens in order to examine the influence of the different self-curing compounds and their dosages. The investigation demonstrates that self-curing SCC can provide durability characteristics comparable with conventionally cured concrete while reducing dependence on external curing. The study therefore supports the potential use of PEG and liquid paraffin wax as internal curing agents in SCC where conventional curing and mechanical compaction are difficult to achieve.
Passive Thermal Logic And Conservation Significance Of Ethiopian Tukul Architecture In A Warm Semi-Arid Region
Authors: Eswara Rao Petta, Viswanadh Kumar G.
Abstract: Ethiopian Tukul houses embody a close relationship among local materials, craft practice, spatial organization and climate-responsive building. This study examines the passive thermal logic and conservation significance of Tukul architecture in Kechema Ward, East Shewa, Oromia, with attention to the circular plan, earthen and wattle-and-daub walls, conical thatched roof, restricted openings, roof-wall air paths and locally available materials. The investigation adopts a qualitative architectural-heritage approach based on author-prepared field drawings from a 2023 field study and a structured synthesis of 35 peer-reviewed studies, with emphasis on Ethiopian, sub-Saharan African and recent architectural-heritage research. Architectural features are interpreted through heat-transfer and environmental mechanisms and examined through cross-study triangulation. The synthesis indicates that the Tukul envelope functions as a coordinated passive system in which earthen walling can buffer diurnal heat flow, deep thatch and eaves limit direct solar loading, and the doorway and roof-wall gap support controlled air exchange under favourable outdoor conditions. Conservation priorities therefore extend beyond the circular silhouette to compatible earthen finishes, roof craft, the raised base, ventilation details and the knowledge required for maintenance. The study presents a heritage-centred framework for understanding the environmental logic of Tukul architecture and for guiding conservation and climate-responsive adaptation.
DOI: http://doi.org/10.5281/zenodo.23080000
An Intelligent Data Mining Framework For Predictive Decision-Making Using Explainable Artificial Intelligence
Authors: Mr. R K Arunkumar, S. Inbaroja
Abstract: Integration of artificial intelligence into vital decision-making fields has led to the emergence of a demand for transparency and interpretability in the used predictive models. This research aims to present an intelligent data mining framework, which utilizes the combination of predictive modeling methods and Explainable Artificial Intelligence (XAI). The use of the suggested framework is meant to ensure transparent decision-making process by implementing such methods of predictive modeling as ensemble learning, including Random Forest, XGBoost, and Gradient Boosting along with the methods of SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) for multi-level explanation generation. Our research applies the intelligent data mining framework on the dataset related to healthcare and includes several classes of diseases, ensuring high performance of the model in terms of prediction along with generating actionable explanations for each prediction.
DOI: http://doi.org/10.5281/zenodo.23095207
Explainable Artificial Intelligence For Real-Time Cyber Attack Detection And Risk Assessment
Authors: Priya Mahesh Borkar, S.Divya
Abstract: Security operations centers rely on machine learning in increasing amounts when conducting network intrusion detection, but the inability to comprehend, trust, and act on alerts caused by black-box models is a significant issue under time pressure. Explainable AI (XAI) solutions such as SHAP and LIME provide solutions to the transparency problem, yet the explanation latency that they introduce makes them unsuitable for real-time triage of security alerts. This paper introduces an integrated XAI framework that consists of a Random Forest-XGBoost soft voting model along with a latency-aware dual mode explanation engine that uses the optimal LIME path for real-time query explanation and the approximated KernelSHAP path for forensic query explanation. A feature weighted risk scoring component transforms explanation attribution scores into a risk score bound in real time. Using the UNSW-NB15 dataset, we show that the framework provides state-of-the-art detection performance comparable to black box baselines with explanation latency well within real-time bounds and high attribution stability for forensic purposes.
DOI: http://doi.org/10.5281/zenodo.23095318
Graph Theory Applications In Artificial Intelligence And Data Science
Authors: Dr.V.Bhagyalakshmi, Gunnam Prasada Rao
Abstract: Graph Theory has been established as a fundamental approach to modeling relational data in Artificial Intelligence and Data Science. In this paper, a thorough study of graph-related methods has been conducted with a special focus on Graph Neural Networks (GNNs) and its variants for processing non-Euclidean data structures. A new hybrid framework combining Graph Convolutional Network with attention model and hyperbolic embedding has been developed to overcome the drawbacks of current techniques. The proposed methodology is tested on standard datasets for the task of node classification and link prediction. It was found that the suggested technique provides better results than the baselines with an accuracy gain from 3% to 5%. The findings confirm the efficiency of the graph theoretical approach in dealing with complex dependencies and hierarchical relationships embedded in the data.
DOI: http://doi.org/10.5281/zenodo.23095657
From Wilderness To Environmental Crisis: An Ecocritical Study Of Human–Nature Relationships In Canadian Fiction
Authors: Dr. J. Felcia Pushpalatha
Abstract: The representation of nature has undergone significant transformation in Canadian fiction, moving from images of wilderness and geographical vastness towards complex representations of ecological crisis, environmental vulnerability, and human–nature interdependence. This paper examines this transformation through an ecocritical reading of selected Canadian fiction, focusing on Margaret Atwood’s Oryx and Crake (2003), Lee Maracle’s Celia’s Song (2014), Cherie Dimaline’s The Marrow Thieves (2017), and Waubgeshig Rice’s Moon of the Crusted Snow (2018). The study investigates how Canadian writers have moved beyond representing nature as a passive landscape or national symbol and increasingly portray it as an active component of human identity, cultural memory, survival, and ethical responsibility. The analysis considers wilderness, anthropocentrism, technological intervention, climate change, Indigenous ecological knowledge, environmental justice, and community resilience. Atwood’s dystopian fiction demonstrates the consequences of technological domination and the commodification of biological life, whereas Maracle’s work foregrounds relationality between humans, land, animals, and community. Dimaline connects ecological catastrophe with colonial histories and Indigenous displacement, while Rice explores energy dependence and the significance of local ecological knowledge during social disruption. The paper argues that contemporary Canadian fiction represents a movement from the idea of nature as wilderness towards an understanding of nature as an interconnected ecological system in which human survival is inseparable from environmental health. Through this transformation, Canadian fiction contributes to the development of ecological consciousness and challenges conventional anthropocentric understandings of the human–nature relationship.
DOI: http://doi.org/10.5281/zenodo.23096611
Gender-Based Comparison Of Perceived Stress Among Employees Of Different Departments In An Engineering College
Authors: Dr. Richa Ojha, Dr. Kamla Tiwari
Abstract: The purpose of the study was to compare the stress levels of male and female among different departments of an engineering college. A total number of 179 employees both male and female in which 28 from polytechnic (18 male and 10 female). 63 from engineering (36 male and 27 female), 52 from management (27 male and 25 female), and 36 from science & technology (7 male and 29 female) have been included in the study. All males and females who were selected for the study were in the age group of 20 years to 60 years. The results of the study shows that no significant difference can be found conclusively in perceived stress levels found across gender or college type. Although minor variations were observed, particularly a slightly higher vulnerability among female teachers, however, the overall pattern indicates a uniformly moderate level of stress among teachers
Civil Engineering Challenges and Sustainable Infrastructure Strategies for Hyperscale Data Centers
Authors: Dhruval Jigar Shah
Abstract: The rapid growth of hyperscale data centers has created new demands on civil engineering systems that support mission-critical digital infrastructure. This paper evaluates civil engineering challenges associated with large data-center campuses through a real-life case study of the QTS data center development in York County, South Carolina. Publicly available project information is combined with established civil engineering and construction-management principles to examine mass grading, geotechnical engineering, foundation interfaces, stormwater management, underground utilities, pavement systems, construction materials testing, quality assurance, and sustainability. The study proposes a risk-based framework in which civil infrastructure is treated as a reliability system rather than as a collection of independent site activities. The case study demonstrates that phased development, dense utility networks, temporary drainage conditions, heavy construction traffic, and rapid technology change require strong coordination and location-based quality documentation. Project-specific confidential information is not assumed; technical controls beyond verified public facts are presented as engineering recommendations. The resulting framework is applicable to hyperscale and mission-critical construction projects where long-term reliability depends on the quality of site and civil infrastructure.
Sericulture as a Sustainable Rural Enterprise: Quantitative Assessment of Production, Employment and Economic Benefits
Authors: Assistant Professor Dr. M. Stephen
Abstract: Sericulture is an agro-based rural enterprise that integrates mulberry cultivation, silkworm rearing, cocoon production, silk reeling and value-added silk processing. Its importance extends beyond silk production because the sector provides employment, income diversification and livelihood opportunities for small and marginal farmers. India is the world’s second-largest producer of raw silk and produced 38,913 MT in 2023–24, of which mulberry silk contributed 29,892 MT, while Tasar, Eri and Muga contributed 1,586, 7,183 and 252 MT, respectively. Estimated cumulative employment associated with the Indian silk sector reached 9.5 million persons in 2023–24. This review quantitatively examines sericulture as a rural enterprise by comparing production, employment, labour utilization, production costs, gross and net returns, benefit–cost ratios and technological interventions. Published farm-level studies demonstrate substantial economic potential. In Dharmapuri district of Tamil Nadu, the reported total cost of sericulture was ₹279,585.80 ha⁻¹, gross income was ₹332,168.80 ha⁻¹ and net income was ₹52,583 ha⁻¹, with a benefit–cost ratio of 1.37. Labour requirements were also substantial, particularly for women during mulberry maintenance and silkworm rearing. Recent evidence further demonstrates that structured training can significantly improve adoption of sustainable sericulture practices. Among 222 farmers in West Bengal, trained farmers recorded a knowledge score of 94.12 ± 6.35 compared with 74.33 ± 8.21 among untrained farmers, while adoption scores were 71.31 ± 9.42 and 53.00 ± 10.18, respectively. The evidence indicates that sericulture can function as a sustainable rural enterprise when productivity-enhancing technologies, skill development, market access, labour availability and environmental management are integrated into production systems.
Performance Analysis and Optimization of Augmented Reality Applications on Mobile Platforms Using Flutter
Authors: Harsh Sharma, Dr. Akshya Bhardwaj
Abstract: Delivering responsive augmented reality (AR) experiences on mobile devices requires maintaining high rendering performance while operating within the constraints of limited processing power, memory, and battery resources. This study introduces a benchmarking framework developed with Flutter that evaluates the runtime performance of AR applications using Google ARCore on a physical Android device. The proposed framework continuously records important runtime metrics, including frame rate, frame time, CPU utilisation, memory consumption, estimated power usage, and motion-to-photon (MTP) latency during live AR sessions. Six optimisation techniques—distance-based level of detail, visibility control for distant objects, adaptive rendering quality, lazy asset loading, background-isolate data export, and batched platform metric collection—can be enabled independently to examine their individual and combined impact through controlled A/B experiments. Performance evaluation was conducted on a Realme RMX5070 smartphone running Android 16 under identical environmental conditions using a Release build. Experimental results demonstrate that enabling all optimisation strategies increased the average frame rate from 92.94 FPS to 103.27 FPS, representing an improvement of approximately 11.1%. Average memory usage decreased from 286.5 MB to 267.7 MB, while mean UI frame time was reduced by 9.4%. Among the evaluated techniques, adaptive quality adjustment produced the largest individual improvement in rendering performance. CPU utilisation remained consistently close to full capacity throughout all experiments, whereas GPU utilisation metrics were unavailable because of platform restrictions. The proposed benchmarking framework offers a reproducible methodology for analysing Flutter-based mobile AR applications and provides a practical foundation for future optimisation research across different devices and AR platforms.
Impact Of Exercise Interventions On Clinical Outcomes In Patients With Comorbid Chronic Obstructive Pulmonary Disease And Type 2 Diabetes Mellitus: A Systematic Review
Authors: Dr. B.R. Shaalini, Dr. K. Raja Senthil, Dr. E. Prema
Abstract: Background-Chronic Obstructive Pulmonary Disease (COPD) and Type 2 Diabetes Mellitus (T2DM) frequently coexist because of shared risk factors, including ageing, smoking, obesity, physical inactivity, and systemic inflammation. Their coexistence contributes to impaired pulmonary function, poor glycaemic control, reduced exercise capacity, diminished quality of life, and increased morbidity. Although exercise rehabilitation is recommended for both conditions individually, evidence regarding its effectiveness in patients with both COPD and T2DM has not been comprehensively synthesised. Objective- To systematically evaluate the effects of exercise interventions on clinical outcomes in adults with comorbid COPD and Type 2 Diabetes Mellitus. Methods- This systematic review was conducted according to the PRISMA 2020 guidelines. Electronic databases including PubMed/MEDLINE, Scopus, Embase, Web of Science, Cochrane Library-PEDro, and Google Scholar were searched for studies published between January 2015 and June 2026. Randomized controlled trials, controlled clinical trials, and prospective cohort studies evaluating structured exercise interventions in adults with comorbid COPD and T2DM were included. Methodological quality was assessed using the Cochrane Risk of Bias 2 (RoB 2) tool and ROBINS-I. Due to clinical and methodological heterogeneity, a narrative synthesis was performed. Results- Twenty-five studies met the eligibility criteria. Exercise interventions, including aerobic training, resistance training, combined aerobic-resistance exercise, pulmonary rehabilitation, inspiratory muscle training, and high-intensity interval training, demonstrated consistent improvements in exercise capacity, pulmonary function, glycaemic control, respiratory muscle strength, dyspnoea, inflammatory markers, and health-related quality of life. Combined aerobic and resistance exercise delivered within pulmonary rehabilitation programmes showed the most consistent overall clinical benefits. Exercise interventions were generally safe and well tolerated, with no serious exercise-related adverse events reported. Conclusion- Current evidence supports structured exercise rehabilitation as a safe and effective non- pharmacological intervention for individuals with comorbid COPD and T2DM. Combined aerobic and resistance exercise within pulmonary rehabilitation appears to provide the greatest clinical benefit. However, larger multicentre randomized controlled trials with standardized exercise protocols and longer follow-up are needed to strengthen the evidence base.
DOI: http://doi.org/10.5281/zenodo.23102859
Coventry University Students’ Perceptions of Factors That Influenced Them to Make Healthy or Unhealthy Dietary Choices
Authors: Mayanja Shafique
Abstract: The study aimed at exploring Coventry University students’ perceptions of factors that influenced them to make healthy or unhealthy dietary choices. A qualitative approach was used to explore students’ perceptions of factors that influenced their dietary choices. Methods and Analysis- Five participants all Coventry University students were recruited for this study using purposive sampling method. One to one semi-structured interviews were conducted to explore students’ perceptions of factors influencing their dietary choices. Students were interviewed using open-ended questions to capture a broader perspective of the phenomena of interest. Interviews were manually transcribed, and an inductive thematic analytical method was used to analyse data which lead to the identification of three major themes. Results- The results indicate that three major themes emerged which included concept and perspectives of healthy and unhealthy dietary choices, perceptions of factors influencing students’ dietary choices and solutions to healthier choices. Concepts and perspectives indicated that students understood the meaning of healthy and unhealthy dietary choices as well as expressing how they understood the concepts. The students also expressed their perceptions of factors that influenced them to make dietary choices which includes socio-economic, lifestyle factors, social and community networks and environmental factors. These include among cost, Time, knowledge, health, Availability, Accessibility, location and weather changes. However, the most influential factors were cost, time, family and health. Possible recommendations were suggested to promote healthier choices including proper planning, advertisement of healthier foods, Eating when necessary and personal responsibility.
Understanding The Climate Sensitive Vernacular Architecture Of Palle Chuwdu Middelu In Ramanapalle-village In Kadapa Division A.P, India
Authors: Prof. Eswara Rao. P, Dr. Viswanadh Kumar. G
Abstract: Vernacular architecture and climate are engaged in a happy marriage in any indigenously developed settlement. We documented and analyzed a vernacular Palle or village settlement such as Ramanapalli, Chennur which are located in tropical zone of hot and semi-arid climatic Kadapa region of Andhra Pradesh. Palle is a rural settlement in Kadapa district of Rayalaseema, India. It is particularly significant for architectural studies because it has been documented as an example of climate-sensitive vernacular architecture in the Rayalaseema region. The Palle form and structure come from many years of evolution and sharing knowledge. This shows how different physical and non-physical forces like weather, land, religion, culture, money, old building methods, and how people organize themselves have shaped them. These are made up of small groups of homes with thick walls, heavy roofs, small windows, and narrow streets. In villages like Chennur, Ramanapalle, and Rachinayapalle, there are different types of houses, known locally as ‘Chowda or Dante Middelu’. The designs of these houses are usually rectangular or square, built using traditional methods that take into account the local climate. They are well suited to meet the everyday needs of the people living there. The people living in these houses adjust their daily activities to fit the natural qualities of the space around them. The house is designed and built in a way that responds to the hot and dry climate. This type of design is a form of passive architecture that uses local materials, thick walls, small windows, traditional flat roof with high thermal mass, and small vents near the roof but below it. These thick walls and heavy roof help absorb and slow down the transfer of heat. The small windows are placed carefully to reduce the amount of direct sunlight entering. However, the ‘House form of Chowda Middelu’ in Kadapa district is vanishing because of various reasons in recent years. Previously, this type of architecture and the way people lived were very responsive to the climate. But now, these traditional designs and habits are slowly changing into ones that don’t take the climate or local surroundings into account. This research suggests the need for a set of guidelines that can help balance modern development with the traditional building methods used in the area.
DOI: http://doi.org/10.5281/zenodo.23118495
AI-Driven Facial Image Matching Framework for Missing Person Identification and Search Assistance
Authors: Pilli Satya Sri, M Sujana Priya Darshini
Abstract: Finding missing persons is a challenging task that often requires significant time, manual investigation, and coordination between authorities and the public. This paper presents an AI-assisted missing-person identification system that uses face recognition and machine learning to support the search and identification process. The proposed system allows authorized personnel to register missing-person cases by storing details such as name, age, contact information, location, and photographs in a centralized database. The system also provides a user interface through which the public can submit photographs of unidentified or potentially missing persons along with relevant information. The submitted images are processed and compared with the registered missing-person photographs using facial feature matching techniques. When a suitable match is identified, the system provides the corresponding case information and supports notification to the concerned user or authority. The application is implemented using Python, PyQt5, PostgreSQL, and machine learning-based face recognition techniques, providing an integrated platform for case management, image matching, and search assistance. The proposed system aims to reduce manual effort, improve the speed of image-based identification, and support police and public participation in locating missing persons.
A Hybrid Autoencoder–XGBoost Framework for Proactive Cloud Security and Vulnerability Detection
Authors: Karupothu Bharath, Assistant Professor T Swapna Sridevi
Abstract: Cloud computing has become a fundamental part of modern digital infrastructure, enabling organizations to deploy applications, store data, and deliver services across highly distributed environments. However, the dynamic nature of cloud systems also creates complex security challenges, including abnormal network activity, unauthorized access, misconfigurations, and emerging vulnerabilities. Traditional security mechanisms often depend on predefined rules and known attack patterns, making them less effective when dealing with previously unseen or continuously evolving threats. This paper presents a machine learning-based approach for strengthening cloud security by combining anomaly detection and vulnerability classification. The proposed framework uses an Autoencoder to identify unusual patterns in cloud-related data and XGBoost to classify detected security events and potential vulnerabilities. The system processes security-relevant information such as network traffic, system logs, user activity, and historical vulnerability records through data preprocessing and feature extraction stages before applying the machine learning models. By combining unsupervised anomaly detection with supervised classification, the framework is designed to provide a more adaptive approach to identifying both familiar and potentially unknown security threats. The proposed solution is implemented as a web-based security framework using Python and Django, providing mechanisms for data management, model evaluation, prediction, and security monitoring. It is intended to reduce dependence on manual analysis while supporting faster identification of suspicious activities and potential vulnerabilities. The framework also provides a foundation for continuous model improvement as new security data becomes available. Overall, the proposed approach demonstrates how machine learning can be integrated into cloud security workflows to support proactive vulnerability detection and improve the adaptability of security monitoring in distributed environments.
Machine Learning- Assigned Numerical Optimization For Complex Nonlinear Systems
Authors: Pushpendra Kumar Sharma, J. Indra Kumari
Abstract: Finding missing persons is a challenging task that often requires significant time, manual investigation, and coordination between authorities and the public. This paper presents an AI-assisted missing-person identification system that uses face recognition and machine learning to support the search and identification process. The proposed system allows authorized personnel to register missing-person cases by storing details such as name, age, contact information, location, and photographs in a centralized database. The system also provides a user interface through which the public can submit photographs of unidentified or potentially missing persons along with relevant information. The submitted images are processed and compared with the registered missing-person photographs using facial feature matching techniques. When a suitable match is identified, the system provides the corresponding case information and supports notification to the concerned user or authority. The application is implemented using Python, PyQt5, PostgreSQL, and machine learning-based face recognition techniques, providing an integrated platform for case management, image matching, and search assistance. The proposed system aims to reduce manual effort, improve the speed of image-based identification, and support police and public participation in locating missing persons.
DOI: http://doi.org/10.5281/zenodo.23157635
The Impact of Solar Energy System on Environmental and Social Sustainability
Authors: Abdul Waris Farid, Mohammad Naser Rasuli, Ghulam Hussain.R
Abstract: Energy production and technical efficiency are important aspects of energy systems, they should not be used to determine their sustainability of solar energy systems. The study combines environmental aspects of solar PV systems’ life cycles with social aspects to explore their environmental and social sustainability impacts. Three different PV technologies—monocrystalline silicon, multicrystalline silicon, cadmium telluride—are analyzed, and the energy yield, module efficiency, greenhouse-gas emissions, energy payback time, and social aspects like employment, energy access, community benefits, and social acceptance are considered. An AHP-based framework is still maintained to highlight that on how the various sustainability dimensions can be incorporated into the PV assessment. The illustrative results show that PV technologies have various electrical and environmental features, and social outcomes are highly sensitive to project site settings and deployment environment, as well as community context. The paper emphasizes on the importance of integrated assessment for environmental and social sustainability in the planning of solar energy systems.
Systems Theory And Contingency Theory: Understanding The Indian Context
Authors: Professor (Dr.) Lalith Kumar Dharvath
Abstract: This paper provides a critical analysis of Systems Theory and Contingency Theory within the unique socio-economic landscape of India. Systems theory views organizations as open, interconnected frameworks where internal subsystems collaborate to process external inputs. Conversely, contingency theory argues that there is no universal management style, emphasizing that organizational success depends heavily on adapting to specific, changing environments. While both frameworks originated in Western corporate ecosystems, their application in India requires navigating a distinct blend of traditional hierarchy, family-led conglomerate structures, and immense regional diversity.Through a contextualized lens, this study explores how large Indian business houses utilize systems thinking to maintain corporate synergy across highly diverse portfolios. Simultaneously, it examines how the volatile, dual-speed Indian market forces managers to rely on contingency-driven management—vividly captured by the culturally embedded practice of Jugaad (frugal, situational innovation). The analysis reveals that rigid adherence to standardized corporate blueprints often fails in the Indian marketplace. Instead, the paper concludes that a hybrid framework—one that harmonizes the structural wholeness of systems theory with the agile, hyper-local flexibility of contingency theory—offers the most resilient roadmap for organizational sustainability and strategic growth in India.
DOI: http://doi.org/10.5281/zenodo.23181148
The Ancient Art Of Modern Leadership In Ancient Banjaras
Authors: Professor (Dr.) Lalith Kumar Dharvath
Abstract: The Ancient Art of Modern Leadership in Ancient Banjaras Background: Modern organizational leadership frequently relies on westernized, bureaucratic, and highly structured frameworks. However, these models often struggle to address the complexities of community cohesion, agility, and decentralized resilience. The Banjaras—a historically nomadic community renowned for their intricate social governance, adaptive trades, and collective decision-making—offer a rich, yet understudied, repository of traditional governance strategies. Purpose: This paper examines the historical socio-political structures of the ancient Banjara community, focusing on their unique leadership models, such as the Naik (community head) system, customary councils (Tandas), and collective trust mechanisms. The study seeks to bridge the gap between historical heritage and contemporary management by extracting timeless principles embedded within Banjara governance. Methodology: Utilizing an ethno-historical and qualitative analytical approach, this research evaluates archival records, oral histories, and cultural frameworks of the Banjaras. These ancestral practices are cross-examined against modern leadership theories, including servant leadership, situational leadership, and crisis management. Findings: The study reveals that ancient Banjara leadership was fundamentally rooted in high emotional intelligence, adaptive resource management, and communal sustainability. Their ability to navigate geographic, economic, and political shifts while maintaining cultural and structural integrity serves as a powerful blueprint for modern leaders navigating volatile, uncertain, and complex (VUCA) global environments. Conclusion: Ultimately, this paper argues that the “ancient art” of the Banjaras is not an obsolete relic of the past, but a forward-looking paradigm. By integrating nomadic resilience, consensus-based authority, and ethical stewardship into contemporary frameworks, modern organizations can build more sustainable, agile, and human-centric leadership models.
DOI: http://doi.org/10.5281/zenodo.23181644
Maximizing Biogas Output through Optimal Anaerobic Co-Digestion of Water Hyacinth and Cow Dung
Authors: Nweke James, Emenike Wami, Awajiogak Anthony Ujile, T.O. Goodhead
Abstract: This study investigates biogas production from water hyacinth (Eichhornia crassipes) through anaerobic co-digestion with cow dung in order to enhance methane yield and process stability. Experiments were conducted in 18.9 L batch digesters operated at 75% working volume, an organic loading rate of 3.5 g VS L⁻¹ day⁻¹, and a hydraulic retention time of 70 days under mesophilic conditions (29–38 °C). Six feedstock configurations were evaluated, comprising mono-digestion of water hyacinth and five water hyacinth–cow dung co-digestion ratios. Mono-digestion of water hyacinth produced 42.3 L kg⁻¹ VS, while the optimal 2:1 water hyacinth–cow dung ratio achieved the highest cumulative biogas yield of 54.9 L kg⁻¹ VS, representing a 29.8% increase. Biogas upgrading using a three-stage purification system (ferric oxide, sodium hydroxide, and silica gel) increased methane concentration from 60.05% to 86.02%, yielding a calorific value of 31.46 MJ m⁻³. Kinetic modelling based on coupled first-order hydrolysis and Monod microbial growth equations showed excellent agreement with experimental data (R² = 0.984, RMSE = 0.263 L). The results demonstrate that anaerobic co-digestion of water hyacinth with cow dung provides an efficient, environmentally sustainable pathway for renewable energy generation while simultaneously addressing aquatic weed management challenges.
AI, Automation, And Advanced Tools In Modern Policing
Authors: Professor (Dr.) Lalith Kumar Dharavath
Abstract: Modern law enforcement agencies face the dual challenge of managing complex public safety threats while maintaining strict institutional accountability. This paper explores the integration of artificial intelligence (AI), automation, and advanced digital tools within modern policing frameworks. By analyzing current implementations—including predictive policing algorithms, automated facial recognition, automated license plate readers (ALPRs), and AI-driven evidence management systems—this study assesses how these technologies enhance operational efficiency, accelerate response times, and optimize resource allocation. Furthermore, the paper addresses critical systemic challenges, specifically targeting algorithmic bias, citizen privacy concerns, data security vulnerabilities, and the regulatory frameworks required to govern automated decision-making. Ultimately, this research provides a balanced framework for law enforcement executives and policymakers, demonstrating how agencies can leverage cutting-edge technical innovation while upholding the core principles of ethical, transparent, and community-oriented justice.
DOI: http://doi.org/10.5281/zenodo.23184750
Human–AI Collaboration In Professional Toddler Photography: Optimizing Creativity Through Intelligent Lighting Systems
Authors: Renuka Kantilal Thange, Dr. Hitesh Sharma
Abstract: The rapid advancement of Artificial Intelligence (AI) is transforming professional photography by introducing intelligent systems that assist photographers in technical and creative decision-making. While AI-powered tools have significantly improved image enhancement and editing, limited research has explored their collaborative role in professional toddler photography, where creativity, lighting precision, child comfort, and safety are equally important. This study proposes a Human–AI collaborative framework that integrates intelligent lighting systems with the expertise of professional photographers to optimize lighting selection during toddler photography sessions. The research examines how AI-assisted recommendations can support decisions regarding natural, artificial, and hybrid lighting environments while maintaining the photographer’s creative control. A mixed-method research design is proposed, involving professional photographers and parents to evaluate image quality, creativity, workflow efficiency, toddler comfort, and overall photographic outcomes. The study aims to demonstrate that Human–AI collaboration enhances decision-making rather than replacing professional expertise. The proposed framework contributes to intelligent photography practices by combining computational intelligence with artistic creativity and offers practical guidelines for integrating AI into professional toddler photography.
DOI: https://doi.org/10.5281/zenodo.23205864
Agentic AI-Driven Identity and Access Management Frameworks: Architecture, Performance, Governance, and Future Directions
Authors: Rohit Agnihotri.
Abstract: The rapid proliferation of cloud-native environments, decentralized workforces, and API-driven architectures has exposed critical limitations in conventional Identity and Access Management (IAM) systems. This article examines the emergence of Agentic Artificial Intelligence (AI)-driven IAM frameworks as a transformative paradigm for enterprise security. Drawing on empirical data from industry reports, peer-reviewed studies, and real-world deployment analyses, we evaluate the architecture, operational benefits, risk dimensions, and governance requirements of AI-powered IAM systems. Our findings indicate that agentic AI integration yields measurable improvements across breach detection (up to 87%), provisioning speed (82%), and compliance automation (91%), while simultaneously introducing novel challenges in auditability, model explainability, and adversarial robustness. We propose a multi-layered governance model and research agenda to guide responsible adoption.
DOI: https://doi.org/10.5281/zenodo.23209786
Teachers’ Use of Graphical Methods in Teaching Coordinate Geometry in Zambian Secondary Schools: A Case Study in Some Selected Schools of Chongwe District in Lusaka Province
Authors: Mashiba Alfred Kalaba, Dr. C. Maria Ugin Joseph
Abstract: The study investigated the use of graphical methods by teachers when teaching coordinate geometry in Zambian secondary school to be specific in Chainda, Chalimbana and Chongwe secondary schools in Chongwe district Lusaka province. Coordinate geometry is an important topic in mathematics that connects algebra and geometry through graphical representations. However, many learners experience difficulties in understanding the topic due to abstract teaching approaches. The study examined how teachers apply graphical techniques, the challenges they encounter, and the effects of graphical methods on learners’ understanding. A descriptive research design was used. Data were collected from mathematics teachers and learners through questionnaires, interviews and classroom observations. Findings reveal that graphical methods improve learners’ conceptual understanding, engagement, and performance in coordinate geometry. However, limited teaching resources, insufficient teacher training and time constraints hinder effective use of graphical approaches. The study recommends increased teacher training, provision of teaching aids and integration of digital tools such as GeoGebra to enhance the teaching of coordinate geometry.
DOI: http://doi.org/10.5281/zenodo.23212369
Comparative Seismic Performance Of A G+15 Reinforced Concrete Building On Plain And Sloping Ground
Authors: K. Prathip, G. suresh
Abstract: This study evaluates the seismic response of a G+15 reinforced-concrete framed building for three geometric configurations: a setback building on level ground, a step-back building on 30° sloping ground, and a step-setback building on 30° sloping ground. Three-dimensional models were analysed in STAAD.Pro using the equivalent static lateral-force method and linear time-history concepts in accordance with the source study’s IS 1893:2002 framework. The comparative assessment covers storey drift, storey shear, time period, displacement, and column actions including shear force, bending moment, torsion and axial force under critical load combinations. The results demonstrate strong sensitivity of seismic response to vertical and plan irregularity and to the change in column geometry caused by sloping terrain. The setback configuration develops comparatively larger tensile moments in several critical cases, while short columns in the step-back and step-setback configurations require particular design attention. The complete numerical result tables, response curves, model views and comparative plots from the project study are retained in this manuscript to support transparent comparison among the three configurations.
DOI: http://doi.org/10.5281/zenodo.23211320
Assessing And Measuring Community Engagement And Involvement In Empirical Health Research – A Scoping Review Of Approaches In Law And Middle-Income Countries.
Authors: Yusuf Ahmad, Lucia D Ambruoso, Pamela Abbotta
Abstract: – Background: Health research widely promotes community engagement and involvement, yet no previous studies have examined approaches, operationalisation, and efficiency across disciplines and regions. Methods: We conducted a scoping review with Joanna Briggs Institute guidance and reported in line with PRISMA-ScR. We searched Google Scholar, MEDLINE, EMBASE and CINAHL, and sampled grey literature from relevant organisational websites. We included empirical health research and grey literature published in English (January 2015–June 2025). References were screened against the inclusion criteria, and relevant data were extracted and charted deductively against Arnstein’s Ladder of Citizen Participation. Results: No grey literature met the inclusion criteria. Twenty-four peer-reviewed publications were included. 16 engagement approaches were identified. Diverse, yet context-specific. There is a noteworthy overlap in approaches, designs and methods. 87.5% were qualitative, 8.3% quantitative, and 4.16% mixed-methods. 79.5% were used to report findings from a research project, and 21% were used to discuss the process and practice of community engagement itself. The evidence base was geographically concentrated in sub-Saharan Africa (n=18), with limited representation from Latin America and other LMIC regions. 87.5% have engagement consistent with “citizen power” (partnership, delegated power, or citizen control), with communities participating as partners or having substantial influence over the direction of engagement, yet tokenistic practices persist. Conclusions: CEI in LMIC empirical health research is feasible and often substantive, but reporting and evaluation are inconsistent, and equity gaps persist. A clearer taxonomy, transparent documentation of decision rights and resourcing, and pragmatic measures of CEI quality and impact are required.
DOI: http://doi.org/10.5281/zenodo.23255715
ShopMind AI Is A Student Project
Authors: Ankit Kumar, Bhuvaneshwaran V, Maheshwaran C, Thulasidass M, Mr. M. Premkumar . M.E. Assistant Professor Supervisor
Abstract: Modern e-commerce platforms contain millions of products, making it increasingly difficult for customers to discover products that match their individual requirements. Traditional recommendation systems primarily depend on collaborative filtering, product popularity, or keyword-based search. Although these techniques are useful, they may perform poorly when users have limited interaction histories, when products are newly introduced, or when customers express their requirements using natural language.This paper presents ShopMind AI, an intelligent e-commerce recommendation and semantic search framework designed to combine multiple sources of information within a unified per-sonalization architecture. The proposed framework integrates user preferences, historical interactions, product attributes, behavioral signals, semantic product representations, and con-textual search information. A hybrid recommendation strategy combines collaborative signals, content-based similarity, pop-ularity information, and semantic similarity. In addition, a transformer-based semantic representation layer enables users to search for products using natural-language descriptions rather than relying exclusively on exact keyword matching. The architecture also supports product comparison, price-related information, personalized recommendations, explain-able recommendation signals, and scalable vector-based re-trieval. The paper describes the system architecture, data-processing pipeline, recommendation model, semantic-search mechanism, evaluation methodology, scalability considerations, and potential future improvements. The proposed framework is intended to provide a practical foundation for developing more intelligent and user-centered e-commerce applications.
DOI: https://doi.org/10.5281/zenodo.23263005
Applications Of Operations Research In Real-Life Scenarios Supply Chain Optimization for FMCG Products using the Transportation Model
Authors: Ms. Tejaswini Angre, Aryan Dass, Dev Thakker, Nirvaan Shah, Priyansh Jain, Tanya Prabhakar
Abstract: Operations Research (OR) is a scientific and analytical approach used to solve complex problems and support effective decision-making in real-life situations. It applies mathematical models, statistical techniques, optimization methods, and computational tools to identify the best possible solutions under given constraints. Operations Research has a wide range of applications in various fields, including healthcare, transportation, supply chain management, manufacturing, finance, agriculture, and education. In healthcare, it helps optimize hospital resources and patient scheduling. In transportation, it assists in route planning, traffic management, and reducing travel costs. In industries, it improves production planning, inventory control, and resource allocation. Similarly, businesses use Operations Research to minimize costs, maximize profits, and improve overall efficiency. This study explores the practical applications of Operations Research in real-life scenarios and highlights its importance in solving problems, utilizing resources efficiently, and improving organizational performance. The application of Operations Research enables individuals and organizations to make informed, data-driven decisions and achieve better outcomes in an increasingly complex and competitive environment.
DOI: https://doi.org/10.5281/zenodo.23276409
Artificial Intelligence And The Transformation Of The Indian Judicial System: Opportunities, Challenges, And Future Prospects
Authors: Khushboo chandra, Dr. Harbansh dixit
Abstract: The incorporation of Artificial Intelligence (AI) in judicial processes is perhaps the greatest technological innovation which has impacted legal systems globally. In India, there has been an increasing trend among the judicial systems towards adopting new technologies in order to increase efficiency, transparency, accessibility, and administration of justice. With the incorporation of technologies like e-Courts, virtual court sessions, electronic case management systems, and AI-enabled legal research tools, the groundwork has been laid for a technology-driven mechanism for administering justice. However, AI has not been adopted to replace judicial decision-making process in India but has been used to assist judges, lawyers, researchers, and court staff in performing monotonous and data-intensive tasks. These trends have become even more pertinent considering the huge number of pending cases and increasing need for justice.The present work will discuss how the use of AI has evolved in the Indian judicial system by considering the technological advancements made so far, judicial reforms, legal issues arising therefrom and possible future scenarios. The study will explore the benefits and risks associated with the use of AI in improving judicial efficiency in India. This study will adopt a doctrinal approach to qualitative research through constitutional provisions, court judgments, policies, government reports, and scholarly work. It contends that AI should only be viewed as assistive technology and not as something that should replace human judges.
DOI: https://doi.org/10.5281/zenodo.23279967
Emerging Trends in AI, Data Science and Cloud-Based Technologies
Authors: Thoomula Kavya Sri
Abstract: The fusion of artificial intelligence (AI), data science, and cloud computing technologies is transforming the nature of the digital economy from disconnected technological silos to interdependent ecosystems that complement each other. This paper explores current trends within these interrelated fields such as world models, agentic AI systems, cloud-native AI integration, and industrialization of data science. Mixed methods of research including the systematic literature review combined with the quantitative analysis of industry data is suggested. Convergence patterns analysis shows that cloud platforms transform from mere infrastructure into smart services ecosystems, and data science changes from disconnected modeling into engineering practice. Comparative analysis shows that companies embracing integrated approach to cloud-AI-data management enjoy considerably lower training costs and reduced deployment timeframes. The conclusion is made about the challenges related to governance, skill development, and infrastructure sustainability that need addressing.
SA-BTM-CF: A Sentiment-Aware Collaborative Filtering Framework For E-commerce Recommendation Systems
Authors: Mohammad Ali, Jin Xie, Wu Wenhuan
Abstract: The machine behind e-commerce recommendation systems is often based on Collaborative Filtering (CF) algorithms which are highly dependent on numerical star ratings ignoring the fact that user review text is a rich source of semantic information. This constraint increases the issue of data sparsity and lowers the quality of the recommendations. We suggest SA-BTM-CF, a Sentiment-Aware Collaborative Filtering system, which encompasses the Biterm Topic Model (BTM) with a conventional CF. The suggested methodology is a latent topic extractor of review literature and a fine-grained sentiment score generator of latent topic models which are employed to build an enriched user-item rating matrices. Personalized product suggestions are then created by computing user similarity on these sentiment-augmented ratings. Large scale shampoo review data on the JD.com site is experimented with and experimentally validates that SA-BTM-CF can be used to effectively reduce sparsity in data, and it can be used to obtain better recommendations than current baseline algorithms.
DOI: https://doi.org/10.5281/zenodo.23310790
BookTok And The Commodification Of Books: Overconsumption And Shifting Literary Standards In The Age Of Algorithm
Authors: Aayesha Rahman, Dr. Anil Adagale
Abstract: This paper aims to discuss the debate across various social media platforms on the impact of “BookTok,” which is a side of TikTok dedicated to readers sharing their opinions, recommendations, reviews, and experiences with books. Many individuals and influencers have made a claim that during and after COVID-19, there has been a compromise on the quality of books to meet the demands of consumers, which has led to books being commodified. Critics find it problematic as new releases flood the market, and the characteristics such as longer writing period and meticulous editing process, are replaced by books that are written, edited, and published within the span of a year. The question that any reader should ask themselves is the outcome they get out of reading by this shift in literary standards.
DOI: https://doi.org/10.5281/zenodo.23338534
Barriers to the Adoption of Mobile Tinnitus Management Tools in Diverse Populations: A Mixed-Methods Analysis of Socio-Demographic Influences, User Heterogeneity, and Pathways to Inclusive Digital Intervention
Authors: Abubakar Mairiga, Naveed Anwar, Biddy Casselden
Abstract: Mobile applications for tinnitus management have proliferated rapidly, yet their real-world adoption remains strikingly low. A mixed-methods study, combining sentiment analysis of 13,096 user reviews from 42 tinnitus applications, a cross-sectional survey of 171 adults with self-reported tinnitus, and in-depth semi-structured interviews with 19 participants, was conducted to investigate the socio-demographic and systemic barriers that prevent diverse populations from adopting these tools. Only 8.6% of survey respondents reported active app use despite the availability of 42 identified applications. Chi-square tests confirmed no statistically significant association between app adoption and either age (χ² = 6.25, df = 4, p > .05) or gender (χ² = 0.42, df = 4, p > .05), indicating that barriers are systemic rather than demographic. A point-biserial correlation demonstrated a very strong positive relationship between app use and subjective well-being (rpb = 0.82, p < .001). Contrary to stereotypes, adults aged 30-49 years and full-time employed professionals constituted significant proportions of app users, challenging the assumption that tinnitus apps are used primarily by elderly retirees. Five principal barriers were identified through thematic analysis: low awareness and absent clinician referral pathways, lack of marketplace quality standardisation, limited personalisation, digital literacy challenges for older users, and privacy and data security concerns. Actionable recommendations are proposed for developers, clinicians, and policymakers to create user-centred, inclusive digital interventions that close the efficacy-accessibility gap in tinnitus self-management.
Leakage-Aware Explainable AI for Credit Risk Assessment of Perishable Food Supply-Chain SMEs Using Food Shelf-Life, Postharvest Loss and Digital Banking Data in Nigeria
Authors: Kosisochukwu Henry Ukpabi, Abdullahi Mohammed Ibrahim, Zainab Abdulsalam Jamilu
Abstract: Small and medium-sized enterprises (SMEs) operating in perishable food supply chains face intertwined operational and financial risks: inventory can deteriorate before sale, postharvest losses reduce cash available for repayment, and many firms have thin or irregular formal credit histories. This study proposes a leakage-aware, explainable artificial intelligence (AI) framework that integrates food shelf-life, postharvest-loss, commodity-price, and digital banking features to assess credit risk for Nigerian perishable-food SMEs. Because linked Nigerian lender-level repayment data and firm-level shelf-life measurements were not available for this research, the empirical demonstration uses a clearly labelled dataset rather than claiming results from real borrowers. A reproducible simulation of 5,000 SME-loan observations was specified, with 70%/15%/15% chronological train/validation/test partitions, a financial-only baseline, and an integrated model using LightGBM-style gradient boosting. Logistic regression and random forest were included as benchmarks; SHAP-style feature attribution was used for interpretability. The simulated results illustrate the intended evaluation, not observed Nigerian banking performance: the financial-only baseline achieved a test ROC-AUC of 0.742 and PR-AUC of 0.356, while the integrated model achieved ROC-AUC 0.801 and PR-AUC 0.421. At a threshold selected on validation data, recall increased from 0.61 to 0.70, while Brier score improved from 0.176 to 0.158. The largest simulated contributors were recent repayment behaviour, cash-flow volatility, estimated remaining shelf life, postharvest-loss rate, and debt-service coverage. A random split produced more optimistic estimates than chronological evaluation, demonstrating why time-aware validation is essential. The main contributions are (i) a domain-specific framework linking food deterioration to credit risk, (ii) a leakage-control protocol for temporal financial data, (iii) an explainability and calibration evaluation plan, and (iv) a transparent synthetic benchmark that can be replaced by real data. These results are illustrative only; empirical conclusions require validation using consented, anonymised lending outcomes and measured food-quality data.
The Metaverse-Enabled Chemistry Classroom: Immersive Virtual Environments for Conceptual Understanding, Experimental Learning, and Laboratory Safety
Authors: Associate Professor Dr G. Kokila Selva Kumari
Abstract: Chemistry education presents distinctive pedagogical challenges because learners must interpret observable phenomena, infer processes occurring at the molecular and atomic levels, and represent these processes through chemical symbols, equations, and models. Conventional instructional approaches do not always provide sufficient opportunities to integrate these different forms of representation or to engage in repeated, exploratory experimentation. Immersive virtual environments offer an alternative learning space in which students can manipulate three-dimensional molecular structures, investigate chemical reactions, rehearse laboratory procedures, and collaborate in simulated scientific settings. This article reveals the educational potential of metaverse-enabled chemistry education through a thematic synthesis of published research on virtual reality (VR), augmented reality (AR), and virtual chemistry laboratories. The discussion is organised around five major themes. The first explores how immersive virtual environments help students visualise molecular structures and understand chemical concepts through different representations. The second examines how virtual experiments provide opportunities for hands-on-style learning, exploration, and scientific investigation. The third focuses on the role of virtual environments in developing laboratory safety awareness and understanding experimental procedures. The fourth considers how these technologies can promote student interest, active participation, teamwork, and collaborative learning. The fifth discusses the practical and ethical challenges of implementing these technologies, including teaching methods, data privacy, accessibility, equipment availability, and infrastructure requirements. Existing evidence indicates that immersive technologies can support selected learning outcomes, even though their effectiveness varies according to instructional design, learning objectives, learner characteristics, and the comparison method employed. Research comparing virtual and physical chemistry laboratories also suggests that virtual environments can support aspects of conceptual learning and procedural practice, but they do not automatically reproduce all the behavioural demands of laboratory work. The article therefore advances a pedagogically integrated model in which immersive environments complement physical experiments, guided inquiry, teacher facilitation, and evidence-based assessment. It concludes that the educational value of the metaverse depends less on technological novelty than on its capacity to make chemical phenomena intellectually accessible, experimentally meaningful, and educationally inclusive.
ATTENDAI: A MULTI-FACTOR STUDENT ATTENDANCE SYSTEM
Authors: Arsath Mohamed M, Gokul M, Rajamanikandan R, Mr.M.Premkumar
Abstract: Manual attendance recording consumes classroom time and may permit proxy attendance. Face-recognition-based attendance systems can automate identity matching, but their reliability may be affected by illumination, image quality, and presentation attacks. This paper presents ATTENDAI, a webbased student attendance system designed to combine face recognition, voice verification, and eye-blink liveness detection in a staged verification workflow. The proposed implementation uses Python and Flask for application services, OpenCV with Haar Cascade face detection and Local Binary Patterns Histograms(LBPH) for face processing, Mel-Frequency Cepstral Coefficients (MFCC) for voice features, and facial landmarks with the Eye Aspect Ratio (EAR) for blink detection. Attendance is recordeonly after the configured verification stages succeed. The system also supports Admin, Staff, and Student roles, departmentwise management, attendance reporting, and a four-hour session rule intended to prevent duplicate entries while retaining historical records. This paper describes the motivation, system architecture, principal modules, session logic, and an evaluation plan. Quantitative performance results are not claimed because they must be obtained from controlled testing of the completed implementation.
DOI: https://doi.org/10.5281/zenodo.23337736
Forensic Evaluation Of Ink Removal Using Vinegar And Ethanol On Contract Bond Paper Under Temperature Variation
Authors: Ms. Shashiprabha V, Assistant Professor Mr. Joel Joseph
Abstract: The present study focuses on the forensic evaluation of ink removal using vinegar and ethanol on contract bond paper under varying temperature conditions. The study aims to investigate the effects of these chemical agents on ink visibility, paper characteristics, and the detectability of potential alterations in written documents. Vinegar, containing acetic acid, and ethanol are examined as treatment agents to determine their effects on ink markings and the physical integrity of contract bond paper at different temperatures. The evaluation considers observable changes such as ink fading, residual ink traces, paper discolouration, surface texture, and fibre damage. Untreated paper samples serve as controls for comparison. Forensic examination methods, including visual inspection and microscopic analysis, may be used to identify characteristics associated with chemical treatment. The study also considers temperature as a factor that may influence the interaction between the treatment agents, ink, and paper substrate. The findings are expected to contribute to the understanding of document alteration and the limitations of detecting chemically treated ink entries in questioned documents. This research may provide useful insights for forensic document examiners in assessing the authenticity and integrity of contract documents. The conclusions will be based on the experimental results obtained.
DOI:
Cyclic Dynamic Performance Of BTRM-Strengthened And Unstrengthened Masonry Hydraulic Piers: An Experimental Comparison
Authors: Ahmed M. Sakr, Ibrahim Abd Ellatif, Ahmed S. Hashad, Mohamed A. Khalaf, Ismail Amer
Abstract: This study experimentally investigates the cyclic dynamic performance of Basalt Textile Reinforced Mortar (BTRM)-strengthened and unstrengthened masonry hydraulic piers under dry and wet conditions. Six specimens, comprising three control and three BTRM-strengthened piers, were tested using a shaking-table setup under progressively increasing harmonic excitation. The primary objective was to compare the number of loading cycles sustained until failure and examine the associated failure mechanisms. The results demonstrated that BTRM strengthening substantially increased the cyclic endurance of the masonry piers compared with the unstrengthened controls. The control specimens experienced severe damage after relatively few loading cycles, whereas the strengthened specimens sustained considerably more cycles before failure. The observed failure of the strengthened specimens involved basalt textile rupture and internal separation of masonry units. The dry and wet specimens were also compared to examine the influence of water exposure on cyclic performance. The findings demonstrate the potential of BTRM strengthening to improve the resistance of masonry hydraulic piers to repeated dynamic loading
DOI: https://doi.org/10.5281/zenodo.23364924
Exam Malpractice Detector: A Real-Time, Rule-Based Computer Vision System with Seat-Level Risk Heatmaps and Automated Evidence Reporting
Authors: Dhakchitha C, Kaviyashree S, Rithika K, Subashini R
Abstract: Maintaining integrity in invigilated examinations is labor-intensive, and human proctors cannot continuously observe every candidate. This paper presents Exam Malpractice Detector, a lightweight, real-time monitoring system built with OpenCV and Python that flags suspicious behavior from a single camera feed. The system combines four inexpensive visual cues: multiple-face detection using a Haar cascade, frame-differencing-based motion analysis, face-position-based "looking away" estimation, and Canny-edge contour analysis for unexpected objects. Detections are mapped to a three-tier status (Normal, Doubt/Review, Confirmed Malpractice) with confidence values. Each alert triggers cooldown-limited, hash-deduplicated, JPEG-compressed evidence capture. Alerts are also projected onto a 5×7 seat grid, where weighted scores produce a color-coded hall heatmap. At session end, a structured PDF report containing session metadata, violation summary, heatmap, event table, and screenshots is generated automatically for staff review. The design keeps a human in the loop: the system flags and documents, and staff decide. Because it needs no GPU and no training data, it can run on commodity hardware.
Fake News Detection Using AI
Authors: Janani A, Nivetha D, Prabhavathi U, Sumathi S
Abstract: The rapid circulation of news through websites and social-media platforms makes it difficult for readers to distinguish trustworthy reporting from fabricated or misleading content. This paper presents an AI-based fake news detection system that applies Natural Language Processing (NLP) and machine learning to classify textual news content into predefined categories such as fake and real. The workflow includes data preparation, text cleaning, feature extraction using Term Frequency–Inverse Document Frequency (TF-IDF), model training, prediction, and presentation of the result through an application interface. Logistic Regression is considered as the baseline classifier because it is efficient for sparse text features and provides a practical reference for text-classification tasks. The proposed system is intended to support early screening of suspicious news, not to replace source verification or professional fact-checking. The paper describes the design, implementation workflow, evaluation metrics, risks, and future improvements. Since verified experimental logs and final test measurements were not supplied for this manuscript, numerical performance is not fabricated; the results section specifies the measurements that the team should insert after running the final model. The work provides a structured, reproducible approach for building and evaluating a news-text classification application.
AUTOMATED FOLLICULAR AND OVARIAN TUMOUR ANALYSIS VIA ULTRASOUND USING U-NET VARIANTS: A REVIEW
Authors: Mrs. Rajeswari M, Dr. Monika G
Abstract: Ultrasound is the first-line imaging modality for assessing the ovary, both for monitoring follicles in fertility care and for characterising adnexal and ovarian tumours. Interpretation, however, depends on operator skill and is affected by speckle noise, low contrast, and ambiguous boundaries. Deep learning, and in particular the U-Net family of encoder-decoder networks, has become the dominant approach for automating segmentation of the ovary, follicles, and masses. This paper reviews the literature on automated follicular and ovarian tumour analysis from ultrasound using U-Net variants. It summarises the clinical background, the architectural variants (plain, attention, nested, self-configuring, recurrent, and transformer-hybrid U-Nets), their application to follicle segmentation and adnexal mass segmentation, and the downstream tasks of quantification and classification. The reviewed studies report high overlap with expert outlines, for example Dice coefficients of 0.912 for the ovary and 0.858 for follicles with a composite U-Net, and 0.91 for adnexal masses using a bounding-box-guided U-Net. Evidence nevertheless remains constrained by small, single-centre, and retrospective datasets, limited external validation, device variability, and the scarcity of Indian data. The paper presents a conceptual analysis pipeline, compares key studies, and outlines research directions toward robust, clinically integrated tools.
DOI: https://doi.org/10.5281/zenodo.23411233
AI-ENABLED DIGITAL TWINS FOR PROACTIVE FAULT PREDICTION AND SELF-HEALING IN DISTRIBUTED CLOUD–EDGE SYSTEMS: A CONCEPTUAL FRAMEWORK
Authors: Dr. Anil Karadwal
Abstract: Distributed cloud–edge systems support modern applications by combining centralised cloud infrastructure with computing resources located near data sources. Despite their advantages, these environments remain vulnerable to hardware faults, software failures, network disruptions, resource contention, and changing workloads. Conventional fault-tolerance mechanisms often respond after a failure has occurred, potentially increasing service interruption and recovery costs. Artificial Intelligence (AI) and Digital Twin (DT) technology offer opportunities to anticipate system degradation, evaluate alternative responses, and support automated recovery. This paper proposes a conceptual framework integrating digital twins, AI-based fault prediction, adaptive resource management, and self-healing mechanisms for distributed cloud–edge environments. The framework comprises six components: real-time monitoring, digital representation, predictive analytics, risk-aware decision-making, self-healing execution, and continuous feedback. It also identifies challenges relating to data quality, prediction uncertainty, model drift, computational overhead, security, and coordination across distributed nodes. A proposed evaluation methodology outlines measurable indicators, including prediction performance, service availability, recovery time, false-alarm rate, and resource overhead. The paper contributes a structured design approach for future investigation of predictive and autonomous fault management. As a conceptual study, it does not claim experimental validation or quantified performance improvements. Its proposed framework requires implementation and empirical evaluation under representative cloud–edge workloads.
DOI: https://doi.org/10.5281/zenodo.23414685
Pregnancy Health Monitoring System Using Artificial Intelligence: A Review
Authors: Anbazhagan R, Dr. Susitha N
Abstract: Pregnancy-related complications such as gestational diabetes, preeclampsia, preterm birth, and fetal distress remain major causes of maternal and perinatal morbidity, and timely detection depends on regular, accurate monitoring that many women cannot easily access. Advances in wearable sensing, mobile health, and artificial intelligence (AI) have made continuous, data-driven pregnancy monitoring technically feasible. This paper presents a structured narrative review of AI-based pregnancy health monitoring, drawing mainly on recent systematic, scoping, and narrative reviews together with selected primary studies. It examines the data sources and sensing modalities used, the machine learning and deep learning techniques applied, and the main application areas: gestational diabetes and preeclampsia prediction, fetal monitoring through cardiotocography, preterm birth and imaging, and wearable and IoT-based remote monitoring.The review finds that AI methods show consistent promise across these areas, but that the evidence base is limited by small single-centre datasets, geographic concentration of research, class imbalance, scarce external validation, and an unresolved tension between accuracy and interpretability. Privacy, sensor accuracy, and user compliance further constrain real-world deployment. The paper proposes a five-layer reference architecture for an AI-based pregnancy health monitoring system and outlines research directions relevant to resource-constrained settings such as India.
DOI: https://doi.org/10.5281/zenodo.23417250
Face Emotion Recognition System
Authors: Pradeepa S, Anamika S S, Mariammal alias Madhu M, Oviya S, Mrs.S.Santhipriya ME. Assistant professor Supervisor
Abstract: Facial expressions provide useful visual cues for human–computer interaction, but automatic interpretation remains challenging because expressions vary across individuals, lighting conditions, head pose, and image quality. This paper presents a face emotion recognition system that combines computer vision with a convolutional neural network (CNN) to classify facial expressions from images and live video. The system detects a face using OpenCV’s Haar Cascade face detector, extracts and preprocesses the facial region, and passes a 48 × 48 grayscale image to a CNN trained using the FER-2013 dataset. The network uses convolutional blocks, batch normalization, max pooling, dropout, and a softmax output layer to classify seven expression categories: angry, disgust, fear, happy, neutral, sad, and surprise. The implementation also integrates separate pretrained age-group and gender models, allowing the application to display multiple facial attributes in a unified interface. Emotion confidence scores and bounding boxes are overlaid on the detected face, while temporal averaging across recent frames is used to reduce rapid changes in predictions. The project demonstrates an end-to-end facial analysis pipeline using Python, TensorFlow/Keras, and OpenCV. Quantitative performance should be reported from the final held-out test run; the project report supplied for this paper does not provide a verified final test-results table, so no unverified performance claim is made here.
DOI: https://doi.org/10.5281/zenodo.23417701
Comparative Assessment Of Seasonal Water Quality In Canals And Barrages Of The Ajay River Influenced By Coal Mining Activities
Authors: Sujit Kumar, Dr. Sanjay Kumar Singh, Dr. Niranjan Kumar Mandal
Abstract: Coal mining can influence riverine water quality through runoff, sediment transport, and the movement of dissolved constituents. This comparative study examines seasonal water-quality patterns reported in two related investigations of the Ajay River system: one covering two canals and the other covering three barrages. Both studies considered the rainy, winter, and summer seasons and reported physicochemical parameters, selected metals, chemical oxygen demand (COD), biochemical oxygen demand (BOD), dissolved oxygen (DO), and total coliform counts. The canal results showed rainy-season turbidity of 20 NTU in both canals, while winter turbidity differed sharply between Canal 1 (0 NTU) and Canal 2 (25 NTU). In the barrage study, turbidity ranged from 17–20 NTU during the rainy season, fell to 0 NTU in winter, and rose to 22–35 NTU in summer. The barrage sites also showed higher summer COD (8–13 mg/L), BOD (3–4 mg/L), and coliform counts (2.3–2.7 × 102 CFU/100 mL) than in the rainy season. Across both studies, reported concentrations of most measured metals were low or below the stated detection limits, although iron varied in the barrage samples. The comparison indicates that seasonal conditions and the type of water body may shape the observed water-quality profile. However, the studies do not provide sufficient site-level information or statistical analyses to establish that coal mining alone caused the observed differences. Consistent sampling, clearly documented analytical methods, and expanded metal and microbial monitoring are recommended.
DOI: https://doi.org/10.5281/zenodo.23417929
BLOCK Chain Technology
Authors: S surya
Abstract: Blockchain technology has emerged as one of the most significant innovations in distributed computing since the advent of the internet. Originally conceived as the underlying architecture for Bitcoin, blockchain has since evolved into a general-purpose technology with applications spanning finance, supply chain management, healthcare, governance, and beyond. This article examines the technical foundations of blockchain, its major variants, real-world applications, and the challenges that continue to shape its adoption.
Confined Stress Analysis Of Consolidated And Unconsolidated Sandstone Reservoir Core Samples.
Authors: Ejokpa Godspower Irikefe, Ikechi Igwe, Emenike N. Wami
Abstract: This research investigates the effects of fluid saturation on the geo-mechanical properties of consolidated and unconsolidated sandstone reservoirs under confined stress conditions. Sandstone formations are critical for hydrocarbon production, but their mechanical behavior is strongly influenced by lithology and pore-fluid interactions. A comparative knowledge gap exists regarding how consolidated and unconsolidated sandstones respond under identical multiphase saturation and stress conditions. This study addresses the gap by experimentally analyzing six core samples obtained from the Niger Delta region of Nigeria. The cores were oven-dried, saturated with water and oil-water mixtures, and subjected to triaxial compression tests at confining pressures of 100 psi and 300 psi. Results revealed marked lithological differences: the consolidated dry core exhibited a Young’s modulus of 22,300 psi, yield strength of 34,800 psi, and peak strength of 36,775 psi, failing suddenly in a brittle manner. In contrast, the unconsolidated dry core showed a much lower Young’s modulus of 65 psi, yield strength of 2,700 psi, and peak strength of 2,915 psi, with gradual strain-softening failure. Saturation kinetics further demonstrated that unconsolidated cores achieved equilibrium within ~1 hour, whereas consolidated cores required 3-5 hours. Fluid type also influenced behavior: under water saturation, consolidated samples weakened to a peak strength of 23,497 psi, while oil-water saturation improved performance, reaching 29,350 psi. The study advances understanding of lithology-dependent saturation kinetics, multiphase fluid effects, and failure mechanisms under confinement. In conclusion, lithology and saturation state jointly control sandstone geomechanics, with consolidated samples showing higher strength but brittle failure, while unconsolidated samples exhibit lower strength with ductile deformation. This study contributes critical experimental data for refining reservoir geomechanical models and supporting sustainable petroleum production.
DOI: https://doi.org/10.5281/zenodo.23489151
Smart Student-Centric Environments In Higher Education: A Focused Literature Review
Authors: Prashant Govardhan, Jatin Patle, Dhananjay Lohakare, saurabh Gowardipe, Suhani Darvehkar
Abstract: Colleges and universities have become increasingly dependent on digital systems for academic records, coursework, communication, assessment, student activities, and institutional services. Yet the presence of multiple digital systems does not necessarily produce a coherent student experience. Students may have access to their grades, course activities, departmental announcements, projects, certifications, and other developmental information, while still having to move between separate platforms to understand their overall progress or determine what they should do next. This paper presents a focused literature review of research on student information systems, digital transformation, learning analytics, student-facing dashboards, e-portfolios, student engagement, and feedback practices in higher education. Twenty-four selected studies from IEEE and other established scholarly venues were examined through a comparative thematic synthesis. The review identifies a recurring shift from institution-centered information management toward student-facing systems that support interpretation, reflection, communication, and action. At the same time, several gaps remain. Information is commonly distributed across independent systems, analytics frequently emphasize measurement rather than subsequent action, and conventional academic records provide only a partial representation of student development. Research on engagement also shows that simple behavioral indicators cannot adequately represent cognitive and emotional dimensions of participation. Based on these findings, this review identifies design considerations for a student-centric departmental environment that can connect academic information, student development, opportunities, communication, and analytics without requiring replacement of existing institutional systems.
DOI: https://doi.org/10.5281/zenodo.23494723
AI-Based Financial Transaction Analysis for Fraud Detection in Digital Commerce
Authors: Mrs.N.Zabina
Abstract: With growing adoption of e-commerce platforms, the problem of identification of fraudulent financial transactions in a real-time manner has increased manifold. Current approaches of rules-based approaches and stand-alone machine learning models have faced difficulty adapting and dealing with novel types of fraud cases when there is heavy class imbalance and only few examples of frauds out of the total volumes. In this research, we propose the novel hybrid system of fraud detection for e-commerce using techniques of isolation forest, representation learning via autoencoder, and supervised learning using xgboost for classification. Unsupervised learning using anomaly detection is used to identify statistical anomalies as well as reconstruction-based outliers and supervised gradient boosting approach is used to improve the decision boundaries on labeled data. Evaluation experiments performed on large-scale anonymous transaction dataset show better discriminative performance of our hybrid approach as compared to the current benchmark models with respect to both the F1-score and AUC score. An explainable approach using SHAP values is provided as part of our framework, complying to regulations on explainability.
DOI: https://doi.org/10.5281/zenodo.23557939
Predictive Analytics for Consumer Spending Patterns in Digital Payment Ecosystems
Authors: Dr. A. Joy
Abstract: The recent explosion in the use of digital payment systems has created large amounts of data relating to customer payments. In this paper, a predictive analytical framework is proposed which will be useful for modelling spending behavior in digital payment systems. The framework uses temporal encoding of data which takes into account the type of data along with the other characteristics of the data. It also utilizes dynamic graph learning of structures and spatial-temporal convolutions in order to take into account the relationships between the transactions as well as their time-based aspects. The key difference between the proposed framework and traditional approaches lies in the fact that it considers the relationships between different spending categories and allows these relationships to change over time for each individual consumer. Experimental evaluations performed on a digital payment system dataset have shown that the proposed framework performs better in terms of accuracy than traditional baselines like ARIMA, Prophet, and LSTM.
DOI: https://doi.org/10.5281/zenodo.23557832
Eye-Controlled Virtual Mouse with Independent Click, Text Selection, Drag-and-Drop and Scroll Operations for Physically Challenged Users
Authors: Janasri S, Kiruthika D, Nandhitha J, Yashwanthini K A
Abstract: Modern law enforcement agencies face the dual challenge of managing complex public safety threats while maintaining strict institutional accountability. This paper explores the integration of artificial intelligence (AI), automation, and advanced digital tools within modern policing frameworks. By analyzing current implementations—including predictive policing algorithms, automated facial recognition, automated license plate readers (ALPRs), and AI-driven evidence management systems—this study assesses how these technologies enhance operational efficiency, accelerate response times, and optimize resource allocation. Furthermore, the paper addresses critical systemic challenges, specifically targeting algorithmic bias, citizen privacy concerns, data security vulnerabilities, and the regulatory frameworks required to govern automated decision-making. Ultimately, this research provides a balanced framework for law enforcement executives and policymakers, demonstrating how agencies can leverage cutting-edge technical innovation while upholding the core principles of ethical, transparent, and community-oriented justice.
DOI: https://doi.org/10.5281/zenodo.23552207
ARIYAAN: A PERSONAL AI ASSISTANT FOR INTELLIGENT CONVERSATION, VOICE INTERACTION, AND PRODUCTIVITY SUPPORT
Authors: Meganathan R, Ramkumar R, Indhuja N
Abstract: Personal AI assistants are increasingly used to support information access, learning, programming, and everyday productivity. However, a useful assistant should combine natural-language interaction with accessible controls, conversation context, voice input and output, and a responsive interface. This paper presents ARIYAAN, a full-stack personal AI assistant designed using React, Vite, Node.js, Express, and the Google Gemini API through the official @google/genai SDK. The application supports conversational question answering, programming assistance, mathematical explanations, Markdown-formatted responses, code highlighting, multi-turn conversation context, and locally stored conversation sessions. Browser-native Web Speech APIs provide speech recognition and speech synthesis where supported by the user’s browser. A voice-intent layer handles selected commands, including greetings, time and date requests, assistant identity questions, and desktop application launch requests. A settings interface supports model selection, voice preferences, speech rate, theme selection, and API connection testing. Gemini requests are routed through a backend service so that the API credential does not need to be embedded in frontend source code. The paper describes the system architecture, functional modules, implementation approach, and an evaluation plan covering response functionality, voice interaction, usability, security, and error handling. As measured performance data have not been supplied, this paper does not claim unverified accuracy, latency, or usability results. The proposed application is intended as an educational and productivity-support tool, with future work focused on persistence, accessibility, multilingual interaction, and stronger system-control safeguards.
DOI: https://doi.org/10.5281/zenodo.23560271
Campus Ai: A Chatbot Using Natural Language Processing for College Assistance
Authors: Athul Krishna S, Chandrika K, Sanjay G, Sanjay S
Abstract: Students and faculty at engineering colleges often search several portals, notices and documents to find routine academic information. This paper presents Campus AI, a web-based chatbot prototype that lets users ask college-related questions in natural language. The system is implemented as a Next.js and React application. A server-side chat endpoint applies per-IP sliding-window rate limiting and input sanitization, assembles a prompt from an institutional system prompt, the latest eight conversation messages and, when available, the authenticated student’s profile, and then queries the Llama 3.1 70B model hosted on the Groq platform in JSON mode. The reply is returned as a structured object containing the answer text, an optional generated visual and source labels, and is rendered in a conversational interface alongside role-specific dashboards for students, faculty and administrators. We describe the architecture, request workflow and implementation, and we separate the features that are implemented in the prototype from those that are only planned. Functional behaviour is demonstrated through screenshots of the working prototype. No quantitative evaluation of answer accuracy, response time or user satisfaction has been carried out; such an evaluation is identified as future work.
DOI: https://doi.org/10.5281/zenodo.23560791
AI-Augmented Zero Trust Architectures With Adaptive Policy Engines Powered By Continuous Risk Assessment And Contextual Analytics
Authors: Rohit Agnihotri
Abstract: Conventional Zero Trust Architecture (ZTA) deployments predicated on static policy rule sets, pre-defined access conditions, and periodic manual policy review cycles are increasingly unable to respond to the dynamic, context-sensitive, and adversarially sophisticated threat environment confronting modern enterprise networks. This article introduces and comprehensively analyzes AI-Augmented Zero Trust Architectures (AI-ZTA): a next-generation security paradigm that embeds self-learning policy engines, continuous risk assessment pipelines, and contextual analytics into the core decision-making infrastructure of Zero Trust enforcement. Drawing on empirical data from over 1,400 enterprise deployments, market growth projections, and controlled benchmarking studies, we demonstrate that AI-augmented adaptive policy engines reduce false positive rates by 86% over twelve months of continuous learning while improving true negative detection rates by 57%. Organizations deploying fully integrated AI-ZTA with adaptive policy engines achieve average breach costs of USD 1.39 million, a 73.7% reduction compared to the USD 5.28 million baseline for non-ZT environments. We introduce a seven-component AI-ZTA Reference Architecture, a five-dimensional contextual risk signal taxonomy, and a four-level AI-ZTA Maturity Model with empirically grounded implementation guidance. The article concludes with a research agenda addressing adversarial attacks on AI policy engines, privacy-preserving risk analytics, and the governance of recursive AI-driven policy modification.
DOI: https://doi.org/10.5281/zenodo.23562205
STUDENT PERFORMANCE PREDICTION USING MACHINE LEARNING
Authors: Dinesh G, Kabilan KM, Sowndar K, Vikram S, Indhuja N
Abstract: Student performance prediction is an important application of machine learning in educational data mining. Academic institutions collect different types of information, including attendance, internal assessment marks, previous academic performance, study habits, and assignment completion. Analyzing these factors manually can be time-consuming, particularly when the number of students increases. This paper presents a machine-learning-based framework for predicting student academic performance using relevant educational attributes. The proposed system consists of data collection, data preprocessing, exploratory data analysis, feature selection, model training, evaluation, and prediction. Classification algorithms such as Logistic Regression, Decision Tree, Random Forest, and Support Vector Machine can be evaluated to identify a suitable model for categorizing students into performance levels. Regression methods can alternatively be used when the target variable is a numerical examination score. The system is intended to help educators identify students who may require additional academic support and plan appropriate interventions. Model performance is evaluated using appropriate classification or regression metrics. Since actual experimental measurements depend on the selected dataset and implementation, the proposed framework does not assume unverified accuracy values. The study also discusses data privacy, fairness, interpretability, and the limitations of predictive educational systems.
DOI: https://doi.org/10.5281/zenodo.23590176
INTELLIGENT WEATHER PREDICTION SYSTEM USING MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE
Authors: Indhuprakash M, Gokul R, Harishragavendra M, Parthasharathy R, M. Premkumar
Abstract: Weather prediction supports agriculture, transportation, travel planning, and public safety. This paper presents an Intelligent Weather Prediction System that combines machine learning techniques with an accessible application interface. The proposed workflow uses weather observations such as temperature, humidity, atmospheric pressure, wind speed, cloud cover, and rainfall, depending on the data source. It includes data collection, preprocessing, exploratory analysis, feature preparation, model training, evaluation, and presentation of predictions. Candidate models include regression methods for continuous weather variables, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Long Short-Term Memory (LSTM) for suitable classification, regression, or sequential forecasting tasks. The application may also provide AI-assisted weather question answering and concise weather-news summaries when these components are implemented and connected to reliable sources. The system is intended to make weather information easier to understand and use. Actual performance must be measured on the selected dataset; no accuracy result is claimed in this draft. The final paper should report the implemented models, dataset, evaluation results, and system screenshots
DOI: https://doi.org/10.5281/zenodo.23586599
FPGA-Based Image EDGE Detection
Authors: Tejas Nanasaheb Rahane, Sakshi Vikram Shelke, Shravani Sambhaji Hinge
Abstract: Edge detection is an important operation in digital image processing because it identifies boundaries where image intensity changes sharply. These boundaries provide useful information for object detection, shape analysis, machine vision and robotics. This paper presents a proposed FPGA-based image edge detection system using the Sobel operator. The input image is acquired from a camera or stored image source and is converted to a suitable grayscale pixel stream. A 3×3 sliding window is formed with line buffers and registers. The Sobel horizontal and vertical kernels calculate gradient components, which are combined and compared with a threshold to generate an edge image. FPGA parallelism and reconfigurable logic can support high-throughput, low-latency processing for suitable designs. The design can be described using Verilog HDL or VHDL and developed with tools such as AMD Xilinx Vivado or Intel Quartus Prime. Performance must be verified on the selected board using measured timing, resource utilization and image-quality results.
DOI: https://doi.org/10.5281/zenodo.23595201
Real-Time Lip Reading Using A 3d Cnn–Bilstm Architecture for Visual Speech Recognition
Authors: Harshini R, Poojabharani S, Sanjana S, Saranya R
Abstract: Lip reading is the task of recognising spoken words from the visible movements of the lips without using any audio signal. This paper presents a real-time visual speech recognition system that captures live video from an ordinary webcam, locates the mouth region, and predicts the spoken word or phrase as text. The system combines 3D convolutional layers, which learn spatial and temporal lip-movement features, with bidirectional LSTM layers that model the whole sequence in both directions, and it is trained with Connectionist Temporal Classification (CTC) loss so that no frame-level alignment is needed. A lightweight pipeline of MediaPipe landmark detection, mouth-region preprocessing and a sliding frame buffer allows the model to run on a standard computer. On a small custom dataset of four phrases, the system achieved a word error rate (WER) of 16.67% and a character error rate (CER) of about 4–5%. The results indicate that a compact, audio-free lip-reading system is feasible for assistive communication and for noisy environments.
DOI: https://doi.org/10.5281/zenodo.23615156
COLLABRO AI Ecosystem: A Collaborative Multi-Agent Architecture for Conversational Assistance, Autonomous Software Engineering and Audio Intelligence
Authors: Ajay Saagar P, Kavihai Arasu S, Ramachandran K, Samuel S
Abstract: Artificial intelligence tools for conversation, programming and speech are usually built and used in isolation, which forces users to carry context manually from one tool to another. This paper presents the COLLABRO AI Ecosystem, a multi-agent platform that unifies conversational assistance, autonomous software engineering and audio intelligence within a single coordinated architecture. Natural Language Processing (NLP), Machine Learning (ML) and Deep Learning (DL) techniques are used to interpret user input, infer intent and decompose goals into tasks. An orchestrator agent routes these tasks to specialised agents for dialogue, code understanding, generation and debugging, and speech and audio analysis. A shared context store lets the agents maintain a unified understanding of the user’s goal, exchange intermediate artefacts and improve through feedback. A governance layer embeds data privacy, transparency and responsible automation into every agent interaction. We describe the architecture, the coordination and routing workflow, candidate implementation techniques and the ethical safeguards, and we propose an evaluation protocol with per-agent and cross-agent metrics. The modular design supports software development, enterprise automation, content creation and accessibility applications. This paper reports design and methodology; no quantitative performance results are claimed, and measured evaluation is identified as future work.
Spam Email Detection System Using Artificial Intelligence and Natural Language Processing
Authors: Bhuvaneswaran R, Dharshan M, Nandhakumar S, Selvaragavan J, Indhuja N
Abstract: Email remains a common channel for personal, educational, and business communication, but unsolicited messages and deceptive email campaigns create security and productivity concerns. This paper presents an AI- and natural-language-processing-based workflow for classifying email text as spam or legitimate (ham). The proposed pipeline validates labelled email data, normalizes text, converts messages into numerical features using Term Frequency–Inverse Document Frequency (TF-IDF), and applies a supervised classifier. Multinomial Naive Bayes is used as the baseline model, while Logistic Regression and a linear Support Vector Machine can be considered for comparison. The workflow separates dataset preparation, feature extraction, training, prediction, and evaluation so that the same fitted pipeline is used for new messages. Model quality is assessed using accuracy, precision, recall, F1-score, and a confusion matrix. The project report does not provide verified numerical evaluation results; therefore, performance values should be obtained by running the implementation on the selected labelled dataset and should not be assumed. The design provides a reproducible educational prototype that can be extended with URL analysis, multilingual processing, and email-client integration.
Pollutant Sources, Removal Techniques and their Impacts on Environmental Quality and Ecological Sustainability
Authors: Peace Onya Ali, Ikechukwu Sampson Chikwe, Olubunmi Abiola Adewusi, Tasie Chimezie Michael, Boniface Tersoo Iorhuna, Edjere Akpoobaro Obadejire, Erienu Obruche Kennedy
Abstract: Environmental pollution is a major global challenge that threatens environmental quality, human health, biodiversity, and ecological sustainability. This review examines the sources of environmental pollutants, pollutant removal techniques, their effectiveness, and their impacts on environmental quality and ecological sustainability. Major sources of pollution include industrial activities, agricultural practices, domestic waste, petroleum operations, transportation, and improper waste disposal. Common pollutants include heavy metals, petroleum hydrocarbons, pesticides, plastics, particulate matter, and emerging contaminants. Conventional removal techniques, such as filtration, adsorption, chemical precipitation, and biological treatment, are discussed alongside emerging approaches, including phytoremediation, nanotechnology, membrane filtration, electrochemical treatment, and advanced oxidation processes. The review highlights that the effectiveness of these techniques depends on pollutant characteristics, treatment conditions, operational costs, and environmental requirements. Although pollutant removal technologies can improve air, water, and soil quality, some generate secondary waste, consume substantial energy, or produce potentially harmful transformation products. These limitations emphasize the need for integrated treatment strategies, effective waste management, pollution prevention, and continuous environmental monitoring. Sustainable pollution control should prioritize affordable, energy-efficient, and environmentally safe technologies that support ecosystem restoration, biodiversity conservation, and resource recovery. The review concludes that combining conventional and emerging remediation techniques with stronger environmental regulations and sustainable production practices is essential for reducing pollution and promoting long-term ecological sustainability.
Principles of Implementing 4d Bim for Time Management in Major Project Delivery
Authors: Ahamed Asif Nagoor Meerasha
Abstract: Time overrun remains one of the most persistent problems in major construction projects, and more than half of UK projects still exceed their agreed programmes. Four-dimensional building information modelling (4D BIM), which links the construction schedule to a 3D model, has been promoted as a way to make planning more reliable, yet its use in practice remains uneven. This paper examines the principles of implementing 4D BIM for time management in major project delivery. It follows a qualitative approach that combines a structured review of literature, industry guidance and policy with a comparative analysis of published surveys and interview studies of 4D BIM use, mainly from the UK and Sweden. The factors that shape adoption are grouped into technical, procedural and people-related categories, and a practical implementation sequence is set out, from reliable data capture and collaborative planning to the integration of model and programme. Applications across the construction phase are then mapped, covering tender planning, construction method planning, programme communication, design interrogation, resource and workspace planning, safety and logistics. The comparative analysis shows a consistent pattern: awareness of 4D BIM is high, but routine use lags behind, and the technology is valued more for communicating and checking programmes than for creating them. The main barriers are a shortage of 4D skills, limited client demand, poor interoperability between authoring and scheduling tools, mismatched levels of development and traditional procurement. On this basis the paper proposes three practical approaches, clarity, collaboration and adaptation, supported by SMART 4D objectives, early involvement of the 4D team, staged training and a ring-fenced implementation budget. The main limitation is that the time savings are not quantified on live projects, which remains a subject for further research
DOI: https://doi.org/10.5281/zenodo.23685128
A Review Of Machine Learning Techniques For Analog-to-Digital Converter Design Automation
Authors: Jai Akkayarajkumar V J
Abstract: ADCs play an important role in converting information from the analog world to the digital one; with modern applications requiring higher resolutions, speed, and power efficiency, it has become very difficult to design them. Traditionally, the design of an ADC follows an iterative design process that includes stages like specification, behavioral modeling, transistor sizing, schematic simulation, layout, and post-layout verification, which is laborious and requires much designer expertise due to the consideration of process-voltage-temperature variations and yield. In this work, we perform a review of the latest advancements in the use of machine learning (ML) to tackle the challenges of ADC design in recent years, with systematic search from 2015 to 2026 in IEEE Xplore, ACM Digital Library, and arXiv. Rather than organizing the literature on different ML algorithms, this paper categorizes ML approaches such as regression, tree-based models, SVM, ANN, CNN, GNN, reinforcement learning, evolutionary algorithms, and Bayesian optimization in ADC design stages such as behavioral modeling, transistor sizing, calibration of comparators and capacitor arrays, calibration of ADC, layout optimization, prediction of PVT/yield variations, and design space exploration. Comparative analysis includes the benefits provided by each technique such as speed and accuracy improvements, as well as their main drawbacks. Open challenges are then presented, including lack of data, poor generalization performance, low explainability, and the largely unexplored topic of co-optimization across multiple stages. Finally, future trends in the field are presented in terms of new directions such as generative AI, GNN-based layout optimization, physics-informed learning, digital twins, and human-AI collaborative design, as well as risks and potential problems such as manufacturability and IP in generative approaches.
DOI: https://doi.org/10.5281/zenodo.23721242
Obstacle Avoiding Robot Using Arduino Uno R4 Minima, HC-SR04 Ultrasonic Sensor And L298N Motor Driver
Authors: Ishaan Sreejith, Vaishnav P, Aby Jacob Varghese
Abstract: This report describes the design and construction of a small wheeled robot that senses obstacles with an HC-SR04 ultrasonic sensor and stops before hitting them. An Arduino Uno R4 Minima measures distance from the echo time of each ultrasonic pulse and drives two DC motors through an L298N H-bridge driver using PWM. The robot moves forward at a fixed duty cycle and halts when an object lies closer than 20 cm or when no echo returns. The hardware, wiring, working principle, software, build procedure and troubleshooting notes are documented so the project can be reproduced.
DOI:
LabVIEW Based Closed Loop DC Motor Speed Control And Performance Analysis Under Variable Load Condition
Authors: Pranav Sajeev, Dilraj N V, Dr. Saravanakumar R
Abstract: This paper describes a low-cost closed-loop speed controller for a 12 V brushed DC motor, written so that the build can be repeated. An ESP32 times the pulses of an optical slot sensor on an 8-slot disc, filters the speed and sends it over USB serial every 20 ms. A LabVIEW VI runs the PID controller and returns a PWM command (0–255) that the ESP32 applies to an L298N driver. All hardware, firmware, VI and test settings are listed, together with the tuning procedure. Mechanical load is applied by hand with a friction pad. We analyse the delay in the loop and show, from the firmware parameters, that the speed filter (about 47 ms of mean lag) dominates the estimated 63 ms loop dead time, while USB-serial timing jitter is only a few milliseconds. In a demonstration run, the PID output stayed between 231 and 248 counts and the mean speed was within about 4 % of the setpoint, but the speed estimate was very noisy because of the hand-cut disc.
DOI: https://doi.org/10.5281/zenodo.23723648
Responsible AI Framework for Trustworthy Decision-Making in Cloud Computing
Authors: Pawar Pritesh Sandip, Mrs. Khushbu Trivedi
Abstract: This paper presents a design in which every decision produced by a cloud-hosted AI model is examined by a dedicated Responsible AI layer before anyone acts on it. Cloud platforms now run models that classify, predict and recommend in settings where errors are costly, yet an accuracy score alone cannot show whether a particular decision was fair, private, secure or adequately justified. The proposed framework adds an assessment stage downstream of the model. This stage tests each output against fairness, privacy, security and reliability criteria, attaches a feature-level explanation generated with SHAP or LIME, merges the findings into a single risk label, and passes high-risk or uncertain outputs to a human reviewer. Every final decision is written to an audit log, and monitoring follows model behaviour, input drift and responsible-AI risks over time. The paper lays out the architecture, a thirteen-step decision procedure, a compact prototype plan built on Python, Scikit-learn, SHAP, LIME, Fairlearn and a public cloud, and an evaluation plan that contrasts the framework with an unmodified baseline. Because the prototype has not yet been built or tested, no experimental results are reported. The contribution is the integrated design together with a measurable plan for validating it.
DOI: https://doi.org/10.5281/zenodo.23726342
Digital Health Literacy and Its Impact on Patient Engagement in Telemedicine Services: A Cross-Sectional Study
Authors: Assistant Professor Mahesh S, Dr. Jayasree P
Abstract: Digital health literacy (DHL) has been recognized as a significant determinant of telemedicine engagement, however, the pathways that link DHL with telemedicine engagement have not been sufficiently quantified. This cross-sectional study aims to establish the relationship between digital health literacy and patient engagement among 412 adults utilizing telemedicine services at an urban academic health system during the period from January to December 2025. DHL has been assessed by means of eHealth Literacy Scale (eHEALS), whereas patient engagement was measured via Patient Health Engagement Scale (PHE-s) and behavioral measures of telemedicine use. Logistic regression and explainable machine learning (CatBoost with SHAP) have been used to determine DHL engagement associations and the most important predictors. Adequate DHL levels were detected in 58.7% of subjects. Increased eHEALS scores were positively correlated with increased PHE-s positions (adjusted OR = 3.42; 95% CI: 2.18-5.37) and completed telemedicine sessions (adjusted OR = 2.89; 95% CI: 1.76-4.74) when adjusted for age, education, income, and chronic conditions presence. SHAP analysis showed self-care confidence, health information searching behavior and number of digital devices as the most influential predictors of high patient engagement level.
Quantum-Inspired Optimization for Energy-Efficient Task Scheduling in Cloud Computing
Authors: Assistant Professor N.Navaneetha, Assistant Professor L Bhavani
Abstract: The increasing demands for energy by cloud data centers have made it even more urgent to devise scheduling techniques that address the competing issues of achieving performance goals and maintaining sustainable practices. The proposed approach in this paper is a Quantum-Inspired Energy-Aware Scheduler (QIEAS) that utilizes concepts of quantum superposition along with a rotation gate-based local search strategy to schedule tasks on virtual machines. Contrary to hardware-assisted quantum-inspired techniques, QIEAS runs on purely classical systems but employs quantum state modeling with probabilistic amplitude coding technique. A multi-objective function consisting of energy consumption, makespan and load balance is employed by the proposed scheduler and a quantum measurement strategy is used to collapse possible solutions. Comparison of QIEAS against three meta-heuristic-based algorithms shows a reduction in energy consumption and scheduling delay in all scenarios.
Artificial Intelligence for predictive surveillance of Infectious Disease Outbreaks
Authors: Research Scholar S.Arockia Vinotha, Madhura B S
Abstract: The rising rate and speed of spread of infectious disease outbreaks require surveillance systems that precede traditional reporting flows. In this paper, we conduct a systematic exploration of the design, development, and assessment of a predictive surveillance framework based on artificial intelligence technologies for outbreak detection and short-term forecasting. The suggested approach entails the integration of multiple types of data sources – open source epidemic intelligence, syndromic data, mobility data, and climatic variables – into a unified pipeline encompassing natural language processing for event extraction, machine learning ensembles for risk classification, and temporal deep learning for trajectory forecasting. An AI framework involving Bidirectional Encoder Representations from Transformers (BERT) technology for signal classification and Gated Recurrent Unit (GRU) network for case forecasting is described in algorithmic pseudocode and illustrated via system diagrams. Retrospective analysis in multiple outbreak cases indicates that the ensembling technique yields better discrimination than single-model approaches, whereas the GRU model decreases forecast error in early outbreak growth stages. Results are summarized in a comparative table of different models' configurations. We conclude the paper with an identification of practical hurdles in the deployment of an AI-based surveillance system and the consideration of governance strategies for such systems.
Research Paper Of Lakhan Tank
Authors: Deepak Singh
Abstract:
DOI: https://doi.org/10.5281/zenodo.23763322
Explainable Machine Learning-Based Flood Susceptibility Mapping And Early Warning Using Multi-Source Geospatial Data
Authors: Dr. Pankaj Malik,, Hamid Ansari, Rajveer Raghuvanshi, Shahid Ali,, Ahmad Raza Qureshi
Abstract: Modern law enforcement agencies face the dual challenge of managing complex public safety threats while maintaining strict institutional accountability. This paper explores the integration of artificial intelligence (AI), automation, and advanced digital tools within modern policing frameworks. By analyzing current implementations—including predictive policing algorithms, automated facial recognition, automated license plate readers (ALPRs), and AI-driven evidence management systems—this study assesses how these technologies enhance operational efficiency, accelerate response times, and optimize resource allocation. Furthermore, the paper addresses critical systemic challenges, specifically targeting algorithmic bias, citizen privacy concerns, data security vulnerabilities, and the regulatory frameworks required to govern automated decision-making. Ultimately, this research provides a balanced framework for law enforcement executives and policymakers, demonstrating how agencies can leverage cutting-edge technical innovation while upholding the core principles of ethical, transparent, and community-oriented justice.
DOI: https://doi.org/10.5281/zenodo.23793526
Experimental Investigation on The Mechanical and Physical Properties of Concrete Incorporating Alkali-Treated Crumb Rubber as Partial Replacement of Fine Aggregate
Authors: Mr. Prashant Patel, Mr. Pankaj Agrawal Assistant Professor
Abstract: Replacing natural sand with crumb rubber from waste tyres can reduce the demand for river sand and the burden of waste tyres, but untreated rubber weakens concrete because it bonds poorly with cement paste. This paper reports an experimental study of M25 concrete in which 5, 10, 15 and 20% of the fine aggregate by volume was replaced with untreated or sodium hydroxide (NaOH)-treated crumb rubber, compared with a conventional control mix. Workability, fresh density, compressive strength at 7 and 28 days, splitting tensile strength, flexural strength, hardened density and water absorption were measured. All properties deteriorated as rubber content increased. At 20% replacement the 28-day compressive strength fell from 33.4 MPa for the control to 18.9 MPa with untreated rubber and 22.0 MPa with treated rubber. Relative to untreated mixes, treatment raised compressive, splitting tensile and flexural strength by up to 16.4%, 12.9% and 10.8% and reduced water absorption by up to 12.9%, but no rubberised mix matched the control. Two-factor analysis of variance showed significant effects of replacement level and rubber condition (p < 0.001) and no significant interaction. Using a minimum of 25 MPa at 28 days, treatment raised the acceptable replacement level from 10% to 15%. The results suggest that treated rubber concrete is better suited to non-structural and low-to-moderate strength applications.
DOI: https://doi.org/10.5281/zenodo.23804716
Governance as a Transmission Mechanism: How Institutional Quality Shapes Fiscal Effectiveness in Nigeria
Authors: Bolarinwa,Sehilat Abike, Adu, Cecilia, Ejiks, Oluwakemi Latifat
Abstract: This study empirically examines how institutional quality moderates the relationship between fiscal policy and fiscal effectiveness in Nigeria from 1996 to 2024. The study employs annual time series data sourced from the Central Bank of Nigeria Statistical Bulletin and World Bank World Governance Indicators. Data were analyzed using Augmented Dickey-Fuller and Phillips-Perron unit root tests, Johansen cointegration, Vector Error Correction Model, Impulse Response Function, Variance Decomposition, Granger Causality, and an interaction regression model. Findings: The results reveal that all variables are integrated of order one I(1) and cointegrated, indicating a long-run relationship. The VECM shows a negative and significant error correction term. The interaction term between fiscal policy and institutional quality is positive and statistically significant, confirming that strong institutional quality amplifies the effect of fiscal policy on economic growth. Impulse response shows that shocks to institutional quality have persistent positive effects on growth. Variance decomposition reveals that institutional quality and fiscal policy jointly explain over 35% of growth variation. Practical Implications: Fiscal reforms in Nigeria will be ineffective without corresponding governance reforms. Policy should focus on strengthening budget credibility, tax administration, and accountability institutions. This study provides direct empirical evidence of governance as a transmission mechanism in Nigeria, moving beyond treating it as a mere control variable.
Sodium Hydroxide Treatment Of Crumb Rubber As Fine Aggregate In Concrete: A Review
Authors: Mr. Prashant Patel, Mr. Pankaj Agrawal
Abstract: Concrete is the most widely used construction material, yet its production consumes large quantities of natural sand, while end-of-life vehicle tyres remain a difficult waste stream. Partial replacement of fine aggregate with crumb rubber from waste tyres addresses both problems, but untreated rubber lowers strength, stiffness and workability and increases water absorption, largely because of a weak bond between the hydrophobic rubber surface and the cement paste. This paper gives a simple review of published work on rubberised concrete, with emphasis on pre-treatment of crumb rubber with sodium hydroxide (NaOH). It covers the properties of waste tyre rubber, the reported effects of rubber on the fresh, mechanical, physical and durability properties of concrete, the nature of the rubber–cement interface, alternative methods of improving that interface, and the mechanisms and parameters of NaOH treatment. The literature agrees on the broad effects of rubber. NaOH treatment is the most widely studied and practical chemical treatment and generally reduces, but does not remove, the strength penalty. Gaps remain in controlled comparison of treated and untreated rubber, reporting of treatment conditions, physical-property data and statistical analysis.
DOI: https://doi.org/10.5281/zenodo.23810899
Machine Learning-Based Cyber Threat Detection And Real-Time Network Security Enhancement
Authors: Vinutha R
Abstract: The emergence of advanced cyber-attacks targeting the latest network infrastructure has highlighted the inefficiencies of signature-based systems employed to detect such attacks, which are unable to recognize new threats due to their lack of adaptability. The current paper offers a machine learning framework for cyber threat detection and network security improvement through real-time application of streaming data analysis techniques together with ensemble learning approaches. The system is based on a hybrid framework comprising of the Random Forest and XGBoost classifiers for detection of known attacks and autoencoder-based anomaly detection method for identifying unknown threats. Feature engineering is used to extract static and behavioral features of the network traffic. In addition, the system includes an adaptive response strategy, which allows the system to dynamically modify the network security policy depending on the threat level. It was shown that the offered system is capable of providing efficient threat detection for different types of attacks, while being computationally efficient enough to be applied in a real-time scenario.
DOI: https://doi.org/10.5281/zenodo.23834126
Dynamic Adaptive Bloom Filter Based Privacy-Preserving Phrase Search For Encrypted Iot Cloud Data
Authors: Sphoorthy Reddy Surakanti, Dr. S. Ramachandram
Abstract: The use of IoT devices in huge numbers results in massive amounts of sensitive data being outsource into cloud storage systems, making it necessary to design efficient search schemes over such encrypted data. Phrase Search allows users to retrieve their documents by matching exact sequences of keywords, but existing solutions have not found a good balance between search time, storage space, and privacy loss due to their static design. In this work, we propose an efficient Dynamic Adaptive Bloom Filter based Privacy-Preserving Phrase Search (DABF-PPS) scheme for encrypted IoT cloud data. We introduce a dynamic parameter optimization method to adaptively adjust Bloom filters according to document set properties and queries. A hierarchical n-gram Bloom filter index is designed to allow phrase searches without exposing keyword locations. Furthermore, our solution supports dynamic updating of indices with the help of counting Bloom filter structures and lazy deletion. Our security analysis proves that our solution provides forward and limited backward privacy against chosen-keyword attacks. Our experiments on real-world IoT datasets show that our approach decreases search time and storage space while maintaining acceptable false-positive rates.
DOI: https://doi.org/10.5281/zenodo.23834235
Fresh, Strength And Confinement Behaviour Of Self-Curing Self-Compacting Concrete Using Polyethylene Glycol
Authors: Karnati surya teja, A.upendar
Abstract: A lot of research work has been going on in the field of concrete and constant efforts are going on to improve its performance with respect to strength and durability. One of major considerations in concrete making is gaining control on water. The water supplied during mixing in concrete is needed to hydrate the cement and to achieve the required rheology properties of various concrete materials in their mixing, transportation, placing and compacting. During hydration, there is a need to maintain a correct relative humidity and temperature for developing good concrete. Decrease in relative humidity will cause self desiccation or chemical shrinkage at micro level. To prevent the above problem, it is advised to make embedded water available for curing. There are a number of methods for providing embedded water for curing viz., using light weight aggregate, super absorbent polymers and hydrophilic materials.
DOI: https://doi.org/10.5281/zenodo.23858878
Seismic Response Of Regular And Irregular Reinforced Concrete Buildings Under Ground Motions Of Different Frequency Content
Authors: Nakirekanti Srinu, A.Upendar
Abstract: Earthquakes produce ground motions with varying frequency characteristics that influence the seismic response of reinforced concrete buildings. This study examines regular and irregular G+9 and G+19 reinforced concrete structures subjected to low-, intermediate- and high-frequency ground motions across different seismic zones. Nonlinear time-history analysis is performed using ETABS to compare base shear, natural time period, storey displacement and storey drift. The study evaluates the effects of building height, structural irregularity, seismic zone and earthquake ground-motion characteristics on structural response. Comparative results are presented through the original numerical tables, structural models and graphical outputs. Keywords: reinforced concrete; seismic response; regular and irregular buildings; ETABS; nonlinear time-history analysis; ground motion frequency.
DOI: https://doi.org/10.5281/zenodo.23861405
Smart Adaptive Water Tap Based On Ambient Temperature: A Review Of Temperature Sensing, Thermoelectric Conditioning And Embedded Control Techniques
Authors: Bhise Pallavi Ramdas, Ganjale Tanishka Jayram, Ghule Srushti Suresh, Prof. M. S. Nalawade, Dr. V. M. Dhede
Abstract: The temperature of tap water changes with the seasons. In summer, water stored in rooftop tanks and exposed pipes can become uncomfortably warm, while in winter it can feel too cold for washing or drinking. Users often wait for the water to change temperature or depend on separate coolers and heaters, which wastes water and energy. A smart adaptive tap addresses this by sensing the ambient conditions, deciding a comfortable water temperature and conditioning the water automatically. This paper reviews the technologies required for such a device. It covers temperature sensors for air and water, flow sensing to detect when the tap is in use, cooling and heating methods with emphasis on thermoelectric Peltier modules and resistive heaters, and control strategies from hysteresis to PID and fuzzy logic. Basic heat-transfer relations are used to estimate the power needed to change the temperature of flowing water, showing why a small conditioned reservoir is more practical than instantaneous cooling for most household flows. A system architecture and control flow are proposed in which a microcontroller maps ambient temperature to a target water temperature, compares it with the measured value and drives cooling or heating only while water is drawn. Applications, safety requirements, limitations and future enhancements such as user profiles and IoT monitoring are discussed.
DOI: https://doi.org/10.5281/zenodo.23879037
Drone-Based Smart Forest Fire Detection: A Survey Of Aerial Sensing, Vision-Based Recognition And Early Warning Systems
Authors: Chaskar Siddhi Dattatray, Chinchawade Tanvi Santosh, Bhore Tanvi Santosh, Prof. P. S. Dumbre, Prof. M. S. Nalawade, Dr. V. M. Dhede
Abstract: Forest fires destroy trees, wildlife habitats and natural resources, release large amounts of smoke and threaten nearby communities. Their impact depends strongly on how quickly they are detected, yet traditional methods based on watchtowers and ground patrols require continuous human effort and are slow to cover remote terrain. Satellites observe large areas but revisit a location only a few times a day and cannot see small fires under cloud or canopy. Unmanned aerial vehicles, commonly called drones, fill this gap by carrying cameras and sensors over the forest on demand. This survey reviews forest fire detection methods and focuses on drone-based systems. It discusses the sensing payload, including visible and thermal cameras and gas and temperature sensors, the detection algorithms, from colour-rule methods to convolutional neural networks, and the communication links that deliver alerts with GPS coordinates to forest authorities. Representative studies and datasets are compared. A layered architecture for a smart drone-based fire detection and alert system is then presented, covering sensing, onboard processing, communication, ground-station verification and alert dissemination. Practical challenges such as limited flight time, false alarms, communication range and regulation are discussed along with future directions such as drone swarms and integration with satellite data.
DOI: https://doi.org/10.5281/zenodo.23862991
Artificial Intelligence For Energy-Efficient Green Cloud Data Centres: A Review Of Workload Prediction, Intelligent Scheduling And Cooling Optimization
Authors: Dhavale Priyal Sandesh, Dhavale Pranjal Sandesh, Gaikar Aditi Bhausaheb, Dr. V. M. Dhede, Prof. M. S. Nalawade
Abstract: The rapid growth of online services, digital education and large-scale artificial intelligence has increased the electricity demand of cloud data centres, where servers, storage, networks and cooling equipment run around the clock. Rule-based management policies, which react to fixed thresholds, often leave servers underused and cooling plants over-provisioned. Artificial intelligence (AI) offers a different approach: models learn from operational data, anticipate demand and choose actions that save energy while protecting service quality. This review examines how AI is applied to four tasks in a green data centre: forecasting workload, allocating and consolidating virtual machines, scheduling tasks with awareness of energy and carbon, and controlling cooling systems. Statistical forecasting, recurrent neural networks, reinforcement learning and data-driven cooling control are explained and compared through representative studies. An AI-based green-cloud framework is then described that combines a learning-based demand predictor, an energy- and carbon-aware scheduler, a cooling optimiser and a renewable-supply monitor in a closed control loop. A simulation-based evaluation plan using energy, response time, utilization and carbon emission as metrics is outlined. Challenges including data quality, model interpretability, safety of autonomous control and the energy cost of the AI models themselves are discussed.
DOI: https://doi.org/10.5281/zenodo.23879779
6G-Enabled Internet Of Things: A Survey Of Enabling Technologies, Network Architecture, Applications And Open Challenges
Authors: Kapadi Lalita Arvind, Kurhe Yashomati Satish, Kute Ruchika Santosh, Dr. V. M. Dhede, Prof. M. S. Nalawade
Abstract: The Internet of Things (IoT) links sensors, machines, vehicles and everyday objects so that they can exchange data and act with little human intervention. Fifth-generation (5G) networks introduced dedicated support for massive machine-type and ultra-reliable low-latency communication, but emerging applications such as autonomous transport, remote surgery, immersive extended reality and large-scale digital twins demand still higher data rates, lower latency, greater reliability, precise positioning and far denser connectivity. Sixth-generation (6G) networks, now being defined under the IMT-2030 framework, aim to meet these needs and to make artificial intelligence a native part of the network. This survey reviews 6G-enabled IoT. It summarises the requirements and key performance indicators, and explains the enabling technologies: terahertz and sub-terahertz communication, intelligent reflecting surfaces, integrated sensing and communication, edge intelligence, AI-native network management and non-terrestrial networks. A five-layer architecture for 6G-enabled IoT is presented, linking devices, access, edge, core and applications under a cross-layer security and energy plane. Applications in smart cities, connected vehicles, healthcare, industrial automation, precision agriculture and environmental monitoring are discussed. Finally, open challenges including infrastructure cost, spectrum regulation, energy consumption, security and privacy, and the management of billions of devices are identified.
DOI: https://doi.org/10.5281/zenodo.23881407
SilentSOS: A Review Of Multimodal Context-Aware Wearable Systems For Silent Emergency Detection And Intelligent Assistance
Authors: Dnyaneshwari Babasaheb Niwdunge, Rasika Jijabhau Gunjal, Priti Vilas Hadawale, Dr. V. M. Dhede, Prof. M. S. Nalawade
Abstract: Many personal safety devices depend on the user pressing a panic button or unlocking a smartphone, yet in a fall, a sudden medical event, an assault or harassment the person may be unable to do either. A wearable that recognises such situations by itself and calls for help silently can close this gap. This review examines the technologies needed for such a device and organises them around SilentSOS, a multimodal context-aware wearable concept. It surveys physiological sensing of heart rate and blood oxygen saturation by photoplethysmography, motion sensing and fall detection with inertial sensors, skin and ambient temperature monitoring, acoustic analysis of surrounding sounds, satellite positioning and cellular alerting. It then reviews how machine learning combines these signals so that the decision does not rest on a single sensor, which reduces both missed events and false alarms. A layered architecture is presented in which an ESP32 microcontroller acquires data from MAX30102, MPU6050, temperature and microphone sensors, extracts features, classifies the situation as normal, suspicious or high-risk, and sends an SMS with location and readings through a GSM module, with a discreet gesture available for manual activation. Design trade-offs including battery life, privacy, on-device inference and validation are discussed, together with future extensions such as ECG and blood-pressure estimation.
DOI: https://doi.org/10.5281/zenodo.23863454
Temperature-Based Automatic Fan Speed Control Using Embedded Microcontrollers: A Review Of Sensing, PWM Drive And Closed-Loop Control Strategies
Authors: Chavan Prathmesh Sambhaji, Dhakne Vaibhav Balasaheb, Giram Devandra Rajandra, Prof. S. D. Chougule, Prof. M. S. Nalawade, Dr. V. M. Dhede
Abstract: Electric fans are used for personal comfort and for cooling equipment such as power supplies, boilers, incubators and ovens. A fan that always runs at full speed wastes energy and produces unnecessary noise, while manual speed selection depends on the user noticing a change in temperature. Automatic fan speed control solves this by measuring temperature and adjusting fan speed continuously. This paper reviews the design of such controllers built around low-cost microcontrollers of the AVR family, such as the ATmega8, ATmega168 and ATmega328 used on Arduino boards. The review covers temperature sensing with the LM35 and alternative sensors, analog-to-digital conversion, pulse width modulation (PWM) drive through a transistor stage, display of set and measured values on a 16×2 LCD, and the power supply. Control strategies ranging from on–off and stepped control to proportional, PID and fuzzy logic control are explained and compared. A reference architecture and control flow are presented for a stand-alone controller using a 12 V, 2 A supply, together with design calculations. Applications in air-conditioning, water heaters, incubators and industrial process equipment, as well as limitations and possible improvements such as wireless monitoring, are discussed.
DOI: https://doi.org/10.5281/zenodo.23881620
Memristor-Based In-Memory Computing For Artificial Intelligence Acceleration: A Review Of Devices, Crossbar Architectures, Benefits And Challenges
Authors: Randive Abhijeet Santosh, Sadaphal Harshal Ravindra, Shinde Prashant Shantaram, Prof. S. E. Shinde, Prof. M. S. Nalawade, Dr. V. M. Dhede
Abstract: Artificial intelligence workloads are dominated by multiply-and-accumulate operations on very large weight matrices. In conventional processors these weights must be moved repeatedly between memory and the arithmetic units, and this data movement, rather than the arithmetic itself, consumes most of the energy and time. Memristor-based in-memory computing (IMC) addresses this limitation by performing computation inside the memory array. A memristor is a two-terminal resistive switching device whose conductance can be programmed and retained, so it can store a weight. When memristors are arranged in a crossbar, Ohm's law and Kirchhoff's current law cause the column currents to equal the product of an input voltage vector and the conductance matrix, so a complete vector–matrix multiplication is obtained in a single analog step. This review explains the memristor concept and its physical realisations, the working principle of crossbar arrays, and the peripheral circuits and system architecture needed to build an accelerator. Representative device-level and system-level studies are compared. The benefits for AI inference and training are assessed against non-idealities such as device variability, limited precision, conductance drift, wire resistance and the cost of analog-to-digital conversion. Applications and research directions are identified.
DOI: https://doi.org/10.5281/zenodo.23881928
Green Cloud Computing: A Survey Of Energy-Efficient Resource Management And Carbon-Aware Scheduling In Cloud Data Centres
Authors: Bhor Aniket Madhav, Boddawar Kunal Chandrakant, Walke Yashraj Dinkar, Prof. S. L. Rahane, Prof. M. S. Nalawade, Dr. V. M. Dhede
Abstract: Cloud platforms deliver compute, storage and software as on-demand services, yet the data centres behind them draw large and continuous amounts of electricity for servers, network fabrics and cooling plants. Green cloud computing is the body of techniques that lowers this energy and carbon footprint without breaking the service-level commitments made to users. This survey organises the field into four layers of intervention: efficient hardware and facility design, virtualization with dynamic consolidation of virtual machines, energy- and carbon-aware scheduling of workloads, and integration of renewable supply. For each layer the paper explains the underlying principle, reviews representative studies, and compares their objectives, metrics and limitations. Building on this review, a layered conceptual framework is described in which a monitoring module gathers utilization, power and grid-carbon signals, a prediction module estimates near-term demand, and a decision engine places and migrates workloads on the most suitable servers while idle hosts are switched to low-power states. Evaluation through simulation toolkits or a small private-cloud test bed is discussed together with metrics such as power usage effectiveness, energy per request and SLA violation rate. Open issues, including the energy cost of migration, accurate carbon accounting and the trade-off between consolidation and performance, are identified as directions for further study.
DOI: https://doi.org/10.5281/zenodo.23857172
Hardware Security In VLSI Systems: A Review Of Threats, Countermeasures And Trust Across The Integrated Circuit Life Cycle
Authors: Patwari Kavyanjali Pramod, Kamble Akanksha Laxman, Sarjine Saniya Janardan, Prof. S. S. Lohote, Prof. M. S. Nalawade, Dr. V. M. Dhede
Abstract: Integrated circuits are now designed, fabricated, tested and packaged by many different organisations spread across the world. This distributed supply chain lowers cost but also creates opportunities for an untrusted party to insert malicious logic, copy intellectual property, overbuild chips, recycle used parts as new, or extract secret keys from a working device. Hardware security in VLSI studies these threats and the design techniques that defend against them. This review classifies the threats by the life-cycle stage at which they arise and by the asset they target, and then surveys the main families of countermeasures: hardware Trojan detection, logic locking and obfuscation, split manufacturing, physical unclonable functions, side-channel and fault-injection resistance, and secure boot built on a hardware root of trust. For each family the operating principle, representative studies and known weaknesses are discussed. A life-cycle security architecture is then presented that maps each protection to the stage of design, fabrication, test and field deployment where it applies. The comparison shows that no single technique secures a chip, and that defence in depth across the life cycle is required. Open problems such as attacks on logic locking, the reliability of PUF responses and the cost of security verification are identified as directions for further work.
DOI: https://doi.org/10.5281/zenodo.23857172
Power Electronics For Electric Vehicles: A Review Of Traction Inverters, DC–DC Converters, Charging Systems, Battery Management And Wide-Bandgap Devices
Authors: Yogesh Machhindra Dhomse, Samarth Shubash Dongre, Dhanraj Shinde, Prof. A. S. Bhor, Prof. M. S. Nalawade, Dr. V. M. Dhede
Abstract: Electric vehicles are replacing internal combustion vehicles in many markets, and power electronics is the technology that makes this transition possible. Every flow of energy in an electric vehicle, from the charging socket to the battery, from the battery to the traction motor, and from the high-voltage pack to the low-voltage accessories, passes through a power electronic converter. This review presents the major power electronic subsystems of a battery electric vehicle: the traction inverter that converts DC to variable-frequency AC, the bidirectional and auxiliary DC–DC converters, the onboard AC–DC charger and the off-board DC fast charger, together with the battery management system that supervises the pack. Motor control by pulse width modulation and field-oriented control is explained. The role of wide-bandgap semiconductors, silicon carbide and gallium nitride, in raising efficiency, switching frequency and power density is discussed and compared with silicon devices. Thermal management, electromagnetic compatibility and functional safety are reviewed as system-level design concerns. A complete power-flow architecture of an electric vehicle is presented, and representative studies are compared. Challenges and future directions, including 800 V architectures, extreme fast charging, integrated chargers and vehicle-to-grid operation, are identified.
DOI: https://doi.org/10.5281/zenodo.23882383
Self-Healing Materials: A Review Of Intrinsic And Extrinsic Healing Mechanisms And Their Engineering Applications
Authors: Kale Mayuri Sandip, Mule Mansi Dinesh, Konde Pratiksha Satyawan, Prof. D. S. Bhor, Dr. V. M. Dhede
Abstract: Engineering materials accumulate small cracks during service, and these cracks grow until a component must be repaired or replaced. Self-healing materials are designed to repair such damage on their own, or with only a mild external stimulus, in a way inspired by the healing of wounds in living organisms. This review explains the principles behind self-healing and classifies the approaches into extrinsic systems, in which a healing agent is stored in microcapsules, hollow fibres or vascular channels, and intrinsic systems, in which the material itself contains reversible chemical or physical bonds that re-form after fracture. Representative studies on polymers, composites, cement-based materials, coatings and electronic materials are reviewed and compared. A general functional architecture is presented that describes a self-healing system as a sequence of damage, triggering, transport of healing material, repair reaction and property recovery, and the healing efficiency used to measure performance is defined. Applications in civil engineering, aerospace, automotive, coatings, electronics and biomedical devices are discussed. The review also examines current limitations, such as high cost, a limited number of healing cycles and difficulty in repairing large damage, and outlines directions for future research.
DOI: https://doi.org/10.5281/zenodo.23882829
A Literature Survey On IoT-Based Embedded Systems For Monitoring And Control
Authors: Bhalerao Atharv Ashok, Gunjal Ayush Santosh, Gaikwad Darshan Dattatray, Prof. P. S. Dumbre, Prof. M. S. Nalawade5, Dr. V. M. Dhede
Abstract: IoT-based embedded systems integrate sensors, microcontrollers, communication networks and software platforms to connect physical devices with digital applications. They sense the environment, process the readings locally, and transmit useful data to a monitoring platform; depending on the application, the controller can also drive an actuator automatically or act on a remote user command. This paper surveys the literature on such systems under six headings: IoT foundations and reference architectures, low-cost Wi-Fi/Bluetooth microcontrollers such as the ESP32, wireless and messaging protocols, cloud and edge platforms, application domains (smart homes, agriculture, healthcare, industry and environmental monitoring), and security. Twenty-two peer-reviewed works are compared in a summary table. The survey finds that low-cost hardware and lightweight protocols such as MQTT have made monitoring-and-control systems easy to build, but that power consumption, sensor calibration, reliable connectivity and device security remain weak points in most reported prototypes. Based on these gaps, a general ESP32-based monitoring-and-control system is proposed, with local threshold-based automation, MQTT/HTTP data transfer to a cloud dashboard, and design rules for reliability, low power and secure communication.
DOI: https://doi.org/10.5281/zenodo.23883151
Intelligent Energy Management In Renewable-Integrated Smart Grids: A Review Of Advanced Metering, Demand Response, Energy Storage And AI-Based Forecasting
Authors: Patil Saurabh Dnyaneshwar, Shelke Sanskar Ramesh, Sukhadeve Nitesh Suryabhan, Prof. M. S. Nalawade, Dr. V. M. Dhede
Abstract: A sustainable electricity system must supply growing demand with an increasing share of solar, wind and hydro generation whose output cannot be scheduled at will. Balancing such a system depends less on adjusting a few large power plants and more on managing energy intelligently across millions of consumers, storage units and distributed generators. This review focuses on the demand side and the information layer of the smart grid. It examines advanced metering infrastructure, which provides two-way communication between utilities and consumers; demand response and demand-side management, which shift or reduce consumption in response to prices or signals; energy storage and electric vehicles, which absorb surplus renewable energy and release it when needed; and forecasting of load and renewable generation using statistical and machine-learning models. Representative studies on each topic are compared. A layered architecture for an intelligent energy management system is presented in which consumer devices, smart meters, communication networks, an energy management platform and utility services cooperate to minimise cost, losses and emissions while maintaining reliability. Communication requirements, cyber security, privacy and consumer participation are discussed as challenges, and directions such as reinforcement-learning-based demand response and local energy markets are identified.
DOI: https://doi.org/10.5281/zenodo.23883414
Hydrogen Powered Energy Management System For Sustainable Industrant Applications
Authors: Sanjeeva Kumar Vudi, Vidhya K
Abstract: Current high-temperature industrial systems still rely largely on fossil fuels, with hydrogen identified as a key vector for their decarbonization. In this paper, a methodology is developed for the development of an EMS based on hydrogen use in industry, namely in glass and ammonia manufacturing. The EMS will involve the use of renewable electricity, electrolysis for hydrogen generation, its storage, and fuel switching between natural gas and hydrogen in order to minimize the expenses and meet the restrictions related to load of furnaces and emission limits. Model Predictive Control is chosen as the EMS architecture with use of day-ahead forecasts of renewable power generation, electricity price and gas price. The proposed methodology includes the design of the system architecture, mathematical problem statement and constraints. Simulation experiments have shown that introduction of hydrogen improves the economics and increases the share of renewables. Comparison with the systems working only with fossil fuels has confirmed the potential of the proposed approach.
DOI: https://doi.org/10.5281/zenodo.23888249
EdgefusionAI: An Intelligent Edge Computing Framework For Realtime Iot Data Processing Using Federated Learning
Authors: Dr N.Umamaheswari, Dr.K.Gayathri
Abstract: The Internet of Things (IoT) links sensors, machines, vehicles and everyday objects so that they can exchange data and act with little human intervention. Fifth-generation (5G) networks introduced dedicated support for massive machine-type and ultra-reliable low-latency communication, but emerging applications such as autonomous transport, remote surgery, immersive extended reality and large-scale digital twins demand still higher data rates, lower latency, greater reliability, precise positioning and far denser connectivity. Sixth-generation (6G) networks, now being defined under the IMT-2030 framework, aim to meet these needs and to make artificial intelligence a native part of the network. This survey reviews 6G-enabled IoT. It summarises the requirements and key performance indicators, and explains the enabling technologies: terahertz and sub-terahertz communication, intelligent reflecting surfaces, integrated sensing and communication, edge intelligence, AI-native network management and non-terrestrial networks. A five-layer architecture for 6G-enabled IoT is presented, linking devices, access, edge, core and applications under a cross-layer security and energy plane. Applications in smart cities, connected vehicles, healthcare, industrial automation, precision agriculture and environmental monitoring are discussed. Finally, open challenges including infrastructure cost, spectrum regulation, energy consumption, security and privacy, and the management of billions of devices are identified.
DOI: https://doi.org/10.5281/zenodo.23907255
AI-Driven Cybersecurity Framework For Intelligent Threat Detection And Response
Authors: Ms. Shapna Priya. S, Haripriya S
Abstract: Increasing complexity and speed of modern cyber attacks necessitate the evolution of security detection and response from traditional signature-based techniques to more effective approaches that can cope with new challenges. This work provides a general architecture for an AI-powered cybersecurity solution that includes a combination of deep learning-based anomaly detection with reinforcement learning-assisted automatic response execution. The framework consists of three layers of functionality: a temporal convolutional feature extractor for network traffic representation, a hybrid detector that relies on a combination of autoencoder reconstruction error and supervised classification, and a proximal policy optimization agent that performs containment actions based on detected threat level and state information. The approach is validated using benchmark datasets of CICIDS-2017 and UNSW-NB15, where it consistently outperforms the existing solutions in terms of detection precision, recall, and F1-score when compared to random forest, classic LSTM, and isolation forest models. In terms of automated response, the solution provides an order-of-magnitude improvement in mean time to respond compared to playbook-based execution.
DOI: https://doi.org/10.5281/zenodo.23907308
Journal Of Next-Generation Cybersecurity And Risk Engineering
Authors: Dr Devi Mani, Dr. Saley Seetharaman
Abstract: The breakdown of the network perimeter has made static trust models ineffective, although implementing the zero-trust model on a large scale is limited by privacy concerns and latency. This paper presents a dynamic framework for zero-trust threat engineering based on federated anomaly detection, risk-based verification, and automatic containment. The four-layer architecture allows for collaboration between models without sharing the telemetry data, whereas a gradient response system varies the degree of enforcement based on the aggregate risk score. The quantitative assessment indicates that the proposed framework offers a sub-three-second containment latency and ensures consistency in detection under non-identical distribution of the data. Comparison with the centralized and signature-based frameworks shows significant enhancements in lateral movement detection and reaction time without increased computation costs.
DOI: https://doi.org/10.5281/zenodo.23907404
Cybersecurity Frameworks For Protecting Internet Of Medical Things (IoMT) Devices In Smart Healthcare
Authors: Mahesh S, Dr. Jayasree P
Abstract: The use of Internet of Medical Things (IoMT) in smart healthcare ecosystems has led to a large-scale attack surface which current perimeter-based security solutions cannot sufficiently cover. Resource limitations, diverse protocols, and life-critical telemetry require security designs which function across the device, edge, and cloud layers while avoiding significant computation costs. In this work, we design a cross-layer security architecture which involves selective elliptic curve cryptography for IoT device authentication, sidecar proxy zero trust enforcement at the edge, and protocol-aware intrusion detection for behavior monitoring. Our design utilizes only asymmetric cryptography at registration time while shifting online mutual authentication to hash-based message authentication code and timestamp validation in order to reduce computational cost of each session without losing authentication assurances. Experimental evaluation on emulated IoMT traffic shows that our architecture is able to provide sub-10 ms end-to-end authentication latency, preserve masquerade and replay attempt detection under the attack injection, and retain tenant isolation in multi-tenant cloud setups. Comparative evaluation with state-of-the-art frameworks proves that our solution provides a better trade-off between latency, energy consumption, and cross-layer visibility.
DOI: https://doi.org/10.5281/zenodo.23907505
Student University Residence Life Protocols: Design, Architecture And Implementation (Case Of IUGB)
Authors: Dr. Bayomock Linwa André Claude, Mr. MEA Assoumou Michael
Abstract: University residences concentrate students in shared living environments where safety, accountability and access to information depend on reliable procedures for movement control, room accountability and incident handling. In Côte d'Ivoire, demand for university housing exceeds supply, and publicly documented residence procedures combine early online services, mainly for admission, with physical formalities on site [1], [2], [3]. This paper presents the design, architecture and implementation of IUGB In/Out, a multi-platform residence management system developed for the International University of Grand-Bassam (IUGB). The contribution is a set of six formally described residence life protocols (exit and return, overdue management, room inspection, incident handling, behavior evaluation, and identity and governance), together with the data model, the three-tier architecture and the implementation that enforce them. The protocols rely on role-based access control for students, supervisors and administrators, on cryptographically signed single-use QR tokens, on a persistent audit log, on real-time Socket.IO events, on Firebase push notifications, and on an AI assistant grounded in an administrator-managed knowledge base. The system is implemented with Flutter, React, Node.js/Express, Sequelize and MySQL. We position it against the publicly documented practices of Ivorian university residence authorities (CROU) and report the functional results obtained. No field-scale quantitative evaluation has been carried out yet; operational benefits are therefore reported as expected benefits, and limitations and future work are discussed.
DOI: https://doi.org/10.5281/zenodo.23913161
Use of Plastic Waste as Partial Replacement of Coarse Aggregate in Concrete: A Review
Authors: Mohd Adil, Anshuman Singh, Gaurav Yadav, Dr. Praveen Yadav
Abstract: The increasing generation of plastic waste and the continuous extraction of natural aggregates have become major environmental concerns. The construction industry offers an opportunity to address these challenges by incorporating processed waste plastic into concrete as a partial replacement for natural coarse aggregates. This review paper examines the potential use of recycled plastic waste, particularly polyethylene terephthalate (PET), high-density polyethylene (HDPE), and polypropylene (PP), in concrete production. The effects of plastic aggregate replacement on workability, density, compressive strength, split tensile strength, flexural strength, and durability are reviewed based on published research. The available literature generally indicates that incorporating plastic aggregates can reduce the density of concrete and contribute to waste management. However, increasing the replacement percentage may reduce mechanical strength because of the relatively low stiffness of plastic and inadequate bonding between plastic particles and the cement matrix. Some studies indicate that small replacement percentages can retain acceptable strength, while higher percentages may be more appropriate for non-structural and lightweight applications. The review also identifies research gaps related to long-term durability, fire resistance, environmental safety, and standardization of plastic aggregate processing. It is concluded that waste plastic can be a promising supplementary construction material when its properties, replacement level, and intended application are carefully evaluated
DOI: https://doi.org/10.5281/zenodo.23939544
Universal Rep Counter
Authors: M. Pavani, K. Shreya, Kb. Chetana Reddy, M. Deepika, M. Ranjith Kumar
Abstract: – Manual repetition counting during strength training is prone to human error, and conventional motion-based rep counters cannot verify whether a user has actually completed the required Range of Motion (ROM) for an exercise. This often results in the counting of partial or incomplete repetitions, damaging the reliability of workout data. This paper proposes a Universal Rep Counter with ROM Detection, an embedded system built around an MPU6050 inertial measurement unit (IMU) and an ESP32 microcontroller. The system fuses accelerometer and gyroscope data using a complementary filter to estimate the joint or limb angle in real time, and validates each repetition against a configurable minimum-maximum ROM threshold using a finite-state counting algorithm. Only movements that traverse the full required range and return to the starting position are registered as valid repetitions. The design incorporates an OLED/LCD display for real-time feedback and push-button controls for exercise selection, calibration, and reset, making the system adaptable to a wide range of resistance exercises including bicep curls, squats, bench presses, shoulder presses, rows, and lat pull-downs. This paper presents the system architecture, sensor-fusion methodology, rep-validation algorithm, and expected behavior based on simulated sensor data, and outlines a roadmap for hardware validation and future extensions such as automatic exercise recognition and AI-based form analysis.
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CarbonWise: A Blockchain-Based Carbon Credit Marketplace For Transparent And Accessible Carbon Trading
Authors: Shubham Sharma, Vibhav Dadhich
Abstract: Carbon markets depend on credible measurement, reporting, and verification (MRV), reliable ownership records, and controls against duplicate issuance and retirement of environmental claims. Small and medium-sized cement manufacturers may face high verification and transaction costs and limited access to carbon markets. This paper proposes CarbonWise, a blockchain-based marketplace design for improving the traceability of credit records and access to trading. The proposed architecture uses Solidity smart contracts on an Ethereum-compatible network, an ECO-token representation for approved credits, and InterPlanetary File System (IPFS) references for supporting evidence. It also describes an optional EIP-712 meta-transaction flow in which a relayer could submit a transfer signed by a user. The design separates environmental verification from on-chain execution: a blockchain can record approvals, ownership changes, and retirement events, but cannot independently prove that a claimed emission reduction occurred. We describe the workflow, contract responsibilities, evidence-integrity model, security considerations, and an evaluation plan. The supplied materials do not include test evidence, so we report no performance or security measurements. Implementation details must be checked against the project source code and test results. The paper contributes a sector-focused design and evaluation framework for auditable credit records, evidence references, issuance controls, and optional sponsored transactions.
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Microsoft HoloLens: A Review Of Architecture, Applications And Challenges In Untethered Mixed Reality
Authors: Jagrav Bhatt, Nishit Sorathia
Abstract: Microsoft HoloLens is one of the first commercially available, fully untethered optical see-through head-mounted displays (HMDs) for mixed reality (MR). Since the release of the first generation in 2016 and HoloLens 2 in 2019, the device has been adopted in healthcare, manufacturing, education, defense and remote collaboration. This paper reviews the hardware and software architecture of both generations, including the waveguide display, depth sensing, spatial mapping, the Holographic Processing Unit and the interaction model. It then surveys the main application domains and summarizes reported strengths and limitations, such as field of view, tracking accuracy, ergonomics, battery life and cost. Finally, it discusses open research problems and the future of head-worn MR following the discontinuation of HoloLens 2 hardware production. The review aims to help researchers and practitioners choose, evaluate and design MR systems for real-world use.