Category Archives: Uncategorized

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.

DOI: https://doi.org/10.5281/zenodo.22769561

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.

DOI: https://doi.org/10.5281/zenodo.22787979

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.

DOI: https://doi.org/10.5281/zenodo.22791801

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.

DOI: https://doi.org/10.5281/zenodo.22805792

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.

DOI: https://doi.org/10.5281/zenodo.22806381

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.

DOI: https://doi.org/10.5281/zenodo.22812490

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

DOI: https://doi.org/10.5281/zenodo.22827360

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.

DOI: https://doi.org/10.5281/zenodo.22872539

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.

DOI: https://doi.org/10.5281/zenodo.22873662

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.

DOI: https://doi.org/10.5281/zenodo.22875171

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.

DOI: https://doi.org/10.5281/zenodo.22895793

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.

DOI: http://doi.org/10.5281/zenodo.22932622

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.

DOI: http://doi.org/10.5281/zenodo.22933192

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.

DOI: http://doi.org/10.5281/zenodo.22934344

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.

DOI: https://doi.org/10.5281/zenodo.23057252

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.

DOI: https://doi.org/10.5281/zenodo.23075989

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.

DOI: https://doi.org/10.5281/zenodo.23076276

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.

DOI: https://doi.org/10.5281/zenodo.23076863

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.

DOI: https://doi.org/10.5281/zenodo.23096932

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.

DOI: https://doi.org/10.5281/zenodo.23097476

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.

DOI: https://doi.org/10.5281/zenodo.23102184

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.

DOI: https://doi.org/10.5281/zenodo.23117855

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.

DOI: http://doi.org/10.61137/ijsret.vol.12.issue5.152

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.

DOI: http://doi.org/10.61137/ijsret.vol.12.issue5.153

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.

DOI: https://doi.org/10.5281/zenodo.23180622

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.

DOI: https://doi.org/10.5281/zenodo.23182991

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

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.

DOI: https://doi.org/10.5281/zenodo.23310822

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

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.

DOI: https://doi.org/10.5281/zenodo.23332874

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.

DOI: https://doi.org/10.5281/zenodo.23333454

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.

DOI: https://doi.org/10.5281/zenodo.23334490

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.

DOI: https://doi.org/10.5281/zenodo.23389223

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.

DOI: https://doi.org/10.5281/zenodo.23394198

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

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