Wearable Device For Diabetes; Emerging Trends Ai Integration And Multi Biomarker Approaches
Authors: Krishnendu Ghosh, Soumyajit Roy, Debayan Bag
Abstract: The integration of artificial intelligence (AI) with wearable sensing technology has emerged as a transformative approach in modern healthcare, particularly for the management of chronic diseases such as diabetes mellitus. AI-enabled wearable devices utilize advanced sensing mechanisms—including electrochemical, optical, microneedle, and sweat-based sensors—to enable continuous, real-time monitoring of physiological parameters. These systems provide significant advantages over traditional invasive techniques by offering non-invasive or minimally invasive solutions, improving patient comfort, compliance, and data accuracy. In diabetes management, AI-driven wearable technologies such as continuous glucose monitoring (CGM) systems, smart insulin pumps, and closed-loop artificial pancreas systems have revolutionized glycemic control. Machine learning and deep learning algorithms analyze large volumes of real-time data to predict glucose trends, detect anomalies, and provide personalized treatment recommendations. Additionally, emerging non-invasive technologies, including smart contact lenses and smartphone-based photoplethysmography, offer promising alternatives for early detection and monitoring. The application of AI in wearable devices also supports remote patient monitoring, enabling timely intervention and reducing the risk of complications. Despite significant advancements, challenges such as sensor accuracy, data interpretation, scalability, and integration into clinical practice remain. Overall, AI-based wearable sensing technology holds immense potential to enhance personalized healthcare, improve clinical outcomes, and reduce the global burden of diabetes.
DOI: http://doi.org/10.5281/zenodo.22763785
Leadership Agility And Organizational Resilience In The Digital Era
Authors: Dr. Rajidi Rammohan Reddy
Abstract: The digital age has brought about new challenges to companies in terms of volatility, uncertainty, complexity, and ambiguity (VUCA), where leadership agility and organizational resilience have emerged as strategic priorities. This research paper explores the connections between leadership agility and organizational resilience and the role digital transformation plays in them as catalysts and contextual variables. Through the review of existing literature and development of a mixed-methodological approach for research, it is revealed what dimensions of leadership agility – adaptive thinking, decentralized decision making, emotional intelligence, transparent communication, and learning – have a significant effect on the organizational resilience factors in a causal way. The results show the impact of digital leadership orientation on organizational resilience through mediation by strategic foresight, agility, and flexibility. Comparative analysis shows how companies with leadership agility have 34 percent more capacity for organizational resilience than companies with lower leadership agility.
DOI: http://doi.org/10.5281/zenodo.22768793
Fintech & Future of Finance Transforming the Financial Sector in the Digital Era
Authors: Harleen kour, Associate Professor Dr. Navneet Seth
Abstract: This research paper studies the role of FinTech in the financial sector and its impact on the future of finance. The study focuses on the development of FinTech, its major applications, benefits and challenges. Financial Technology, commonly referred to as FinTech, represents the integration of technology with financial services to improve the way financial activities are delivered, managed and accessed. The rapid development of digital technologies has transformed traditional financial systems from branch-based and manually operated services into increasingly digital, automated and data-driven financial ecosystems.This research examines the role of FinTech in finance with particular emphasis on digital payments, digital banking, online lending, artificial intelligence, blockchain, financial inclusion and data-driven financial services.The study identifies that FinTech has become an important component of modern finance, while challenges related to cybersecurity, data privacy, digital fraud, regulatory compliance, technological dependence and digital literacy remain significant. The research further discusses the future potential of artificial intelligence, blockchain, open banking, embedded finance and other emerging technologies.The study concludes that FinTech is not simply replacing traditional finance; rather, it is contributing to the development of a more digital, accessible, efficient and technology-driven financial ecosystem.
ActLeak: Action-Channel Memory Leaks in Tool-Using LLM Agents
Authors: Monish Kanungo, Seemant Kaushal
Abstract: Classical software memory leaks are allocations that outlive their intended lifetime. Tool-using large language model (LLM) agents exhibit an analogous failure that current “forgetting” mechanisms do not measure. A user or policy issues a forget request for a memory m*; the underlying store correctly drops the record, and content probes no longer reproduce the fact; yet the agent’s tool-selection behavior on the next turn continues to favor the same tool that m* had taught it to prefer. We call this an action-channel memory leak. Formally, let π(t∣q,M) denote an agent’s distribution over tools t in a catalog T given a query q and memory state M, let F(M,m*) denote a declared forget operator, and let M★ denote an oracle store from which m* and all of its derived traces have been removed. The leak is the total-variation distance between π under the forgotten store MF and π under M★. This paper contributes a three-layer leak taxonomy (store, shadow, action), an interventional audit built on masking, upweighting, and swapping retrieved memories, and Forget-Consistent Coupling (FCC) a reference monitor that withholds confirmation of a successful forget until the measured action-channel leak falls under a budget ε. We further specify ActLeak-Bench, an evaluation protocol addressing tool selection after forgetting, as distinct from tool-parameter deflection while a memory remains live. We situate this contribution relative to closely adjacent work on memory-driven tool-selection bias and behavioral unlearning verification, present the protocol, evaluation conditions, and pre-registered hypotheses, and discuss what is required to execute the protocol and what a small-scale pilot would look like.
Preparation and Characterization of Nanocomposite Cotton Fabrics (NCCFs) With in Situ Generated Silver Nanoparticles
Authors: Associate Professor N.Chandanaa, Assistant Professor Vijaya Ushasreeb
Abstract: Nanocomposite cotton fabrics (NCCFs) embedded with in situ generated silver nanoparticles (AgNPs) were successfully prepared using an eco‑friendly green synthesis approach. Lemon leaf extract was employed as a natural reducing and stabilizing agent to functionalize cotton fabrics and facilitate the formation of AgNPs within the cotton matrix. The incorporation of phytochemicals from the extract enabled effective reduction of silver ions and strong adhesion of nanoparticles onto the cellulose fibers. The synthesized NCCFs were characterized using X‑ray diffraction (XRD), scanning electron microscopy (SEM) coupled with energy dispersive X‑ray analysis (EDX), Fourier transform infrared spectroscopy (FTIR), and thermogravimetric analysis (TGA). XRD results confirmed the presence of crystalline silver nanoparticles along with cellulose structure, while SEM analysis revealed uniformly distributed spherical AgNPs with sizes increasing with higher precursor concentration. EDX analysis verified the elemental composition of silver within the fabric. FTIR studies indicated that the fundamental structure of cotton remained intact, with interactions between AgNPs and functional groups occurring primarily through electrostatic forces. TGA results showed a slight reduction in thermal stability of NCCFs compared to the untreated cotton matrix. The antibacterial activity of NCCFs was evaluated against Gram-positive (Staphylococcus aureus, Bacillus subtilis) and Gram-negative (Escherichia coli, Pseudomonas aeruginosa) bacteria using the agar diffusion method. The prepared nanocomposites exhibited significant antibacterial performance, with inhibition zones ranging from 11 to 25 mm, whereas untreated cotton fabrics showed no antibacterial activity.
Recovery of Crude Oil Losses to Effluent Water Disposal System in the Production Platform Using Sand Filter
Authors: Ephraim Anthony Jacob, Jackson.G. Akpa, Fidelis. O. Wopara
Abstract: The work entails research on recovery of crude oil losses to effluent water disposal system in the production platform using sand as the filtration media. In this dissertation a glass column filled with sand is used as a low-cost filtration media for the removal of crude oil from synthetic oil produced water (SOPW) and real produce water PW from oil and gas wells. The objective of this work is to examine the filtration efficiency of sand grains tilled in a glass column as sand filter in recovering crude oil from produced water. This work investigates the influence of process parameters such as physical characteristics of sand, column height, types of effluent (oilfield produced water and synthetic oil produced water), and oil concentration on oil removal efficiency. The sand fractions used for this work was selected using a set of Tyler sieves. After the different grain sizes were selected, the sand was washed several times to remove impurities. The sand samples were oven dried at 1000C for 24h. The gravel was washed and also dried at 1000C, for l2h. Oil was removed from laboratory-produced water in a batch process at standard atmospheric conditions. The effect of contact time, physical characteristics of sand, column height, pH as well as temperature on the removal efficiency of oil was investigated. The optimum parameters for oil removal were: pH 9.5, contact time 40.0 minutes and temperature 55.00C. The results showed that the oil concentration decreased significantly after the filtration process. The oil removal efficiency was influenced by the sand particle size and bed height, reaching 90% using a 30 cm high sand bed composed by mixed grain sizes.
A Refined Framework for Incorporating Facial Stimulation into Hybrid SSVEP-P300 Brain-Computer Interfaces
Authors: Research Scholar Ms. Monali Khune, Dr. Amol Y. Deshmukh
Abstract: Background: Brain–computer interface (BCI) systems frequently utilize P300 and steady-state visual evoked potential (SSVEP) paradigms. Because neither approach yields universal success across users, recent research has explored hybrid BCI designs that merge multiple techniques to expand user compatibility. Although hybrid P300/SSVEP systems are a relatively recent innovation with limited performance optimization studies to date, they represent a promising avenue for improving system accessibility. New method: In this research, we contrast a conventional hybrid P300/SSVEP BCI framework with an innovative approach. Specifically, shape alterations are utilized instead of color modulations to evoke the P300 wave, aiming to minimize any adverse impact on SSVEP signal strength. Result: The new hybrid paradigm presented in this paper yields much better performance than the traditional hybrid paradigm. Comparison with existing method: The novel hybrid paradigm yields an SSVEP classification improvement of close to 20% over the standard approach. Furthermore, all tested paradigms—excluding the conventional hybrid model—achieve a perfect 100% accuracy rate in P300 classification. Conclusions: The innovative hybrid P300/SSVEP brain-computer interface paradigm replaces traditional color-shifting stimuli with shape-altering visual elements, matching the classification accuracy of conventional SSVEP and P300 setups. Furthermore, the author explored how presenting multiple visual stimuli at once triggers overlapping brain responses and evaluated the resulting interference on signal detection.
The Dynamics of Bank Credit and Industry Credit in India: An Empirical Study of the MSME Sector
Authors: Rajpurohit Priya Bhavani Singh, Assistant Professor Ms Manisha Kalra
Abstract: Micro, Small and Medium Enterprises (MSMEs) are widely described as the backbone of the Indian economy, contributing close to 30 per cent of national income, a little over a third of manufacturing output, and roughly 45 per cent of the country's merchandise exports, while sustaining employment for more than 110 million people. Despite this scale, the sector has historically operated under a severe financing constraint, with credible estimates of the gap between demand and formally supplied credit ranging from about ₹20 lakh crore to ₹30 lakh crore in recent years. This paper examines the evolving relationship between bank credit and industrial credit in India, with specific reference to the MSME sector, using secondary data drawn from the Reserve Bank of India (RBI), the Ministry of Micro, Small and Medium Enterprises, Parliamentary disclosures, and other government and industry publications. Employing a Pearson correlation framework on annual data for scheduled commercial bank (SCB) credit to MSMEs and to the industrial sector as a whole, the study finds a very strong, statistically significant positive association between the two series (r ≈ 0.98, p < 0.001), leading to rejection of the null hypothesis of no relationship. The paper further documents that credit to MSMEs grew faster than credit to any other major sector in 2024-25, even as overall bank credit growth dece Fund lerated, reflecting the cumulative effect of policy interventions such as the Credit Guarantee Trust for Micro and Small Enterprises (CGTMSE), the Emergency Credit Line Guarantee Scheme (ECLGS), and priority sector lending norms. At the same time, the study finds that formal credit penetration remains low — by some estimates only about one in seven MSMEs has access to institutional finance — and that collateral requirements, asset-quality caution among lenders, and disparities across enterprise size and gender continue to constrain the sector. The paper concludes with a set of policy-relevant observations on deepening formal credit flow to MSMEs.
Overcoming Interfacial Bottlenecks in Commercial Hard Carbon Anodes: SEI Dynamics, Pre-sodiation Strategies, and Safety Protocols for High-Energy Sodium-Ion Batteries
Authors: Daniel Karikari Frempong, Hannah Owusu Ansah, Gabriel Oduro Asirifi
Abstract: One of the most promising anode materials for high-energy sodium-ion batteries (SIBs) is hard carbon (HC); however, low initial Coulombic efficiency (ICE), unstable solid electrolyte interphase (SEI) formation, irreversible sodium loss, and sodium-plating risks at low potentials continue to limit its commercial implementation. The interfacial mechanisms controlling HC performance are critically examined in this paper, which also assesses methods for getting beyond these restrictions in realistic full-cell systems. The effects of surface imperfections, specific surface area, and open microporosity are highlighted in the link between HC microstructure, electrolyte breakdown, SEI composition, and sodium-ion transport. In addition, surface and artificial interphase engineering through atomic layer deposition, chemical vapour deposition, thermal-pyrolysis carbon coatings, and conductive protective layers is discussed for suppressing parasitic reactions and stabilising the interface; the mechanisms and operational triggers of sodium plating, such as high-rate charging, low temperature, and mass-transfer polarisation, are also examined; and finally, operando characterisation, scalable manufacturing, thermal management, and techno-economic considerations are identified as critical paths for converting HC-based SIBs into safe, long-lasting, and commercially viable high-energy cells
Nanotechnology At The Frontiers Of Societal Transformation: Opportunities, Impacts, And Future Directions
Authors: Hannah Owusu Ansah, Daniel Karikari Frempong, Gabriel Oduro Asirifi
Abstract: Nanotechnology has emerged as a transformative field with the potential to reshape multiple dimensions of society through the manipulation and application of materials at the nanoscale. The distinctive optical, electrical, mechanical, chemical, and biological properties of nanomaterials have enabled advances across healthcare, environmental sustainability, agriculture, energy, electronics, transportation, and consumer products. This review provides an overview of major nanomaterial classes, their synthesis, fabrication, and characterization, and examines their growing contributions to societal transformation. Particular attention is given to nanomedicine, targeted drug delivery, medical diagnostics, cancer therapy, nano-enabled agriculture, water purification, environmental remediation, nanoelectronics, energy generation and storage, and lightweight automotive materials. Beyond technological applications, the review discusses the broader socioeconomic and societal implications of nanotechnology, including employment opportunities, public acceptance, privacy, security, and environmental risks. Although nanotechnology offers considerable opportunities for improving human health, resource efficiency, environmental sustainability, energy performance, and economic development, its widespread implementation is accompanied by challenges related to nanomaterial toxicity, environmental fate and bioaccumulation, high production costs, technological scalability, uncertainty, public awareness, privacy, and security. Addressing these challenges requires interdisciplinary research, improved risk assessment and characterization methods, responsible innovation, effective regulation, transparent communication, and stronger consideration of the entire nanomaterial life cycle. The responsible development and commercialization of nanotechnology can contribute substantially to societal transformation while ensuring that technological progress is aligned with human health, environmental protection, security, and sustainable development
DOI: http://doi.org/10.5281/zenodo.22827579
Produced Water Treatment Using Bacteria And Microalgae Consortia
Authors: Okorinama E, Nmegbu CGJ
Abstract: Due to the many processes involved in oil and gas production, Produced Water is usually given off as a major by-product. This wastewater constitutes of contaminants like dissolved salts, hydrocarbons, organic matter and other toxins which pose significant risks to the environment if discharged without the necessary treatments. This study analyzed the treatment potential of hydrocarbon-degrading bacteria and microalgae species, utilizing Produced water gotten from the Obaji Oil field, Rivers State, Nigeria. A total of five microalgae species -Chlorella vulgaris, Scenedesmus obliquus, Arthrospira platensis, Monoraphidium sp. and Neochloris oleoabundans -were screened and acclimatized, while bacterial isolates were obtained from rhizosphere soil and identified as Pseudomonas aeruginosa, Bacillus subtilis, Acinetobacter calcoaceticus, Rhodococcus erythropolis and Sphingomonas paucimobilis. The treatments were carried out in batch reactor systems at 50%, 60%, 75% and 100% produced water concentrations for seven days. Notable changes in temperature, pH, Total Dissolved Solids (TDS), Biological Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Total Hydrocarbon Content (THC) and Dissolved Oxygen (DO) were observed. Microalgae treatment reduced the major pollutants and produced biomass, while bacterial treatment gave noticeable reductions in TDS, BOD and THC. At 100% produced water concentration, bacterial treatment achieved 12.7%, 16.8%, 23.7% and 14.6% reductions in TDS, COD, BOD and THC respectively. At the same concentration, microalgal treatment reduced TDS by 11.9%, COD by 24.7%, BOD by 31.4% and THC by 22.8%. Comparing the given results with Nigerian Upstream Petroleum Regulatory Commission (NUPRC) Onshore Discharge standards showed that the treated water still surpassed the limits for major parameters. The study indicated that biological treatment alone, of the produced water sample, was inadequate for direct discharge into the environment. The findings from this study demonstrated the viability of microbial treatment and aids further development of combined bacteria-microalgae systems for produced water remediation.
DOI: http://doi.org/10.5281/zenodo.22828486
Communication Skills
Authors: Amit Jalindar Sawant
Abstract: Learning English language’ is essential for career growth. There are different ways of learning English language. Using different ways of learning English language is really helpful to be comfortable and fluent. If we don’t practice enough to be habitual then definitely it does not work. We often learn different things knowingly-unknowingly because of being practiced or habitual after revising many more times. Similar thing works for learning English language. So many tactics are there to learn, generally we notice, how people are there, who they use differente words, even sentences but they don’t know, which word they should use in their mother tongue, and that is the practice and habitual process. Surrounding really well important to become a part of learning English language. It says, what people are there in our surrounding, so we become one of them, likewise, if there are English language speakers, we tend to speak English language without any hesitation. Actually, there are different expectation, demands, requirements of English language users, average, proficient, expert etc. And definitely no one is born perfectionist, we become educated through process, different ways, ‘Learning English language’ is a part of ways of learning.
Intelligent Phishing Website Detection System Using Hybrid Machine Learning and Deep Learning Approaches
Authors: Rajesh Chauhan, Akshay Bhardwaj, Shubham Rana
Abstract: Phishing websites are still one of the most common infection vectors for stealing credentials, financial information and other sensitive data from users of digital platforms, and there is a growing demand for accurate and deployable automated phishing-detection systems. However, models trained on a set of phishing URLs may experience a large performance drop when transferred to another feature scheme or a different collection window outside the scope of their training. In this work, we compare four classical machine-learning models, and five deep-learning and hybrid architectures for within-domain and cross-domain phishing-website detection. We evaluate our approach over five publicly available benchmark datasets, which contain more than 445k labeled instances in total, including UCI Phishing Websites Dataset, PhiUSIIL Phishing URL Dataset, Web-Page Phishing Detection Dataset, Mendeley Web-Page Phishing Dataset and Phishing Dataset based on Machine Learning. The models are evaluated in terms of classification accuracy, F1-score, transfer of cross domain performance, computational cost, data needs and interpretability. The Hybrid CNN-BiLSTM with attention model achieved the highest accuracy of 98.1% on PhiUSIIL and 85.6% on the transfer task from the PhiUSIIL to the Mendeley Web-Page dataset, outperforming the Linear SVM model by 13.1 percentage points. However, classical models still work well in resource-limited and interpretability-sensitive settings, and a simple BiLSTM with additive attention offers a balanced compromise. The results suggest that model selection for phishing detection should not only focus on predictive performance, but also consider operational deployment constraints.
Artificial Intelligence in Digital Marketing: Connecting Personalization, Consumer Trust, Engagement, and Purchase Intention
Authors: Sagar Shivaji Thakare
Abstract: Artificial intelligence (AI) is becoming an important part of digital marketing. Marketers now use AI for personalized recommendations, customer service chatbots, targeted advertising, content creation, customer analysis, and other activities across the digital customer journey. These applications can make marketing communication faster and more relevant, but they also raise questions about privacy, transparency, trust, authenticity, and the appropriate role of human interaction. This conceptual paper reviews and connects research on AI and marketing to explain how AI-enabled marketing activities may influence consumer engagement and purchase intention. The paper uses a structured literature-based approach and does not use primary survey data. The review suggests that AI capability alone does not determine consumer response. Instead, consumer perceptions of relevance, usefulness, convenience, transparency, privacy, and trust play an important role in shaping outcomes. Based on the literature, the paper develops a conceptual framework and six propositions linking AI-enabled digital marketing with consumer engagement, trust, and purchase intention. The paper also discusses practical implications for marketers and identifies directions for future empirical research.
Microbiological Profile and Phenotypic Antimicrobial Susceptibility Patterns in Chronic Suppurative Otitis Media Patients at A Tertiary Care Hospital
Authors: Pawandeep Kaur, Jasleen Kaur, Priya Bhat, Upasana Bhumbla
Abstract: Objective: To characterize the aerobic bacterial and fungal isolates recovered from patients with chronic suppurative otitis media (CSOM) and to evaluate their antimicrobial susceptibility profiles along with selected phenotypic resistance mechanisms. Methods: A prospective cross-sectional study was conducted over six months at Adesh Institute of Medical Sciences & Research, Bathinda. A total of 178 ear discharge specimens obtained from patients clinically suspected of having CSOM were processed for isolation and identification of aerobic bacteria and fungi. Organisms were identified by conventional microbiological methods and, where applicable, confirmed using the VITEK® 2 Compact system. Antimicrobial susceptibility was assessed by the Kirby–Bauer disk diffusion method in accordance with CLSI M100 (2024). Phenotypic testing included detection of extended-spectrum β-lactamase (ESBL) production among selected Enterobacterales, metallo-β-lactamase (MBL) production among Gram-negative isolates, and methicillin resistance in Staphylococcus aureus. Colistin susceptibility among multidrug-resistant Gram-negative isolates was determined by broth microdilution following EUCAST recommendations. Results: Of the 178 specimens examined, 132 (74.16%) yielded microbial growth, whereas 46 (25.84%) showed no growth. All culture-positive specimens demonstrated monomicrobial growth. Culture-positive cases were predominantly male (88/132, 66.67%), and the 41–50-year age group accounted for the largest proportion (50/132, 37.88%). Tubotympanic disease constituted 66.67% of cases, while atticoantral disease accounted for 33.33%. Pseudomonas aeruginosa was the predominant bacterial isolate (60/132, 45.45%), followed by Staphylococcus aureus (28/132, 21.21%). Among fungal isolates, Candida albicans was more frequently recovered (5/132, 3.79%) than Aspergillus niger (3/132, 2.27%). S. aureus exhibited complete susceptibility to vancomycin, linezolid, and teicoplanin. Conclusion: The study demonstrated a predominance of Gram-negative organisms, particularly P. aeruginosa, in CSOM, along with notable antimicrobial resistance among several bacterial isolates and the presence of phenotypic resistance mechanisms. These findings emphasize the value of periodic local antimicrobial resistance surveillance and microbiologically guided treatment for CSOM. Such surveillance may assist in optimizing antimicrobial selection and strengthening antimicrobial stewardship.
DOI: http://doi.org/10.5281/zenodo.22845104
Analysis Engineering Material Feasibility of RAP & RAS Modified Hot Mix Asphalt for Bahadurganj–Araria Section of NH-327E
Authors: Dhananjay Kumar Singh, Assistant Professor Dr. Amit Kumar Ahirwar
Abstract: The use of reclaimed asphalt pavement (RAP) and recycled asphalt shingles (RAS) can support material circularity in highway construction by returning aged aggregate and binder to productive use. This study evaluates the resource-substitution potential and engineering feasibility of a dense-graded hot mix asphalt containing a combined 31.53% RAP and tear-off RAS. The mixture incorporated 19.89% virgin 13.2 mm aggregate, 43.66% virgin 6 mm aggregate, 1.94% hydrated lime, 2.48% VG-30 virgin bitumen, and 0.50% rejuvenator. A material-flow assessment was combined with gradation compliance and Marshall design results. Recycled binder supplied 3.33 percentage points of the 5.80% total binder content, corresponding to 57.4% of the binder system; RAS alone supplied 1.88 percentage points. All sieve results remained within the adopted Ministry of Road Transport and Highways limits. At the selected 5.8% binder content, the mixture achieved 1232.70 kg Marshall stability, 3.27 mm flow, bulk specific gravity of 2.443, 3.99% air voids, 14.19% VMA, and 71.89% VFB. These results demonstrate that substantial recycled-material and recycled-binder contributions can be achieved without losing basic Marshall and volumetric compliance. The design offers a technically credible pathway for reducing dependence on virgin pavement materials. However, project-specific cost, energy, emissions, leaching, durability, and field-performance data are required before claiming quantified environmental or economic benefits.
Marshall Mix Design and Performance Evaluation of RAP RAS Modified Hot Mix Asphalt with Rejuvenator
Authors: Dhananjay Kumar Singh, Assistant Professor Dr. Amit Kumar Ahirwar
Abstract: The stiff aged binder contained in reclaimed asphalt pavement (RAP) and recycled asphalt shingles (RAS) can reduce the demand for virgin binder, but it may also limit workability and cracking tolerance. This study develops a dense-graded hot mix asphalt containing a combined 31.53% RAP and tear-off RAS, 0.50% rejuvenator, 1.94% hydrated lime, and VG-30 virgin bitumen. The aggregate blend was checked against the applicable Ministry of Road Transport and Highways grading envelope, and Marshall specimens were evaluated at total binder contents of 5.4, 5.6, 5.8, 6.0, and 6.2%. The measured responses included bulk and maximum specific gravity, air voids, voids in mineral aggregate, voids filled with bitumen, stability, flow, and Marshall quotient. All combined gradation values remained within the specified limits. Marshall stability increased from 902.12 kg at 5.4% binder to a maximum of 1232.70 kg at 5.8%, then decreased to 905.33 kg at 6.2%. At 5.8% binder, the mixture achieved a bulk specific gravity of 2.443, maximum specific gravity of 2.545, 3.99% air voids, 14.19% VMA, 71.89% VFB, 3.27 mm flow, and a Marshall quotient of 3.77 kN/mm-equivalent as reported from the adopted calculation basis. The convergence of maximum stability, maximum bulk density, and approximately 4% air voids established 5.8% as the optimum binder content. The results show that a properly graded RAP-RAS blend with a modest rejuvenator dosage can satisfy Marshall volumetric and strength requirements. Direct wheel-tracking, fatigue, moisture-susceptibility, and field validation are recommended before project-scale implementation.
Herbal Contraceptives: Phytochemistry, Mechanisms of Action, Therapeutic Potential, Safety, and Future Perspectives
Authors: Sambhaji Borgude, Vinayak Kanchan, Pranav Ghumare, Sohel Shaikh, Tanmay Gangawane, Bhagyshree Gaikwad
Abstract: The rising worldwide need for safe, cost-effective, and reversible contraception has sparked new interest in using medicinal plants as alternative fertility-controlling solutions. Conventional medical systems have utilized various plants for contraception by impacting ovulation, spermatogenesis, fertilization, implantation, and uterine functions. Recent pharmacological studies have discovered various phytochemicals such as alkaloids, flavonoids, saponins, terpenoids, coumarins, lignans, and phytoestrogens that influence reproductive endocrine pathways and gamete activity. This review thoroughly outlines existing evidence related to phytochemistry, action mechanisms, pharmacological effects, and safety profiles of herbal contraceptives. Key medicinal plants like Azadirachta indica, Carica papaya, Ruta graveolens, Juglans regia, Ricinus communis, Daucus carota, and other commonly utilized species are thoroughly examined with a focus on their antifertility properties, active compounds, and empirical support. Despite considerable in vitro and animal research supplementing contraceptive effectiveness via anti-ovulatory, spermicidal, anti-implantation, endocrine-modulating, and uterotonic processes, strong clinical evidence continues to be scarce. Moreover, issues related to toxicity, reproductive safety, teratogenic effects, dose consistency, and long-term reversibility still impede clinical translation. Future studies must focus on standardized phytochemical profiling, mechanistic molecular investigations, GLP-compliant toxicity assessments, and rigorously structured randomized clinical trials. Herbal contraceptives thus serve as encouraging options for creating new contraceptive methods; however, significant scientific confirmation is necessary before their regular clinical use.
AI-Based Coordinated Traffic Load Scheduling for Rail-Road Freight Corridors
Authors: Krishna Kumar Singh, Professor Vinay W. Deulkar, Professor Piyush Mahajan
Abstract: The rapid growth of freight transportation has increased the need for efficient traffic load scheduling in integrated rail-road freight corridors. Conventional scheduling methods often suffer from poor coordination, traffic congestion, delivery delays, and inefficient resource utilization. This research proposes an Artificial Intelligence (AI)-based Coordinated Traffic Load Scheduling Framework using an Artificial Neural Network (ANN) to predict traffic conditions, freight demand, travel time, and scheduling performance. Historical and simulated transportation data are used to train and validate the ANN model, and the predicted outputs are integrated into a traffic scheduling simulation for intelligent freight allocation and route optimization. The performance of the proposed framework is evaluated using key indicators such as transportation cost, travel time, delivery delay, vehicle utilization, rail utilization, fuel consumption, carbon emissions, and scheduling efficiency. The results demonstrate that the ANN-based approach improves prediction accuracy, optimizes multimodal freight scheduling, reduces operational costs and congestion, and enhances the overall efficiency of rail-road freight transportation. The proposed framework provides an effective and intelligent solution for sustainable freight corridor management and future smart logistics systems.
Development of Ultra-High-Performance Concrete (UHPC) for High-Traffic Highway Pavements
Authors: Girish Prasad, Professor Vinay Deulkar, Assistant Professor Piyush Mahajan
Abstract: Ultra-High-Performance Concrete (UHPC) is increasingly recognized as an advanced construction material for high-traffic highway pavements because of its outstanding mechanical performance, durability, and extended service life. This review provides a comprehensive assessment of UHPC, covering its development, constituent materials, mix-design principles, mechanical characteristics, durability performance, and recent developments in pavement applications. Particular attention is given to its superior compressive and flexural strength, very low permeability, high abrasion resistance, and enhanced resistance to freeze–thaw cycles and chemical deterioration. These properties make UHPC particularly suitable for pavements subjected to heavy traffic and demanding environmental conditions. Despite these benefits, its wider adoption is constrained by factors such as relatively high initial cost, limited availability of specialized materials, and challenges associated with large-scale construction and implementation. Overall, the reviewed research indicates that UHPC offers considerable potential for improving pavement performance, extending service life, reducing maintenance requirements, and supporting the development of more durable and sustainable highway infrastructure.
Review on AI-Based Durability Prediction of RCC Buildings with Floating Columns Using STAAD.Pro Analysis
Authors: Vishal Sahu, Sandeep Choudhary
Abstract: Reinforced Cement Concrete (RCC) buildings with floating columns are increasingly adopted in modern urban construction to achieve architectural flexibility, open parking spaces, and large column-free areas. However, the discontinuity introduced by floating columns significantly alters the load transfer mechanism and may adversely affect the structural durability and long-term performance of buildings, particularly under seismic and environmental loading conditions. Recent advances in Artificial Intelligence (AI) provide an opportunity to predict the durability and service life of such complex structural systems more accurately than conventional empirical methods. This review paper presents a comprehensive analysis of AI-based durability prediction techniques for RCC buildings incorporating floating columns, with structural behavior evaluated using STAAD.Pro. The review summarizes the influence of floating column configurations on stress distribution, deflection, drift, and load-carrying capacity, while examining AI approaches such as Artificial Neural Networks (ANN), Machine Learning (ML), Deep Learning (DL), Support Vector Machines (SVM), Random Forest (RF), and ensemble models for predicting durability indicators including crack development, corrosion potential, service life, and structural degradation. The paper also discusses the integration of finite element analysis results obtained from STAAD.Pro with AI algorithms to improve prediction accuracy and facilitate intelligent structural health assessment. Furthermore, existing research gaps, challenges, and future opportunities in AI-assisted durability evaluation are highlighted. The review concludes that the combination of STAAD.Pro-based structural analysis and AI-driven predictive models offers a reliable and efficient framework for enhancing the safety, durability, maintenance planning, and sustainable design of RCC buildings with floating columns. This integrated approach supports data-driven decision-making and contributes to the development of resilient and smart infrastructure.
The Role of Training and Development in Employee Retention
Authors: Mansi Mishra
Abstract: Employee retention has become an important concern for organisations because employee turnover can increase recruitment costs, disrupt organisational continuity, and lead to the loss of knowledge and experience. Training and development can influence employees’ perceptions of organisational support by providing opportunities to acquire skills, improve performance, and develop their careers. This research paper examines the role of training and development in employee retention, with particular attention to the mediating role of employee satisfaction. The proposed study will examine whether access to relevant training, career development opportunities, learning support, and development programmes is associated with employee satisfaction and employees’ intention to remain with their organisations. A quantitative research design is proposed, using a structured questionnaire administered to employees from different organisations. The data may be analysed using descriptive statistics, reliability analysis, correlation, regression, and mediation analysis. The study is expected to contribute to human resource management literature by clarifying the relationship between development opportunities, employee satisfaction, and retention. It may also provide practical guidance to organisations seeking to design training and development practices that support employee retention.
A Behavioural And Quantitative Analysis Of FinTech Adoption And Financial Mathematics Among Young Investors
Authors: Mataprasad Chaurasia
Abstract: The past five years have produced one of the fastest demographic shifts in the history of retail investing: tens of millions of people under 30 have opened their first brokerage or demat account, most of them through a mobile app rather than a bank branch or broker's office. This paper examines that shift at the intersection of three forces — investor behaviour, financial technology, and financial mathematics — drawing on primary market data (NSE, RBI, SEBI), international survey research (the FINRA Investor Education Foundation, CFA Institute, TIAA Institute-GFLEC, Gemini, YouGov, IPX1031), and peer-reviewed academic literature. Using secondary-data analysis, the study documents how young investors are entering markets earlier than any prior generation, disproportionately through low-cost, app-based fintech platforms, while simultaneously scoring lowest of any generation on standardised tests of financial literacy. A worked compound-interest illustration then quantifies what this “literacy-behaviour gap” is worth in practice. The paper concludes that fintech has solved the problem of market access far faster than anyone — fintech included — has solved the problem of financial education, and it proposes concrete, evidence-based measures for investors, platforms, and policymakers to close that gap.
DOI: http://doi.org/10.5281/zenodo.22895860
A Behavioural And Quantitative Analysis Of FinTech Adoption And Financial Mathematics Among Young Investors
Authors: Mataprasad Chaurasia
Abstract: The past five years have produced one of the fastest demographic shifts in the history of retail investing: tens of millions of people under 30 have opened their first brokerage or demat account, most of them through a mobile app rather than a bank branch or broker's office. This paper examines that shift at the intersection of three forces — investor behaviour, financial technology, and financial mathematics — drawing on primary market data (NSE, RBI, SEBI), international survey research (the FINRA Investor Education Foundation, CFA Institute, TIAA Institute-GFLEC, Gemini, YouGov, IPX1031), and peer-reviewed academic literature. Using secondary-data analysis, the study documents how young investors are entering markets earlier than any prior generation, disproportionately through low-cost, app-based fintech platforms, while simultaneously scoring lowest of any generation on standardised tests of financial literacy. A worked compound-interest illustration then quantifies what this “literacy-behaviour gap” is worth in practice. The paper concludes that fintech has solved the problem of market access far faster than anyone — fintech included — has solved the problem of financial education, and it proposes concrete, evidence-based measures for investors, platforms, and policymakers to close that gap.
DOI: http://doi.org/10.5281/zenodo.22895860
AI Adoption Among Gen Z Learners: A Critical Examination Of Cognitive Engagement And Critical Thinking
Authors: Aarya Moossaddee
Abstract: The swift adoption of artificial intelligence (AI) tools in higher education has greatly impacted the academic activities and study approaches of a large number of students, as well as their thinking styles. This research is concerned with the investigation of the parties’ AI employment extent in multiple academic work, as well as exploring how patterns of AI use relate to level of critical thinking. Based on primary data, the study makes use of a structured questionnaire to assess the frequency and purpose of AI-aided learning, as well as self-reported measures of critical thinking, among undergraduate and graduate students. This descriptive–correlational study can reveal patterns of use and it can explore potential relationships. Descriptive statistics, reliability tests as well as correlation analyses will be used to analyse the data to investigate the relationship between the degree of AI implementation in academic work and students’ cognitive engagement. The results will likely add substance to continuing conversations surrounding the potential educational fallout from AI and also offer actionable recommendations for educators attempting to right the scale between tech adoption and essential skills development.
DOI: http://doi.org/10.5281/zenodo.22912929
Lexical-Semantic Features of French Diplomatic Texts and Methods of Their Translation
Authors: Khasanova Shakhnoza
Abstract: This article investigates the lexical-semantic organization of French diplomatic discourse and the principal methods used to translate it into English. Diplomatic documents combine terminological precision with political caution; consequently, their meaning is produced not only by dictionary definitions but also by institutional convention, modality, collocation, politeness, and controlled ambiguity. The study applies descriptive, contextual, contrastive, and functional analysis to representative units drawn from recurrent diplomatic genres, including notes verbales, communiqués, declarations, agreements, and official statements. The analysis identifies eight central features: standardized terminology, formulaic expressions, polysemy, nominalization, modality, euphemistic mitigation, constructive ambiguity, and institutional collocation. It demonstrates that established equivalence is the safest method for conventional legal-diplomatic terms, whereas transposition, modulation, functional equivalence, explicitation, borrowing, calque, and cautious adaptation are required when structural or pragmatic asymmetry occurs. Particular attention is paid to the preservation of legal force and degrees of commitment. The article proposes a controlled translation workflow that integrates genre identification, terminology verification, pragmatic analysis, drafting, and bilingual quality assurance. It concludes that high-quality diplomatic translation is a form of disciplined intercultural mediation in which semantic fidelity, institutional consistency, and pragmatic equivalence must be evaluated together.
DOI: http://doi.org/10.5281/zenodo.22913354
AI-Powered Observability in Cloud-Native DevOps: LSTM-Based Anomaly Detection for Kubernetes Microservices
Authors: Parav Sharma, Rajesh Chauhan, Akshay Bhardwaj
Abstract: In today's complex cloud-native microservice architectures, the traditional rule-based monitoring approaches are insufficient for system reliability. The solution to this is the integration of Artificial Intelligence (AI) into DevOps, often known as AIOps. This paper introduces an AI-powered observability framework that combines the LSTM (Long Short-Term Memory) autoencoder with the Prometheus-Grafana observability stack for anomaly detection in Kubernetes-based microservice environments. The system collects real-time CPU utilization metrics from different microservices, trains an LSTM model on metric records, and detects anomalies using reconstruction error thresholding. Under controlled CPU stress injection, the characteristic scale of reconstruction error, measured as the mean plus two standard deviations of each condition's own reconstruction errors, is approximately 27 times higher than under baseline conditions, indicating that the model's reconstruction error responds strongly to abnormal workload patterns. Because a single training run may not be representative, the model is retrained ten times on a 6,407-timestep dataset; the reconstruction-error threshold is 0.000676 +/- 0.000038 and the anomaly rate 4.38% +/- 0.26% (mean +/- SD), a coefficient of variation of 5.6%, indicating that the result is reproducible rather than an artifact of one random initialization. The trained model is then deployed as a continuous detector that scores live metrics every 60 seconds; in a controlled test it flagged all 17 readings taken while an injected workload was active and returned to normal after its removal. The LSTM-based model detects anomalies without relying on fixed alert boundaries, offering a more adaptive alternative to static threshold approaches and enabling proactive detection of system abnormalities in cloud-native DevOps environments.
Climate Resilient Horticulture Infrastructure and Market Connectivity in Kangra District
Authors: Assistant Professor Hakam Chand
Abstract: The paper analyses infrastructure and market connectivity as joint determinants of climate-resilient horticulture in Kangra district, Himachal Pradesh. It uses a secondary-data case-study approach and the latest consistent state horticulture series available in the supplied publications, covering 2011-12 to 2024-25. Although horticulture output expanded during the period, climate variability, fragmented production, seasonal irrigation, post-harvest losses and costly transport threaten the stability of smallholder returns. Kangra’s traditional kuhl systems, road network, urban centres and crop diversity provide a strong base, but infrastructure must function as an integrated service chain. The study develops an Infrastructure Readiness Matrix covering water, planting material, extension, protection, aggregation, grading, cold chain, processing, roads, digital information and finance. It also proposes indicators for reliability, utilisation and inclusion. The analysis suggests that water harvesting and micro-irrigation should be combined with crop planning; pack houses should be located only where verified throughput exists; and producer aggregation should precede capital-intensive cold-chain investment. Climate resilience requires diversified crops and seasons, quality planting material, local weather and pest advisories, and multiple market channels. A phased investment and monitoring framework is recommended for Kangra.
Hybrid InceptionV3-LSTM Framework for Real-Time Deepfake Video Detection
Authors: Professor Vikram Singh, Research Scholar Naresh Kumar
Abstract: The rapid generation of deepfake videos poses significant challenges to real-time detection systems, which often encounter a wide range of quality variations and novel manipulation techniques. In order to deal with the challenges faced we propose a hybrid InceptionV3-LSTM network that encapsulates spatial feature extraction and action sequence over time. This architecture employs InceptionV3 pretrained on ImageNet for modeling frame-level artifacts, then stacks a bidirectional LSTM to jointly learn the temporal problem from multiple sequences. The model displays robustness under high compression and when tested on low compression, it shows a decline of just 2.5% in accuracy which implies that the model is sensitive. According to temporal analysis, LSTM proves capable of detecting manipulations as illustrated due to reconstruction loss variance being higher by 3.2× of low-fidelity deepfakes. The deepfake inference data under tool-based evaluation shows this detection. The InceptionV3-LSTM’s full-fledged version achieves 150 ms latency and 96% accuracy while real-time performance granted 20 ms/frame latency using MobileNet-LSTM with no accuracy loss (96%). The solution presented in this study can be deployed, balancing detection accuracy and computational efficiency. However, limitations still exist for novel manipulation techniques and multi-modal integration could be an avenue for future work. The objective of this experimental study is to achieve the enhanced detection accuracy using separate training and testing datasets of deepfake videos which can help get a better security performance and robustness. By integrating these advances, the application allows users to check the authenticity of people and deep fake videos. A framework for real-time detection of visual deepfake videos analyzes pixel-level artifacts including blending boundaries, double edges, eyes, ears, nose, head pose inconsistencies and other possible parameters sub-pixel noise patterns unique to manipulation videos.
Controlling The Pitch Angle Of A Variable Wind Speed On A Wind Turbine Using Fuzzy-Logic
Authors: Hosea, J. O., Ogbonna, B. O.
Abstract: One of the problems associated with wind energy is fluctuations in wind speed, which cause unstable and inefficient operation of variable-speed wind turbines, especially above the rated operating range where blade pitch control is necessary to adjust aerodynamic power and maintain generator speed within the rated range. This study investigates the effect of a Fuzzy Logic Controller (FLC) on pitch-angle regulation compared with the dynamic performance of a proportional-integral (PI) controller. The same wind turbine model, generator reference speed, wind-speed profile, and operating conditions were used for both controllers. The proposed Mamdani-type FLC is based on generator-speed error and its rate of change, using scaling, saturation, fuzzification, rule-based inference, and defuzzification to derive an appropriate pitch-angle command. The FLC adjusts the pitch command according to changing turbine operating conditions, enabling control of the aerodynamic power coefficient, aerodynamic torque, generator speed, and output power under wind-speed disturbances. Controller performance was evaluated using rise time, settling time, percentage overshoot, steady-state error, RMSE, IAE, ISE, ITAE, and ITSE. The FLC increased rise time from 1.7677 s to 3.2920 s, while reducing settling time from 17.2793 s to 5.5018 s and overshoot from 7.662% to 0.880%. Furthermore, the FLC reduced steady-state error, IAE, ITAE, and ITSE by 19.20%, 20.76%, 71.56%, and 61.97%, respectively. The PI controller produced lower RMSE and ISE. Overall, the results showed that FLC-based pitch-angle control provides better damping, disturbance rejection, steady-state accuracy, and long-term tracking performance under variable wind operation, although its initial response is slower than conventional PI control.
Green Computing: Approaches, Techniques And Its Implementation
Authors: Vishal Sankhyan, Dr.Rajesh Chauhan, Dr. Akshay Bhardwaj
Abstract: Green computing refers to the environmentally responsible design, manufacture, operation, and disposal of computing systems and information technology (IT) infrastructure. The rapid growth of data centers, cloud computing, artificial intelligence, Internet of Things (IoT) devices, and other digital technologies has increased energy consumption and electronic waste. Green computing seeks to reduce the environmental impact of IT while maintaining performance, reliability, security, and economic efficiency. This research paper examines major approaches, techniques, and implementation strategies for green computing. The study discusses energy-efficient hardware, virtualization, server consolidation, cloud computing, power management, green software development, efficient data centers, sustainable networking, renewable energy, and electronic-waste management. It also presents an implementation framework for measuring and reducing the energy consumption of computing infrastructure. The paper identifies important challenges, including initial investment, compatibility, performance requirements, lack of awareness, measurement difficulties, and e-waste management. Finally, it discusses future directions such as artificial intelligence-based energy optimization, edge computing, sustainable data centers, and carbon-aware computing. The study concludes that combining hardware, software, infrastructure, and organizational strategies can significantly improve the sustainability of modern information technology.
Adaptive Trust-Based Continuous Authentication Framework Using Risk-Aware Zero Trust Architecture
Authors: Atharv Sharma
Abstract: In this paper, we propose Adaptive Trust-Based Continuous Authentication Framework (ATCAF), a lightweight architecture to extend Zero Trust principles with continuous risk aware authentication. The proposed framework does not make a single authentication decision at login but continuously calculates a dynamic trust score by combining multiple contextual security indicators such as device trust, behavioral consistency, location confidence, network reputation and historical user reputation. Access decisions are made in real-time based on the continuously evolving trust score, enabling adaptive responses such as seamless access, step-up multi-factor authentication, privilege reduction or session termination. The paper describes the system architecture, mathematical trust model, continuous evaluation workflow, implementation methodology, public evaluation datasets, security analysis and experimental design to evaluate authentication accuracy, False Acceptance Rate (FAR), False Rejection Rate (FRR), Equal Error Rate (EER), trust score stability and response latency . The framework we propose aims at improving identity security, while maintaining usability and reducing unnecessary authentication interruptions.
DOI: https://zenodo.org/records/22976298
A Safety-Bounded Multi-Agent Medical Assistant: Retrieval-Augmented Generation, Medical Vision, And Human Validation
Authors: Dr. K. Himabindu, Faruk Ahmed, Pankaj Kumar Bara, Krishna Kumar Yadav, Soham Parmar
Abstract: Healthcare chatbots are gradually moving beyond simple question-and-answer systems by bringing together large language models, information retrieval, multimodal analysis, and human oversight. This paper explores the open-source Multi-Agent Medical Assistant as a safety-focused tool for providing medical information. The system includes separate components for input screening, conversation, retrieval-augmented generation (RAG), web-based evidence retrieval, medical image processing, response generation, and human validation. Rather than attempting to provide autonomous diagnoses, the system emphasizes evidence-based responses, transparent communication of uncertainty, and human review.