Authors: Ramanuj Singh, Assistant Professor Hariram Sahu

Abstract: Flexible pavements constructed over clay subgrades are highly susceptible to premature distress on account of the low bearing capacity and pronounced moisture sensitivity of clay soils. This paper presents an experimental investigation into the effectiveness of geosynthetic reinforcement in improving the California Bearing Ratio (CBR) of a high-plasticity clay (CH) subgrade collected from Damoh, Madhya Pradesh, India. Two commercially available geosynthetics, a Mirafi HP370 woven polyester geotextile and a Tensar BX1200 biaxial polypropylene geogrid, were evaluated at three reinforcement depths within the CBR mould, namely H/3, H/2, and 2H/3, under both soaked and unsoaked conditions. The soil was classified as CH under IS 1498, with a liquid limit of 55.36%, plasticity index of 30.03%, optimum moisture content of 19.5%, and maximum dry density of 1.69 g/cm³. The unreinforced clay exhibited unsoaked and soaked CBR values of 4.8% and 1.9%, respectively, both indicative of a very weak subgrade. Reinforcement with Tensar BX1200 at H/2 produced the greatest improvement, raising the CBR to 9.6% (unsoaked) and 4.9% (soaked), corresponding to gains of 100.0% and 157.9% over the unreinforced control. The Mirafi HP370 geotextile performed best at H/3, with improvements of 85.4% and 121.1%. The results confirm that both reinforcement type and placement depth significantly influence subgrade performance, and that biaxial geogrid reinforcement at mid-depth offers the greatest structural benefit for the clay investigated. The experimentally derived CBR values are discussed in relation to their implications for flexible pavement thickness design over weak clay subgrades.

DOI: https://doi.org/10.5281/zenodo.21887185

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Authors: Prateek Anand, Assistant Professor Rishi Sharma, Assistant Professor Gaurav Morghare

Abstract: Low Earth Orbit (LEO) satellite communication has emerged as a key enabler of sixth-generation (6G) Non-Terrestrial Networks (NTNs), offering global coverage, low propagation delay, and high-capacity broadband connectivity. However, the high orbital velocity of LEO satellites introduces significant challenges, including severe Doppler frequency shifts, rapidly varying channel conditions, and frequent handovers, which adversely affect communication reliability, throughput, and Quality of Service (QoS). Existing Doppler compensation and handover mechanisms are generally treated as independent processes and often rely on static threshold-based strategies or computationally intensive artificial intelligence (AI) models, limiting their adaptability in highly dynamic satellite communication environments. This paper proposes an Adaptive AI-Assisted Doppler Compensation and Predictive Handover Optimization (AIDCPHO) framework for LEO satellite communication in 6G Non-Terrestrial Networks. The proposed framework integrates real-time Doppler estimation, AI-assisted predictive handover decision-making, adaptive beam selection, and dynamic link quality assessment into a unified optimization model. A predictive mobility module estimates future satellite-user link conditions using orbital dynamics and user mobility information, while an adaptive Doppler compensation module minimizes frequency estimation errors before communication degradation occurs. Furthermore, a multi-parameter handover decision algorithm utilizes Signal-to-Noise Ratio (SNR), Doppler shift, elevation angle, received signal strength, and predicted link quality to proactively initiate seamless handovers, thereby reducing service interruption and packet loss. The proposed framework is implemented and evaluated using MATLAB-based simulations that model realistic LEO satellite orbital movement, time-varying communication channels, and user mobility scenarios.

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Yuvika Priyadarshini
Affiliation	Software Engineer,Department of Computer Science, YRCAIRI TECH (OPC) PVT. LTD.
Email-Id:	info@thecairi.com
Publication:  Growth and Performance of Data Mining in Banking Industry” International Journal Engineering Science & Research Technology (IJESRT) Volume 6, Issue 7,July 2017. Data Analytics Integration in Banking Industry “at the International conference on Recent Innovations in  Electrical,Electronics,Computer,Information,Communicationand Mechanical Engineering (ICRIEECICME) at Pune , India on  21st May 2017. Effectiveness of Data Mining Techniques in Banking “ at Computer Applications: An International journal  (CAIJ), Nov 2017. Growth Pool of Knowledge Management in Banking Industry" at  International Conference on Emerging Technologies, Systems & Applications. April 2018. Information Secrecy and Security in AI" at International Journal of Science Engineering and Technology. June 2024.

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Mohamed Razeed Mohamed Nowfeek
Affiliation	Visiting Lecturer, Demonstrator in IT at SLIATE,Srilanka Intitute of Advanced Technological Education .
Email-Id:	nowfeekmit@gmail.com
Publication:  Mohamed Razeed Mohamed Nowfeek (2026), An Analysis of Online Learning Issues during the Covid-19 Pandemic." International Journal of Research and Innovation In Social Science (Ijriss),Volume X Issue V May 2026, pp. 402-420 2026. Mohamed Razeed Mohamed Nowfeek (2021), A Survey: Amazon’s Digital Commerce Evolution and Its Potential Impact on Sri Lanka." International Journal of Latest Technology in Engineering, Management & Applied Science-IJLTEMAS, Vol. 14 No. 6 Volume XIV Issue VI June 2025, pp. 198-204 2025. M. R. M. Nowfeek, Dr. Lakmal Rupasinghe, "Development of a Virtual Learning Environment (VLE) During the COVID -19 Pandemic”: A Study with special reference to Advanced Technological Institute" International Journal of Latest Technology in Engineering, Management & Applied Science-IJLTEMAS vol.11 issue 1,pp.32-46 2022. Mohamed Razeed Mohamed Nowfeek, “A Review of Android operating system security issues” International Journal of Research and Scientific Innovation (IJRSI) vol.9 issue 1, pp.26-30 January 2022. MRM.Nowfeek& M. Farwis (2021). Effectiveness of Analyzing Data in the Cloud: Agenda for Future Research. Asian Journal of Social Science and Management Technology 3(3): 110-115, 2021.

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Authors: Jackson Matsanga

Abstract: Teacher knowledge and teacher efficacy have long been recognised as important determinants of effective teaching and learning. This study examined the influence of teacher knowledge and teacher efficacy on students’ learning outcomes in Mathematics in selected secondary schools in Bududa District, Uganda. A cross-sectional survey design incorporating quantitative and qualitative approaches was used. Data were collected using questionnaires, interviews, classroom observations and Mathematics achievement measures. Quantitative data were analysed using descriptive statistics and Spearman’s rank correlation, while qualitative evidence was organised thematically and used to enrich interpretation. The findings indicated positive relationships among teacher knowledge, teacher efficacy and students’ learning outcomes. Teachers who demonstrated stronger subject-matter understanding, pedagogical competence and confidence in instructional practice were better positioned to explain mathematical concepts, manage learning activities, motivate learners and respond to learning difficulties. The study further indicated that teacher knowledge and efficacy were mutually reinforcing: professional competence strengthened instructional confidence, while successful teaching experiences reinforced teachers’ beliefs in their capabilities. The study concludes that improving Mathematics achievement requires simultaneous attention to teachers’ content knowledge, pedagogical competence and professional efficacy. Continuous professional development, supportive instructional supervision, adequate teaching resources and strengthened teacher preparation are recommended.

DOI: http://doi.org/10.5281/zenodo.21868584

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Authors: Ramina. K

Abstract: Lead sulphide (PbS) thin films have attracted significant research interest because of their narrow band gap, excellent infrared sensitivity, and promising applications in optoelectronic devices, infrared detectors, solar cells, and photoconductive systems. The present study investigates the synthesis and characterization of PbS thin films deposited on glass substrates using the Successive Ionic Layer Adsorption and Reaction (SILAR) technique. The films were prepared under controlled deposition conditions and subsequently subjected to different cooling durations to evaluate their influence on structural and optical characteristics. Structural analysis was performed using X-ray Diffraction (XRD), while surface morphology and optical properties were examined using Scanning Electron Microscopy (SEM) and UV–Visible spectroscopy. XRD analysis confirmed the formation of polycrystalline PbS thin films with a face-centred cubic crystal structure. Variations in grain size, strain, and dislocation density were observed with different cooling durations, indicating that post-deposition treatment significantly affects crystal growth. SEM images revealed uniform surface morphology with densely packed grains, whereas UV–Visible analysis demonstrated favourable optical absorption characteristics suitable for photovoltaic and infrared sensing applications. The findings indicate that the SILAR method is an economical, simple, and effective technique for producing high-quality PbS thin films with potential applications in advanced optoelectronic devices.

DOI: https://doi.org/10.5281/zenodo.21847782

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Authors: Research Scholar Reeta, Assistant Professor Dr Meentu Grover

Abstract: Neuro-marketing has emerged as an interdisciplinary field that integrates marketing, psychology, and neuroscience to understand the cognitive and emotional processes underlying consumer decision-making. The present study empirically examines the influence of key neuro-marketing factors consumer attention, emotional response, memory, sensory appeal, and subconscious influence on consumer behaviour. The study adopts a quantitative research design and considers a sample of 200 respondents. Data are measured using a five-point Likert scale and analyzed through descriptive statistics, Pearson correlation, and multiple regression analysis. The empirical analysis indicates that attention, emotional response, memory, and subconscious influence have positive and significant relationships with consumer behaviour, whereas sensory appeal demonstrates a positive but comparatively weaker influence when other factors are considered simultaneously. The regression model indicates that the selected neuro-marketing factors collectively explain a substantial proportion of the variation in consumer behaviour. Among the factors examined, emotional response emerges as one of the most influential predictors, highlighting the importance of affective engagement in shaping consumer responses. The findings suggest that marketers can improve consumer engagement by designing marketing stimuli that effectively capture attention, generate positive emotions, strengthen brand memory, and influence automatic decision processes. The study contributes to the growing empirical literature on neuro-marketing by demonstrating a structured framework for examining the relationship between psychological marketing stimuli and consumer behaviour. However, the findings should be interpreted in light of the study's methodological limitations and should be validated through larger samples and actual consumer data in future research.

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Authors: Associate Professor M MGanganallimath, Associate Professor S J Sanjay, Abhishekh M Hiremath, Prajwal Mane, Raghavendra I Yaragudri, Siddalingesh M Matoli

Abstract: The fast expansion of the textile and garment sector has greatly increased waste clothing creation, causing major environmental and resource management concerns. Most wasted clothing are disposed of by landfilling or incineration, leading to resource depletion, pollution, and greenhouse gas emissions. Waste clothes recycling has emerged as an efficient approach for encouraging resource conservation and fostering a circular economy. This study discusses important recycling technologies, including mechanical, chemical, thermal, and biological processes, and their potential for transforming textile waste into value-added goods such as regenerated fibers, composites, insulating materials, geotextiles, and construction items.The study also addresses how new technologies, like as artificial intelligence and the Internet of Things, can enhance the effectiveness of textile collection, sorting, and recycling. Additionally, the economic and environmental advantages of recycling are emphasized, including less carbon emissions, decreased landfill waste, resource conservation, and job creation. There is also discussion of current issues such contamination, insufficient infrastructure, mixed-fiber separation, and policy constraints. The analysis comes to the conclusion that effective waste clothing recycling presents important chances for value development, sustainable resource management, and the shift to a circular textile economy.

DOI: https://doi.org/10.5281/zenodo.21838501

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Authors: Nagabathula Poorna Praveen, Assistant Professor K V V Ramana

Abstract: Early and accurate cancer detection is essential for improving patient survival rates and enabling timely clinical intervention. However, conventional diagnostic approaches and classical machine learning techniques often encounter challenges in processing high-dimensional biomedical datasets due to feature redundancy, computational complexity, and limited predictive performance. To address these limitations, this paper proposes a quantum-assisted intelligent framework for early cancer detection by integrating advanced quantum-inspired preprocessing, feature optimization, and hybrid predictive modeling techniques. The proposed methodology employs Quantum-Normalized Adaptive Refinement (Q-NAR) to enhance data quality through effective preprocessing, Wrapper Component Attribute Analysis (WCAA) to rank significant biomedical features, and Swing L-Bee Mustard Optimization (SLBMO) to identify the optimal feature subset while reducing dimensionality. Finally, a Quantum Boosted Vector Fusion Network (QBVFN) is developed to perform accurate cancer classification and treatment outcome prediction. The proposed framework is validated using biomedical data from The Cancer Genome Atlas (TCGA) within a Python-based implementation environment. Experimental evaluation demonstrates that the proposed approach achieves superior prediction accuracy, improved feature optimization, and enhanced computational efficiency compared with conventional machine learning models. The obtained results highlight the effectiveness of integrating quantum computing principles with machine learning techniques to support next-generation intelligent cancer diagnosis and precision healthcare systems.

DOI: http://doi.org/10.61137/ijsret.vol.12.issue4.161

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Authors: Nishant Kumar, Jitendra Chauhan

Abstract: Asphalt binder aging significantly affects the long-term performance and durability of flexible pavements, leading to various pavement distresses such as cracking, rutting, and raveling. Accurate prediction of asphalt binder aging and associated pavement deterioration is essential for effective pavement design, maintenance planning, and lifecycle cost management. In recent years, Artificial Intelligence (AI) techniques have emerged as powerful tools for modeling complex, nonlinear relationships among environmental factors, material properties, traffic loading, and aging characteristics of asphalt binders. This work explores the application of Artificial Intelligence–based methods for predicting asphalt binder aging and pavement distress. Various AI techniques such as Artificial Neural Networks (ANN), Support Vector Machines (SVM), Machine Learning (ML), Deep Learning (DL), and hybrid optimization models are examined in terms of their ability to analyze large datasets and provide accurate predictions. The study existing literature on AI-driven predictive models that incorporate factors such as temperature variation, oxidation processes, traffic load, binder composition, and environmental conditions. Furthermore, the paper highlights the advantages of AI models over traditional empirical and mechanistic methods, including improved prediction accuracy, adaptive learning capability, and efficient data-driven decision-making. The work also identifies current research gaps, challenges in data availability, and opportunities for integrating AI with mechanistic–empirical pavement design approaches. Overall, the study provides a comprehensive overview of recent advancements in AI-based prediction of asphalt binder aging and pavement distress, emphasizing its potential to enhance pavement performance evaluation, optimize maintenance strategies, and support sustainable infrastructure development.

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