Category Archives: Uncategorized

Authors: Aravind Chagantipati

Abstract: A persistent obstacle in deploying artificial intelligence within enterprise network defense systems is the tension between operational accuracy and human transparency. Conventional, rule-guided systems such as decision trees lack the scale and sophistication required to detect multi-stage, contemporary cyber threats. Conversely, complex neural networks (including LSTM and CNN designs) provide superior classification rates but act as opaque models, making validation difficult within strict enterprise compliance structures. This study presents a tiered, dual-layered architecture that integrates high-capacity deep learning classifiers with post-hoc explainability engines, effectively bridging the gap between classification performance and regulatory auditing requirements.

DOI: http://doi.org/10.5281/zenodo.21412923

Published by: vikaspatanker

Authors: Sheid, Avidime Momohjimoh, Sheidu Omeiza Momoh, Oyem Onyekachi Anslem, Shuaibu, Muhib Amoto

Abstract: This study presents a comprehensive numerical investigation of magnetohydrody-namic (MHD) blood-based nanofluid flow with coupled heat and mass transfer for cardiovascular prosthetic applications. The research addresses critical gaps in thermal regulation and targeted drug delivery modeling by developing a physiolog-ically realistic mathematical framework that incorporates electromagnetic effects, nanoparticle dynamics, and the non-Newtonian rheology of blood. The govern-ing conservation equations for mass, momentum, energy, and species concentration are formulated within a boundary layer framework, incorporating Lorentz forces, variable thermal conductivity, viscous dissipation, Joule heating, Brownian motion, thermophoresis, and chemical reactions. Through similarity transformations, the nonlinear partial differential equations are reduced to a system of coupled ordinary differential equations, which are solved numerically using a collocation method im-plemented in Python (scipy.integrate.solve_bvp). A comprehensive parametric analysis reveals that thermal and solutal buoyancy significantly enhance momentum transport, while the Prandtl number governs thermal boundary layer characteristics. Nanoparticle transport is predominantly controlled by thermophoresis and Brown-ian motion, with thermophoresis promoting nanoparticle accumulation and Brown-ian motion enhancing diffusion. The Schmidt number suppresses species diffusion, while the Casson parameter exhibits minimal influence on velocity but significantly affects thermal distribution. The magnetic parameter introduces resistive Lorentz forces that modify both momentum and thermal fields. These findings provide valu-able insights for the design of cardiovascular prosthetics, thermal therapy systems, and targeted drug delivery platforms.

DOI: http://doi.org/10.5281/zenodo.21407708

Published by: vikaspatanker

Authors: Jereen Susan John

Abstract: Design thinking has emerged as an innovative, human-centered approach to addressing complex challenges in education by fostering creativity, collaboration, critical thinking, and problem-solving. This paper explores the versatility of design thinking and its application across diverse educational contexts, including school, higher, and professional education. It examines the core stages of the design thinking process—empathize, define, ideate, prototype, and test—and their role in promoting learner-centered, experiential, and inquiry-based learning. The study highlights how design thinking enables educators to develop inclusive teaching strategies, redesign curricula, enhance student engagement, and encourage interdisciplinary collaboration. Furthermore, it discusses the integration of digital technologies and real-world problem-solving activities that prepare learners with essential 21st-century skills. The paper argues that adopting design thinking as both a pedagogical framework and an institutional innovation strategy can transform teaching and learning environments by encouraging adaptability, empathy, and continuous improvement. It concludes that the widespread implementation of design thinking can contribute significantly to educational quality, innovation, and lifelong learning.

Published by: vikaspatanker

Authors: Shri. Suresh Lamani, Dr.M.B. Chalawadi

Abstract: Groundwater depletion presents a critical threat to agrarian viability within the semi-arid cratonic terrains of the Deccan Plateau. This study develops a comprehensive geospatial framework using Remote Sensing (RS), Geographic Information Systems (GIS), and the Analytical Hierarchy Process (AHP) to delineate Groundwater Potential Zones (GWPZ) across the seven taluks of Gadag district, Karnataka. Nine thematic layers geology, geomorphology, lineament density, drainage density, slope, soil type, land use/land cover (LULC), rainfall distribution, and vadose zone lithology—were integrated utilizing multi-criteria decision-making weights. The validation matrix indicates that GWPZ classes conform strongly to historical borewell yield records. High potential zones encompass 18.4% of the district, localized primarily within alluvial plains and highly fractured shear zones in parts of Shirhatti and Lakshmeshwar. Conversely, Nargund and Mundargi taluks display severe structural limits, with over 45% of their area classified under 'Poor' to 'Very Poor' potential due to steep horizons, high drainage runoff, and massive crystalline basements. These geospatial normalizations offer definitive structural models for targeting managed aquifer recharge zones and planning sustainable water distribution networks.

DOI: http://doi.org/10.5281/zenodo.21370627

Published by: vikaspatanker

Authors: Dr Hari Gangadhar Kale

Abstract: The solar energy is generated by the Sunlight is a sustainable renewable energy source that doesn't harm the environment. The earth receives enough solar energy per hour to cover all of the world's energy needs for a full year. In the modern era we require electricity on a daily basis. This solar energy is produced for commercial, residential and industrial uses. It can readily absorb energy from direct sunshine. As a result it is highly effective and pollution free. We have analyzed solar energy from sunlight and spoken about its future developments and characteristics in this essay. Additionally, the page attempts to clarify how different types of solar panels operate and highlights the numerous uses and strategies for promoting the advantages of solar energy.

Published by: vikaspatanker

Authors: Hammed, Hammidat D, Enoma, Samuel, Donkoh, Christian J. K, Kikeh, Emric N, Agboola, Anthonia O, Benin, Sandra

Abstract: Neurodegenerative illnesses pose a significant problem, partly due to the challenge of getting therapeutic drugs across the blood-brain barrier (BBB), as well as the complex interactions between neuroinflammation and metabolic dysregulation. As a way to address these issues, we have created a lipid nanoparticle (LNP) system. It combines two different types of transporters with the intended goal of delivering omega-3 fatty acids through the use of plant lectins to help facilitate the movement of DHA and EPA across the BBB. As a special feature of this created LNP system, when utilizing the GLUT1 and LAT1 transporters located on the endothelium of the BBB in order to move the LNPs across the BBB from the circulation to the brain, both GLUT1 and LAT1 are used simultaneously, allowing a more efficient means of delivering the LNP system across the BBB without being limited by saturation kinetics when both GLUT1 and LAT1 are engaged. Within the LNP, both DHA and EPA are contained in an optimized ratio for both optimal delivery and maximal effect, supporting the activation of neuroprotective pathways (NF-κB suppression) and the promotion of mitochondrial biogenesis. The use of lectin (a binding agent derived from plant sources) as a means by which to reduce inflammation and provide a pathway to help the LNP system penetrate the BBB and provide an inflammatory reduction via helping to change microglial polarity towards an anti-inflammatory phenotype was also demonstrated. The experimental validations done with this LNP system, using human induced pluripotent stem cell (iPSC) derived human BBB models, clearly showed significantly greater levels of transcytosis flow than what is typically expected. As well, in transgenic Alzheimer's mouse models, the oxidative stress levels were significantly decreased and the synaptic structure was maintained. The novel nature of this work is due to the ability of each of the transporters, GLUT1 and LAT1, to target neurodegenerative disorders while also utilizing immunomodulatory and metabolic pathways in tandem. The strategy applied here provides an innovative and effective platform to enhance neuronal resiliency through the combination of neuroprotection and directional/neural specific drug delivery with applicability across a broad range of neurodegenerative diseases.

DOI: http://doi.org/10.5281/zenodo.21357715

Published by: vikaspatanker

Authors: Ms. Neha Yadav, Dr. Nitin Kumar

Abstract: Smartphone addiction has become a growing concern due to excessive dependence on mobile devices for communication, entertainment, and social interaction. This research focuses on a data-centric machine learning framework for detecting smartphone addiction by analyzing user behavioral patterns such as screen time, unlock frequency, app usage, and night-time activity. Unlike traditional model-focused approaches, the proposed framework emphasizes data quality, preprocessing, feature engineering, and reliable labeling to improve prediction performance. The study aims to support early identification of addiction risk and contribute to the development of intelligent digital well-being systems for healthier smartphone usage habits.

DOI: https://doi.org/10.5281/zenodo.21355847

Published by: vikaspatanker

Authors: Ms. Nibha kumari, Associate Professor Dr. Pramod K

Abstract: The rapid growth of digital technologies in healthcare has led to the generation and storage of vast amounts of sensitive medical data, making security and efficient data processing critical concerns. Deep learning, a powerful subset of artificial intelligence, has shown significant potential in addressing these challenges by enabling accurate analysis of complex healthcare data and enhancing system security. This study examines the application of deep learning models in healthcare systems with a focus on improving security, disease detection, and data reliability. The research reviews existing studies related to artificial intelligence and deep learning techniques used in medical image analysis, disease prediction, and healthcare data protection.

DOI: https://doi.org/10.5281/zenodo.21355298

Published by: vikaspatanker

Authors: Prince Pawar, Sujal Sisodiya, Associate Professor Pradeep Patel

Abstract: Rapid microclimatic fluctuations often evade detection by sparse, conventional meteorological networks, necessitating hyper-local, real-time monitoring solutions. This paper presents an analysis of an 8-hour diurnal environmental dataset (10:00 AM to 5:00 PM) captured via an Internet of Things (IoT)-based monitoring node. The system integrates low-cost sensors to continuously log ambient temperature, relative humidity, air quality (particulate/gas concentrations in ppm), light intensity, and precipitation. The empirical data reveals distinct thermodynamic transitions and strong inter-parameter correlations. Specifically, the dataset captures a textbook meteorological shift: midday solar heating—evidenced by a peak temperature of 34∘C, peak light intensity (100%), and a concurrent relative humidity drop to 45%—followed by a sudden convective afternoon rain shower. The onset of precipitation at 3:00 PM triggered an immediate environmental inversion, characterized by a 3∘C drop in temperature, a sharp moisture surge to 68% relative humidity by 5:00 PM, and a significant reduction in airborne pollutants (from a peak of 180 ppm down to 125 ppm) due to the atmospheric scrubbing effect of the rain. These findings demonstrate that high-frequency IoT sensor networks provide highly reliable, granular data essential for unmasking the velocity and impact of localized weather fronts. The proposed approach offers scalable, actionable insights applicable to urban climate mapping, smart agriculture, and industrial environmental compliance.

DOI: https://doi.org/10.5281/zenodo.21353849

Published by: vikaspatanker

Authors: Assistant Professor Dr.S.Sujatha, Assistant Professor Dr Sayantani Chakraborty

Abstract: In times when organizations face a high level of challenges, they need sophisticated models that help them to reconcile conflicting goals and maintain sustainable development. In this paper, we propose an analytical strategic decision-making model combining the use of business analytics with multi-objective optimization and sustainability principles. We base our research on Multi-Objective Optimization (MOO) concept, Pareto frontier theory, and Triple Bottom Line (TBL) framework. As a result, we have developed a model that allows organizations to deal with conflicting goals concerning profitability and risk level and maintain sustainable development at the same time. The analysis of quantitative data from different industries shows that application of the proposed model significantly increases the efficiency of decision making and strategic planning in organizations.

DOI: https://doi.org/10.5281/zenodo.21350647

Published by: vikaspatanker