Daily Archives: July 14, 2026

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