Category Archives: Uncategorized

Authors: Ar. Suman Sharma, Muskan Gour

Abstract: Vastu Shastra is an ancient Indian architectural science that establishes harmony between buildings, nature, and human activities through orientation and spatial planning. Traditional Indian architecture evolved according to climatic conditions, environmental understanding, and sustainable planning principles. The orientation principles of Vastu are closely related to thermal comfort, daylight performance, natural ventilation, and passive cooling strategies. This research paper studies the climatic relevance of Vastu orientation and analyses how traditional Indian architecture responded effectively to environmental conditions using climate responsive architectural techniques. The study follows a qualitative and analytical research methodology based on literature review, comparative analysis, and case studies of traditional Indian houses. The paper examines the relationship between orientation, sunlight, wind movement, thermal comfort, and passive environmental control. Traditional architectural elements such as courtyards, verandahs, jaalis, shaded openings, and thick walls are analysed in relation to Vastu principles. The study also compares these traditional concepts with modern sustainable architectural practices. The findings indicate that many Vastu principles are scientifically relevant and environmentally responsive. Proper orientation improves daylight quality and ventilation while reducing heat gain and energy consumption. East-facing openings provide healthy morning sunlight, while reduced western exposure minimizes thermal discomfort. Courtyard planning enhances air circulation and creates thermal balance within buildings. The research concludes that Vastu orientation is not merely a cultural or spiritual concept but also a climate responsive architectural strategy based on environmental understanding and passive design principles. Many concepts of Vastu remain relevant in contemporary sustainable architecture and energy-efficient building design.

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Authors: Kovuru. Rasi, Ch.Praveen Kumar, V.Haribaskar

Abstract: Artificial Intelligence (AI) is transforming the pharmaceutical industry by introducing advanced computational approaches for formulation design, drug delivery optimization, and personalized medicine. Conventional pharmaceutical development methods are often time-consuming, expensive, and dependent on repeated experimental trials. AI-based technologies such as machine learning, deep learning, artificial neural networks, and predictive analytics provide innovative solutions by analyzing large datasets, predicting formulation behavior, and optimizing drug delivery systems with improved precision and efficiency. AI assists researchers in identifying critical formulation variables, predicting drug–excipient interactions, enhancing stability, and improving bioavailability while reducing development time and manufacturing costs. In drug delivery optimization, AI supports the development of targeted, controlled, and patient-specific delivery systems including nanoparticles, liposomes, transdermal systems, and smart drug carriers. Furthermore, AI-driven models facilitate quality-by-design approaches, real-time monitoring, and automated decision-making during pharmaceutical manufacturing. The integration of AI with pharmaceutical sciences also promotes personalized therapeutics by enabling dose optimization according to patient-specific factors such as genetics, age, disease condition, and metabolic profile. Despite its significant advantages, challenges including data reliability, regulatory concerns, ethical issues, and the need for interdisciplinary expertise remain barriers to widespread implementation. This review highlights recent advancements, applications, benefits, challenges, and future prospects of AI in pharmaceutical formulation development and drug delivery optimization, emphasizing its potential to revolutionize modern pharmaceutics and improve healthcare outcomes.

DOI: http://doi.org/10.5281/zenodo.20696741

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Authors: Associate Professor Vaibhav Narwade, Ms. Shraddha Nitin Ghadage, Ms. Sakshi Dilip Dadge, Dr. Vijaykumar Kale, Associate Professor Mahesh Thakare

Abstract: The present study focuses on the formulation and evaluation of a herbal immunity booster powder prepared using natural medicinal herbs known for their immunomodulatory, antioxidant, and health-promoting properties. In recent years, there has been increasing interest in herbal formulations due to their safety, effectiveness, affordability, and minimal side effects compared to synthetic preparations. The formulated immunity booster powder was developed using herbal ingredients such as turmeric, ginger, Tulsi, amla, cinnamon, black pepper, giloy, and ashwagandha, which are traditionally used in Ayurvedic medicine for enhancing body resistance and improving overall health. The selected herbal ingredients were collected, dried, powdered, and sieved individually before being blended in suitable proportions to obtain a homogeneous formulation. The prepared powder was evaluated for various physicochemical and organoleptic parameters including color, odor, taste, texture, bulk density, and tapped density, angle of repose, ash value, moisture content, pH, and solubility. Stability studies were also carried out under suitable storage conditions to determine the stability and shelf life of the formulation. The evaluation results indicated that the prepared herbal immunity booster powder possessed good flow properties, acceptable physicochemical characteristics, and satisfactory stability. The formulation showed potential antioxidant and immunomodulatory activity due to the presence of bioactive phytoconstituents such as flavonoids, phenolics, alkaloids, and vitamins. The study concludes that the developed herbal immunity booster powder can be used as a safe and effective natural health supplement for improving immunity and maintaining overall wellness.

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Authors: Hannah Owusu Ansah, Daniel Karikari Frempong, Gabriel Oduro Asirifi

Abstract: Wearable sensors have emerged as a transformative technology in healthcare, sports, and fitness, enabling continuous monitoring of physiological and environmental conditions. Advances in stretchable substrates, microfluidic channels, and skin-integrated electronics now facilitate real-time, high-fidelity information from the human body. Integration into textiles and garments has led to the development of smart e-textiles with sensing capabilities for motion, pressure, and sweat composition. These systems operate on principles such as piezoresistivity, piezoelectricity, electrochemistry, and triboelectricity, converting physical or chemical stimuli into quantifiable electrical signals. As self-powered platforms, they minimize reliance on conventional batteries, enabling energy-autonomous sensing. Consequently, extensive research efforts are ongoing to innovate and overcome current limitations in wearable sensor technologies. This literature review explores the fundamental principles, current research status, and development trends of wearable sensors, with a focus on their integration into smart textiles, flexible electronics, and real-time health monitoring systems. Despite remarkable progress, challenges remain in sensor durability, data accuracy, energy management, and large-scale manufacturing. Nonetheless, the integration of flexible electronics, artificial intelligence, and Internet of Things (IoT) infrastructure continues to propel wearable sensors toward broader applications in telemedicine, ageing care, industrial safety, and human–machine interfaces. Importantly, this work serves as a blueprint for researchers, engineers, and policymakers committed to advancing wearable sensor technologies toward practical, scalable, and human-centric applications.

DOI: http://doi.org/10.5281/zenodo.20682359

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Authors: A. Siddu Nayak, K. Jyothi, M. Jeevan, P.V.R.Ravindra Reddy

Abstract: The increasing demand for sustainable and environmentally friendly engineering materials has promoted the utilization of agricultural waste as reinforcement in polymer composites. Among various agro-based materials, rice husk (RH), a by-product obtained during rice milling, has emerged as a promising reinforcing material due to its low density, abundant availability, renewable nature, and unique silica-rich composition. Rice husk contains cellulose, hemicellulose, lignin, and a considerable amount of silica, which contribute to its stiffness and thermal resistance. However, the hydrophilic nature of rice husk and the hydrophobic nature of most polymer matrices often lead to weak interfacial adhesion, limiting the mechanical performance of composites.This review paper presents a comprehensive analysis of the reinforcing potential of rice husk in thermoplastic and thermosetting polymer matrices. The influence of rice husk content, particle size, chemical treatment, and processing techniques on the mechanical, thermal, morphological, and water absorption characteristics of composites is critically reviewed. The effects of coupling agents such as maleic anhydride grafted polypropylene (MAPP) and silane treatments in improving fiber–matrix compatibility are discussed. The recent advancements in hybrid rice husk composites and bio-based polymer systems are also highlighted. The review concludes that rice husk has significant potential as a low-cost and eco-friendly reinforcement for manufacturing lightweight materials for automotive, construction, packaging, and consumer product applications.

DOI: http://doi.org/10.5281/zenodo.20680516

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Authors: Dr. Nidhi Mishra, Sunil Vishwakarma, Sahil, Sneha Pandey, Shirish Shukla

Abstract: The growing integration of artificial intelligence (AI) in healthcare has greatly enhanced clinical decision-making and predictive capabilities. However, conventional centralized training approaches introduce significant concerns related to data privacy, security, and regulatory compliance. Patient data, often distributed across multiple healthcare institutions, cannot be easily shared due to strict privacy laws and ethical considerations. To overcome these limitations, this study presents a secure and scalable federated AI framework designed for privacy-preserving healthcare analytics, allowing collaborative model development without the need for centralized data collection. The proposed system employs federated learning to build a global model by combining locally trained updates from decentralized healthcare nodes, ensuring that sensitive patient information remains within institutional boundaries. To strengthen security and reliability, the framework incorporates secure aggregation techniques, encryption-based protection of model updates, and anomaly detection methods to defend against adversarial threats and data poisoning attacks. Additionally, the architecture supports scalability through adaptive client selection and communication-efficient update mechanisms, making it well-suited for large-scale and heterogeneous healthcare environments. Experimental results using distributed healthcare datasets indicate that the proposed federated AI approach achieves performance comparable to traditional centralized models while substantially minimizing privacy risks and communication costs. These findings demonstrate the potential of the framework to enable secure, compliant, and efficient analytics across distributed medical systems. Overall, this work establishes a practical pathway for deploying trustworthy AI solutions in real-world healthcare settings while safeguarding patient confidentiality.

DOI: http://doi.org/10.5281/zenodo.20679368

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Authors: SAM, Fredrick P, ALABI, Omowumi, MD, Tawey, MORRIS, Susannah D, UGBALA, E.N, Nimely, DENNIS R

Abstract: This paper investigated the spatial and temporal dynamic pattern of rainfall over four decades (1981-2024) in Monrovia, Liberia. These rainfall data were used, a combined rainfall data that combines surface observations of the Liberia Meteorological Services (LMS) and the satellite-based Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) estimates. The presence of variability, anomalies, and extremes has been measured using the Mann-Kendall trend test and Sen’s slope estimator and rainfall indices like the Precipitation Concentration Index PCI), Standardized Precipitation Index (SPI), and Rainfall Anomaly Index (RAI). Analysis showed that there is no statistically significant long-term trend in annual rainfall totals (Mann-Kendall, p > 0.05), but there are significant intra-seasonal changes. Drying patterns as identified in the early rainy season (April-May) with slope of Sen’s values between -2.1 mm/yr and -3.7 mm/yr. Conversely, late rainy season months (August-September) showed an increasing part of rainfall with the slope between 1.456 mm/year and 1.966 mm/year, indicating redistribution in the seasonal rainfall time. Moderate rainfall concentration and non-equal seasonal distribution were characterized by PCI values (12.93 to 16.34). The SPI analysis found repeat drought and extreme wet years (1982, 1994, 2009, 2015, 2020, 2022, and 2024) and extreme wet years (1995, 1996, 2006, 2007, 2008, and 2010). The Aggregate outcome of RAI indicated that a greater proportion of the years were in the negative anomaly as opposed to the wet years; this translates to prevalent dry years with high inter-annual variability. The redistribution and increment of extremes, although resulting in no notable reductions in total rainfalls, make it impossible to reinstate only significant declines in the whole annual rainfalls. Water resources management, agriculture, irrigation, and urban flooding control in Monrovia have very significant implications under such circumstances. The implications of the findings reflect evidence-based knowledge in consonance with Sustainable Development Goals (SDG 6: Clean Water and Sanitation, SDG 11: Sustainable Cities and Communities, and SDG 13: Climate Action), the urgency of which relates to adaptive climate strategies of the urban environment in Monrovia.

DOI: http://doi.org/10.5281/zenodo.20679212

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Authors: Ashwini Angadi, Adarsh GS, Janaki R Torvi, Preeti V Kulkarni, Chetan Savant, Venkatrao H Kulkani

Abstract: COVID-19 vaccination has been a major public health intervention, significantly decreasing the incidence of severe infection, hospitalization, and death caused by SARS-CoV-2. The safety of currently authorized vaccines has been confirmed through extensive clinical trials and post-marketing surveillance. However, uncommon cardiovascular complications, including myocarditis and pericarditis, have been identified in a small number of vaccinated individuals, especially after administration of mRNA-based vaccines. In very rare situations, vaccine-associated myocarditis can progress to serious cardiac complications such as arrhythmias, impaired ventricular function, and, in exceptional cases, cardiac arrest. This review provides an overview of the available literature on cardiac arrest occurring after COVID-19 vaccination, focusing on potential pathophysiological mechanisms, clinical presentation, diagnostic evaluation, treatment strategies, and patient outcomes.

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Naveen Reddy Burramukku
Affiliation	Caterpillar Lead Infrastructure And Security Engineer .
Email-Id:	naveenreddyburramukku01@gmail.com
Publication:  Books: Self-Defending Enterprise Infrastructure: AI-Driven Security, Zero Trust, and Autonomous Cyber Defense. Publications: Burramukku, N. R. (2024). Flood Nexis: Intelligent multi-layer flood monitoring system. Spanish Journal of Innovation and Integrity, 37, 13. 2024. Burramukku, N. R. (2024). Implementation of secure hybrid cloud infrastructure using infrastructure-as code and zero trust principles. South Asian Journal of Science and Technology, 141, 4–15. 2024. Burramukku, N. R. (2023). Automated vulnerability detection and mitigation in virtualized datacenter environments. Journal of Management and Science, 13(4), 46–55 2023. Burramukku, N. R. (2023). Infrastructure-as-code security: Risks, best practices, and compliance considerations. International Journal of Science, Engineering and Technology, 11(6) 2023. Burramukku, N. R. (2022). Identity and access management in cloud and on-prem infrastructure environments. International Journal of Scientific Research & Engineering Trends, 8(5) 2022.

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Authors: Dr. Harsha Sammangi, Aditya Jagatha, Navyasri Maddukuri

Abstract: Climate disruption has become a persistent organizational condition rather than an episodic event, yet most information systems designed to support organizational resilience treat each disruption as an isolated incident. Existing digital resilience platforms, disaster recovery systems, and AI-driven decision support tools lack the capacity to accumulate, encode, and reuse organizational knowledge across successive climate-related crises. This paper introduces Algorithmic Resilience Memory (ARM), a novel IS construct defined as an AI-enabled organizational capability through which agentic AI systems sense climate-related disruptions, encode prior organizational responses, preserve decision rationale, generate contextually adaptive recommendations, and reconfigure future actions through structured outcome feedback. Drawing on Design Science Research (DSR), we propose and develop an Agentic AI-Based Algorithmic Resilience Memory Framework as the primary artifact. The framework integrates six interdependent functional layers—environmental sensing, knowledge encoding, agentic AI reasoning, explainable decision support, human governance, and adaptive learning—grounded in organizational memory theory, dynamic capabilities theory, sociotechnical systems theory, and responsible AI governance principles. We demonstrate the framework through a detailed scenario involving a regional flood disrupting a manufacturing firm's supply chain operations and evaluate its utility using scenario-based assessment and expert panel validation. The paper makes three primary contributions: it introduces ARM as a theoretically grounded IS construct that advances digital resilience research; it offers a design-science artifact that organizations can adopt for AI-driven climate-crisis adaptation; and it establishes design principles for building agentic AI systems capable of institutional learning across repeated climate disruptions.

DOI: http://doi.org/10.5281/zenodo.20676882

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