Category Archives: Uncategorized

Authors: Mohammed Swaleh Mohammed, Bostley Muyembe Asenahabi, Alice Nambiro, Eric Sifuna

Abstract: The purpose of the study was to evaluate the learning analytics usability factors towards e-learning learner performance assessment in Kenyan Universities. The study used quantitative methodology toward achieving the purpose of the study. Quantitative approach was attained through using five- point Likert scale distributed through random sampling to eight universities in Kenya. A focus on those students using e-learning whether blended or virtual learning. The findings revealed two factors: Perceived Usefulness and Perceived Ease of Use.

DOI: http://doi.org/10.5281/zenodo.16737143

Published by: vikaspatanker

Authors: Tushar Sharma, Dr. Rakesh Kumar

Abstract: Plastic pollution continues to pose a severe environmental threat worldwide. Simultaneously, the common practice of burning wheat and rice straw in agricultural fields leads to hazardous air quality and soil degradation. This study introduces a novel, sustainable alternative: transforming crop residues into biodegradable nanocellulose bioplastics. Through enzyme-assisted extraction, cellulose from straw can be processed into high-quality packaging material. This innovation not only curbs plastic pollution but also reduces air pollution and provides farmers with an alternative income stream. The paper emphasizes the need for policy support, industry collaboration, and rural engagement to scale this solution.

Published by: vikaspatanker

Authors: Dr. P. Lokesh Muni Kumar, J Priya Chandana

Abstract: This study focuses on the pricing and performance analysis of Initial Public Offerings (IPOs) listed on the National Stock Exchange (NSE) of India, aiming to understand the behavior of newly listed equities in the primary market and their performance in the secondary market post-listing. IPOs play a vital role in corporate financing and capital market development, making it crucial to assess their underpricing, listing gains, and long-term returns. The research investigates the issue price, listing price, closing price, and after-market performance of selected IPOs over a defined period to determine the efficiency of IPO pricing mechanisms and the investor sentiment at the time of listing.The study uses quantitative analysis based on secondary data collected from NSE official records, annual reports, and stock market databases. Statistical tools such as average market-adjusted returns, volatility analysis, and cumulative abnormal returns (CAR) are used to evaluate both short-term listing performance and medium to long-term returns. The study also compares the performance across sectors and examines whether underpricing is prevalent in the Indian IPO market.Findings suggest that a majority of IPOs are underpriced, offering significant listing day gains, but do not always sustain positive performance in the long run. The results provide insights for retail and institutional investors, underwriters, and regulatory bodies to improve IPO pricing strategies, manage market expectations, and assess the efficiency of the capital market. The research contributes to a better understanding of IPO market dynamics in India and offers recommendations to enhance transparency, investor confidence, and market efficiency.

Published by: vikaspatanker

Authors: Rahul Solanki, Murlidhar Chourasia, Rahul Kumar Satbhaiya

Abstract: The seismic performance of reinforced concrete (RC) structures is significantly influenced by the geometry and detailing of their columns, which act as the primary load-bearing and energy-dissipating elements during ground motion. Among the available column cross-sections, rectangular shapes have traditionally been used due to ease of construction and integration with architectural plans. However, in high-seismic zones, circular columns are gaining attention for their superior ductility, confinement efficiency, and uniform stress distribution.This study presents a comparative analysis of the seismic behavior of RC frames with rectangular and circular columns, focusing on their performance under lateral loading conditions. An extensive literature review was conducted, highlighting the influence of column shape on key seismic performance parameters such as base shear capacity, lateral drift, energy dissipation, plastic hinge formation, and failure mechanisms. Analytical studies, experimental investigations, and code-based assessments consistently indicate that circular columns outperform rectangular ones in terms of ductility and post-yield behavior. Their symmetrical geometry allows better confinement of core concrete, resulting in enhanced resilience during strong ground shaking.Additionally, the review underscores the limitations of conventional force-based design methods in accurately predicting the inelastic behavior of structures, especially those with irregular geometries. Nonlinear static (pushover) analysis, displacement-based approaches, and accurate modeling of infill-wall interaction emerge as essential tools for realistic seismic performance evaluation.The findings of this study support the strategic replacement or incorporation of circular columns in RC frames to improve seismic resistance, particularly in retrofitting and performance-based design scenarios. While practical challenges such as increased formwork complexity exist, the benefits in structural safety and energy absorption justify their use. This paper serves as a reference for engineers and researchers aiming to optimize RC structures for improved seismic resilience through column geometry selection.

DOI: https://doi.org/10.5281/zenodo.16728432

 

Published by: vikaspatanker

Authors: Sandeep Gajanan Sutar, Dr. Praveen B M, Dr. Amolkumar Jadhav

Abstract: In the era of pervasive cloud computing, ensuring the privacy and integrity of sensitive data has emerged as a critical challenge. Traditional data protection methods often fall short in addressing sophisticated security threats and compliance demands. This study explores the synergistic integration of blockchain technology and homomorphic encryption (HE) as a transformative approach to privacy management in cloud environments. Blockchain's decentralized and immutable architecture ensures transparent, tamper-proof data transactions, while homomorphic encryption enables computation on encrypted data without revealing its contents—thus preserving confidentiality throughout the data lifecycle. The research discusses layered architectural frameworks, real-world implementations across healthcare, IoT, and supply chains, and presents empirical findings that highlight improvements in computational efficiency, security, and regulatory compliance. Despite challenges in scalability and computational overhead, the combined use of blockchain and HE presents a promising pathway for developing resilient, privacy-preserving cloud infrastructures. This integration not only fortifies data governance but also lays the groundwork for next-generation secure cloud services.

DOI: https://doi.org/10.5281/zenodo.16728278

 

Published by: vikaspatanker

Authors: Rahul Solanki, Murlidhar Chourasia, Rahul Kumar Satbhaiya

Abstract: Designing earthquake-resistant structures necessitates ensuring their safety throughout both the construction phase and their operational lifespan, regardless of the intensity or frequency of seismic events. Ground motion effects are particularly complex due to their dynamic and multifaceted impact on structural behaviour. Among the various analytical techniques available, pushover analysis is considered one of the most dependable for evaluating a structure's response to intense seismic forces. This approach is based on the assumption that during seismic events, buildings predominantly respond in their fundamental or lower vibration modes. Therefore, a multi-degree-of-freedom (MDOF) system can be effectively transformed into an equivalent single-degree-of-freedom (ESDOF) model. This ESDOF model is derived using nonlinear static analysis and subsequently subjected to nonlinear time history or response spectrum analysis utilizing constant-ductility or damped response spectra. The seismic demand parameters obtained from the ESDOF model are then mapped back to the MDOF system through modal transformation techniques. In this research, the seismic behaviour of a moment-resisting RC frame structure is analyzed using the pushover method. The study focuses on the effect of changing column shapes and sizes, specifically replacing rectangular columns with circular ones, to assess variations in seismic performance. The static pushover approach incorporates both constant gravity loads and incrementally applied lateral loads at each story level. Capacity curves, illustrating the relationship between base shear and total story drift, are generated using ETABS 2015 to extract critical seismic response parameters. Throughout the simulation, the overall plan dimensions of the building are maintained constant, while only the column dimensions are varied. Three sets of rectangular column sizes are examined for their nonlinear seismic response and subsequently compared with equivalent circular columns. All structural models are designed following the provisions of IS 456:2000 for concrete design and IS 1893:2002 for seismic loading conditions..

DOI: http://doi.org/

Published by: vikaspatanker

Authors: Kiran Kumar, V. Narmada, Sagarika Kulkarni

Abstract: Cardiovascular diseases cause morbidity and mortality. Early detection is critical, as it can detect and the use of AI plays a significant role in and impacts cardiovascular diseases. Since cardiovascular diseases (CVDs) continue to be the world's leading cause of death, improvements in early diagnosis, treatment, and management techniques are vital. The integration of artificial intelligence (AI), machine learning, and deep learning into cardiovascular medicine offers promising avenues to improve patient outcomes. This review explores recent progress in AI applications for CVDs, including automated electrocardiogram (ECG) analysis, medical imaging, wearable sensor technologies, and telemedicine. AI-driven systems have demonstrated potential in enhancing diagnostic accuracy, enabling remote monitoring, predicting disease risk, and supporting clinical decision-making. Despite significant advancements, challenges such as data bias, algorithmic fairness, and the need for rigorous clinical validation remain. Continued research and the responsible deployment of AI technologies can help address the global burden of CVDs through more precise, efficient, and personalized care

DOI: https://doi.org/10.5281/zenodo.16719088

 

Published by: vikaspatanker

Authors: Azar Djamali

Abstract: This study reviews the application of Artificial Intelligence (AI) and robotic tools in terrestrial and extraterrestrial architecture. The rapid advancement of AI and robotics is transforming the architectural profession, enhancing creative exploration through data aggregation and analytics while complicating decision-making due to the vast datasets involved. Architects must balance technical proficiency with strategic discernment to harness AI's potential while addressing operational challenges. AI significantly impacts architectural design throughout the building lifecycle, influencing predictive analytics, construction supervision, and facility maintenance (Yangluxi Li et al.). As humanity moves toward interplanetary colonisation, architects are designing habitable structures for extreme environments. Notable projects include the Mars Habitat Concept by Foster + Partners and NASA (2015–2016), Mars Science City by Bjarke Ingels Group (BIG) for the UAE (2017–present), and Skidmore, Owings C Merrill’s (SOM) Moon Village concept, featuring inflatable modules, showcased at the Venice Architecture Biennale 2025. AI and AI-assisted robots, such as the Perseverance rover, enable adaptive and efficient design for harsh environments. This article synthesises insights from over 100 research papers, exploring how machine learning (ML), generative design, and autonomous systems will redefine extraterrestrial construction. By optimising structural integrity and resource utilisation, AI facilitates spatial optimisation and energy-efficient designs (Yangluxi Li et al., 2025). This study highlights the roles of AI, robotics, and automation in optimising terrestrial and extraterrestrial architecture, presenting scalable solutions for future colonies and providing actionable insights for space agencies and architects. By leveraging AI and robotic technologies, architecture can adapt to extreme environments and advance innovative designs for future inhabitants.

 

 

Published by: vikaspatanker

Authors: Nanthini.N.L, Sheeja.B

Abstract: Microplastics are tiny plastic pieces that are less than 5 mm in size. These small plastics enter the oceans through the breakdown of larger plastic waste or through products like face wash and toothpaste. They pose threats to marine life and potentially human health. This paper explains how microplastics are detected and removed, including advanced technologies such as spectroscopy and AI, as well as the challenges involved and the need for improved solutions.

 

 

Published by: vikaspatanker

Authors: Mohammed Swaleh Mohammed, Alice Nambiro, Bostley Muyembe Asenahabi, Eric Sifuna

Abstract: The Covid-19 pandemic significantly accelerated the adoption of e-learning, resulting in increased investment in digital education platforms and the generation of vast amounts of learner data, commonly referred to as big data. This shift has propelled the development of learning analytics, a field focused on the systematic collection, analysis, and reporting of data to better understand learner behavior and enhance educational outcomes. Despite growing interest in learning analytics, there remains limited understanding of its usability, particularly in assessing student performance within virtual learning environments. This paper presents a critical literature review examining the usability factors of learning analytics in the context of Kenyan universities. Using a desktop research approach, the study synthesizes existing research to identify key gaps and highlight usability dimensions relevant to performance assessment. The review identifies four primary factors influencing the usability of learning analytics tools: perceived usefulness, perceived ease of use, user satisfaction, and learnability. These findings provide a foundation for future empirical studies aimed at evaluating the impact of these factors on learner performance in virtual settings.

DOI: http://doi.org/10.5281/zenodo.17158417

Published by: vikaspatanker