Category Archives: Uncategorized

Authors: Dr. Rajidi Rammohan Reddy

Abstract: The digital age has brought about new challenges to companies in terms of volatility, uncertainty, complexity, and ambiguity (VUCA), where leadership agility and organizational resilience have emerged as strategic priorities. This research paper explores the connections between leadership agility and organizational resilience and the role digital transformation plays in them as catalysts and contextual variables. Through the review of existing literature and development of a mixed-methodological approach for research, it is revealed what dimensions of leadership agility – adaptive thinking, decentralized decision making, emotional intelligence, transparent communication, and learning – have a significant effect on the organizational resilience factors in a causal way. The results show the impact of digital leadership orientation on organizational resilience through mediation by strategic foresight, agility, and flexibility. Comparative analysis shows how companies with leadership agility have 34 percent more capacity for organizational resilience than companies with lower leadership agility.

DOI: http://doi.org/10.5281/zenodo.22768793

Published by: vikaspatanker

Authors: Krishnendu Ghosh, Soumyajit Roy, Debayan Bag

Abstract: The integration of artificial intelligence (AI) with wearable sensing technology has emerged as a transformative approach in modern healthcare, particularly for the management of chronic diseases such as diabetes mellitus. AI-enabled wearable devices utilize advanced sensing mechanisms—including electrochemical, optical, microneedle, and sweat-based sensors—to enable continuous, real-time monitoring of physiological parameters. These systems provide significant advantages over traditional invasive techniques by offering non-invasive or minimally invasive solutions, improving patient comfort, compliance, and data accuracy. In diabetes management, AI-driven wearable technologies such as continuous glucose monitoring (CGM) systems, smart insulin pumps, and closed-loop artificial pancreas systems have revolutionized glycemic control. Machine learning and deep learning algorithms analyze large volumes of real-time data to predict glucose trends, detect anomalies, and provide personalized treatment recommendations. Additionally, emerging non-invasive technologies, including smart contact lenses and smartphone-based photoplethysmography, offer promising alternatives for early detection and monitoring. The application of AI in wearable devices also supports remote patient monitoring, enabling timely intervention and reducing the risk of complications. Despite significant advancements, challenges such as sensor accuracy, data interpretation, scalability, and integration into clinical practice remain. Overall, AI-based wearable sensing technology holds immense potential to enhance personalized healthcare, improve clinical outcomes, and reduce the global burden of diabetes.

DOI: http://doi.org/10.5281/zenodo.22763785

Published by: vikaspatanker

Authors: Dr. Lalith Kumar Dharavath

Abstract: Background: The global pursuit of justice is increasingly challenged by systemic inefficiencies, lack of transparency, and unequal access to legal remedies. In response, modern judicial frameworks are undergoing a digital transformation. Objective: This paper examines how emerging technologies—specifically blockchain, artificial intelligence (AI), predictive analytics, and digital surveillance tools—can be leveraged to enhance accountability, reduce institutional bias, and democratize legal access. Methodology: Utilizing a comparative case study approach, this research analyses recent technological implementations across global criminal justice systems and public administration frameworks. It evaluates their direct impact on institutional transparency, evidence integrity, and marginalized communities. Results: The findings indicate that while technologies like body-worn cameras and decentralized ledgers significantly increase public accountability and safeguard chain-of-custody protocols, the deployment of automated risk-assessment tools often exacerbates historical algorithmic biases. Conclusion: This paper argues that technology is an invaluable tool for modernising justice, but its success depends on strict regulatory oversight. To achieve true accountability for all, systems must balance technological innovation with ethical design and human-centric legal frameworks.

Published by: vikaspatanker

Authors: Howells Frank Amalagha Brown, Ikechi Igwe, Kingdom. K. Dune

Abstract: Accurate reservoir simulation initialization plays a vital role in predicting hydrocarbon recovery and assessing the economic potential of oil and gas projects. However, many traditional models assume uniform fluid composition and often ignore compositional grading (CG) effects such as gravity, temperature gradient, and thermal diffusion. This limitation can lead to unrealistic production forecasts and poor economic evaluations. Therefore, this study evaluated the economic viability of initializing reservoir simulation models with CG models that better capture real reservoir behavior. Using secondary data from the literature, cumulative oil and gas production data were analyzed for five models such as constant composition, isothermal, zero thermal diffusion, Haase’s, and Kempers’ CG models, respectively. Deterministic discounted cash flow (DCF) analysis was performed using Microsoft Excel at a 12% discount rate to compute Net Present Value (NPV) and Profitability Index (PI) for each model. The findings revealed that all models produced positive NPVs, ranging from $1.85 × 10⁸ to $3.67 × 10⁸ and PIs greater than 1, indicating strong profitability. The constant composition model achieved the highest PI of 3.675, mainly due to its overestimation of oil production of about 41.7 million barrels. However, the nonisothermal CG models recorded slightly lower oil output but showed improved financial performance through additional gas revenues exceeding $25 million, which compensated for their higher modeling costs. The study concludes that incorporating CG effects in reservoir models not only enhances simulation accuracy but also improves economic viability, making it a practical approach for sustainable reservoir development.

Published by: vikaspatanker

Authors: Godfrey I.P.Kageuka

Abstract: Digital transformation is becoming more and more important for small businesses if they are to improve their efficiency, gain access to customers and stay competitive. Yet the capacity of small businesses to adopt and make effective use of digital technologies is hindered by a variety of financial, technological, skills-related and security-related obstacles. The present study looked at the barriers influencing the adoption of digital tools by small businesses in Morogoro Municipality, Tanzania. It was based on the Unified Theory of Acceptance and Use of Technology (UTAUT) and used a mixed-methods approach with a descriptive cross-sectional research design. Data were gathered from small business owners and managers through the use of structured questionnaires and semi-structured interviews. Out of the 318 questionnaires distributed, 276 valid responses were received, which gives a response rate of 86.79 per cent. The quantitative data were analysed using frequencies and percentages, while the qualitative responses were examined thematically. The results indicate that the lack of capital was the most significant barrier, cited by 92.7 per cent of the respondents, then came high internet costs (86.9 per cent), lack of digital skills (74.6 per cent), poor network connectivity (71.7 per cent), lack of training (67.0 per cent), fear of cybercrime (55.07 per cent) and lack of trust in online systems (52.9 per cent). The qualitative findings supported these results, particularly by pointing out the difficulty of buying digital devices, the problem of meeting ongoing internet costs, the difficulty of coping with unreliable connectivity and the challenge of building confidence in online transactions. The study demonstrates that digital transformation among small businesses in Morogoro Municipality is not merely a matter of willingness to use technology but is instead limited by insufficient facilitating conditions, especially in the areas of finance, infrastructure, skills and digital trust. The study suggests that integrated interventions be put in place which combine affordable finance, better internet infrastructure, practical digital skills training, increased cybersecurity awareness and stronger mechanisms for building trust in online systems.

DOI: https://doi.org/10.5281/zenodo.22793598

Published by: vikaspatanker

Authors: Assistant Professor S. C. Suke, Aryan Kosare, Dewansh Lonarwar, Hardik Hinge, Raj Biranwar, Vivek Bhagat

Abstract: Battery Management System (BMS) are essential for rechargeable battery applications such as electric vehicles, portable electronics, renewable energy storage, and industrial equipment. In multi-cell battery packs, manufacturing variations and operating conditions can cause cell voltage and state-of-change (SoC) imbalance, excessive temperature rise, creating potential safety risks. This paper presents an IoT-enabled BMS that integrates individual cell voltage sensing, passive cell balancing, and temperature monitoring for safe and reliable battery operations.

DOI: https://doi.org/10.5281/zenodo.22743809

Published by: vikaspatanker

Authors: Associate Professor Dr. Channakeshava RN

Abstract: The rapid integration of 5G networks, Cellular Internet of Things (C-IoT), and Vehicular Ad Hoc Networks (VANETs) has enabled intelligent and highly connected vehicular environments. However, the high mobility of vehicles, rapidly changing channel conditions, varying traffic density, and heterogeneous service requirements create significant challenges for mobility management and radio resource allocation. Conventional mobility and resource management approaches generally react to network changes after they occur, resulting in increased handover latency, resource utilization inefficiency, communication failures, and degraded Quality of Service (QoS). This paper proposes an AI-driven framework for predictive mobility management and context-aware resource allocation in 5G-enabled Cellular-IoT VANETs. The proposed approach utilizes contextual information such as vehicle location, velocity, direction, traffic density, channel conditions, network load, and service requirements to predict future mobility and network states. Artificial Intelligence (AI) and Machine Learning (ML) techniques are employed to anticipate handover requirements and dynamically allocate communication resources according to predicted network conditions. The framework aims to jointly optimize mobility decisions and resource allocation while considering QoS, latency, throughput, spectrum utilization, and energy efficiency. The proposed approach is intended to reduce unnecessary handovers, improve resource utilization, minimize communication interruptions, and enhance the reliability of vehicular-IoT services. Simulation-based evaluation can be performed against conventional mobility management and resource allocation techniques to demonstrate the effectiveness of the proposed framework under varying vehicle mobility and network-load conditions.

DOI: https://doi.org/10.5281/zenodo.22725151

Published by: vikaspatanker

Authors: Gargi Mishra, Shankar Thalla

Abstract: The implementation of Artificial Intelligence (AI) algorithms in clinical decision support systems (CDSS) and biomedical diagnostics has already shown its impressive potential for positive patient impact. At the same time, there are several challenges that need to be addressed when it comes to the deployment of advanced machine learning models because of their 'black box' characteristics that cannot satisfy the needs of clinical practice where transparency is vital for trust and validation of generated advice. This paper introduces the concept of Explainable Artificial Intelligence (XAI) for application in clinical diagnostics through the use of both post-hoc explanation methods and inherently interpretable models. We offer an approach that includes using fuzzy probabilistic trees in order to create an interpretable model along with mapping of biomarkers activation to explain a generated solution visually. Evaluation performed on multimodal biomedical data shows that explanation-enabled models can provide comparable diagnostics accuracy while providing actionable explanations that help to increase clinician acceptance by 23.7%.

DOI: http://doi.org/10.5281/zenodo.22724968

Published by: vikaspatanker

Authors: Dr Vijay Temley

Abstract: The combination of AI with BI is a revolutionary paradigm that would allow organizations to have sustainable performance and make green decisions. This research suggests the development of a framework based on the combination of MIS, BA, and AI to be used for ESG strategy realization. Algorithms used by this framework for the purpose of predicting sustainability indicators include Random Forest, XGBoost, ARIMA, and LSTM networks, which allow for the prediction of sustainability indicators such as energy consumption, carbon emission, and waste production. The results of the quantitative analysis indicate that the use of AI-supported BI systems allows to predict emission trends with the accuracy of 89.2%. Thus, organizations will have an opportunity to conduct proactive, and not reactive, sustainability management.

DOI: http://doi.org/10.5281/zenodo.22724899

Published by: vikaspatanker

Authors: Roshni Thakre, Assistant Professor Rahul Patidar, Professor Akrati Shrivastava

Abstract: Bio-inspired metaheuristics can be useful for the optimization of complex systems. Wireless sensor networks (WSNs) are massively distributed cyber-physical systems whose efficient operation requires appropriate design and control strategies. This work proposes an IoT network optimization model that performs node clustering and efficiently routes data packets from sensing IoT devices to the base station. In the proposed approach, clustering is carried out using the Intelligent Water Drops (IWD) algorithm, which intelligently identifies suitable cluster centers based on the network conditions. The algorithm can operate effectively in dynamic WSN environments without requiring prior knowledge of node configurations. Furthermore, an optimized routing strategy is employed to reduce energy consumption during data transmission. Extensive experiments were conducted using different node distributions and network regions. The results demonstrate that the proposed IWD-based clustering and routing approach significantly improves energy efficiency and extends the overall lifetime of the IoT network.

Published by: vikaspatanker