Daily Archives: September 15, 2026

Authors: 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