Authors - Md. Mizanur Rahman, Ankan Roy, Shovan Kumar Paul, Anupam Singha Abstract - Nowadays, energy or power dissipation has become a major concern in digital integrated circuit design. This high-density design of the chip increases power dissipation. One of the primary causes of energy dissipation is irreversible computation, necessitating improved power optimisation techniques. Consequently, the reversible logic methodology provides an efficient means to minimise energy dissipation in the circuit. Addition is a fundamental arithmetic operation that underpins other regularly utilised operations, including multiplication, division, and subtraction. In computing systems, adders, which are digital circuits that add numbers, are a fundamental component. With the continuous development of technology, the need for efficient and high-performance processing units has become inevitable, and these must be made from reversible logic elements. The Carry Skip Adder(CSA) is among the most efficient adders utilised in numerous data processing units to do swift arithmetic operations. This study introduces a design that is effective of a CSA utilising reversible logic gates and evaluates its performance. The proposed design achieves notable improvements over existing works by reducing garbage outputs, optimizing constant inputs, minimizing delay, enhancing quantum cost efficiency, and decreasing the overall gate count. Additionally, the performance of the suggested adder surpasses that of others in terms of transistor count and power dissipation.
Authors - Badrun Nahar Luna, Nazifa Jerin Reshni, Shefat-E-Ara Khan, Md. Munna Khan, Md. Rakibul Hasan Antor, Sadah Anjum Shanto Abstract - This autonomous healthcare monitoring system enhances patient care by tracking vital signs and automating essential needs via IoT-Edge computing. It uses MAX30100 and DS18B20 sensors to measure heart rate, oxygen levels, and body temperature, with real-time data displayed on an LCD screen and transmitted to the Blynk app for remote monitoring. The system automates food and medicine dispensing using a servo motor, ensuring timely intake through Blynk notifications for caregivers. To maintain uninterrupted operation, a hybrid power system integrates solar panels, battery storage, and an SMPS, with a relay switch enabling automatic transitions based on power availability. The Blynk app also monitors the voltage and efficiency, comparing the solar and SMPS performance. Paired NodeMCU microcontrollers power the system, making it extensible and sustainable. Anticipated for hospitals, clinics, and home healthcare, this smart monitoring system leverages IoT technology to strengthen patient safety, accommodation, and authenticity while reducing manual interference.
Authors - Suruchi Pandey, Hemlata Gaikwad, Vatsala Saxena, Sweta Rani, Sonal Kumari Abstract - The fast pace of development in blockchain technology is transforming corporate Learning and Development (L&D) by addressing challenges related to credential verification, transparency in training, compliance tracking, and work-force mobility. Current training management systems are typically plagued by inefficiencies in the form of manual verification, data security breaches, and lack of interoperability with Learning Management Systems (LMS). This research explores how blockchain can enhance training management through decentralized, tamper-evident learning records, providing trust, security, and efficiency for corporate training programs. The study utilises a secondary data research design, scrutinizing industry reports, peer-reviewed scholarly articles, firm case studies, and government policy documents. Results indicate that blockchain enables secure credentialing, time-real skill verification, automated compliances verification, and easy transfer of certification from one company to another. The study employs the ADDIE Model to assess the use of blockchain in L&D for effective training program analysis, design, development, implementation, and evaluation. This study concludes that blockchain will play an important role in the future of business education but requires standardized models, policy backing, and scalable integration models to achieve large-scale adoption. Companies must leverage blockchain-based AI learning systems to enhance workforce training, compliance automation, and global credential recognition in the digital economy.
Authors - Anika Yadav, Ananya V Holla, Animesh Giri Abstract - With the growing use of connected devices, geographic barriers diminish, enabling communication of critical data in near real-time. As the frequency of natural disasters increases, timely communication forms the backbone of efficient disaster management. With the number of recipients varying based on the population density, any disaster notification framework must be highly scalable. Big Data technologies like Apache Kafka, Apache Flink, RabbitMQ, Apache Spark, and Apache Hadoop can be employed to enhance scalability. This study proposes a hybrid framework that classifies disasters based on severity and then routes them through one of three pipelines. This approach ensures that the Quality of Service requirements for each disaster severity type are satisfied. The pipeline assigned to handle high-severity traffic demonstrates notification delivery with latency in the order of 0.88 seconds on average across the disaster types.
Authors - Chauhan Priyank Hasmukhbhai, Ritu Khanna Abstract - The integration of wind-solar hybrid systems presents a transformative pathway to bolster renewable energy resilience, yet their optimal deployment in geographically diverse environments remains hindered by spatiotemporal intermittency, conflicting stakeholder priorities, and dynamic ecological constraints. This study proposes a novel fuzzy meta-goal programming (FMGP) framework to reconcile techno-economic, environmental, and social objectives in wind-solar hybridization, with a focus on arid and coastal ecosystems—regions characterized by contrasting meteorological volatility and land-use sensitivities. By embedding fuzzy set theory into meta-goal structures, the model quantifies uncertainties in renewable resource availability (e.g., wind shear variability, solar irradiance fluctuations) while balancing antagonistic criteria such as levelized energy cost minimization, carbon footprint reduction, and biodiversity preservation. The FMGP approach uniquely incorporates synergistic complementarity metrics to exploit temporal offsetting between wind and solar generation cycles, enhancing grid stability in resource-erratic zones. Empirical validation through case studies in a hyper-arid desert and a storm-prone coastal region reveals Pareto-optimal solutions that achieve up to 23% improvement in annual energy yield reliability and 18% reduction in land-use conflicts compared to conventional multi-objective models. Furthermore, the framework introduces a stochastic acceptability index to evaluate policy robustness under climate change scenarios, demonstrating adaptive capacity in mitigating energy-water nexus pressures in arid areas and storm resilience trade-offs in coastal grids. This research advances sustainable hybrid system design by harmonizing multi-scale environmental governance with precision energy planning, offering policymakers a decision-centric tool to navigate the socio-ecological complexities of the renewable transition.
Authors - Kala V Krishnan, Ramanathan P V Abstract - India is currently experiencing a demographic dividend, with the youngest working population predicted to remain until 2050. Scholars emphasize that this demographic advantage alone cannot guarantee the economic growth of the country unless accompanied by effective policy interventions. Skill development has thus emerged as a national priority to harness this opportunity, leading to large-scale initiatives such as the Pradhan Mantri Kaushal Vikas Yojana (PMKVY). As India’s flagship skill certification scheme, PMKVY aims to provide industry-relevant training to youth. Despite its magnificent scale and substantial investment, the program faces a major challenge of high dropout rates among participants, which warns its long-term effectiveness and results in significant economic and social costs. Existing research highlights the need for analysing these dropout trends, especially at the state level, where disparities may reflect underlying regional and structural factors. This study adopts a quantitative cross-sectional comparative approach to assess the dropout rates of PMKVY across Indian states and examine the impact of selected state-specific factors: poverty rate, literacy rate, population density, and industrial development. The objective is to identify the extent of regional variation and analyse the relationship between these state-specific factors and program attrition. By doing so, the study aims to generate insights that can inform policy reforms and targeted interventions to improve retention and enhance the efficiency of government-supported skill development programs in India.
Authors - Chauhan Priyank Hasmukhbhai, Ritu Khanna Abstract - The strategic allocation of marine oil resources involves complex trade-offs between economic performance, operational efficiency, and environmental sustainability—often under significant uncertainty. This paper develops a robust decision-support framework that integrates Fuzzy Logic with Nonlinear Goal Programming (NLGP) to address the multi-objective optimization problem inherent in marine oil extraction and resource allocation. Uncertainty in key parameters— such as extraction costs, production yields, and environmental impact limits—is modeled using fuzzy sets, enabling a more flexible representation of real-world ambiguity. The model simultaneously optimizes multiple nonlinear and conflicting goals, including profit maximization, cost minimization, and ecological risk reduction. To efficiently solve the resulting nonlinear programming problem, a hybrid solution approach is proposed that combines fuzzy goal programming techniques with metaheuristic optimization, specifically a tuned Genetic Algorithm. The framework is applied to a representative offshore oil field scenario, demonstrating superior performance in solution quality and robustness compared to traditional linear and crisp optimization methods. The results underscore the potential of fuzzy NLGP models in supporting high-stakes operational decisions in uncertain and dynamic environments. This work contributes to the growing body of operations research methods that address multi-criteria decision-making under uncertainty, with direct implications for energy resource planning and sustainable marine operations.
Authors - Smita Kalokar, Ritesh Sule, Dinesh Mirkute Abstract - A nation's progress is supported by its effective administration. To ensure the betterment of the country the governments of all the world has accepted the sustainable development goals which is announced by United Nation Development programme (UNDP) in 2015,as acceptable target by the member country.The transformation in digital Technology has introduced a new focus for government in India. Nowadays, Information and Communication Technology (ICT) is essential. In particular, for developing nations, it is viewed as vital for economic, social, and political progress. The introduction of low-cost smart phones and the lowest-priced data packages have increased the opportunities for both citizens and government equipment to take advantage of e-Government's advantages. In recent years, e-Governance initiatives in India have shown their effectiveness in reducing processing expenses, enhancing transparency, and fostering economic growth through income-generating activities, increased agricultural output, and advancements in health and education sectors. These improvements collectively enhance the quality of life for Indian citizens. Nevertheless, e-Governance has not fully reached all demographics, particularly in rural areas. In rural India, there are specific basic and cultural challenges that hinder the achievement of e-Governance objectives. Consequently, the government must address these issues and ensure that service delivery mechanisms are user-friendly for these communities. This paper will explore ICT and Governance in India, along with the fundamental challenges and acceptance of e-Governance.
Authors - Chauhan Priyank Hasmukhbhai, Ritu Khanna Abstract - Real-time optimization of virtual reality (VR) models in dynamic systems demands adaptive decision-making frameworks capable of reconciling conflicting objectives such as computational efficiency, latency reduction, and user experience fidelity. While traditional optimization techniques often struggle with the non-linear, high-dimensional, and time-sensitive nature of VR environments, this paper introduces a novel hybrid framework that synergizes Goal Programming (GP) and Neural Network Artificial Intelligence (NN-AI) to address these challenges. The proposed methodology leverages GP to formalize multi-objective decision-making under constraints, while a dynamically trained neural network predicts and prioritizes system states in real time, enabling context-aware adjustments to VR model parameters. By integrating GP’s structured optimization with NN-AI’s predictive adaptability, the framework achieves Pareto-optimal solutions that balance competing objectives across fluctuating operational conditions. The study validates the framework through a series of simulated and real-world VR scenarios, including immersive gaming and industrial training systems, where dynamic variables such as user interactions, environmental complexity, and hardware limitations are present. Results demonstrate a 22–35% improvement in rendering efficiency and a 40% reduction in latency compared to conventional single-objective optimization approaches, without compromising visual quality. Furthermore, the system exhibits robust generalization capabilities, adapting to unseen scenarios within 5–10 iterations. This research bridges a critical gap in real-time multi-objective optimization for VR, offering a scalable, AI-driven solution for industries reliant on immersive technologies. The framework’s ability to harmonize human-centric objectives with computational constraints positions it as a transformative tool for next-generation dynamic systems in entertainment, healthcare, and Industry 4.0 applications.
Authors - Thuan Nguyen Dinh, Truong Nguyen Xuan Abstract - In this paper, the authors suggest and compare models for industrial oven Time-to-Failure (TTF) prediction within a critical 60-minute timeframe using sensor data. They compare traditional methods such as LSTM, GRU + Attention, and XGBoost with a new hybrid approach: CNN-Autoencoder + XGBoost (CNN-AE+XGBoost). Experimental outcomes, in terms of RMSE, MAE, and R², confirm the performance superiority of the proposed system. For the complete dataset, R² for the hybrid model was 0.89, well ahead of LSTM (0.27), GRU + Attention (0.47), and XGBoost (0.83). Importantly, for the focused TTF ≤ 60 minutes frame, it also had a low Mean Absolute Error (MAE) of 6.57. These results present the CNN-AE+XGBoost model as an effective predictive maintenance tool for curbing production downtime within the food processing sector.
