Stuti Pandey, Soma Ghosh
College of Engineering, Pune (COEP), India

Abstract: This paper presents a modular framework that automates multiple-choice question (MCQ) generation and high-quality distractor creation using large language models (LLMs). The pipeline decouples (i) question generation, implemented with a T5 model fine-tuned on SQuAD for passage-anchored prompts, and (ii) distractor synthesis and selection, where RACE-derived candidates are filtered and re-ranked via an ensemble that combines BERT-based semantic similarity, a fluency scorer, and a monotonic mapping constraint to preserve learning objectives. To ensure item variety and minimize clueing, a hybrid scoring function integrates Jaccard dissimilarity, classifier confidence, and syntactic plausibility checks. The system is lightweight and containerizable, enabling deployment in memory-constrained environments (e.g., Kaggle) while maintaining near-real-time throughput. Evaluation includes automatic metrics (BLEU, ROUGE-L, F1) for content fidelity and a structured human study assessing coherence, pedagogical alignment, and distractor plausibility. Against conventional automatic question generation baselines, the framework shows consistent gains in fluency and plausibility, with reduced lexical overlap between keys and distractors. We discuss safeguards for academic integrity (answer-key leakage tests, adversarial distractor stress-tests) and outline extensions for curriculum alignment and item banking. The approach reduces authoring effort and supports scalable, objective-aligned assessment generation for digital learning platforms.

Keywords: Automated MCQ Generation, Distractor Quality, Large Language Models, Educational NLP, Assessment Design

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Harish Kasireddy
Virginia International University, United States

Abstract: This study explores the critical role of parameter optimization in improving the predictive performance of data mining algorithms used in software analytics, with a focus on software defect prediction. Although data-driven models are increasingly employed to support decision-making in software engineering, their effectiveness is often constrained by default parameter settings. To address this, the research applies differential evolution as an optimization heuristic to systematically tune algorithm parameters across multiple open-source Java-based software systems. Empirical results demonstrate that neglecting this tuning step can result in significant degradation of prediction accuracy and model robustness. By integrating parameter optimization into the analytics pipeline, the study achieves enhanced predictive performance, validating the approach as a necessary component of rigorous software analytics workflows. The findings have practical implications for software quality assurance and underscore the importance of combining automated optimization techniques with domain-specific analytics tools.

Keywords: Software Defect Prediction, Parameter Tuning, Differential Evolution, Predictive Analytics, Software Quality

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Diman Zuhair Jacksi
University of Zakho, Iraq

Abstract: Iraq, despite its wealth in fossil fuel resources, faces an urgent need to address climate change and environmental degradation. With over 3,000 hours of solar radiation annually, the country has significant untapped potential for solar energy generation. This study assesses the technical, geographic, and institutional feasibility of transitioning Iraq's fossil fuel-dependent energy sector to a more sustainable model aligned with Sustainable Development Goal 7 (SDG 7). The research synthesizes national statistics, policy documents, and geospatial data to evaluate Iraq's current energy mix and maps the role of solar photovoltaics (PV) in decarbonization. Results highlight substantial barriers including regulatory fragmentation, low public awareness, outdated grid infrastructure, and financing gaps. Nevertheless, recent government-backed initiatives, such as solar procurement programs, international partnerships, and public sector electrification plans, indicate a shift toward renewable integration. The paper outlines a phased implementation roadmap through 2030, emphasizing GIS-based site selection, policy reform, investment incentives, and public engagement as critical enablers. Findings suggest that solar energy could play a transformative role in reducing Iraq's fossil fuel dependence by up to 30%, with co-benefits for SDGs 3, 4, 11, and 13. This study contributes to the discourse on clean energy transitions in post-conflict, resource-dependent contexts.

Keywords: Solar Energy Transition, SDG 7, Iraq Energy Policy, Renewable Infrastructure, Climate Mitigation

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Ahmed Sarwar Mohammed
Judson University, United States

Abstract: This research introduces a reinforcement-learning-driven monitoring framework, IPro, designed to dynamically optimize traffic monitoring in Software-Defined Networks (SDNs). The proposed system leverages the Knowledge-Defined Networking (KDN) paradigm to regulate the cadence of probing intervals in real time, balancing flow data accuracy with overhead and CPU usage constraints. IPro incorporates a Q-learning agent that observes network conditions, specifically control plane bandwidth and controller CPU load, and adjusts the monitoring frequency to maintain these parameters within predefined thresholds. Experimental evaluation in a simulated campus network environment demonstrates that IPro reduces monitoring overhead to 1.23% and CPU usage to below 8%, while achieving over 90% accuracy in flow data collection. Comparative results against fixed-interval monitoring confirm IPro's superior efficiency and adaptability. The architecture, built on four SDN planes, integrates state-aware reinforcement learning for real-time responsiveness. This work offers a scalable, intelligent, and resource-efficient solution to SDN monitoring challenges, contributing to the advancement of autonomous and sustainable network management systems.

Keywords: SDN Monitoring, Intelligent Probing, Reinforcement Learning, Network Optimization, Knowledge-Defined Networking

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KwangMin KIM
KTL (Korea Testing Laboratory), Republic of Korea

Abstract: As electric vehicle adoption accelerates, the range of DC Electric Vehicle Supply Equipment (EVSE) from portable IC-CPD units to wall-mounted systems with integrated installations, continues to diversify. While IEC 61851 outlines general safety standards for EVSE, its scope is limited in addressing the design heterogeneity and evolving configurations of modern DC charging technologies. This study examines structural and functional variations among emerging DC EVSE categories and proposes a systematic framework for safety evaluation. Key components and configurations were compared to identify failure modes and potential risks. Based on this analysis, the study suggests tailored test items and procedures suited to each EVSE type, including insulation resistance, overcurrent protection, and thermal behavior under load. The recommendations aim to complement IEC standards by providing context-specific testing methods that improve coverage of safety-critical aspects unique to portable and mobile chargers. The findings contribute to the development of robust, application-oriented certification protocols for next-generation EVSE.

Keywords: DC EVSE Safety, Electric Vehicle Charging Standards, Testing Protocols, IEC 61851 Limitations, EVSE Certification

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Yunjoo Cho
University of Michigan, United States

Abstract: This study explores the integration of artificial intelligence (AI) and nature-based solutions (NbS) into corporate biodiversity strategies, with a comparative focus on South Korea and the United States. As global expectations grow for private sector engagement in biodiversity conservation, companies are increasingly employing digital tools and ecosystem-based approaches. Using a comparative case study methodology, the research analyzes corporate sustainability reports, biodiversity-related disclosure policies (including TNFD), and expert interviews to assess how firms implement AI for monitoring ecological risks and deploy NbS such as green infrastructure and restoration. The study highlights key institutional differences: U.S. companies (e.g., Microsoft, Google) leverage advanced AI capabilities and investor-driven ESG mandates despite the absence of national biodiversity commitments. In contrast, Korean firms (e.g., Samsung, POSCO) operate under policy frameworks linked to the UN Convention on Biological Diversity and national action plans but face constraints in digital implementation. The research reveals that technological readiness and regulatory alignment significantly shape biodiversity strategy execution. These findings offer insights into context-sensitive pathways for advancing corporate environmental responsibility and inform policymakers, investors, and practitioners aiming to enhance private-sector contributions to biodiversity protection.

Keywords: Corporate Biodiversity Strategy, AI for Sustainability, Nature-Based Solutions, ESG Disclosure, Cross-National Comparison

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Abdul Latif Junejo
Sindh Lakhra Coal Mining Company, Energy Department Sindh, Pakistan

Abstract: This case study presents a multi-dimensional Corporate Social Responsibility (CSR) program implemented by the Sindh Lakhra Coal Mining Company in Lakhra, a remote mining region of Pakistan characterized by environmental degradation, water scarcity, and inadequate healthcare infrastructure. The initiative addresses critical socio-environmental vulnerabilities through targeted interventions. A 6,000-gallon-per-day Reverse Osmosis (RO) plant was deployed to improve access to potable water, while free medical camps provided essential healthcare services to underserved communities. Environmental initiatives include the creation of a mini forest for microclimate enhancement, supported by a greywater recycling system that enables drip irrigation using filtered wastewater. A seed ball campaign during the monsoon season promotes ecological restoration of native flora. Furthermore, the company's green mining agenda prioritizes resource efficiency, emission reduction, and environmental stewardship across operational processes. This integrative approach demonstrates how extractive industries can operationalize Environmental, Social, and Governance (ESG) principles to deliver co-benefits for communities and ecosystems. The case underscores the potential for localized CSR strategies to contribute to broader Sustainable Development Goals (SDGs), particularly those focused on health, clean water, ecosystem restoration, and responsible consumption.

Keywords: CSR in Mining, Water Resilience, Environmental Rehabilitation, Green Mining Practices, Community Sustainability

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Tomoya Yabuzaki, Trang Nakamoto, Kozo Taguchi
Department of Electrical and Electronic Engineering, Ritsumeikan University, Shiga, Japan

Abstract: Microbial fuel cells (MFCs) offer a promising avenue for sustainable energy generation by converting organic waste into electricity. However, their practical deployment is constrained by challenges in maintaining stable long-term performance, particularly under submerged, aerated conditions. This study investigates the effect of cathode material composition and structural design on the sustainability of power generation in flooded MFCs. Two conductive cathode materials-cobalt- and copper-based electrodes-were evaluated for their antibacterial properties and oxygen reduction efficiency. To enhance oxygen retention and delivery, an umbrella-shaped cathode cover was incorporated to direct aeration bubbles inside the cathode zone. Experiments simulated conditions of aeration tanks with controlled dissolved oxygen (2-6 mg/L) and chemical oxygen demand (650 ± 100 mg/L). Power density and electrode potentials were compared across different configurations using Ag/AgCl reference electrodes. The cobalt-based cathode equipped with an umbrella-type cover demonstrated the highest power generation stability, sustaining output over a three-month period. These results suggest that the proposed design offers a scalable, maintenance-efficient MFC configuration suitable for long-term energy recovery applications in wastewater treatment systems and aquatic environments.

Keywords: Microbial Fuel Cells, Sustainable Power Generation, Submerged Electrodes, Cobalt Cathodes, Aerated Wastewater Environments

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Tan Dao Duy, Trang Nakamoto, Kozo Taguchi
Department of Electrical and Electronic Engineering, Ritsumeikan University, Shiga, Japan

Abstract: High-performance sustainable composites have attracted significant attention in recent years for their potential in water splitting applications. The fabrication of multicomponent heterostructured composites with both high durability and superior catalytic activity for the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) remains a global research challenge. In this work, three-component composites based on transition-metal layered double hydroxides (LDHs) and metal oxides, NiCoFe-LDH@NiCo₂O₄-rGO/NF (NCF@NCO-rGO/NF), were successfully synthesized for the first time via a simple two-step hydrothermal process. The formation of a sea urchin-like NiCo₂O₄ structure, combined with reduced graphene oxide (rGO), produced nanorod-assembled microspheres anchored to the porous Ni foam surface. This architecture significantly increased the electroactive surface area, providing a robust scaffold for the hierarchical growth of NiCoFe-LDH nanoflowers. The resulting NCF@NCO-rGO/NF heterostructure exhibited outstanding bifunctional electrocatalytic performance, achieving low overpotentials of 108 and 209 mV (vs. RHE) for HER, and 278 and 324 mV for OER, at current densities of 10 and 50 mA·cm⁻², respectively. Electrochemical impedance spectroscopy revealed a remarkably low charge-transfer resistance (Rct = 0.2 Ω), while durability tests confirmed a stable current density of 10 mA·cm⁻² over 24 hours of continuous operation. This study demonstrates a practical and efficient strategy for constructing advanced, sustainable heterostructured composites from transition metals, offering great promise for future alkaline water electrolysis technologies.

Keywords: Hydrogen Evolution Reaction, Oxygen Evolution Reaction, Layered Double Hydroxides, Reduced Graphene Oxide, Water Splitting Catalysts

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Manolo Alexander Cordova
Universidad Nacional de Chimborazo, Ecuador

Abstract: As climate change intensifies, sustainable alternatives for biodiesel production are essential to mitigate environmental impacts, especially in processes utilizing recycled frying oil (FO). This study evaluates the potential of ethanol to reduce the carbon footprint (CF) in the biodiesel production process compared to the conventional use of methanol. A life cycle assessment (LCA) was conducted for the production of 1 kg of biodiesel under two transesterification conditions: methanol-FO at a 1:6 molar ratio and ethanol-FO at 1:9, both using 0.35% potassium hydroxide (KOH) as a catalyst and processed at subcritical temperatures. The LCA framework followed ISO 14067:2018 standards, incorporating a greenhouse gas (GHG) emissions inventory across five defined production stages. Calculations were performed using CCalC2 software. The results revealed that biodiesel synthesized with methanol produced 5.79 kg CO₂eq per functional unit (FU), while ethanol-based production yielded 5.35 kg CO₂eq/FU, indicating a 7.6% reduction in emissions. The findings demonstrate that ethanol offers a more environmentally favorable option for biodiesel synthesis from FO, suggesting its broader adoption could contribute to carbon mitigation goals in biofuel industries.

Keywords: Biodiesel Production, Carbon Footprint Reduction, Life Cycle Assessment, Ethanol Transesterification, Recycled Cooking Oil

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Jay Narayan
Agra University, India

Abstract: This study explores the impact of motion on the curvature of space-time, building upon Einstein's theory of general relativity and offering an extension relevant to Keplerian orbital dynamics. According to general relativity, massive bodies deform the space-time fabric, and this curvature manifests as gravity. The central idea proposed in this research is that the curvature caused by a moving body is directionally dependent-compressed along the direction of motion and elongated in the opposite direction. This anisotropic curvature is hypothesized to influence the behavior of surrounding planetary orbits, particularly when the central star is in motion. The concept introduces a potential explanation for minor perturbations in planetary orbits as a relativistic response to stellar velocity. While this is a theoretical investigation, the idea attempts to reconcile Newtonian orbital mechanics with relativistic spatial deformation, offering a modified perspective on the behavior of gravitational fields in dynamic reference frames. The study invites further mathematical modeling and observational correlation to assess the physical implications of the proposed space-time distortion gradient. It may contribute to more precise models of orbital prediction, especially in high-velocity stellar systems.

Keywords: Space-Time Curvature, General Relativity, Kepler's Law, Orbital Dynamics, Relativistic Gravity

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Stanley O. Nwaugo
Federal University of Technology, Owerri, Nigeria / Grand Challenges Renewable Energy & Robots Manufacturer & Contractor Ventures, Nigeria

Abstract: This study introduces an innovative solar-powered system, the “Solar Lister Zero Fuel Technology,” which enables conventional Lister or fossil fuel generators to operate without the use of diesel, petrol, or gas. Instead, the system harnesses 100% incident solar radiation to power heavy-duty appliances with zero emissions and no carbon footprint. It achieves operational efficiencies between 80% and 90%, a significant improvement over the typical 30%-41% thermal efficiency of traditional diesel generators. Unlike conventional solar inverters, which may overheat due to current-induced resistance in aluminum coils or temperature-sensitive FET transistors, the Solar Lister system remains stable under high loads. It offers superior thermal stability by mitigating energy losses described by the formula E=I2RtE = I^2Rt. Economically, it is four times more efficient in battery usage and eight times more efficient in solar panel utilization compared to standard inverters of the same capacity. This results in significantly reduced long-term costs and minimal maintenance, contrasting with the frequent overhauls required by fossil fuel generators. A comparative analysis demonstrates its cost-effectiveness and resilience in high-demand applications. The proposed technology presents a scalable, environmentally sustainable alternative for off-grid or remote power systems where fuel logistics and maintenance are ongoing challenges. Further research may explore system integration in microgrid applications and performance under variable climate conditions.

Keywords: Zero-Fuel Generator, Solar Lister Technology, High-Efficiency Solar Power, Off-Grid Energy Systems, Fossil Fuel Alternatives

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Emily Marlen Moreira-Tircio, Mónica Enid Tillaguango-Jirón, René Faruk Garzozi-Pincay, Yamel Sofia Garzozi-Pincay
Universidad Estatal Península de Santa Elena, Ecuador

Abstract: This study assesses public perceptions of municipal solid waste collection services in the canton of Santa Elena, Ecuador, using a descriptive approach grounded in survey responses from local residents. The analysis focuses on key service dimensions including reliability, responsiveness, and citizen communication. Findings reveal widespread dissatisfaction, citing inconsistent adherence to collection schedules, limited responsiveness to complaints, and insufficient coverage in select urban zones. Additionally, a lack of clear and timely communication regarding routes and collection times contributes to citizen uncertainty. The research identifies the need to strengthen the relationship between service providers and residents by implementing participatory oversight strategies and enhancing communication mechanisms. Recommended improvements include integrating digital tools for real-time service updates and adopting more efficient, transparent management models. Overall, the study concludes that participatory governance and community engagement are essential to improving waste service delivery and building citizen trust in public infrastructure.

Keywords: Solid Waste Management, Citizen Engagement, Service Quality Assessment, Public Utilities, Participatory Governance

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Hira Ajmal
Lahore School of Economics, Pakistan

Abstract: In today's digital payment ecosystem, optimizing merchant incentives plays a vital role in increasing transaction volumes and enhancing customer engagement. This study presents a novel data-driven strategy utilizing graph neural networks (GNNs) to model and analyze complex interactions between merchants and customers. By capturing transactional behaviors and applying representation learning, the framework predicts merchant responsiveness to different incentive types, enabling dynamic allocation of marketing budgets. The approach integrates linear programming and monotonic mapping techniques to align commercial objectives with performance metrics. Real-world experiments indicate a significant uplift in merchant activity and a measurable reduction in overall marketing expenditures. Furthermore, A/B testing validates the framework's effectiveness in refining incentive structures. The proposed method offers a scalable solution for fintech firms aiming to design more personalized and cost-efficient incentive campaigns within digital marketplaces. Future extensions may focus on incorporating real-time feedback loops, integrating alternative behavioral variables, and adapting the model for emerging markets with limited data infrastructure.

Keywords: Merchant Incentive Design, Digital Payments, Graph Neural Networks, Budget Optimization, Customer-Merchant Modeling

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