Yesim Sirakaya, St. Clements University, UK
Rapidly changing regulations in the energy sector (environmental standards, carbon emission limits, occupational safety laws, sustainability goals) affect not only technical processes but also the psychology of employees. This article discusses both the negative outcomes of regulatory changes-such as uncertainty, stress, burnout, and resistance-and the positive effects, including enhanced safety, ethical commitment, and motivation for sustainability. The study develops a conceptual framework based on key theories from the Industrial and Organizational Psychology literature, including the Job Demands-Resources (JD-R) Model, Organizational Justice, Psychological Safety, and Change Management. Furthermore, within the principle of “just transition,” the paper offers recommendations on leadership, communication, and human resources policies to ensure that regulations strengthen employee motivation and commitment. Ultimately, it argues that successful regulatory processes in the energy sector depend not only on technical compliance but also on meeting employees’ psychological needs.
Energy regulations, employee psychology, organizational justice, psychological safety, job demands-resources model, just transition.
Mohammad Ali Khan1 and Christopher Greenfield2, 1Department of Physics Undergraduate, 19th St - Al Safa - Al Safa 1 - Dubai, 2Department of Astrophysics, 19th St - Al Safa - Al Safa 1 - Dubai
For decades, fusion energy has been hindered by one major limitation: plasma containment time. Traditional tokamak designs rely on lithium as a plasma-facing material, but lithium suffers from rapid neutron interactions, excessive cooling, and material degradation. It is severely limiting plasma sustainment and energy efficiency. By applying quantum mechanics—specifically the momentum operator, de Broglie wavelength, and Planck’s radiation law—we identified xenon as a superior alternative due to its high atomic mass, low reactivity, and ability to suppress radiative losses. Our solution? A matrix of xenon and lithium that combines the neutron-absorbing benefits of lithium with the stability and wave-diffraction properties of xenon. This approach minimizes plasma losses, extends containment time beyond 30 minutes to over an hour : eliminates lithium’s cooling drawback. By overcoming fusion’s greatest bottleneck, this breakthrough paves the way for commercially viable fusion energy, something the industry has yet to achieve. settings.
Materials and Structural Analyses, Nuclear Fusion, Energy Storage, Plasma.
Amir Anvarov, Department of Economics, Branch of the Russian State University of Oil and Gas (National Research University) named after I.M. Gubkin, Tashkent, Uzbekistan
Energy companies are integral to advancing the United Nations Sustainable Development Goals (SDGs), as they play a crucial role in addressing environmental, economic, and social challenges. Positioned at the forefront of the global energy transition, such companies are essential in promoting sustainability and environmental responsibility. This paper explores the role of PJSC Lukoil in integrating sustainability into its operations, with a specific focus on renewable energy, decarbonization, and social programs. Using financial data from 2021–2023, the study evaluates Lukoil’s contributions to multiple SDGs, including affordable and clean energy (SDG 7), climate action (SDG 13), and decent work and economic growth (SDG 8). Findings indicate substantial progress but also highlight inconsistencies in investment flows, raising concerns over the stability of long-term sustainability commitments. The paper concludes that while Lukoil demonstrates leadership in SDG integration, greater strategic balance and innovative financing mechanisms are required to ensure sustained progress toward global climate and social goals.
Sustainable Development Goals (SDGs), Renewable Energy, Climate Change, Lukoil, Energy Transition, Corporate Sustainability.
Emeka Emmanuel Otti, Case Western Reserve University, USA.
Despite effort by public health officials to attenuate head lice, it remains endemic in several part of the globe both in developing and developed countries. This work presents a deterministic mathematical model to attenuate head lice infection. We validate the proposed model by studying it existence and uniqueness solution, computed the basic reproduction number and the local stability analysis associated with the disease-free equilibrium point. Furthermore, we carefully selected some sensitive parameter and performed numerical simulation on the subdivided population to see their effects for better understanding during decision making.
Pediculosis, Stability Analysis, Existence Solution, Uniqueness Solution, Basic Reproduction Number and Mathematical Model.
Ziqi Zheng, United States of America
In this paper two kinds of algorithms are proposed to improve the power grid distribution in which one uses static methods (Dijkstra’s, Ford-Fulkerson) to consider the capacity/loss from plant and transmission and another is probability/reinforcement learning based method (Markov Decision Processes, and Q-Learning) to take into consideration the uncertainties, such as fuel shortages and wind variability for the goal of optimizing its energy flow. We took the dataset for Cuba’s power plants as a case study to test the effectiveness of these algorithms for power distribution. Our results show a major potential improvement of 22-68% in energy generation from using these two kinds of algorithms (static/probability) compared to the current country’s available operating power.
energy optimization, graph theory, reinforcement learning, Markov Decision Processes, Ford-Fulkerson, Dijkstras, Q-Learning.
Mou Sarkar, Pratham Prajapati, Rahul Dewangan, S Abhinav Raj, and Sanjay Chatterji, Indian Institute of Information Technology Kalyani, Kalyani, India
This paper presents the design, implementation, and evaluation of a Soybean Crop Advisory Chatbot that leverages Retrieval-Augmented Generation (RAG) techniques and Large Language Models (LLMs) to provide farmers with accurate, context-specific advice. The system integrates diverse data sources (research articles, extension bulletins, crop tables) into a unified knowledge base. Using semantic embedding and vector storage (via ChromaDB), the chatbot retrieves relevant information in response to user queries and formulates answers through a language model pipeline (LangChain) with prompt tuning for clarity and farmer-friendly language. Key challenges, such as extracting English content from bilingual PDFs, merging sentence fragments, choosing optimal text chunk sizes, and simplifying technical language for non-expert users, are addressed with custom processing strategies. We report development details including system architecture, data preprocessing, embedding generation, and prompt design. Sample queries and responses demonstrate the chatbot’s capabilities. Evaluation on test queries indicates high retrieval precision and user-friendly performance, suggesting the system’s potential to improve soybean farming practices. The work concludes with discussion of limitations and future enhancements.
Soybean Crop Advisory, Chatbot, Retrieval-Augmented Generation (RAG), Large Language Models (LLMs), Semantic Embedding, ChromaDB, Data Preprocessing, Prompt Tuning, LangChain.
