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.
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.
Juan Sebastian Baena Cock, Independent Researcher, Spain
This paper presents QuantumContact, a Python- and AI-based pipeline designed to detect and quantify micro-fluctuations in light intensity recorded by multiple photodiode sensors in a double-slit experiment under different cognitive states of a human observer. A continuous laser, a double-slit grid and three sensors (S1–S3) generate high-resolution interference data, while a digital channel (S4) encodes two conditions: focused observation (ON) and relaxed, off-task viewing (OFF). CSV recordings are segmented into ON→OFF pairs following the QC-MicroFirst v1.4 protocol, and for each pair and sensor we compute micro-activity features such as mean, standard deviation, coefficient of variation and z-score–based spike counts. These features feed classical statistics and machine-learning models (Random Forests, SVMs and clustering) that test whether the observer state can be predicted from micro-activity patterns. The pipeline automatically produces plots, verdict tables and reproducible reports, providing a reusable computational framework for AI-assisted analysis of double-slit and related optical experiments.
Double-slit experiment, Micro-activity analysis, Machine learning, Python pipeline.
Kishan Sai Saguturu, Tirth Chheta, Venkata sai kumar Erla, Lokesh Umamaheswari Ethirajan, Dr Moin Bhuiyan and Abinav Satya Sripathi, University of New Haven, West Haven, CT, USA
The rapid growth of online information has made automatic text summarization essential for productivity and knowledge access. While transformer-based abstractive summarizers dominate current research, they often demand high computational resources and reduce interpretability. This work presents a lightweight extractive summarization framework with a web-based interface that balances efficiency, scalability, and usability. The system accepts both user-provided text and live news articles, applying frequency- and position-weighted ranking to identify key sentences. Built with Python libraries such as spaCy, BeautifulSoup, and Streamlit, it generates concise summaries with low latency and supports real-time interaction. Experimental evaluation with ROUGE metrics and tokenization accuracy confirms the framework’s reliability and effectiveness. Designed for extensibility, the system outlines future directions including neural abstractive integration, multilingual support, and sentiment-aware summarization. By combining the interpretability of classical methods with pathways to modern advancements, this work provides a practical bridge between academic study and real-world applications.
NLP, Extractive Framework, BeautifulSoup, spaCy, Text summarization
Eva Anton, Kaust University, Saudi Arabia
The Hajj pilgrimage, an annual Islamic ritual in Makkah, Saudi Arabia, attracts 2–3 million participants, presenting unparalleled crowd management challenges due to its spatial and temporal constraints, diverse demographics, and inherent safety risks. This article synthesizes insights from four seminal studies to assess how advanced technologies, ranging from crowd simulation models to artificial intelligence (AI), machine learning (ML) and a wide range of digital tools, can mitigate these challenges. Researchers propose a multi-layered framework that integrates predictive planning, real time monitoring, and pilgrim-centric support systems to enhance safety, efficiency, and scalability. Findings highlight the transformative potential of these technologies while identifying critical gaps, such as scalability and real-world validation, that future research must address to ensure their efficacy during this massive gathering.
AI, Crowd Management, Hajj, CNN
Bryan Anton, Kaust University, Saudi Arabia
Dense crowd analysis is crucial for ensuring safety, security and good resources usage during large scale gatherings, particularly the Hajj and Umrah pilgrimages, where high crowd density, limited available space and continuous motion, challenge conventional computer vision and deep learning models. Although lot of progress has been made in crowd counting, tracking, and identification, existing research remains fragmented, and to our knowledge, no dedicated state-of-the-art review focused on pilgrimage crowds currently exists. This work addresses that gap by synthesizing recent AI-based approaches and evaluating their applicability under extreme cultural, visual, and logistical conditions of the Hajj.
Dense crowds, Hajj, Umrah, CNN, Dataset, Related work
Eva Anton, Kaust University, Saudi Arabia
The Hajj pilgrimage, an annual Islamic ritual in Makkah, Saudi Arabia, attracts 2–3 million participants, presenting unparalleled crowd management challenges due to its spatial and temporal constraints, diverse demographics, and inherent safety risks. This article synthesizes insights from four seminal studies to assess how advanced technologies, ranging from crowd simulation models to artificial intelligence (AI), machine learning (ML) and a wide range of digital tools, can mitigate these challenges. Researchers propose a multi-layered framework that integrates predictive planning, real time monitoring, and pilgrim-centric support systems to enhance safety, efficiency, and scalability. Findings highlight the transformative potential of these technologies while identifying critical gaps, such as scalability and real-world validation, that future research must address to ensure their efficacy during this massive gathering.
AI, Crowd Management, Hajj, CNN
Moses Boudourides1 and Eleni Savvidou2, 1Northwestern University, USA, 2USA
This article presents a structured review of machine learning (ML) applications in lighting design, with a particular emphasis on smart lighting systems and user comfort optimization. Situated within the context of rapidly evolving building technologies, the paper synthesizes current developments in supervised, unsupervised, deep, reinforcement, and ensemble learning approaches as applied to lighting control, energy efficiency, activity recognition, and human-centric illumination strategies. To demonstrate practical applications of machine learning in lighting design, we present an illustrative case study based on simulated data that represents a realistic office lighting optimization system implementation. This case study uses synthetic data generated to illustrate typical performance ranges and methodological approaches found in the literature, rather than empirical data from an actual deployment. The review also identifies implementation challenges, evaluation frameworks, and real-world deployment issues. Special attention is given to ethical and societal implications of AI in lighting environments and to emerging trends that will shape the next decade of intelligent lighting systems. This synthesis aims to inform both the lighting research community and practitioners by clarifying the scope of ML techniques in lighting and by identifying critical research and development directions.
Machine Learning, Smart Lighting, Lighting Design, User Comfort, Activity Recognition, Energy Efficiency, Artificial Intelligence.
Sam Osmanagich Archaeological Park: Bosnian Pyramid of the Sun Foundation, Visoko, Bosnia-Herzegovina
This paper presents a quantitative mathematical analysis of spatial similarity between key summit points in the Bosnian Valley of the Pyramids and the angular configuration of the Pleiades star cluster (M45). Leveraging LiDAR-derived elevation models, high-precision geodesy, and Gaia celestial catalog data, this study tests geometric and statistical correspondence using reproducible computational techniques. Analytical procedures include Fibonacci-ratio evaluation, distance-matrix comparison, angular deviation measurements, Procrustes geometric alignment, and Monte-Carlo spatial randomization with 100,000 iterations. Results reveal multiple inter-summit proportional relationships approximating the golden ratio within a ≤2% tolerance, angular alignment convergence under ≤2°, and statistically significant Procrustes similarity. Monte-Carlo null modeling further indicates that the observed coherence has a low probability (p < 0.05) of arising by random terrestrial placement. No cultural or chronological claims are asserted; rather, this work provides a formal, statistically grounded framework for testing potential celestial-terrestrial spatial correspondences. Findings demonstrate measurable geometric coherence and support further application of advanced statistical geometry to archaeological and geomatic research.
Fibonacci ratio; golden ratio; spatial alignment; celestial-terrestrial geometry; Procrustes transformation; Monte-Carlo simulation; Bosnian Valley of the Pyramids; Pleiades star cluster; geomatics; LiDAR.
Amor Boulouma, Ecole Nationale Supérieure de Technologie et d’Ingénierie d’Annaba, Algeria
We introduce a groundbreaking mathematical framework that fundamentally advances the analysis of polynomial root systems. While the classical discriminant has served for centuries as the primary tool for detecting repeated roots, it provides only binary information about root coincidence, offering no insight into the geometric organization of roots in the complex plane. This work presents a novel class of discriminants that transcend this limitation by detecting multiplicities in the geometric attributes of complex conjugate pairs—specifically their moduli/argument, and real/imaginary components. Our method enables, for the first time, the algebraic identification of sophisticated root constellations directly from polynomial coefficients, without requiring explicit root computation. For a real polynomial P(a), we construct discriminants that vanish if: 1. Complex conjugate pairs share identical moduli (lying on common circles centered at the origin) or possess equal (or complementary) arguments (exhibiting specific angular relationships) 2. Roots demonstrate multiplicities in their real or imaginary coordinates (aligning along vertical or horizontal lines in the complex plane) The theoretical foundation rests on a the new innovative SPRS (Step−by−step recursive polynomial remainder sequence) method, symmetric function theory, and geometric invariant theory. We establish explicit constructions that generalize elegantly to polynomials of arbitrary degree, with particular significance for the rich but poorly understood case of real quintics. Our discriminants provide a complete geometric classification system that captures the hidden structure of root distributions. The engineering and physical implications are profound. In control theory, these geometric discriminants enable early detection of resonant pole configurations that lead to instability in aerospace systems, power grids, and mechanical structures. For signal processing, they facilitate the systematic design of filters with prescribed frequency and phase characteristics. In quantum mechanics, they classify degenerate energy states and identify symmetry breaking patterns through analytical continuation pole geometry. Numerical examples demonstrate applications including: Detecting flutter conditions in aircraft wings through specific pole constellations Identifying inter−area oscillation modes in power systems from characteristic equations Designing photonic crystals with prescribed band-gap structures Classifying black hole quasi-normal mode relationships in gravitational wave analysis This work bridges pure mathematics with engineering applies sciences, offering a new paradigm for polynomial analysis that emphasizes geometric insight over mere algebraic classification. The resulting framework provides powerful tools for researchers across disciplines including dynamical systems, condensed matter physics, electrical engineering, and cosmological modeling, establishing a new language for understanding and designing complex systems through their fundamental polynomial characteristics.
Polynomial discriminants, root geometry, complex analysis, control theory, spectral analysis, dynamical systems, resonant frequencies, stability analysis.
Kimberley “Jinrei” Asher1, Kaia Asher1, Aneska Asher1, Dino Ducci2 and Christopher Ducci2, 1Orchard Harmonics, Churston, Devon, UK, 2Dustlabs, Gauteng, Johannesberg, South Africa
We recast the Riemann Hypothesis in terms of a concrete Unified Torsion Operator on a weighted L2 space. Alpha builds a positive, compact Gaussian log-convolution operator; Beta implements the functional equation as a unitary intertwiner; Gamma adds an even convex potential that produces a coercive Rayleigh landscape with a unique minimum on the critical line; and Delta T defines a regulator flow. The main analytic gate is a spectral determinant identity: the zeta-regularised determinant of the shifted operator equals the completed zeta packet, so the nontrivial zeros of zeta are encoded by the spectrum. Appendix E recasts Delta T as a Fokker–Planck gradient flow with a strict Lyapunov functional and shows that any stable equilibrium off the critical line would contradict monotone entropy dissipation. Together these pieces give an operator-theoretic reformulation of RH plus a falsifiable program of analytic and numerical stress tests.
Riemann Hypothesis, Mellin analysis, zeta-determinant, Phragmén–Lindelöf; spectral symmetry.
George Buop and Junghwan Kim, The University of Toledo, USA
Orthogonal frequency division multiplexing (OFDM) has become a popular technique for transmission of signals over wireless channels and has been adopted in several wireless communication standards. Multiple-input multiple-output (MIMO) antennas can be combined with OFDM to achieve diversity gain and/or to increase system spectral efficiency. MIMO technology uses multiple antennas at both the transmitter and receiver to increase the systems capacity and improve its performance by reducing interference and improving signal quality. Additionally, OFDM modulation divides the data stream into multiple subcarriers and modulates them separately. This helps to mitigate the effects of channel fading and improves the systems spectral efficiency. MIMO OFDM systems combine these two technologies to provide high-speed, reliable wireless communication over a wide range of distances. In this work, we investigate the performance of Fractional Fourier Transform (FrFT) as a suitable replacement of the Fast Fourier Transform (FFT) used in conventional OFDM. The bit error rate (BER) performance of conventional OFDM is first investigated, using BPSK, 16PSK and 16QAM, to determine the best performing modulation scheme. A FrFT based MIMO-OFDM system is then observed and compared to FFT based MIMO-OFDM system under 16-QAM modulation over AWGN. The simulation results indicate that BER performance of FrFT based MIMO-OFDM system is better than the FFT based MIMO OFDM system
Wireless communication, MIMO-OFDM, FrFT, BER, SNR.
