Artificial intelligence in sustainable energy industry: Status Quo, challenges and opportunities

The rapid evolution of the energy sector is significantly influenced by the integration of Artificial Intelligence (AI) technologies. This paper reviews the work in the areas of AI applications in energy trading platforms, focusing on three broad domains. Firstly, the energy industry is undergoing a transformative phase, where AI-driven digitalization is optimizing energy supply, trade, and consumption. Emphasis is laid on AI’s role in integrating solar and hydrogen power generation, supply-demand management, and the latest advancements in AI technology. These techniques have shown superior performance in areas like big data handling, cyberattack prevention, and energy efficiency optimization. Secondly, the manufacturing sector is witnessing a shift towards smart factories, where AI is enhancing value-added manufacturing by integrating various information communication technologies. The characteristics of these factories include operations optimization and intelligent decision-making, with AI technologies enabling systems to adapt to external needs. Lastly, while AI promises transformative changes in the energy sector, it also brings forth challenges. A multidisciplinary approach identifies these challenges, offering insights and recommendations for successful AI integration in the energy sector.

Advances in Artificial Intelligence (AI) technologies and Autonomous Unmanned Vehicles are shaping our daily lives, society, and will continue to transform how we will fight future wars. Advances in AI technologies have fueled an explosion of interest in the military and political domain. As AI technologies evolve, there will be increased reliance on these systems to maintain global security. For the individual and society, AI presents challenges related to surveillance, personal freedom, and privacy. For the military, we will need to exploit advances in AI technologies to support the warfighter and ensure global security. The integration of AI technologies in the battlespace presents advantages, costs, and risks in the future battlespace. This chapter will examine the issues related to advances in AI technologies, as we examine the benefits, costs, and risks associated with integrating AI and autonomous systems in society and in the future battlespace.

This study focuses on the future of AI in the energy sector, examining how AI can be used to improve efficiency and sustainability in the sector. The study aims to provide a realistic baseline of AI technology that can be used to compare efforts, ambitions, new applications, and challenges around the world. We covered three main topics: (i) how AI is being used in solar and hydrogen power generation; (ii) how AI is being used in supply and demand management control; and (iii) the latest advances in AI technology. In this research we explored how AI techniques outperform traditional models in controllability, energy efficiency optimization, cyber-attack prevention, IoT, big data handling, smart grid, robotics, predictive maintenance control, and computational efficiency. Our study found that AI is becoming an important tool for a new and data-intensive energy industry, which is providing a key magic tool to increase operational performance and efficiency in an increasingly cut-throat environment. KEYWORDS: Artificial Intelligence; Renewable Energy; Energy Demand; Decision Making; Big Data; Energy Digitization

Masters or Slaves? AI and the Future of Humanity

Purpose/Rationale Sport officials operate within settings that dynamically change and shift. While they gather, synthesise, and store experiences related to task, performer, and environmental constraints, their internal mental models of judgement and decision-making individually evolve as they perform in different contexts. However, while a large body of work in psychology and behavioural economics has attempted to capture the way humans make decisions, there is a growing realisation among researchers, evaluators, and educational designers that quality improvement interventions cannot be understood outside of the context in which they occur [Ramaswamy, R., Reed, J., Livesley, N., Boguslavsky, V., Garcia-Elorrio, E., Sax, S., Houleymata D., Kimble L., Parry, G. (2018). Unpacking the black box of improvement. International Journal for Quality in Health Care, 30(suppl_1), 15–19. https://doi.org/10.1093/intqhc/mzy009]. Approach In this futuristic proposal, we put forward our vision of how artificial intelligence technologies can unpack and support the internal collections of cognitive knowledge, context, task goals, and on-field experiences that influence sport officiating development. Findings In what follows, we define what we mean by artificial intelligence and machine learning technologies, briefly highlighting their histories in sport analytics contexts. We outline how education is a promising field for the adoption of artificial intelligence/machine learning technologies and conclude by providing a theoretical case study scenario that describes a potential platform through which perspectives of environment, task and performer expertise might be developed for amateur and elite sport officials. Practical implications Using advanced AI technologies as the basis through which to examine on-field data provides tremendous potential to theoretically tackle the idiosyncrasies of officiating development in a range of sports as it can close the gap between a descriptive analysis (i.e. understanding the interactions undertaken by officials in the presence of others), and a more prescriptive one (i.e. suggesting the actions such officials should have executed). Research contribution We put forward that artificial intelligence technologies can offer sport organisations sophisticated, constructively based help with opening the “black box” of learning related to sport officiating development. By using ecological dynamics as the fundamental framework through which to filter the data collected, statistical information and qualitative ecological outcomes can be linked and managed into understandable and stable development content [Liu, A., Mahapatra, R. P., & Mayuri, A. V. R. (2021). Hybrid design for sports data visualization using AI and big data analytics. Complex & Intelligent Systems, https://doi.org/10.1007/s40747-021-00557-w], that strongly benefits the online development of amateur sport officials.

The continuous goal of discovering extraterrestrial life has driven scientific interest in exoplanets—celestial bodies orbiting stars outside our solar system. Traditional methods of exoplanet detection, reliant on manual analysis and prone to human error, presented unavoidable challenges given the vastness of the universe. This paper aims to discuss the benefits and limitations of AI within the exoplanet detection field, and determine whether the highly-regarded artificial intelligence is as beneficial to astronomical fields as we think. With the introduction of the first ever fully-robotic exoplanet detector which takes high-precision radial velocity measurements to measure the gravitational reflex motion, and advancing computer algorithms that avoid human errors in data analysis, modern advancements in artificial intelligence (AI) technology have not only transformed the efficiency and accuracy of exoplanet detection, but also extended our understanding of these distant worlds. While the use of AI does have its benefits, there are several drawbacks that could potentially hinder further advancement in the field of exoplanet detection.

The integration of Artificial Intelligence (AI) and Big Data Analytics (BDA) in project management has become a critical enabler of efficiency in managing large-scale, complex projects. This research paper delves into how AI-driven big data analytics can revolutionize traditional project management methodologies by introducing dynamic scheduling, real-time risk prediction, and automated task prioritization strategies. These advanced techniques, which leverage machine learning (ML) models and extensive historical project data, enable a shift from reactive to proactive project management, ensuring that risks and resource constraints are identified and addressed before they impact project delivery. By analyzing massive datasets, including historical performance metrics, resource availability, and project timelines, AI-driven systems can forecast delays, assess risk levels dynamically, and adapt schedules in real-time. This proactive approach facilitates better decision-making, optimized resource allocation, and improved project outcomes. The study is anchored on the premise that the sheer volume of data generated in large-scale projects often overwhelms traditional project management systems. By incorporating AI and BDA, project managers can better utilize this data, turning it into actionable insights that inform intelligent decision-making. Machine learning algorithms, particularly those specializing in predictive analytics, are capable of identifying patterns that elude human analysis, allowing for the accurate forecasting of project risks, schedule slippage, and task dependencies. This ability to predict potential issues, such as resource bottlenecks or unforeseen delays, enables project teams to implement mitigative actions in advance, thus reducing the likelihood of project failure. Furthermore, dynamic scheduling is a key focus of this research, as AI-powered models can continuously adjust project timelines based on real-time data. These models consider variables such as resource utilization rates, task dependencies, and evolving project constraints, offering adaptive scheduling mechanisms that evolve throughout the project lifecycle. The automated task prioritization system, powered by BDA, ensures that the most critical tasks receive the appropriate level of attention at the right time, improving project performance and enhancing resource efficiency. Through natural language processing (NLP) and advanced data mining techniques, AI models can also analyze project documentation and communication channels to detect potential risks and suggest task adjustments. The paper also discusses the application of AI in risk prediction, focusing on how AI models can analyze risk factors from historical data, including resource constraints, financial limitations, and market volatility, to produce risk profiles that project managers can use for strategic planning. Real-time risk assessments, made possible by the integration of AI and BDA, can help project teams stay ahead of potential disruptions. This allows for more accurate contingency planning and reduces the overall risk to project timelines and budgets. Practical applications of these AI-driven strategies are presented through case studies of large-scale projects in various industries, including construction, information technology, and healthcare. These case studies demonstrate how AI-powered analytics have been successfully implemented to enhance project efficiency, optimize resource allocation, and minimize risks in complex projects. The study underscores the importance of integrating these technologies into modern project management frameworks to cope with the increasing complexity of projects in today’s fast-paced business environment. While the potential benefits of AI and BDA in project management are substantial, this paper also addresses the challenges associated with their implementation. One significant challenge is the quality and availability of data required to train AI models effectively. Incomplete or inaccurate data can lead to unreliable forecasts, compromising the project’s success. Additionally, the paper explores the issues of data privacy and security in AI-driven project management systems, highlighting the need for robust data governance frameworks to ensure the ethical use of AI technologies. Another key consideration is the resistance to change within organizations, where traditional project management methods are deeply ingrained. The paper emphasizes the need for a cultural shift towards data-driven decision-making and suggests strategies for fostering an environment conducive to AI adoption. This includes training project management teams to work alongside AI systems and fostering collaboration between AI experts and project managers to ensure smooth implementation and operation. Finally, this research outlines future trends in AI and BDA for project management, suggesting that further advancements in AI technologies, such as reinforcement learning and more sophisticated natural language processing algorithms, will drive the next generation of intelligent project management systems. These future systems are expected to be even more adept at handling the complexities of large-scale projects, offering real-time solutions to unforeseen challenges and adapting dynamically to changing project requirements.

The rapid development and adoption of artificial intelligence (AI) technology has sparked debates about its implications for labor markets, yet the micro-level relationship between AI and labor share remains underexplored. Based on the theory of skill-biased technological change, this study aims to examine whether AI technology increases labor share by labor structure upgrading at the enterprise level. Using panel data for China’s listed companies from 2012 to 2022, this study tests this relationship using a two-way fixed effects model. The empirical results reveal that AI technology significantly increases labor share, with labor structure upgrading playing a mediating role in this relationship. Heterogeneity analysis reveals that the influence of AI technology on labor share is stronger for enterprises characterized by low labor market rigidity, high labor market supply, and talent policy support in external environments, as well as among labor-intensive, high-tech, and non-state-owned enterprises. Notably, this study finds that advancements in AI technology have achieved mutually beneficial outcomes of improving labor share and enhancing total factor productivity. Our research findings provide detailed empirical evidence for enterprises to formulate and implement AI strategies.

Artificial intelligence (AI) is increasingly shaping enterprise operations, yet evidence remains limited on how firms in Poland acquire AI capabilities, which AI technologies they adopt, and where they apply them functionally. This study examines (i) AI acquisition modes, (ii) types of AI technologies used, and (iii) application areas and assesses how these patterns differ by firm characteristics (size, operating scope, ownership structure, sector, and market experience). Data were collected through a questionnaire survey of 118 enterprise representatives in Poland (February–July 2025), of whom 49.15% reported using AI and were included in the detailed usage analysis. Using Ward’s minimum variance method, we identify systematic associations between firm characteristics and AI acquisition choices, technology types, and application domains. The findings suggest that larger, internationally active, and foreign-owned firms tend to combine multiple acquisition sources and report more advanced AI technologies and broader application portfolios, while smaller firms more often rely on ready-made solutions and narrower use cases. This study concludes with practical recommendations for managers regarding staged AI adoption, capability building, and governance.

With the accelerated advancement of educational digitalization, the intelligent transformation of academic management has gradually evolved into an inexorable trend in the progression of the education field. This paper delves into the significance of applying the integration of Big Data and Artificial Intelligence (AI) technologies in student academic monitoring and early warning systems. It elaborates on the entire technical architecture, from data collection and processing to model construction and application. Through multi-source data acquisition, efficient data processing, precise model building, and intuitive warning applications, the academic monitoring and early warning system integrating Big Data and AI technologies enables dynamic monitoring of students' academic status and timely alerts, providing robust support for the intelligent transformation of educational management. The innovation of this system lies in its comprehensive utilization of multi-source data and advanced AI technologies, which not only accurately predicts academic risks but also offers personalized intervention suggestions. It provides innovative ideas and practical guidance for the intelligent transformation of educational management, propelling educational management to new heights of intelligence.

Fruits and vegetables preservation based on AI technology: Research progress and application prospects

With the rapid development of modern social economy, big data, artificial intelligence, block chain, cloud technology and other science and technology industries have penetrated into all sectors of society. The application of big data, cloud computing, artificial intelligence and other information technologies in enterprises has changed traditional enterprise models and promoted the formation of new business management models. Based on this background, the purpose of this paper is to provide a new thinking mode for enterprises with artificial intelligence technology, and to promote enterprises to transform from quantitative development to qualitative development. On the basis of traditional enterprises, this paper introduces advanced artificial intelligence technology to enhance the innovation ability of enterprises, and combines literature research method and case analysis method to design the scheme of promoting enterprise innovation by artificial intelligence technology from the perspective of theory and practice. The research results of this paper show that artificial intelligence is a strategic opportunity brought by the technological revolution. Under the guidance of artificial intelligence technology, the production efficiency of enterprises can be increased by 56% and the business opportunity of 30% increment can be brought, thus achieving the expected goal of bringing innovation and vitality to enterprises.

The article explores the potential role of artificial intelligence (AI) technologies in training design professionals. The article emphasizes the significance of developing digital competencies in future designers in line with current trends in digitalization and innovation. Success and competitiveness in the labor market are increasingly determined by the ability to work effectively with digital technologies, including AI. The essence of the concept of 'artificial intelligence' and its relationship with the concept of 'Education 4.0' in Ukraine are examined. The text emphasizes the importance of modernizing education and introducing innovative teaching technologies, such as AI, to train highly qualified personnel capable of creating innovative design solutions based on AI technologies. The text analyzes the problem of insufficient use of such technologies in the training of future designers in Ukraine, which leads to a shortage of specialists with the necessary digital skills in the labor market. The benefits of incorporating AI technologies into the training of designers are numerous. These include personalized learning, optimized distribution of teaching resources and methods, automated assessment and quality control of competencies, and an effective combination of independent and distance learning. It is important to note that these advantages are objective and supported by evidence. The article discusses the potential of AI in developing fundamental knowledge, practical design skills, 3D modeling, visualization, big data analysis, and interactive modeling of design solutions. It is important to avoid making claims about AI's capabilities that are not supported by evidence. The authors propose various methods for implementing AI technologies in designer training. These include creating adaptive interactive courses on design and 3D modeling based on neural networks, developing systems for automated evaluation of creative works using machine learning, using Data Science to optimize the learning process based on big data analysis, and creating virtual 3D laboratories for simulating and modeling design processes using AI. The article highlights the potential of artificial intelligence technologies to modernize and improve the efficiency of training future designers in the context of the digital transformation of society and the economy. The authors suggest the active implementation of AI technologies in the educational process to form highly professional and competitive design professionals who are ready for innovation.

This paper explores the application of artificial intelligence (AI) in mitigating the effects of global warming, which stands as one of the most pressing and complex challenges of our time. The purpose of this research is to examine how various AI technologies, including machine learning, neural networks, and big data analytics, can be leveraged to enhance climate modeling, optimize energy systems, improve agricultural practices, and support carbon capture and storage efforts. By conducting a comprehensive literature review, this paper aims to highlight current advancements, practical applications, and relevant case studies that demonstrate the potential of AI to reduce greenhouse gas emissions and promote sustainable practices across different sectors. The study synthesizes findings from recent academic research, industry reports, and real-world implementations to provide an in-depth analysis of the benefits and challenges associated with integrating AI into climate action strategies. The methodology involves a thorough examination of the existing literature, identifying key areas where AI has shown significant promise in addressing various aspects of global warming. This includes enhancing the accuracy of climate predictions, optimizing the efficiency of renewable energy systems, improving precision agriculture techniques, and increasing the effectiveness of carbon capture and storage technologies. The conclusions drawn from this research underscore the transformative potential of AI in combating global warming. The findings highlight the necessity for interdisciplinary collaboration, advancements in AI technologies, and the development of supportive policy frameworks to maximize the impact of these innovations. The paper emphasizes that while AI offers significant potential to address global warming, realizing this potential requires addressing several challenges, including data quality and availability, integration with existing systems, ethical considerations, and economic and policy barriers. Furthermore, this paper discusses the critical role of AI in enabling more effective climate adaptation strategies. As the impacts of global warming become increasingly apparent, AI-driven tools and solutions can help communities and ecosystems adapt to changing environmental conditions. This includes providing early warning systems for natural disasters, optimizing resource allocation during climate-related crises, and supporting the development of resilient infrastructure. In addition to technological advancements, the paper also explores the importance of public engagement and citizen science in enhancing the effectiveness of AI applications in environmental monitoring and climate action. By involving citizens in data collection and environmental monitoring, AI models can access more diverse and localized data, improving their accuracy and relevance. Public engagement can also raise awareness about AI's role in addressing climate change and foster greater support for sustainable practices. Overall, this paper provides a comprehensive overview of the current state of AI applications in mitigating global warming, offering insights into the future directions and emerging trends in this rapidly evolving field. The research highlights the need for continued innovation, interdisciplinary collaboration, and supportive policy measures to fully harness the potential of AI in the fight against global warming and to ensure a sustainable future for all. DOI: https://doi.org/10.52783/pst.464

Big Data, Machine Learning, and Artificial Intelligence to Advance Cancer Care: Opportunities and Challenges