ARTIFICIAL INTELLIGENCE IN THE MILITARY

This study evaluates the impact of Artificial Intelligence (AI) applications in power systems on energy security and to determine relevant policy implications. We use a mixed methods approach to analyze the benefits and risks associated with AI implementation on the European power grid, focusing on four key dimensions of energy security: availability, affordability, accessibility, and acceptability. We investigated the benefits of AI using PyPSA, a Python-based model of the European electricity system. Three AI applications were parametrized: load reduction, load shifting, and wind wake steering. We compared scenarios in which these AI applications are widely deployed against a baseline scenario without these applications to determine if AI improves energy security. The study also analyzes risks associated with AI deployment in the power grid. We developed a risk taxonomy centered around six high-level categories: cybersecurity, jurisdictional or sovereignty issues, unexplained or unexpected actions by the model, unethical or illegal decision-making, reliance and trust in decision-making, and supplier dependency and vendor lock-in. Additionally, we conducted a back-casting exercise with subject-matter experts to determine positive and negative future outcomes of AI deployment and identify actions to create positive outcomes and avoid negative ones. The paper presents a set of policy implications for AI on the European synchronous grid. We find that AI applications can improve energy security in power systems. In the scenarios we tested, behind-the-meter applications have a greater impact on energy security than front-of-meter applications. The results suggest that AI applications targeting energy consumption may significantly improve energy security metrics. Keywords: Energy Security, AI, Power Grid, European Electricity System

BackgroundDespite the extensive hopes and expectations for value creation resulting from the implementation of artificial intelligence (AI) applications in healthcare, research has predominantly been technology-centric rather than focused on the many changes that are required in clinical practice for the technology to be successfully implemented. The importance of leaders in the successful implementation of innovations in healthcare is well recognised, yet their perspectives on the specific innovation characteristics of AI are still unknown. The aim of this study was therefore to explore the perceptions of leaders in healthcare concerning the innovation characteristics of AI intended to be implemented into their organisation.MethodsThe study had a deductive qualitative design, using constructs from the innovation domain in the Consolidated Framework for Implementation Research (CFIR). Interviews were conducted with 26 leaders in healthcare.ResultsParticipants perceived that AI could provide relative advantages when it came to care management, supporting clinical decisions, and the early detection of disease and risk of disease. The development of AI in the organisation itself was perceived as the main current innovation source. The evidence base behind AI technology was questioned, in relation to its transparency, potential quality improvement, and safety risks. Although the participants acknowledged AI to be superior to human action in terms of effectiveness and precision in some situations, they also expressed uncertainty about the adaptability and trialability of AI. Complexities such as the characteristics of the technology, the lack of conceptual consensus about AI, and the need for a variety of implementation strategies to accomplish transformative change in practice were identified, as were uncertainties about the costs involved in AI implementation.ConclusionHealthcare leaders not only saw potential in the technology and its use in practice, but also felt that AI’s opacity limits its evidence strength and that complexities in relation to AI itself and its implementation influence its current use in healthcare practice. More research is needed based on actual experiences using AI applications in real-world situations and their impact on clinical practice. New theories, models, and frameworks may need to be developed to meet challenges related to the implementation of AI in healthcare.

This article explores the integration of artificial intelligence (AI) applications into project‐based learning (PBL) education as a means to enhance students' education. Specifically, the implementation of AI in the context of power generation is addressed, focusing on achieving power generation at minimum wind velocity and classifying turbine faults using AI techniques. The researchers have proposed a novel generating unit which is going to generate 1 KW of electric power at a specific flow rate of air and the generated power will be stored in the battery bank through the charge controller and then the load is driven from the battery through an inverter. Iron or core losses (Hysteresis, Eddy Current losses) can be acknowledged as one of the major reasons for the inefficiency of conventional generators, therefore anovel coreless model generator was proposed which also improved efficiency and reduces drag. Wind Turbine prototype was fabricated and deployed for the testing and validation of the proposed novel design. The design produced outstanding power ratings and electrical generation characteristics compared with other existing strategies at minimal air flow. Results proved that the proposed coreless axial flux generator has the capability to produce a better power rating compared with the existing wind turbine generators. Proposed Axial flux achieved 10.73 watt power at wind velocity at around 80 rpm. At a wind velocity of 10 m/s and around 800 rpm 313–330 kwh was produced by the proposed generator while the conventional generator produced around 300 kwh. The proposed generator design performed 35% better in terms of production efficiency under load and no load conditions. Moreover, faults in turbines are very common due to the various temperatures, therefore the faults have been classified using state‐of‐the‐art AI‐based classifiers. A comparison of space vector modulation (SVM) and Naive Bayes classifiers was performed in the study to classify wind turbine faults. It was found that both classifiers performed well in achieving high accuracy. SVM achieved a slightly higher accuracy of 0.9861 compared with Naive Bayes, which achieved an accuracy of 0.967. Based on the results, it can be inferred that SVM may be a more suitable classifier for wind turbine fault classification. The case study results demonstrated the potential of AI applications in PBL education, offering students a multidisciplinary learning experience that enhances their technical knowledge, problem‐solving skills, and teamwork abilities.

When ChatGPT-3.5 entered the consumer-grade market, artificial intelligence (AI) technology has developed at a rapid pace and become an important driving force for the progress of human civilization. Up to now, the landscape of large AI models has gradually presented a situation of a hundred schools of thought contending, playing important roles in industries such as industry, healthcare, education, and finance. However, it is undeniable that there are structural resistances to the application of artificial intelligence technology in social, ethical, and other aspects. Therefore, this paper, from an organizational perspective, comprehensively analyzes existing literature on organizational inertia and its impact on the application of artificial intelligence, clarifying the definition and dimensions of organizational inertia as well as the impact of each dimension on the application of artificial intelligence technology. The study found that organizational inertia has six internal dimensions: structure, resources, cognition, path, routines, and culture. Moreover, through searching for studies on the impact of organizational inertia on the application of artificial intelligence, it was found that the number of relevant representative papers is relatively limited, and only the structural, resource, cognitive, and path dimensions of organizational inertia have been verified to hinder organizations' application of artificial intelligence technology, while there is a lack of research on the impact of the routines and cultural dimensions of organizational inertia on the application of artificial intelligence technology

Globalisation of the strategy for the development and implementation of artificial intelligence (AI) in Ukraine and/or deepening the direction in nominal areas of social life, i.e. individuals in society, respectively, in the fields of art, design, medicine, commercial activity, science and education, banking and deep military and national strategy and national security in cadence formats such as ChatGPT, i.e: • Artificial intelligence – AI; • Artificial Neural Networks – ANN – computing systems. The dynamic position in the context of the emergence of artificial intelligence (AI) and its further application in the educational process in relation to the first stage, i.e. the species concept on the part of educators and students of education from among the convicts in the status of a special contingent, respectively, was remade in relation to the nominal field of vocational training in the context of penitentiary education, In other words, artificial intelligence (AI) in the form of a contact line requested a penitentiary trajectory for the format of the social and creative model teacher-education student from among prisoners in the status of a special contingent. Accordingly, the special contingent in the form of students from the prison population in the status of a special contingent needs to be fluent in digital technologies in accordance with the requirements of modern society, to improve and enhance the nominal abilities that will be needed for further employment in the digital world. The motivation of the present regarding the project perspective of artificial intelligence (AI) in penitentiary education in the status of a special contingent, that is, the involvement of the social and creative model of teacher-educator from among the convicts of imprisonment in the status of a special contingent is characterized by the intense development of artificial intelligence (AI) technologies and the introduction of. The generation of the relationship between artificial intelligence (AI) and education goes beyond the application of artificial intelligence (AI) during the acquisition of theoretical training, i.e., taking into account the study of artificial intelligence (AI) technologies and the professional training of students from among prisoners of imprisonment in the status of a special contingent within the framework of penitentiary education.

Technological progress, especially in the field of information, has led to reducing the gap between science, information and knowledge. This accelerating change has forced all educational institutions to comply with this new mission by using information technology in the teaching process, as artificial intelligence has become an integral part of the educational and learning process, and a means of Effective in the teaching process, in addition to being research tools in obtaining information and knowledge, the research problem focused on (what are the attitudes of the kindergarten teacher towards using artificial intelligence applications in the effective teaching process in the early education stage in the third Rusafa Education Directorate?), where the importance of The research is considered one of the researches that deals with an important and vital topic (artificial intelligence), as it gains its importance in that it represents the role that kindergarten teachers play in the early education stage in developing the skills and abilities and refining the information of the child through their use of artificial intelligence methods and applications, as well as The positive role of such research in stimulating kindergarten teachers’ motivation to benefit from artificial intelligence applications, which increases the efficiency and effectiveness of their teaching. The aim of the research is to reveal kindergarten teachers’ attitudes towards using artificial intelligence in the effective teaching process in the early education stage, as well as to reveal the most important The challenges facing kindergarten teachers using artificial intelligence in the effective teaching process, and are there any statistically significant differences between the average kindergarten teacher’s attitudes to using artificial intelligence in the effective teaching process in the early education stage according to the research variables? The research reached several conclusions, perhaps the most important of which is the existence of differences. There is statistical significance between the average kindergarten teacher’s attitudes to using artificial intelligence in the effective teaching process in the early education stage according to the research variables. The research recommends holding many workshops and training courses for kindergarten teachers on how to use artificial intelligence in effective teaching, and including programs and rehabilitation plans for kindergarten teachers. About the use of artificial intelligence according to the plans of the Ministry of Education, which are consistent with its vision, and encouraging teachers to proceed and benefit from applications and websites for safe and available artificial intelligence applications in the teaching process

Introduction: In todays rapidly changing technology, artificial intelligence (AI) is changing the way we live, work and create art at an unprecedented speed. As an important part of human culture, dance art has also ushered in new development opportunities and challenges in this scientific and technological revolution. Objectives: This paper aims to explore the innovative application and influence of artificial intelligence (AI) in dance creation and performance. With the rapid development of AI technology, its application is increasingly wide in the field of art, and dance art is no exception Methods: This paper first outlines the application of AI technology in dance creation, including the generation of new dance movements through algorithms, auxiliary choreography, etc. These applications provide unprecedented creative sources and tools for dance creators. Then, this paper analyzes the role of AI in dance performance, such as the accurate movement performance of dance robot, the immersive experience of virtual reality technology in dance performance, etc. Results: These technologies not only enrich the form of dance performance, but also broaden the viewing experience of the audience. In addition, this paper also discusses the application of AI in dance teaching, such as intelligent guidance system, distance dance teaching, etc., which bring convenience and personalization to dance teaching. However, the application of AI in the field of dance also faces technical challenges and art ethical issues, which need further research and discussion. Conclusions: In conclusion, this paper believes that the application of AI technology in dance creation, performance and teaching has broad prospects, but it also needs to pay attention to its potential impact and challenges to promote the harmonious coexistence of dance art and AI technology. The research of this paper provides new perspectives and thoughts in the field of dance art, Provides a reference for future research and practice.

ObjectivesThis study aimed to gain insight into radiographers’ views on the application of artificial intelligence (AI) in Saudi Arabia by conducting a qualitative investigation designed to provide recommendations to assist radiographic workforce improvement.Materials and methodsWe conducted an online cross-sectional online survey of Saudi radiographers regarding perspectives on AI implementation, job security, workforce development, and ethics.ResultsIn total, 562 valid responses were received. Most respondents (90.6%) believed that AI was the direction of diagnostic imaging. Among the respondents, 88.5% stated that AI would improve the accuracy of diagnosis. Some challenges in implementing AI in Saudi Arabia include the high cost of equipment, inadequate knowledge, radiologists’ fear of losing employment, and concerns related to potential medical errors and cyber threats.ConclusionRadiographers were generally positive about introducing AI to radiology departments. To integrate AI successfully into radiology departments, radiographers need training programs, transparent policies, and motivation.

This study investigates the scientific development of Artificial Intelligence (AI) and Machine Learning (ML) applications in Human Resource Management (HRM) through bibliometric analysis. To this end, 522 academic publications indexed in the Scopus database between 2020 and 2024 were analyzed using the R-based bibliometrix package and VOSviewer software. Descriptive analysis, scientific productivity metrics, and content analysis techniques were employed. The findings revealed three main patterns. First, research on AI and ML applications in HRM has grown significantly—particularly between 2022 and 2024—driven by post-pandemic digital transformation. Second, India, China, and the USA lead in research output, while the UK and France demonstrate strong citation impact, indicating a globally expanding research ecosystem. Third, the thematic focus of research is shifting from technical infrastructure toward more human-centered and ethical dimensions. Additionally, keyword co-occurrence network analysis identified three major thematic clusters: HRM functions, AI applications, and machine learning analytics, highlighting the field’s interdisciplinary nature. Compared to the previous studies, this research provides a more comprehensive bibliometric analysis of AI and ML applications in HRM. It is the first extensive study to map the intellectual evolution of the field from a multidisciplinary perspective. Furthermore, it charts research trends and collaboration networks, revealing a shift from technical implementations to strategic integration. In conclusion, this analysis offers new insights to the literature by illustrating the technological evolution in HRM and highlighting the growing significance of cutting-edge approaches such as AI and ML, reaffirming the field as a timely and impactful area of research.

Imaging in the emergent setting carries high stakes. With increased demand for dedicated on-site service, emergency radiologists face increasingly large image volumes that require rapid turnaround times. However, novel artificial intelligence (AI) algorithms may assist trauma and emergency radiologists with efficient and accurate medical image analysis, providing an opportunity to augment human decision making, including outcome prediction and treatment planning. While traditional radiology practice involves visual assessment of medical images for detection and characterization of pathologies, AI algorithms can automatically identify subtle disease states and provide quantitative characterization of disease severity based on morphologic image details, such as geometry and fluid flow. Taken together, the benefits provided by implementing AI in radiology have the potential to improve workflow efficiency, engender faster turnaround results for complex cases, and reduce heavy workloads. Although analysis of AI applications within abdominopelvic imaging has primarily focused on oncologic detection, localization, and treatment response, several promising algorithms have been developed for use in the emergency setting. This article aims to establish a general understanding of the AI algorithms used in emergent image-based tasks and to discuss the challenges associated with the implementation of AI into the clinical workflow.

Purpose This study examines the extent to which artificial intelligence (AI) applications predict psychological flourishing and sustainable development among Fayoum University students. It further explores gender differences in AI usage, flourishing levels, and sustainability orientations, as well as the interrelationships among the three variables. Methods A quantitative correlational design was employed using validated scales measuring AI application use, psychological flourishing, and sustainable development. The sample consisted of 190 university students from Fayoum University (Egypt) there ages were between 20 and 25 years. The mean age was 22.54 years, with a standard deviation of 3.22 years. Data were analyzed using t-tests, Pearson’s correlation coefficients, and simple linear regression to test the study hypotheses. Results Findings revealed no significant gender differences in AI application use, while significant differences were found in psychological flourishing and sustainable development in favor of male students. Significant positive correlations emerged among AI use, flourishing, and sustainability. Regression analyses showed that AI application use moderately predicted psychological flourishing (R 2 = 0.098) but strongly predicted sustainable development (R 2 = 0.604), indicating that AI plays a substantial role in shaping students’ sustainability-related behaviors. Conclusions The study demonstrates that AI applications contribute meaningfully to enhancing students’ well-being and sustainability practices, with a stronger influence on sustainability outcomes. These findings underscore the growing relevance of AI-driven learning environments in promoting sustainable education and student development. Implications The results highlight the need for integrating AI-based tools into higher education policies, fostering responsible AI use, and developing institutional initiatives that enhance students’ well-being and sustainability competencies.

With the continuous development of science and technology, exploration in the field of life sciences has become increasingly profound, encompassing the understanding of human beings themselves and the exploration of disease etiology and treatment methods.Artificial intelligence is defined as the intelligence exhibited by artificially created systems. The application of artificial intelligence is quite extensive and can replace humans in various aspects of work, significantly enhancing efficiency in daily life and work. Artificial intelligence has a dual nature, as it could provide convenience to human society but could also bring about various adverse effects.This paper summarizes the current applications of artificial intelligence in the field of life sciences. By analyzing the applications of artificial intelligence in areas such as gene editing, drug development, and bioinformatics, it demonstrates the potential of artificial intelligence in the field of life sciences. At the same time, this paper also points out the ethical, technical, social, and various challenges that the application of artificial intelligence technology in the field of life sciences faces, with the aim of providing reference for research in related fields.

Technology used to perform tasks requiring intelligence is referred to as artificial intelligence. It is a technological device intended to perform tasks that a human could. Artificial intelligence has resulted in significant changes to today’s organisations. Since human resource management is the area where the majority of employees work, it is important to examine not just one aspect of the company but all of its activities and how they impact it separately. This study proposes a conceptional framework of artificial intelligence (AI) technology application for human resource management (HRM). This review highlights the role of AI in human resource management. Most of the organization has been using artificial intelligence or digital technologies in HR like chatbot, machines learning, and robot process automation in human resource management which support in recruitment, screening, onboarding, and interviewing etc. Therefore, AI implementation should be considered a good expectation opportunity, because AI improves lives, AI builds a better future if properly understood and applied to the right way.

Organisations are adopting to artificial intelligence (AI) very rapidly which is revolutionizing business processes and decision making. AI applications in businesses span multiple areas, including content creation, data analytics, automation as well as pattern recognition Purpose: This study investigates the role and impact of AI technologies on various organisational practices, including decision-making, process automation etc. In this article, we explore how organisations are adopting AI tools to enhance operational efficiency, drive innovation, and gain competitive advantage. It also examines the challenges and ethical considerations associated with AI implementation. Methodology: The research is conducted by collecting data from secondary sources like scholarly literature, industry reports, and case studies. Results: Adoption of AI is influenced by factors like AI readiness, change capability, leadership involvement etc Artificial Intelligence is revolutionizing how organisations operate and make decisions on a daily basis. Conclusion: The evolution of AI has greatly transformed the nature of organisational framework and integration of AI is crucial for organisation success.

Artificial intelligence (AI) applications pave the way for innovations in the healthcare (HC) industry. However, their adoption in HC organizations is still nascent as organizations often face a fragmented and incomplete picture of how they can capture the value of AI applications on a managerial level. To overcome adoption hurdles, HC organizations would benefit from understanding how they can capture AI applications’ potential.We conduct a comprehensive systematic literature review and 11 semi-structured expert interviews to identify, systematize, and describe 15 business objectives that translate into six value propositions of AI applications in HC.Our results demonstrate that AI applications can have several business objectives converging into risk-reduced patient care, advanced patient care, self-management, process acceleration, resource optimization, and knowledge discovery.We contribute to the literature by extending research on value creation mechanisms of AI to the HC context and guiding HC organizations in evaluating their AI applications or those of the competition on a managerial level, to assess AI investment decisions, and to align their AI application portfolio towards an overarching strategy.