Multi-dimensional Path Exploration of Artificial Intelligence Empowering Postgraduate Research Ability Improvement

In this study, the training data of graduate students of Xi'an University of Posts and Telecommunications for the past three years was used to construct a predictive model for graduate students' high-quality employment based on support vector machine through 13 feature attributes that may affect the high-quality employment of graduate students.Six important characteristics of high quality employment were studied, including gender, student origin, postgraduate score, innovation fund, book reading and graduation thesis score. After many experiments, the accuracy of the model was 87.41%.According to the calculation results of the model, it is concluded that the graduate students should make corresponding improvements in the postgraduate training sessions, encourage graduate students to engage in cutting-edge and groundbreaking research work; continuously expand the knowledge of students; strengthen the guidance of instructors, improve graduate students' ability to analyze and solve problems.

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

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

Graduate student wellbeing is a public health issue in the United States. The COVID-19 outbreak exacerbated the mental health burden on graduate students worldwide. Culture of Wellness (PH 104) is a 2-week wellbeing elective course that teaches evidence-based wellbeing strategies for graduate students at a university in the United States. Our study aimed to evaluate the impact of this pilot wellbeing elective on Master of Public Health students' mental health and wellness during the COVID-19 pandemic. Participants included 22 Master of Public Health students from the class of 2021 at a university in the United States. We provided a pre-course survey to students that assessed their perception of their own personal wellbeing, their knowledge about various wellbeing strategies, and their confidence in applying 13 wellbeing strategies before taking the course. Post-course students completed the same survey following course completion, as well as a matching evaluation and a five-month follow up survey. Of the 13 strategies taught, students reported significant improvements in their ability to apply 10 strategies. There was a significant increase in self-reported emotional and physical wellbeing, as well as a significant decrease in burnout. Five months post-course, more than three quarters of respondents used strategies taught in the course on a weekly basis or more. This pilot study is limited by its small sample size, which may restrict the generalizability of the findings. The PH 104 Culture of Wellness course was effective in improving graduate students' wellbeing and confidence in applying wellbeing strategies.

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.

In the era of artificial intelligence (AI), physical education (PE) teachers face unprecedented challenges in integrating advanced technologies into their teaching practices. This study aims to identify the specific challenges PE teachers encounter in the AI era and propose practical strategies to enhance the quality and relevance of school-based physical education. This study employed a multi-method approach, including literature review, mathematical statistics, questionnaire surveys, and expert interviews. The participants were 120 PE teachers from urban and rural schools across different regions. The study began with a literature review to establish a theoretical foundation. Questionnaires were used to gather teachers' perceptions of challenges related to AI integration, student engagement, and professional development. In-depth interviews with 25 PE experts provided deeper insights into their experiences and coping strategies. Data were analyzed using SPSS software. The results showed that 80% of PE teachers reported difficulties in integrating AI tools into their lessons, while 70% struggled with engaging students in technology-enhanced activities. Analysis of the questionnaire and interview data revealed three main challenges: (1) limited access to AI resources, (2) lack of training in AI applications, and (3) concerns about the impact of AI on traditional teaching methods. Teachers emphasized the need for continuous professional development and support to effectively utilize AI in teaching. The study concludes that PE teachers face significant challenges in adapting to the AI-driven educational environment, particularly in integrating AI tools and maintaining student engagement. These findings align with previous research on educational technology integration but highlight the unique concerns of PE teachers regarding the impact of AI on traditional teaching. This study proposes the following strategies: (1) increasing investment in AI tools and establishing resource-sharing platforms to improve access to AI resources; (2) providing continuous professional training in AI applications and integrating AI education into teacher development programs; (3) offering technical support for AI integration and encouraging teachers to use AI tools to optimize traditional teaching methods.

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.

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.

This research examines what impact the Big Data Processing Framework (BDPF) has on Artificial Intelligence (AI) applications within Corporate Marketing Communication (CMC), and thereby the research question stated is: What is the potential impact of the BDPF on AI applications within the CMC tactical and managerial functions? To fulfill the purpose of this research, a qualitative research strategy was applied, including semi-structured interviews with experts within the different fields of examination: management, AI technology and CMC. The findings were analyzed through performing a thematic analysis, where coding was conducted in two steps. AI has many useful applications within CMC, which currently mainly are of the basic form of AI, so-called rule-based systems. However, the more complicated communication systems are used in some areas. Based on these findings, the impact of the BDPF on AI applications is assessed by examining different characteristics of the processing frameworks. The BDPF initially imposes both an administrative and compliance burden on organizations within this industry, and is particularly severe when machine learning is used. These burdens foremost stem from the general restriction of processing personal data and the data erasure requirement. However, in the long term, these burdens instead contribute to a positive impact on machine learning. The timeframe until enforcement contributes to a somewhat negative impact in the short term, which is also true for the uncertainty around interpretations of the BDPF requirements. Yet, the BDPF provides flexibility in how to become compliant, which is favorable for AI applications. Finally, BDPF compliance can increase company value, and thereby incentivize investments into AI models of higher transparency. The impact of the BDPF is quite insignificant for the basic forms of AI applications, which are currently most common within CMC. However, for the more complicated applications that are used, the BDPF is found to have a more severe negative impact in the short term, while it instead has a positive impact in the long term.

We appreciate Dr. Qi and Dr. Niu for their insightful comments on our study, "Exploring the Unknown: Evaluating ChatGPT's Performance in Uncovering Novel Aspects of Plastic Surgery and Identifying Areas for Future Innovation." Their observations underscore significant considerations in the application of artificial intelligence (AI) in plastic surgery. We agree with their concern about potential biases in ChatGPT's responses. The AI's frequent attribution of the title "parent of plastic surgery" to Sir Harold Delf Gillies, despite gender-neutral terminology, highlights underlying biases from training data. These biases often reflect historical texts and contemporary writings. Addressing them requires refining training datasets for balanced representation and developing algorithms that adjust dynamically to diverse inputs. The authors also question the criteria ChatGPT uses to identify key contributions to plastic surgery. The AI's focus on microsurgery, minimally invasive techniques, and tissue engineering, while significant, may prioritize keyword prevalence over a holistic evaluation. Enhancing ChatGPT's capabilities through targeted training and input from subject matter experts could improve the AI's ability to generate more balanced outputs. The identified bias favoring reconstructive over cosmetic procedures is another critical point. While reconstructive advancements are transformative, cosmetic surgery also has significant innovations. Ensuring ChatGPT presents a balanced view of both reconstructive and cosmetic advancements is essential. This can be achieved by diversifying training data and calibrating the AI to give equitable weight to different subspecialties within plastic surgery. AI models like ChatGPT are proficient in processing and generating information but lack the human elements of creativity, intuition, and emotional depth critical for groundbreaking innovations. AI should complement, not replace, the expert judgment and innovative thinking of skilled plastic surgeons. Ensuring the accuracy of AI-generated responses is crucial. Clinicians must verify AIgenerated information against established medical literature and clinical guidelines to maintain accuracy in medical practice. Continuous feedback and improvement mechanisms are vital to enhance AI's clinical utility. The improvement of AI in plastic surgery will be driven by active involvement from surgeons, providing comprehensive and balanced data for training to ensure AI systems evolve to support and enhance clinical practice effectively.Level of Evidence V This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

With the rising demand for personalized learning and increased digital natives, the traditional education model is under pressure to reform. the rapid development of artificial intelligence (AI) provides new education solutions, and the application of AI is becoming widespread, from intelligent tutoring to automated grading to learning analytics. However, the application of AI in education is not without challenges. This literature review aims to explore the impact of AI on education, analyze its potential advantages, and propose strategies to address the challenges and provide guidance for the future development of education. Understanding the impact of AI on education is instructive for the future direction of AI in education and reveals the importance of AI for education to the extent that it is important for formulating effective educational policies and cultivating future talents in AI technology. The final results of the study show that although AI in the education industry there are various difficulties and limitations, but AI offers great potential for smarter and more efficient education levels and education systems. In today's era, computer programming education is emerging as a pivotal force in shaping the future, as it not only hones students' logical thinking and innovative capabilities but also lays a solid foundation for their career paths in the digital world. By integrating knowledge across various disciplines, programming education stimulates students' enthusiasm for applying their learning to real-world problems and enhances their sense of social engagement, enabling them to tackle challenges through technological means.

Recent advancements in artificial intelligence (AI) applications in medicine have been significant over the past 30 years. To monitor current research developments, it is crucial to examine the latest trends in AI adoption across various medical fields. This bibliometric analysis focuses on AI applications in cardiology. Unlike existing literature reviews, this study specifically examines journal articles published in the last decade, sourced from both Scopus and Web of Science databases, to illustrate the recent trends in AI within cardiology. The bibliometric analysis involves a statistical and quantitative evaluation of the literature on AI application in cardiovascular medicine over a defined period. A comprehensive global literature review is conducted to identify key research areas, authors, and their interrelationships through published works. The leading institutions and most influential authors in research on the role of AI in cardiology were located in the United States, the United Kingdom, and China. This study also provides researchers with an overview of the evolution of research in AI and cardiology. The main contribution of this study is to highlight the prominent authors, countries, journals, institutions, keywords, and trends in the development of AI in cardiology.

A 3D indicator for guiding AI applications in the energy sector

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.

In this paper, the application of Artificial intelligence (AI) methods in power system protection and inspection has been discussed. In this paper particularly AI application of line scout Robot is used. Line scout robot is an inspection based Artificial intelligence Robot. Line Scout robot is able to work on the power lines while they are operating (at very high tension). The robot can patrol in a substation and inspect equipment with a visible-light camera and thermograph. It has possess a vast array of cameras and tools that allow the remote operators to inspect and conduct (albeit minor) repairs on the power lines without needing to put the off-service or risking their lives. It is preventive arrangement for detection of fault (very high temperature). Keywords: Artificial intelligence (AI), artificial general intelligence (AGI), corona pulse, Line Scout Robot, power system