Exploration of Artificial-intelligence Oriented Power System Dynamic Simulators

The integration of artificial intelligence (AI) in education has the potential to revolutionize teaching and learning, particularly in the development of students’ critical thinking skills. This study explores science instructors' familiarity, perceptions, and experiences with using AI to enhance students' critical thinking skills, as well as the level of institutional support for AI integration in teaching. A quantitative survey was conducted among 20 science instructors from higher education institutions in Isabela, Philippines. The findings reveal that while instructors acknowledge AI's potential to improve educational outcomes, there is a significant gap in formal AI training and literacy among educators. Positive correlations were found between AI literacy, AI integration, and critical thinking development, suggesting that as AI literacy increases, AI integration and enhancement of critical thinking skills also increase. Regression analysis identified AI integration as a significant predictor of critical thinking development. Challenges remain in the effective implementation of AI, including concerns about overreliance on AI-generated responses and the need for clear assessment guidelines. Interestingly, years of teaching experience did not significantly influence participants’ AI literacy, perceptions, or integration. This study highlights the importance of developing comprehensive AI literacy programs for educators and integrating AI into curriculum structures to balance AI-enhanced learning with human-centered pedagogy. These findings emphasize the need for thoughtful implementation and ongoing research to effectively leverage AI in promoting critical thinking skills in science education.

Methods for incorporating digital controllers in power system dynamic simulations

Background: Artificial intelligence (AI) is increasingly embedded in routine primary care, yet how the levels of integration might affect its acceptability is unknown, especially in relationship-based service models where patients expect visible human stewardship. Prior experimental studies often treat AI adoption as a binary condition, leaving the "intensity gradient" of automation and the role of model specialization under-explored. We examine whether increasing AI integration in the clinical encounter erodes patients' intention to seek care from family doctors in China, and whether labeling the AI as a medical-specific model buffers such erosion. Methods: We conducted a nationwide online survey experiment in China (N = 2790). Participants were randomly assigned to vignettes that varied by (i) the level of AI integration (low, medium, high) and (ii) the AI type (general-purpose vs. medical-specific large language model), with a human-only care scenario as a reference. Care-seeking intention from family doctors was assessed immediately after exposure. We estimated treatment effects using OLS regression with heteroskedasticity-robust standard errors, and examined the buffering hypothesis through an interaction term between AI integration intensity and AI type. Results: Care-seeking intention declined steadily as AI integration increased (p < 0.001), with the sharpest drop under high-intensity AI integration where clinical decisions were delegated to the AI system. Across all intensity levels, framing the system as a medical-specific AI consistently resulted in higher care-seeking intention than a general-purpose model. However, the interaction between AI intensity and the AI type was not statistically significant (p = 0.508). Conclusions: Patient acceptance of AI in primary care depends not only on whether AI is involved, but on how deeply AI is positioned in the encounter. Medical-specific AI labeling may enhance acceptance across all AI integration levels. The findings underscore the need to preserve human clinical agency in AI-embedded primary care. The results contribute to research on healthcare systems, digital health, and AI-patient interaction.

Integrating blockchain technology with artificial intelligence (AI) i.e., blockchain Intelligence makes an extremely powerful tool that solves many multidimensional problems in several domains. Blockchain technology has the potential to provide links to shared data, transactions, and records in a decentralized, safe, and reliable manner, including the information and decision-making capability of AI which makes machines similar as capable as humans. This study is intended to present an updated systematic review of the integration of Blockchain and AI in various application areas. We have studied and summarized more than 100 research papers to present an updated version of the review. We also discuss the future of Blockchain technologies with AI. By integrating these two technologies results increases the security, efficiency, and productivity of the applications. Past works feature a few possible advantages of integration of Blockchain and AI, yet just give a restricted hypothetical system to depict forthcoming certifiable combination instances of the two advances. We survey and orchestrate surviving exploration on the integration of AI and Blockchain are other ways around to thoroughly build up a future research plan on the fusion of the two innovations. We also proposed an agenda to develop a secure system of cyber threat intelligence information exchange by using features of blockchain and artificial intelligence. This paper mainly focusses on explaining how collaboration of blockchain and AI gives immense boost in latest domains like Cybersecurity, Healthcare, Supply Chain Management, Finance and Banking and Social Media Analytics.

Nonlinearity of power system is always one of the difficulties when dealing with dynamic simulation of power systems. Solving differential-algebraic equations representing power systems are difficult without losing nonlinearity, especially for large power systems. This thesis shows an alternative method to solve nonlinear dynamical power system by producing a purely differential representation of the power systems. This new representation converts the algebraic equations to differential equations in order to have an absolute differential system. By using Runge-Kutta algorithm to solve this differential system, the results of the power system simulations are compared to trapezoidal integration algorithm commonly used to solve the differential-algebraic equations. In this thesis, IEEE 14-bus system and IEEE 118-bus system are tested with both classical generator model generator model and two-axis generator model in MATLAB. The proposed algorithm shows significantly faster convergence comparted to trapezoidal integration method in larger power systems. It is a great improvement to shorten the simulation time in while keeping the same accuracy in large power systems.

This study investigates the influence of artificial intelligence (AI) integration on teachers’ professional identity and job satisfaction using a quantitative research design involving 251 respondents. Descriptive statistics showed relatively high levels of AI integration (M = 3.98, SD = 0.62) and professional identity (M = 4.12, SD = 0.58), indicating strong engagement with AI tools and a well-defined sense of professional role among teachers. Pearson correlation analysis revealed a moderately strong, statistically significant positive relationship between AI integration and professional identity (r = 0.612, p = 0.000), demonstrating that increased use of AI is associated with a strengthened professional identity. Mediation analysis further indicated that institutional factors significantly influence the relationship between AI integration and job satisfaction, with AI integration positively predicting institutional support (β = 0.54, p = 0.000) and institutional factors strongly predicting job satisfaction (β = 0.47, p = 0.000). Both a significant direct effect (β = 0.29, p = 0.001) and a strong indirect effect (β = 0.25, p = 0.000) were found, confirming partial mediation. These findings highlight that AI not only enhances teachers’ identity and satisfaction but that successful implementation relies heavily on institutional readiness and support. Overall, the results underscore the importance of adopting teacher-centered AI strategies that reinforce professional identity, reduce workload, and enhance well-being.

IntroductionRadiographer reporting is accepted practice in the UK. With a national shortage of radiographers and radiologists, artificial intelligence (AI) support in reporting may help minimise the backlog of unreported images. Modern AI is not well understood by human end-users. This may have ethical implications and impact human trust in these systems, due to over- and under-reliance. This study investigates the perceptions of reporting radiographers about AI, gathers information to explain how they may interact with AI in future and identifies features perceived as necessary for appropriate trust in these systems. MethodsA Qualtrics® survey was designed and piloted by a team of UK AI expert radiographers. This paper reports the third part of the survey, open to reporting radiographers only. Results86 responses were received. Respondents were confident in how an AI reached its decision (n = 53, 62%). Less than a third of respondents would be confident communicating the AI decision to stakeholders. Affirmation from AI would improve confidence (n = 49, 57%) and disagreement would make respondents seek a second opinion (n = 60, 70%). There is a moderate trust level in AI for image interpretation. System performance data and AI visual explanations would increase trust. ConclusionsResponses indicate that AI will have a strong impact on reporting radiographers’ decision making in the future. Respondents are confident in how an AI makes decisions but less confident explaining this to others. Trust levels could be improved with explainable AI solutions. Implications for practiceThis survey clarifies UK reporting radiographers’ perceptions of AI, used for image interpretation, highlighting key issues with AI integration.

Artificial Intelligence (AI) is poised to revolutionize healthcare by enhancing clinical practice, diagnostics, and patient care. Although AI offers potential benefits through data-driven insights and personalized treatments, challenges related to implementation, barriers, and ethical considerations necessitate further investigation. We conducted a cross-sectional survey using Qualtrics from October to December 2023 to evaluate U.S. rheumatology fellows' perspectives on AI in healthcare. The survey was disseminated via email to program directors, who forwarded it to their fellows. It included multiple-choice, Likert scale, and open-ended questions covering demographics, AI awareness, usage, and perceptions. Statistical analyses were performed using Spearman correlation and Chi-Square tests. The study received IRB approval and adhered to STROBE guidelines. The survey aimed to reach 528 U.S. rheumatology fellows. 95 fellows accessed the survey with response rate to each question varying between 85 and 95 participants. 57.6% were females, 66.3% aged 30-35, and 60.2% in their first fellowship year. There was a positive correlation between AI familiarity and confidence (Spearman's rho = 0.216, p = 0.044). Furthermore, 67.9% supported incorporating AI education into fellowship programs, with a significant relationship (p < 0.005) between AI confidence and support for AI education. Fellows recognized AI's benefits in reducing chart time (86.05%) and automating tasks (73.26%), but expressed concerns about charting errors (67.86%) and over-reliance (61.90%). Most (84.52%) disagreed with the notion of AI replacing them. Rheumatology fellows exhibit enthusiasm for AI integration yet have reservations about its implementation and ethical implications. Addressing these challenges through collaborative efforts can ensure responsible AI integration, prioritizing patient safety and ethical standards in rheumatology and beyond.

The increasing capacities of wind power bring influence on power system stability because wind power can produce stochastic behaviors due to special fluctuant characteristics of natural wind. The dynamic simulation method and software considering effect of wind power mainly concentrated on electromagnetic transient and electromechanic transient process, whereas the analysis for the long term dynamic simulation for power system including wind farms is relatively scarce at present. This paper adopts mathematic dynamic model of induction wind generating set and double-fed induction generators respectively, and makes use of dynamic simulation method based on variables grouping and seceding grouping strategy, realizes long term dynamic simulation for power system including wind farms under different wind condition and operating mode based on a modified IEEE-30 bus network. The numerical example shows that dynamic simulation method being adopted is suitable for long term dynamic simulation of power system including wind farms, and also shows that doubly fed induction wind turbines have better stability characteristic than asynchronous induction wind turbines and can improve system stability characteristic.

A national survey on how to improve the integration of traditional Chinese medicine and artificial intelligence: Attitudes and perceptions from medical staff.

The rise of artificial intelligence (AI) heralds a significant revolution in healthcare, particularly in mental health.AI's potential spans diagnostic algorithms, data analysis from diverse sources, and real-time patient monitoring. It is essential for clinicians to remain informed about AI's progress and limitations. The inherent complexity of mental disorders, limited objective data, and retrospective studies pose challenges to the application of AI. Privacy concerns, bias, and the risk of AI replacing human care also loom. Regulatory oversight and physician involvement are needed for equitable AI implementation. AI integration and use in psychotherapy and other services are on the horizon. Patient trust, feasibility, clinical efficacy, and clinician acceptance are prerequisites. In the future, governing bodies must decide on AI ownership, governance, and integration approaches. While AI can enhance clinical decision-making and efficiency, it might also exacerbate moral dilemmas, autonomy loss, and issues regarding the scope of practice. Striking a balance between AI's strengths and limitations involves utilizing AI as a validated clinical supplement under medical supervision, necessitating active clinician involvement in AI research, ethics, and regulation. AI's trajectory must align with optimizing mental health treatment and upholding compassionate care.

Academic Summary Firms face intensifying pressure to innovate amid escalating US–China trade tensions. This study investigates how they integrate artificial intelligence (AI) to respond to such disruptions. We reconceptualize induced innovation theory as adversity‐induced innovation theory and test hypotheses using data from listed Chinese manufacturing firms, WTO tariff records, and customs data (2015–2022). Results indicate that higher tariff exposure increases AI integration, particularly among firms with stronger customer engagement needs. AI integration improves supply chain efficiency, sales performance, and market value. However, performance benefits follow an inverted U‐shape: extreme tariff exposure diminishes AI's compensatory effects. The study extends induced innovation theory to geopolitical adversities and advances stakeholder engagement theory by identifying customer engagement as a boundary condition for technology adoption. We establish causal relationships between tariff‐induced AI integration and firm performance through instrumental variable analysis and robustness checks. Findings inform how firms can strategically use technological innovation and customer collaboration to strengthen resilience under external shocks. Managers should view trade tensions as catalysts for AI integration rather than merely as obstacles. Managerial Summary Firms facing trade tensions should view them as catalysts for strategic AI integration rather than merely as obstacles. Our research shows that firms experiencing higher tariff exposure that invest in AI achieve measurable improvements in supply chain efficiency, sales performance, and market valuations. However, AI's compensatory benefits diminish at extreme tariff levels, revealing an optimal investment threshold that managers should consider. The benefits of AI integration are particularly strong for firms with high customer engagement needs, such as those with concentrated customer bases, substantial international sales, or significant customer‐specific investments. To maximize returns, managers should prioritize AI technologies that strengthen customer relationships by enhancing communication, responsiveness, and service personalization during trade disruptions. Successful implementation requires navigating technical and organizational challenges, including talent acquisition, data quality concerns, and integration complexities. Despite these hurdles, our findings confirm AI's strategic value as a compensatory innovation tool that not only mitigates tariff impacts but also transforms supplier‐customer relationships. By aligning AI initiatives with customer engagement objectives, firms can turn geopolitical adversity into a competitive advantage. Companies that integrate AI strategically can maintain operational continuity, preserve market position, and emerge stronger from trade disruptions.

Artificial intelligence (AI) is transforming the education sector of different nations of the world. In spite of its commonly attested huge prospects, AI is yet to be integrated into Nigerian educational systems at a significant extent. This study examines the prospects and challenges of AI integration into Nigerian educational systems. The study adopts Venkatesh’s Unified Theory of Acceptance and Use of Technology, which explains how technologies get accepted and used in society. Questionnaire, and library and internet materials are used to source data. Survey design and mixed method with their allied analytical tools are employed. The results show pedagogic, technological, informative, administrative and socio-economic benefits of AI integration into Nigerian educational systems. Besides, it identifies the challenges constraining AI integration into the sector and proffers some solutions. The study concludes that the current low level of AI awareness and integration in Nigeria limits the ideal extent of AI impact on Nigerian educational systems. Stakeholders should collaborate significantly in making efforts to increase AI integration and find lasting solutions to the problems of AI integration into the educational systems.

With the rapid development of artificial intelligence (AI) technology, its application in the field of art design is expanding increasingly. The cross and integration of AI and art design have become the core driving force for promoting the reform of art design teaching and an inevitable path to achieve high-quality development of the art design industry. Firstly, based on the current development status of AI in the field of art design and professional education, this paper analyzes the transformation opportunities brought by AI to art design, including creativity generation, image recognition, style transfer, and design efficiency improvement. Secondly, it explores the paths for AIdriven reform of art design teaching from the dimensions of building an AI intelligent design support system, innovating teaching content, applying teaching methods, promoting interdisciplinary cooperation, and constructing teaching platforms. Finally, combined with the transformation trend of art design teaching, it puts forward countermeasures for reflections triggered by the integration of AI into teaching practice (such as copyright protection, avoiding technical dependence, and making up for emotional loss), thereby promoting the cross and integration of AI and art design teaching, providing practical methods for talent training of art design majors in various universities, and helping the art design industry achieve sustainable innovative development in the digital era.

BackgroundThe rapid trajectory of artificial intelligence (AI) development and advancement is quickly outpacing society's ability to determine its future role. As AI continues to transform various aspects of our lives, one critical question arises for medical education: what will be the nature of education, teaching, and learning in a future world where the acquisition, retention, and application of knowledge in the traditional sense are fundamentally altered by AI?ObjectiveThe purpose of this perspective is to plan for the intersection of health care and medical education in the future.MethodsWe used GPT-4 and scenario-based strategic planning techniques to craft 4 hypothetical future worlds influenced by AI's integration into health care and medical education. This method, used by organizations such as Shell and the Accreditation Council for Graduate Medical Education, assesses readiness for alternative futures and effectively manages uncertainty, risk, and opportunity. The detailed scenarios provide insights into potential environments the medical profession may face and lay the foundation for hypothesis generation and idea-building regarding responsible AI implementation.ResultsThe following 4 worlds were created using OpenAI’s GPT model: AI Harmony, AI conflict, The world of Ecological Balance, and Existential Risk. Risks include disinformation and misinformation, loss of privacy, widening inequity, erosion of human autonomy, and ethical dilemmas. Benefits involve improved efficiency, personalized interventions, enhanced collaboration, early detection, and accelerated research.ConclusionsTo ensure responsible AI use, the authors suggest focusing on 3 key areas: developing a robust ethical framework, fostering interdisciplinary collaboration, and investing in education and training. A strong ethical framework emphasizes patient safety, privacy, and autonomy while promoting equity and inclusivity. Interdisciplinary collaboration encourages cooperation among various experts in developing and implementing AI technologies, ensuring that they address the complex needs and challenges in health care and medical education. Investing in education and training prepares professionals and trainees with necessary skills and knowledge to effectively use and critically evaluate AI technologies. The integration of AI in health care and medical education presents a critical juncture between transformative advancements and significant risks. By working together to address both immediate and long-term risks and consequences, we can ensure that AI integration leads to a more equitable, sustainable, and prosperous future for both health care and medical education. As we engage with AI technologies, our collective actions will ultimately determine the state of the future of health care and medical education to harness AI's power while ensuring the safety and well-being of humanity.