Role of Artificial Intelligence in Large Wind Turbine Designs

Design of wind turbines with shroud and lobed ejectors for efficient utilization of low-grade wind energy

The role of Artificial Intelligence (AI) and Generative Artificial Intelligence (Gen AI) in digital healthcare

PurposeThis study aims to investigate the significance of an emerging concept – green talent management (TM) and its influence on employees’ innovative work behavior, together with the moderating roles of transformational leadership and artificial intelligence within the context of higher educational institutions.Design/methodology/approachTwo hundred and thirty-five structured questionnaires were administered to the academic staff in five universities located in Northern Cyprus, and the data was analyzed using partial least square structural equation modeling with the aid of WarpPLS (7.0).FindingsThis study provides evidences that green hard and soft TM exerts significant influence on employees’ innovative work behavior. Similarly, transformational leadership and artificial intelligence were confirmed to have a significant impact on employees’ innovative work behavior. Moreover, the study found transformational leadership and artificial intelligence to significantly moderate the relationship between green hard TM and employees’ innovative work behavior.Research limitations/implicationsThe study provides theoretical and managerial implications of findings that will assist the leaders in higher educational institutions in harnessing the potential of green TM in driving their employees’ innovative work behavior toward the achievement of sustainable competitive advantage in the market where they operate.Originality/valueThe attention of researchers in the recent time has been on the way to address the challenge facing organizational leaders on how to develop and retain employee that will contribute to the sustainability of their organization toward the achievement of sustainable competitive advantage in the market they operate. Meanwhile, the studies exploring these concerns are limited. In view of this, this study investigates the significance of an emerging concept – green talent management and its influence on employees’ innovative work behavior, together with the moderating roles of transformational leadership and artificial intelligence within the context of higher educational institutions.

This review aims to delve into the role of artificial intelligence in medicine. Ulcerative colitis (UC) is a chronic, inflammatory bowel disease (IBD) characterized by superficial mucosal inflammation, rectal bleeding, diarrhoea and abdominal pain. By identifying the challenges inherent in UC diagnosis, we seek to highlight the potential impact of artificial intelligence on enhancing both diagnosis and treatment methodologies for this condition. A targeted, non-systematic review of literature relating to ulcerative colitis was undertaken. The PubMed and Scopus databases were searched to categorize a well-rounded understanding of the field of artificial intelligence and its developing role in the diagnosis and treatment of ulcerative colitis. Articles that were thought to be relevant were included. This paper only included articles published in English. Artificial intelligence (AI) refers to computer algorithms capable of learning, problem solving and decision-making. Throughout our review, we highlighted the role and importance of artificial intelligence in modern medicine, emphasizing its role in diagnosis through AI-assisted endoscopies and histology analysis and its enhancements in the treatment of ulcerative colitis. Despite these advances, AI is still hindered due to its current lack of adaptability to real-world scenarios and its difficulty in widespread data availability, which hinders the growth of AI-led data analysis. When considering the potential of artificial intelligence, its ability to enhance patient care from a diagnostic and therapeutic perspective shows signs of promise. For the true utilization of artificial intelligence, some roadblocks must be addressed. The datasets available to AI may not truly reflect the real-world, which would prevent its impact in all clinical scenarios when dealing with a spectrum of patients with different backgrounds and presenting factors. Considering this, the shift in medical diagnostics and therapeutics is coinciding with evolving technology. With a continuous advancement in artificial intelligence programming and a perpetual surge in patient datasets, these networks can be further enhanced and supplemented with a greater cohort, enabling better outcomes and prediction models for the future of modern medicine.

This study investigates the relationship between technology quality and consumer behavior in the field of virtual reality tourism. An online survey was conducted with 403 customers in Vietnam, spanning various age groups, who have a passion for travel and have used virtual reality (VR) and augmented reality (AR) technologies in tourism. The research findings indicate that technology quality, including information quality, system quality, and usefulness, positively influences users' perceived value of VR/AR quality. This perceived VR/AR value, in turn, has a positive impact on travel intention. However, privacy concerns were found to negatively affect users' perceived value of VR/AR. The study also examines the role of Explainable Artificial Intelligence (XAI) in positively moderating the relationship between privacy concerns and perceived VR/AR value. An interesting discovery is that XAI helps alleviate users' privacy concerns when experiencing virtual tourism technologies, addressing a limitation that conventional AI has not yet resolved. Several data analysis and validation methods were applied, including reliability testing of the measurement scale (Cronbach's alpha), Exploratory Factor Analysis (EFA), and Confirmatory Factor Analysis (CFA). Hypothesis testing and model evaluation were conducted using Structural Equation Modeling (SEM). Additionally, data analysis was supported by SPSS 26 and AMOS 28 software. The results of the study provide critical insights for technology manufacturers to enhance product quality, enabling users to optimize their virtual tourism experiences. Furthermore, the findings serve as a foundation for businesses leveraging smart tourism technologies to design and implement more effective VR/AR-based tourism products. By doing so, they can attract more tourists to real-world destinations, strengthen their competitive advantage, and promote the sustainable development of the tourism industry. This study highlights the importance of integrating advanced technologies like XAI to address user concerns and improve the adoption and satisfaction of virtual tourism solutions, ultimately contributing to the growth and sustainability of the tourism sector.

Utilization of wind energy in urban areas — Chance or utopian dream?

Wind energy is an important strategic energy over the world. Wind power is a useful form of energy conversed from wind energy. The main conversion of wind energy is using wind turbines to make electricity. Generally, studies focus on the specific technology of wind energy. There is, however, little qualitative information on status of wind energy utilization and development. This research tended to focus on systematic analysis of global utilization and development of wind energy, rather than on particular technology. The results shown that from 1997 to 2010, the global cumulative installed wind turbine capacity is exponential growth with years. The top eleven wind energy utilization countries account for 87.7% of total. The global utilization of wind energy increased 24.6% in 2010. The highest increasing country is Romania with an increasing rate of 264.3%. The cumulative installed wind turbine capacity has increased by 73.2% in China, which is the largest cumulative installed wind turbine capacity country. US increased by 14.5% in 2010 as the second cumulative installed wind turbine capacity country.

Review of Wind Energy Utilization in South Asia

Artificial Intelligence (AI) has found a lot of scope in diversified applications including health care systems. Due to the rapid increase in digitization and change in the life style lot of people are facing health care issues like mental diseases. Now the days AI is use to help health care members with its analysis like tumor, cyst, cancer, dermatology issues etc. Looking towards the increasing cases there is a urgent demand of AI in medical specially in mental health care.Many electronic systems are used for the health data analysis so the combination of AI within system can help the patients. Due to the pandemic there is increase in health issues and it has pushed the limits for increase in need of mental health care system using AI. Since AI can provide services like personalize care, remote access, guiding patient, online doctor’s advice etc. AI can be used to identify the individual with high risk also it can provide intervention to treat and prevent mental illness. This work presents the comparison and role of different AI based mental healthcare analysis. As AI using electronic health record, brain imaging and other sensing system can predict the issues in individual and help to monitor patient’s progress and helps the doctor to alter treatment if needed and can help in decrease in suicidal issues. Apart from indentifying the particular issue in patient AI can help the patient to assign the right therapist as per his/her problem. Thus the patient is been given with right therapy at right time. It can also, guide the care taker to give medicine at given time. Natural language processing and Machine learning can be used to find the problem in individual along with its social media presence can be an effective tool to identify once mental health. This information can assist the healthcare practitioner to identify particular problem and guide for treatment. There is also a limitation for collecting data and training the AI based system which is discussed in this work. Along with that the technology limitation and challenges are well described.

At present, coal is the most important resource for utilization and development in China, but the environmental pollution caused by coal is serious. It is urgent to improve the development of clean energy. China is gradually promoting wind energy, the development and utilization of wind energy is difficult. The difficulty of storing wind energy and its instability limit the country’s spread. Wind energy can only be developed better by solving the current problems. The research on the development and utilization of wind energy also becomes the key to solve the problem.

Purpose: The growing sophistication of financial fraud in the banking sector has necessitated the adoption of advanced technical solutions such as artificial intelligence (AI) and robotic process automation (RPA) to enhance fraud detection and prevention. This study examines the role, effectiveness, and challenges of AI and RPA in combating financial fraud, addressing gaps left by traditional rule-based systems. Methodology: This study employs a literature review methodology, synthesizing existing research, case studies, and industry reports to evaluate the impact of AI and RPA on fraud detection. Key themes analyzed include real-time analytics, anomaly detection, predictive modeling, operational efficiency, and implementation challenges. Findings: The findings reveal that AI significantly improves fraud detection accuracy, reduces false positives, and adapts to emerging threats, while RPA enhances compliance and operational efficiency by automating repetitive tasks. However, challenges such as algorithmic bias, adversarial AI attacks, data privacy concerns, high implementation costs, and ethical dilemmas around transparency and accountability hinder widespread adoption. Despite these obstacles, financial institutions report substantial reductions in fraud-related losses after integrating AI and RPA. Unique contribution to theory, practice and policy (recommendations): This study contributes to theory by consolidating insights on AI and RPA’s transformative potential in fraud detection. For practice, it recommends investing in explainable AI, robust adversarial defense mechanisms, and cost-effective RPA integration. Policymakers should establish ethical AI governance frameworks, promote regulatory alignment, and incentivize innovation to ensure financial security and transparency. The study underscores that maximizing the benefits of AI and RPA requires continuous technological advancement, ethical oversight, and collaborative regulatory efforts.

Artificial Intelligence (AI) has an important role to play in shaping the future of software development. AI responds to complex challenges in the information technology industry and expands the scope of future possibilities, which include increased automation, personalization, and security. The research aims to identify the role of AI in education and research from various aspects of software development, and evaluate the resulting implications for information technology as a whole. The research adopted the Systematic Literature Review Method following PRISMA guidelines. A total of 320 articles were collected from Scopus, Web of Science and Google Scholar and applying predefined criteria, 42 relevant articles were included for analysis. The research findings show that the role and integration of artificial intelligence (AI) has a significant impact in improving efficiency, bringing software innovation in education, learning and research in the future. AI has proven effective in personalizing learning, adapting teaching materials and improving student learning outcomes. AI accelerates the process of analyzing big data, identifying patterns and trends that conventional methods may miss. The implications of the findings suggest that the integration of AI in education and research not only improves the efficiency and effectiveness of the process, but opens up new opportunities for innovation and development of more adaptive and data-driven learning and research methods. The challenges of AI in education and research include data privacy, potential bias in algorithms, and the need for adequate technological infrastructure to support effective and secure implementation, avoid inequality of access, and ensure accurate results.

The development and utilization of wind energy is of great significance to the sustainable development of China’s economy and the realization of the “dual carbon” goal. Under typhoon conditions, the randomness and volatility of wind speed significantly impact the energy efficiency and design of wind turbines. This paper analyzed the changes in wind speed and direction using the BFAST method and Hurst index based on data collected at 10 m, 30 m, 50 m, and 70 m heights from a wind power tower in Yancheng, Jiangsu Province. Furthermore, the paper examined the causes of wind speed and direction changes using wind speed near the typhoon center, distance from the typhoon center to the wind tower, topographic data, and mesoscale system wind direction data. The conclusions drawn are as follows: (i) Using the BEAST method, change points were identified at 10 m, 30 m, 50 m, and 70 m heights, with 5, 5, 6, and 6 change points respectively. The change points at 10 m, 30 m, and 50 m occurred around node 325, while the change time at 70 m was inconsistent with other heights. Hurst index results indicated stronger inconsistency at 70 m altitude compared to other altitudes. (ii) By analyzing the wind direction sequence at 10 m, 30 m, 50 m, and 70 m, it was found that the wind direction changes follow the sequence Southeast (SE)—East (E)—Southeast (SE)—Southwest (SW)—West (W)—Northwest (NW). Notably, the trend of wind direction at 70 m significantly differed from other altitudes during the wind speed strengthening and weakening stages. (iii) Wind speed at 10 m and 70 m altitudes responded differently to the distance from the typhoon center and the wind near the typhoon center. The correlation between wind speed and the distance to the typhoon center was stronger at 10 m than at 70 m. The surface type and the mesoscale system’s wind direction also influenced the wind speed and direction. This study provides methods and theoretical support for analyzing short-term wind speed changes during typhoons, offering reliable support for selecting wind power forecast indicators and designing wind turbines under extreme gale weather conditions.

The ultimate goal of distributed renewable energy control is to minimize disorder disturbance to the power grid while ensuring maximum energy utilization. Taking wind power as an example, a coupling system using flywheel energy storage system to balance the output variables of wind power system was proposed while using hill climb searching (HCS) to maximize the utilization of wind energy. These methods can obtain stable DC output voltage. In the case of wind speed variation, as a limited capacity energy pool structure, flywheel energy storage system can ensure the maximum utilization of wind energy. Based on fully utilization of wind energy resources, the rapid and comprehensive compensation of wind power output can effectively smooth the output voltage of grid-connected wind power system, thus achieving the purpose of improving the power quality of grid-connected wind power system.

Based on a literature review of the development of new energy, especially the status quo in the development and use of wind energy, this paper analyzed the main factors affecting the development and utilization of wind energy in Jilin Province, China, including law and policy, as well as environmental, economic, social, and industrial factors. The industrial factor mainly involved wind power industry infrastructure, the R&D level of the wind power industry, wind farm construction, and the wind power market. A questionnaire was designed to interview experts and scholars in the field with the help of the Sedentary Time and Activity Reporting Questionnaire network platform. It contained 10 latent variables and 34 measurable indexes and used a Likert-type scale. In total, 368 questionnaires were collected, including 268 valid ones and 100 invalid ones determined by automatic and artificial screening. The AMOS software was used to examine the data and build the structural equation. As a result, the wind power market was found to be the main factor affecting the development level of wind energy in Jilin Province, followed by the economic factor, law and policy, and the environmental factor. It was also found that the main factor hindering the utilization level of wind energy in Jilin Province was infrastructure, which restricted the role of wind energy in promoting economic growth in the province. Therefore, the main problems that urgently need to be solved include speeding up the power grid along with other infrastructure construction.