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Article Citation - WoS: 11Citation - Scopus: 13Choice Functions for Autonomous Search in Constraint Programming: Ga Vs. Pso(Univ Osijek, Tech Fac, 2013) Soto, Ricardo; Crawford, Broderick; Misra, Sanjay; Palma, Wenceslao; Monfroy, Eric; Castro, Carlos; Paredes, Fernando; Computer EngineeringThe variable and value ordering heuristics are a key element in Constraint Programming. Known together as the enumeration strategy they may have important consequences on the solving process. However, a suitable selection of heuristics is quite hard as their behaviour is complicated to predict. Autonomous search has been recently proposed to handle this concern. The idea is to dynamically replace strategies that exhibit poor performances by more promising ones during the solving process. This replacement is carried out by a choice function, which evaluates a given strategy in a given amount of time via quality indicators. An important phase of this process is performed by an optimizer, which aims at finely tuning the choice function in order to guarantee a precise evaluation of strategies. In this paper we evaluate the performance of two powerful choice functions: the first one supported by a genetic algorithm and the second one by a particle swarm optimizer. We present interesting results and we demonstrate the feasibility of using those optimization techniques for Autonomous Search in a Constraint Programming context.Conference Object AI Trustworthiness and Student Pilots: Exploring Attitudes, Anxieties, and Adaptation Performance(Elsevier B.V., 2025) Ceken, S.; Yilmaz, A.A.; Acar, A.B.This research explores the attitudes of student pilots toward artificial intelligence (AI) applications within the aviation sector, with a focus on their adaptation processes and potential challenges. The recent release of the "EASA AI Roadmap 2.0"by the European Union Aviation Safety Agency (EASA) underscores the growing impact of AI on aviation, driving the emergence of new business models and emphasizing a human-centric approach to AI integration within the aviation industry. This study addresses a significant gap in the literature by examining student pilots' perspectives on AI, specifically focusing on AI trustworthiness, attitudes, anxieties, and adaptation performance. The study utilizes a quantitative research approach, collecting data from 150 student pilots through surveys. Preliminary results from 106 respondents indicate varied attitudes toward AI, with significant implications for AI-supported cockpit assistant systems and the broader aviation industry. The study sample consisted of 106 (Mage = 23.6, SDage= 4.64; 79% male) student pilots from of university pilot training departments and various flight school in Turkey. Collected data were analyzed on SPSS 29. The study revealed that Sociotechnical Blindness AI anxiety is a significant predictor of general attitudes toward AI among student pilots. This finding suggests that higher levels of anxiety related to the perceived complexity and potential unintended consequences of AI are associated with more positive general attitudes toward AI. The findings emphasize the need for a human-centric approach to AI integration, highlighting the importance of trust, transparency, and adaptive training in the successful adoption of AI technologies in aviation. © 2024 The Authors. Published by ELSEVIER B.V.Article An Investigation Into The AI-Assisted Visualization Of Children’s Songs: The Case Of Ali Baba’s Farm(Nilgun SAZAK, 2025) Südor, S.; İpekçiler, B.This study aims to visualize children’s songs, which are part of primary-level music education, using AI-supported tools. The objectives of the Ministry of National Education’s music course curriculum were examined, and both the themes to be emphasized in song selection and the pedagogical functions of children’s songs were analyzed. In the literature review, the Web of Science and Google Scholar databases were used. The obtained source data were analyzed with the VOSviewer software to generate conceptual maps, through which thematic trends in the field were identified. In the practical part of the study, the children’s song “Old MacDonald’s Farm” was visualized in detail using two different AI-supported tools: RunwayML and WZRD.ai. In RunwayML, prompt-based scenes were generated using the “text-to-video” feature, and visuals compatible with the lyrics of the song were created. On the WZRD.ai platform, visuals were automatically generated in response to sound waves, and the limitations of the platform were examined. Based on the findings, it was concluded that RunwayML offers more effective results for pedagogical content production, while WZRD. ai, despite its technical capabilities, falls short in delivering child-appropriate visual stimuli. The study also provides a theoretical foundation on synesthesia and discusses how AI tools can be integrated into music education in early childhood and primary school levels. The findings indicate that AI-supported visualization tools have the potential to provide engaging and flexible educational materials that support learning at the primary school level. It is recommended that teacher training programs develop hands-on modules for these tools, and that future research focus on how these technologies can be adapted to various songs, age groups, and learning domains. © © 2025 The Author(s). This is an open access article distributed under the Creative Commons Attribution License (CC BY), which permits unrestricted use, distribution, and reproduction in any medium or format, provided the original work is properly cited.Review Citation - WoS: 5Citation - Scopus: 8Monkeypox: a Comprehensive Review of Virology, Epidemiology, Transmission, Diagnosis, Prevention, Treatment, and Artificial Intelligence Applications(Shaheed Beheshti University of Medical Sciences and Health Services, 2024) Rahmani,E.; Bayat,Z.; Farrokhi,M.; Karimian,S.; Zahedpasha,R.; Sabzehie,H.; Farrokhi,M.Monkeypox (Mpox), an uncommon zoonotic Orthopoxvirus, is commonly manifested by blisters on the skin and has a mortality rate of approximately 0-10%. Approximately two decades after the cessation of global smallpox vaccination, the number of confirmed cases of Mpox has been growing, making it the most common Orthopoxvirus infection. Therefore, in this narrative review, we aimed to shed light on recent advancements in the pathophysiology, transmission routes, epidemiology, manifestations, diagnosis, prevention, and treatment of Mpox, as well as the application of artificial intelligence (AI) methods for predicting this disease. The clinical manifestations of Mpox, including the onset of symptoms and dermatologic characteristics, are similar to those of the infamous smallpox, but Mpox is clinically milder. Notably, a key difference between smallpox and Mpox is the high prevalence of lymphadenopathy. Human-to-human, animal-to-human, and animal-to-animal transmission are the three main pathways of Mpox spread that must be considered for effective prevention, particularly during outbreaks. PCR testing, as the preferred method for diagnosing Mpox infection, can enhance early detection of new cases and thereby improve infection control measures. JYNNEOS and ACAM2000 are among the vaccines most commonly recommended for the prevention of Mpox. Brincidofovir, Cidofovir, and Tecovirimat are the primary treatments for Mpox cases. Similar to other viral infections, the best approach to managing Mpox is prevention. This can, in part, be achieved through measures such as reducing contact with individuals displaying symptoms, maintaining personal safety, and adhering to practices commonly used to prevent sexually transmitted infections. © This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0).Article Factors Affecting Dentists' Intention To Adopt Artificial Intelligence: An Extension of the Unified Theory of Acceptance and Use of Technology (UTAUT) Model(Emerald Group Publishing Ltd, 2025) Alqaifi, Faten; Tengilimoglu, DilaverPurposeAdvancements in science and technology have integrated artificial intelligence (AI) into dentistry, improving treatment processes, operational efficiency, and clinical outcomes. However, AI adoption among dentists remains underexplored, hindering progress in oral healthcare. This study aims to identify key barriers to AI adoption and examine factors influencing dentists' intention to use AI.Design/methodology/approachA quantitative cross-sectional approach was employed, utilizing self-administered questionnaires distributed online and across various dental clinics and hospitals in Ankara, Turkey. A total of 440 dentists participated in the study. Data analysis was conducted using SPSS and SmartPLS.FindingsThe study found that AI-anxiety negatively affects the intention to adopt AI in dentistry, showing a medium (almost large) effect that is stronger than other UTAUT factors such as performance expectancy, effort expectancy, and social influence, which demonstrated only small effects. Dentists with higher anxiety about learning and sociotechnical blindness are less likely to adopt AI, while concerns about job replacement and AI-configuration have less but still significant impact.Research limitations/implicationsThese results contribute to the growing body of knowledge on technology adoption in oral healthcare and provide practical implications for technology developers, policymakers, and other stakeholders seeking to facilitate AI integration in dentistry.Originality/valueThis study provides novel insights into AI adoption in dentistry, offering guidance for future development and integration, and addressing a critical research gap in a growing field-particularly in Turkey, where implementation is still in its early stages.
