LBMS-SAM: Segment anything model guided SEM image segmentation for lithium battery materials.

Flavor perception in the oral processing of mixed grain foods: Flavor release and AI.

Water knowledge, understanding the current and future availability and needs of water, has been critical in negotiating international water disputes. Drawing from expert interviews, this article examines how Artificial Intelligence (AI) tools influence knowledge production and exchange in water diplomacy. The findings suggest that technical strides from AI technologies can enhance data and information objectivity and social learning, potentially benefiting water negotiations and consensus building. However, without addressing political and human challenges, AI tools canexacerbate the risk of eroding trust and spreading dis- and mis-information about politically sensitive water issues. The malicious use of AI poses a serious risk, as negotiators may face increased pressure from public opinion, potentially undermining cooperative progress and escalating tensions over water.

Background: Amblyopia (lazy eye) is the leading cause of preventable monocular vision loss in children worldwide. In low-resource regions, early detection and ongoing treatment are hindered by a shortage of trained specialists and inadequate healthcare infrastructure. Methods: We developed BRIGHTEYE, a mobile health platform designed to provide accessible screening, monitoring, and gamified therapy for pediatric amblyopia. The platform integrates a lightweight artificial intelligence (AI) engine, a secure blockchain-inspired data management framework, and telemedicine features, all optimized for low-end smartphones and limited-bandwidth environments. The AI component employs a compact convolutional neural network (CNN) architecture trained on 1,200 annotated ocular images and evaluated on a held-out test set. Model quantization and pruning were implemented to ensure efficient inference on mobile devices. Results: The BRIGHTEYE AI engine achieved an accuracy of 91.2%, sensitivity of 87.5%, and specificity of 96.3%, with an Area Under the Curve (AUC) of 0.94, indicating strong discriminative capability. Inference times averaged under one second on entry-level Android devices, demonstrating the model’s suitability for deployment in low-resource environments. Conclusion: The BRIGHTEYE platform offers a technically feasible and robust approach to pediatric amblyopia management. Its capacity to deliver accurate screening and continuous therapy monitoring through affordable devices provides a promising foundation for clinical validation and large-scale implementation, potentially transforming eye care accessibility in underserved communities.

Artificial Intelligence in Diagnostic Oral and Maxillofacial Imaging, Surgical Applications, and Teledentistry.

Generalized green and red concepts for artificial intelligence, big data, and information and communication technologies: Multidimensional framework and countermeasures

Adopting Artificial Intelligence (Al) Technology in Advertising from the Customer’s Perspective in Egypt

A comprehensive review of emerging protein modification methods: modified properties and potential applications.

The value of artificial intelligence combined with multimodal data analysis in tumor immunotherapy and targeted therapy.

Unlocking component-level chemical structural information for AI-driven targeted nanoparticle design.

PreLora: A fine-tuning approach with low-rank matrix decomposition and prefix tuning for pre-hospital emergency text classification.

AI Anxiety, AI Use, Motivation This study aims to analyze the effect of artificial intelligence (AI) anxiety on the use of AI technology with learning motivation as a moderating variable among Economics Education students at the Faculty of Teacher Training and Education, Sebelas Maret University. The study employed a descriptive quantitative approach with a sample of 70 students from the 2020 cohort, collected through a Likert scale questionnaire and analyzed using hierarchical regression with the assistance of SPSS 26. The results show that AI anxiety has a positive and significant effect on AI usage, indicating that the higher the level of student anxiety towards AI, the higher the intensity of AI usage as an adaptive strategy in completing academic tasks. However, learning motivation was not found to moderate the relationship between AI anxiety and AI usage, so that the effect of AI anxiety on AI usage remained consistent among students with different levels of learning motivation. These findings confirm that AI anxiety can function as a driver of adaptive behavior in the use of AI technology in higher education.

Research on Artificial Intelligence (AI) and second/foreign language (L2) education has recently become a flourishing line of thinking. However, the psycho-affective states of teachers regarding AI tools have received insufficient scholarly attention. To address this void, this cross-cultural study examined the causes and solutions of Iranian and Turkish English as a foreign language (EFL) teachers' AI adoption reluctance. A total of 40 EFL teachers participated in a semi-structured interview and composed a narrative frame. The results of inductive thematic analysis indicated that a wide range of factors had caused Iranian and Turkish EFL teachers' AI adoption reluctance. Additionally, the participants in both contexts suggested some solutions for AI adoption reluctance, which involved similarities and dissimilarities. The findings are discussed, and implications are provided for EFL teachers and educators to encourage their acceptance and adoption of AI technologies in L2 education.

Generative artificial intelligence technologies are increasingly being used in professional preparation by pre-service teachers, although there are limited empirical data on how these technologies facilitate authoring emergent pedagogical identities. This qualitative research study used the Framework Method to explore the experiences of twenty-three female pre-service teachers pursuing Initial Teacher Education programmes in Punjab, Pakistan, using generative AI in reflection, planning lessons, and writing about their professional experiences. The semi-structured interviews, which were held over Zoom, covered the repetitive and iterative nature of creating, critiquing, and revising AI-mediated pedagogical texts by the participants. Systematic cross-case analysis based on framework matrices demonstrated that there are four broad themes identified through analysis: Strategic Appropriation; Recursive Editing in Identity Formation; Conflicts Between Efficiency and Authenticity as Imperatives; and Different Patterns of Critical Engagement Influenced by previous Technological Experiences. The results show that revision patterns are reproduced, instead of offering first-time AI performances, indicating pedagogical sophistication, making generative tools a kind of mediational resource as novice teachers rehearse, challenge, and otherwise perform the construction of professional selves.

This review explores the integration of artificial intelligence (AI) in mobile health applications for diabetes care. It focuses on key AI methodologies - machine learning, deep learning, and natural language processing - and their roles in glucose monitoring, personalized self-management, risk prediction, and clinical decision support. Drawing on recent literature (2018-2024), the study outlines the benefits of AI in improving accuracy, engagement, and precision in diabetes treatment. Challenges such as data privacy, algorithmic bias, and regulatory barriers are also examined. A new section discusses when AI technologies may become burdensome, especially in low-resource settings or for users with limited digital literacy. The review concludes with directions for enhancing model explainability and integrating AI with wearable and Internet of Things devices, emphasizing the need for ethical and equitable implementation in future diabetes management strategies.

Artificial Intelligence (AI) has rapidly emerged as a silent manufacturer of collective memory, shaping what societies remember and what they are urged to forget. While extensive scholarship has examined AI’s role in producing and distributing content, far less attention has been paid to its influence on the retrieval, management, and accessibility of historical media archives. This paper pioneers a critical exploration of this under-researched dimension, positioning AI not only as a technical tool but as an invisible editor of media history. Through real-world case studies, the research highlights how AI-driven systems selectively govern visibility: social media algorithms suppressing politically sensitive footage, search engines obscuring content deemed commercially or ethically contentious, and automated tools privileging certain historical materials while erasing others. These processes, often concealed within the technical structures of digital platforms, quietly dictate what resurfaces in public interactions and what remains buried. The study further reveals how these dynamics pose unique challenges for journalists, researchers, and news organizations, where access to archives is critical for documentation, accountability, and scholarship. By interrogating the mechanisms of AI-mediated memory, the paper calls on media professionals, policymakers, and scholars to recognize the ethical, professional, and social consequences of these opaque practices. Ultimately, the findings underscore an urgent need for greater transparency and accountability in the ways AI technologies curate, conserve, or eliminate historical media content.

Cancer is one of the leading causes of death worldwide, and early tumor detection can significantly reduce mortality rates. Liquid biopsy is a minimally invasive, repeatable method with a high economic benefit ratio, and it shows excellent prospects for tumor diagnosis. However, the detection methods relying on classical biomarkers have limited sensitivity and accuracy. The application of auxiliary reagents, such as iRGD, promotes the release of alpha-fetoprotein (AFP) to improve the detection efficiency of liver cancer. Artificial intelligence (AI) technology is increasingly being applied as an assistant in tumor diagnosis. It can automatically identify tumor lesions in imaging, analyze tumor-related gene mutations, classify circulating tumor cells (CTCs), and integrate multi-omics data. These auxiliary means have enhanced the efficiency of tumor screening or detection. In this review, we summarize the combined applications of iRGD and AFP. We also discuss emerging new detection techniques, including CTCs, circulating tumor DNA (ctDNA), exosomes, and tumor-educated platelets (TEPs), specifically with the help of AI. The aim is to better understand the auxiliary role of the iRGD reagent and AI technology in early tumor detection.

The rapid rise of artificial intelligence (AI) and digital health technologies presents new opportunities for personalized care in multiple sclerosis (MS). However, implementation in routine practice is limited by regulatory hurdles, fragmented infrastructure and a lack of agile real-world evaluation methods. Living Labs (LLs) emerge as dynamic environments for advancing MS care and research, supporting early testing and iterative development of digital tools, while fostering structured collaboration among patients, clinicians, researchers and regulators. In this review, we conceptually frame LLs in MS and provide a concrete, clinic-ready implementation framework for AI-enabled application in real-world practice. Using a digital-based voice task as an exemplar with automated feature extraction, we detail integration patterns and define key performance indicators for feasibility, data quality, usability and clinical utility. We show how this co-designed model can generate decision-relevant evidence, may help shorten time-to-action and embed innovation seamlessly into clinical workflows. Finally, we align LL operations with ethical and regulatory standards and outline strategies to responsibly scale across centres.

This study introduces a SMARTphone-based, expert annotated dataset of Oral Mucosa images (SMART-OM), collected to facilitate the development of Artificial Intelligence and Machine Learning (AI/ML) technologies for automated diagnosis of Oral Cancer (OC) and Oral Potentially Malignant Disorders (OPMD). The dataset consists of 2,469 images from 331 subjects from four distinct classes: healthy/normal, variations from normal, OPMD, and OC. The images are captured using Android and iOS smartphone cameras under real-world clinical conditions in visible light. Each image is annotated by expert dental surgeons using the open-source VGG image annotator. Elaborate patient metadata, including clinical diagnosis, age, sex, and lifestyle-based risk indicators such as smoking, smokeless tobacco usage, alcohol consumption, and areca nut chewing, are recorded via a customized Jotform. The data collection and handling procedures are adhered to the ethical guidelines outlined in the Declaration of Helsinki and its amendments for research involving human subjects, with informed consent obtained from each subject. The SMART-OM dataset is intended to advance research and development of AI/ML algorithms for automated oral lesion detection.

ABSTRACT The rapid scaling of generative artificial intelligence (GenAI) technology presents opportunities for personalised learning experiences and facilitates collaborative learning, including collaborative argumentation (CA). However, empirical research examining students' perceptions of GenAI‐assisted CA within classroom contexts remains limited. This study explored university students' experiences with GenAI‐assisted CA through in‐depth interviews with 36 students following a CA activity using a ChatGPT4‐embedded argumentation platform developed by the research team. Findings indicate that students viewed GenAI as serving multiple roles, including tool, facilitator, teaching assistant and machine buddy. Students perceived that GenAI‐assisted CA could empower task performance and create a collaborative learning environment. Meanwhile, they found three challenges, including students'‐, AI‐ and learning environment‐related challenges during GenAI‐assisted CA. These findings offer evidence‐based strategies for educators seeking to integrate GenAI into the design of collaborative learning activities and guidelines for developers on the design of pedagogical AI for creating student‐centered GenAI‐powered collaborative learning.