With the surge in the use of Artificial Intelligence (AI) in recent years, educators have been introduced to new tools that may influence and change the future of education as it is known. How educators adapt to and evolve with AI depends on how they view AI in relation to who they are and how they teach. This article defines identity in education, from academic identity to specific disciplinary identities like STEM identity for educators, which involves an integrated understanding of science, technology, engineering, and mathematics. It introduces the critical concept of AI Identity, defined as a professional self-concept encompassing comfort, competence, and agency in leveraging AI for teaching. Developing an AI Identity is a four-step iterative process: Understanding, Exploration, Implementation, and Reflection and Criticality. Key competencies for an AI-identified educator include theoretical knowledge (episteme), practical skills (techne), and crucial professional judgment (phronesis). The article emphasizes the urgent need for teacher education programs to integrate AI competencies across curricula, preparing future educators to use AI, ensuring they are AI-ready and can model critical engagement with students responsibly and effectively.

This paper explores the potential of Artificial Intelligence (AI) as a transformative tool to support the achievement of the Sustainable Development Goals (SDGs). It presents a theoretical and analytical framework linking AI technologies to the three core dimensions of sustainable development: economic growth, social inclusion, and environmental protection. Drawing on recent literature (2020–2024) and global case studies, the paper analyzes practical applications of AI in key sectors such as health, environment, education, and governance. It identifies critical challenges—technical, ethical, institutional, and educational—that hinder AI adoption, particularly in developing countries. Finally, the paper proposes an integrated model to localize AI within national development strategies, emphasizing digital governance, capacity building, inclusive innovation, and strategic partnerships. The study concludes that responsible and inclusive deployment of AI can significantly accelerate progress towards SDGs by 2030, provided that ethical, infrastructural, and policy-related gaps are addressed. Keywords: Artificial Intelligence, Sustainable Development Goals, Digital Transformation, Ethical AI, Public Policy, Developing Countries.

4-Hydroxy-2,5-dihydrothiazole derivatives as a new class of small-molecule antibiotics for MRSA: AI-integrated design, chemical synthesis and biological evaluation.

Decoding strategies for enhancing immunotherapy in head and neck squamous cell carcinoma.

This study aims to conceptually analyze how Islamic Religious Education (PAI) undergoes a significant transformation in the era of digital intelligence through changes in pedagogical approaches, content reconstruction, and the redefinition of the teacher’s role. The research is grounded in the urgent need to align religious education with rapid technological developments that have created a new learning ecosystem—one that is more flexible, interactive, and future-oriented. The research employs a descriptive qualitative method with a literature-based approach, reviewing books, journal articles, educational policies, and recent research reports related to educational digitalization and its integration into PAI instruction. The findings reveal three major themes. First, PAI pedagogy has shifted from traditional methods toward digitally integrated approaches that utilize artificial intelligence, interactive media, learning analytics, and collaborative digital learning models. These approaches enhance student engagement and the effectiveness of value transmission. Second, PAI content has undergone substantial reconstruction by incorporating emerging digital issues such as digital ethics, information security, academic integrity, media literacy, and character formation in virtual spaces. This ensures that Islamic values remain relevant to contemporary challenges. Third, the role of PAI teachers has significantly evolved from being mere transmitters of information to becoming facilitators of digital literacy, curators of learning resources, and guides in shaping students’ character and digital ethics. Teachers are now required to master digital tools while maintaining the humanistic dimension of learning. The study concludes that the transformation of PAI in the digital intelligence era is inevitable and requires simultaneous integration between pedagogical approaches, content development, and the evolving role of teachers. These findings are expected to inform policy development, curriculum design, and further research on the implementation of digitally integrated PAI instruction across different educational contexts.

Ultralow-power consumption bimodal synaptic transistors for high-efficiency neuromorphic vision system.

In recent decades, digital transformations have significantly impacted knowledge production and dissemination, especially in Islamic sciences, leading to issues such as digital religious content proliferation, text fragmentation, and misuse of fatwas. This study aims to present an integrated framework entitled "Intelligent Knowledge Engineering for Islamic Texts" to address these challenges through a maqasid-based and contextual approach. The research tackles cognitive challenges in digital religious content by establishing theoretical foundations for Islamic knowledge engineering that leverage generative artificial intelligence while respecting maqasid principles. It also proposes an applied model linking texts to their objectives and contexts through semantic processing techniques. The study adopts an analytical-descriptive methodology, reviewing Islamic and linguistic sources and analyzing digital religious data using AI tools. Findings indicate that employing AI in Islamic research enhances fatwa accuracy, organizes digital religious content, and supports maqasid-oriented ijtihad in contemporary issues. The study also provides a set of guidelines for the effective and safe application of this intelligent knowledge engineering framework. Keywords: Islamic text, knowledge engineering, maqasid, generative artificial intelligence, digital religious content

Measurable residual disease (MRD) assessment by flow cytometry (FC) plays an essential role in prognosis and therapy escalation of B-cell acute lymphoblastic leukemia (B-ALL). However, the high degree of expertise and manual analysis time required limits the availability of this assay. To overcome this limitation, we developed a data-enhancing artificial intelligence (AI) pipeline that accelerates and simplifies MRD analysis. Unaltered FC files from 171 B-ALL MRD-positive and 89 MRD-negative cases were processed through an AI pipeline trained with 31 expert-gated negative controls. Cluster-informed downsampling reduced FC files from 1.2 million to 155 884 cells per case, on average, (87% cellularity reduction), whereas preserving small MRD populations (median, 100% retention for MRD of <1%) and allowing for true percentage MRD estimates using a correction factor. A deep neural network cell classifier automatically identified normal hematopoietic subsets (macro-averaged F1 score of 0.86); and an AI measure of anomaly discriminated B-ALL from benign mononuclear (area under the curve [AUC] of 0.98) or B-lymphoid cells (AUC of 0.94). Manual analysis of AI-enhanced files was completed in only 1.01 minutes per case, on average (standard deviation of ±0.57); with 100% positive agreement with conventional analysis (for MRD of ≥0.01%), 100% negative agreement, and excellent quantitative correlation (R2 = 0.92). Our cloud-based AI-enhancement solution accelerates B-ALL MRD identification without compromising test performance and has the potential of facilitating B-ALL MRD analysis by more clinical laboratories.

The skeletal system exhibits remarkable mechanical properties, serving as the primary supportive framework in vertebrates. Dysregulation of bone metabolic activity can alter bone quality and quantity, leading to skeletal deformity, pain, or disease. Accurate assessment, therefore, necessitates robust methods capable of evaluating both mechanical competence and structural characteristics across various scales. Numerous bone assessment techniques have been developed to comprehensively assess these parameters ex vivo and increasingly in situ. We discuss a spectrum of modalities, including mechanical testing, radiological imaging, microscopic, and spectroscopic analysis, evaluating their operational principles, applications in understanding bone health and pathophysiology, inherent advantages, and current limitations. Notably, we also highlight how a synergistic combination of two or more techniques provides information exceeding the capabilities of individual methods alone and hence advance our understanding for future diagnostic avenues. Integration of artificial intelligence (AI) further holds considerable potential to augment the analytical prowess of these methods, offering enhanced insights for decision-making. By elucidating these robust methodologies, we aim to expand the understanding of bone health and disease, with implications for improved patient management and treatment strategies. This review consolidates a spectrum of interconnected bone assessment techniques, each offering unique insights and collectively enabling diverse modalities for opportunistic screening of skeletal disorders in an aging global population.

India generates over 500 million tonnes of agricultural waste annually, much of which is lignocellulosic biomass rich in silica, calcium, potassium, and carbon elements, which are favourable for catalytic applications. This study highlights the valorisation of abundant agro-wastes such as rice husk (up to 20% silica), coconut shell (74%-78% fixed carbon), sugarcane bagasse (45%-55% cellulose), and tamarind seed (rich in polysaccharides and carbon), as cost-effective and sustainable catalysts. Various preparation techniques, such as calcination (450°C-700°C), acid/base activation (e.g. H2SO4, KOH), and nanoparticle impregnation (e.g. CaO, ZnO, Fe3O4), are explored to enhance the surface area (up to 250 m2/g) and activate functional groups. Agro-waste-derived catalysts exhibit high performance, achieving over 90% conversion in transesterification, efficient alcohol oxidation under mild conditions, and up to 98% dye degradation (e.g. methylene blue) within 60-90 min. Economic evaluations estimate production costs at $30-50 per ton, positioning them as competitive alternatives to conventional catalysts. Comparative insights from African innovations reveal opportunities for regional scalability. The study further explores Artificial Intelligence (AI)-assisted catalyst design, with life-cycle assessments indicating a potential reduction of up to 40% in greenhouse gas emissions, and integration prospects within decentralised biorefineries, supporting the transition to a circular, low-carbon chemical economy.

Circulating microRNAs in viral myocarditis: Advancements in biological understanding and potential clinical applications.

Metal-organic frameworks (MOFs) are crystalline materials with exceptionally high surface areas (up to 7000 m2/g), tunable pore structures, and versatile chemical functionalities, making them attractive for diverse environmental and industrial applications. Simultaneously, cold plasma, an ionized, low-temperature gas enriched with reactive species, has gained recognition for its environmentally friendly, rapid, and solvent-free processing capabilities, particularly in material synthesis and surface functionalization. Integrating cold plasma with MOFs presents a synergistic approach that enhances material properties and process efficiency. Recent studies have reported up to a 40%-60% increase in surface reactivity, improved catalyst dispersion by 30%, and reduced particle size to below 100 nm through plasma-assisted synthesis. These hybrid systems have demonstrated enhanced performance in areas such as air and water purification (achieving over 90% pollutant removal), carbon capture (exceeding 4 mmol/g CO2 uptake), energy conversion, and waste-to-resource technologies. Despite their promise, key challenges remain, including scalability, long-term structural integrity, and economic viability. This review also discusses recent advances in MOF design, innovations in plasma engineering, and the potential integration of artificial intelligence to optimize synthesis and functionality. Future perspectives emphasize the importance of green chemistry principles and interdisciplinary collaboration for the development and commercialization of MOF-plasma technologies aimed at sustainable environmental solutions.

The identification of drug targets remains one of the most critical challenges in pharmaceutical research. The rapid progress of artificial intelligence (AI) is significantly advancing this landscape by enabling more efficient and accurate drug-target interaction prediction. AI-driven approaches can integrate and analyze massive biomedical datasets, elucidating complex signaling networks and providing systematic insights into drug mechanisms of action. These developments have greatly accelerated virtual screening, binding affinity estimation, and target identification. However, despite these advancements, key challenges persist, such as ensuring the precision of predictions and overcoming the barriers to integrating AI tools with drug target discovery. This review provides a comprehensive overview of recent public databases, advanced computational methods, and user-friendly AI tools, highlighting both their potential and challenges. It also offers practical guidance for researchers without computational expertise, illustrating how these technologies can be effectively incorporated into current research workflows to advance drug target discovery and ultimately accelerate the development of novel therapeutic drugs.

BackgroundDiabetic kidney disease (DKD) is a major complication of diabetes and the leading cause of end-stage renal disease globally. Artificial intelligence (AI) technologies have shown increasing potential in DKD research for early detection, risk prediction, and disease management. However, the landscape of AI applications in this field remains incompletely mapped, especially in terms of collaboration networks, thematic evolution, and clinical translation.ObjectiveThis study aims to perform a comprehensive bibliometric and translational analysis of AI-related DKD research published between 2006 and 2024, identifying publication trends, research hotspots, key contributors, collaboration patterns, and the extent of clinical validation and explainability.MethodsA systematic search of the Web of Science Core Collection was conducted to identify English-language original articles applying AI technologies to DKD. Articles were screened following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines. Bibliometric visualization was performed using CiteSpace and VOSviewer to assess coauthorship, institutional and country collaboration, keyword evolution, and citation bursts. A qualitative review was conducted to evaluate clinical validation, model explainability, and real-world implementation.ResultsOut of 1158 retrieved records, 384 studies met the inclusion criteria. Global publications on AI in DKD increased rapidly after 2019. China led in publication volume, followed by the United States, India, and Iran. Keyword analysis showed a thematic transition from early biomarker and proteomic research to deep learning, clinical prediction models, and management tools. Despite methodological advances, few studies included external validation or explainability frameworks. Notable translational efforts included DeepMind’s acute kidney injury predictor and a chronic kidney disease prediction model developed by Sumit, yet widespread real-world integration remains limited.ConclusionsAI research in DKD has grown substantially over the past 2 decades, with expanding international collaboration and diversification of research themes. However, challenges persist in clinical applicability, model transparency, and global inclusivity. Future research should prioritize explainable AI, multicenter validation, and integration into clinical workflows to support effective translation of AI innovations into DKD care.

Abstract The advancement of Chip-on-Wafer-on-Substrate (CoWoS) technology has positioned three-dimensional (3D) packaging as a key enabler for next-generation artificial intelligence (AI) chips, thereby sustaining the trajectory of Moore’s Law. However, the continued scaling of critical dimensions (CDs), such as through-silicon vias (TSVs) and redistribution layers (RDLs), presents major challenges for CD metrology, primarily due to the difficulty of characterizing submicron hidden structures and limited light penetration. This study introduces an optimization framework based on global sensitivity analysis (GSA) to quantify the impact of CDs on optical responses and assess interaction effects on metrology performance. By combining GSA with rigorous electromagnetic simulations, the proposed approach enhances the extraction of CD information in optical critical dimension (OCD) metrology. In particular, this work systematically analyzes key structural parameters in both submicron silicon trench structures and copper redistribution layers (Cu RDLs), providing a unified evaluation across two representative classes of semiconductor architectures. Key structural parameters, including depth, top critical dimension (TCD), sidewall angle (SWA), and trench spacing, are systematically analyzed in submicron silicon structures. Furthermore, polarization optimization guided by sensitivity analysis is applied to maximize optical response sensitivity. Experimental validation shows that the GSA-optimized scatterometry setup achieves high measurement accuracy, maintaining a bias below 2% relative to focused ion beam/scanning electron microscope (FIB/SEM) benchmarks. The findings demonstrate that critical dimensions traditionally difficult to measure, such as depth and SWA of hidden submicron microstructures, can be accurately determined. Overall, the proposed methodology significantly enhances the accuracy and robustness of OCD metrology, providing valuable insights for advancing measurement strategies in 3D semiconductor packaging.

Legal Artificial Intelligence in Interventional Cardiology: Ethical Boundaries and Opportunities for Decision Support in the Turkish Context.

ABSTRACT The rise of Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs) is accelerating the integration of social robots into education. These technologies enhance robots' abilities in natural language interaction, adaptive behaviour, and personalised learning support. To advance real-world implementation, it is essential to identify the main challenges and opportunities in this field. We conducted a two-round Delphi study with 16 experts in human-robot interaction and educational technology. In the first round, participants outlined opportunities, challenges, and potential robot roles expected in the short term (1 year) and medium term (5 years). Content analysis revealed 8 opportunities, 10 challenges and 10 roles. In the second round, experts ranked their importance and feasibility across both time horizons. The results show that the most critical opportunities and challenges are also the least feasible to achieve in practice. Conversely, the proposed roles of educational robots demonstrated alignment between importance and feasibility. Experts highlighted three promising roles for robots in the GenAI era: supporting teachers in boosting learner engagement, serving as conversational interfaces for students to access knowledge and assisting teachers in supporting disadvantaged learners. These findings provide a roadmap for prioritising feasible innovations in educational robotics.

BackgroundThe emergence of artificial intelligence (AI) is driving digital transformation and reshaping medical education in China. Numerous medical schools and institutions are actively implementing AI tools for case-based learning, literature analysis, and lecture support. This expanding application is accelerating the adoption of localized AI platforms, which are poised to become integral components in the coming years.ObjectiveThe primary aim of this study was to investigate the current use of AI tools among medical students, including usage frequency, commonly used platforms, and purposes of use. The second aim was to explore students’ needs and expectations toward AI-powered medical education platforms by collecting and assessing student feedback, and to identify practical requirements across disciplines and academic stages to inform more effective platform design.MethodsBased on the task-technology fit model and 5 hypotheses, an anonymous online questionnaire was conducted to assess AI usage in learning, gather student feedback on AI-powered medical education platforms, and evaluate expected functionalities. The survey was conducted from March 1 to May 31, 2025, using a convenience sampling method to recruit medical students from various disciplines across Shanghai, China. The sample size was determined at 422, accounting for a 10% rate of invalid responses. The questionnaire was developed and distributed online via Wenjuanxing and promoted through WeChat groups and in-person interviews. Data analysis was conducted employing IBM SPSS Statistics (v 27.0).ResultsA total of 428 valid questionnaires were collected. The average frequency of AI-assisted learning among medical students was 5.06 (SD 2.05) times per week. Over 90% (388/428) of the students used more than 2 AI tools in their daily tasks. Students from different disciplines, educational stages, and academic systems demonstrated different usage patterns and expectations for AI-powered medical education platforms.ConclusionsAI technology is widely accepted by medical students and is extensively applied across various aspects of medical education. Significant differences are observed in usage patterns across disciplines, educational stages, and academic systems. Understanding the actual needs of students is crucial for the construction of AI-powered medical education platforms.

Objective In this study, we aimed to examine the responses given by ChatGPT (OpenAI), Copilot (Microsoft), and Gemini (Bard) artificial intelligence applications to questions about the active ingredient isotretinoin in terms of accuracy, readability, applicability, and understandability. Material and methods The readability of the answers given by the artificial intelligence programs was evaluated using the Flesch-Kincaid ease score, and the applicability and understandability levels were evaluated using the Patient Education Materials Evaluation Tool scales. The accuracy of the answers was compared by two dermatologists who scored them between 1 and 5. Results No significant difference was found between the groups in terms of Flesch Kincaid reading ease scores (p = 0.671), and all three programs were found to be at a difficult level of reading. In the Patient Education Materials Evaluation Tool scales, it was observed that Gemini and ChatGPT rates were >70% and there was a significant difference in favor of these programs between the groups (p < 0.001). In the accuracy scores of the answers, Gemini (4.90 ± 0.31) and ChatGPT (4.60 ± 0.69) had high scores and there was a significant difference between the groups (p < 0.001). Conclusion While the AI chatbots we used in the study demonstrated reasonable accuracy in answering questions about isotretinoin, they performed limited in terms of readability and usability. These findings suggest that AI programs alone are not sufficient for patient education and need to be improved to simplify responses.

Abstract High-resolution (HR) sea surface temperature (SST) is crucial for understanding ocean dynamics, climate variability, and nearshore ecosystems. While demand for fine-scale SST data in coastal regions grows, HR observations remain limited, and numerical modeling is constrained by prohibitive computational costs. Efficient and accurate downscaling approaches are therefore essential, and recent advances in artificial intelligence (AI) offer promising alternatives. However, current AI-based methods often use artificially degraded low-resolution (LR) data, overlooking the systematic mismatches where LR simulations misrepresent complex, fine-scale ocean features. This study employed a Convolutional Block Attention Module-enhanced UNet model (CBAM-UNet) trained on realistic LR-HR data pairs from the three-layer nested regional ocean model system (ROMS) simulations to enhance practical reliability. The LR multi-mode inputs include SST and sea surface currents, which help encode essential physical ocean processes. Compared with bilinear interpolation and a traditional UNet model using SST-only input, the proposed model reduced Root Mean Square Error (RMSE) by 21.93% and achieved a spatial Pearson Correlation Coefficient (R) of 0.86. In addition, interpretability analysis revealed the contribution of each input channel, aligning well with temperature transport and seasonal variability, and confirming the underlying natural physical constraints learned from real-world data. Beyond pixel-value precision, the physically interpretable behavior of the multi-mode downscaling method demonstrates its capability to reconstruct accurate and dynamically consistent HR SST fields, which is vital for operational applications.