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A framework for developing university policies on generative AI governance: a cross-national comparative study
PolicyEvidence-backed gain

A framework for developing university policies on generative AI governance: a cross-national comparative study

As generative AI (GAI) becomes increasingly embedded in higher education, universities worldwide are developing policies to govern its ethical, pedagogical, and institutional use. However, these policies vary across national and institutional contexts. We undertake a cross-national analysis of GAI guidelines issued by leading universities in the United States, Japan, and China, identifying key policy orientations and proposing a structured framework to support policy development. Using an extended Technology Acc…

Studies in Higher Education
Algorithmic Justice and Responsible AI Journalism: A Comparative Communication Policy Perspective in East Asia
Both readings

Algorithmic Justice and Responsible AI Journalism: A Comparative Communication Policy Perspective in East Asia

As generative artificial intelligence and automated content curation rapidly reshape the global media landscape, the intersection of algorithmic justice and media governance has become a critical frontier for sustainable development. This study provides a comparative communication policy analysis of China and South Korea, focusing explicitly on how their distinct regulatory toolkits address the tension between algorithmic fairness and platform accountability in AI journalism. While China utilizes a top-down, sta…

Machine learning prognostication in nasopharyngeal carcinoma: a european multicentre analysis of survival and risk of second malignancy
Evidence-backed gain

Machine learning prognostication in nasopharyngeal carcinoma: a european multicentre analysis of survival and risk of second malignancy

Nasopharyngeal carcinoma (NPC) is rare in Europe, and emerging data suggest poorer outcomes in Caucasian patients compared with Asian populations, highlighting the need for region-specific prognostic tools. Inflammation-based biomarkers and artificial intelligence show promise for risk stratification and prediction of survival and second primary cancers (SPCs). We conducted a retrospective multicentre study including 405 NPC patients from six European institutions. Demographic, clinicopathological, and haematolo…

Predictive value of the uric acid to high-density cholesterol ratio (UHR) combined with intact parathyroid hormone for protein-energy wasting after incident hemodialysis: a multicenter study
Evidence-backed gain

Predictive value of the uric acid to high-density cholesterol ratio (UHR) combined with intact parathyroid hormone for protein-energy wasting after incident hemodialysis: a multicenter study

Protein-energy wasting (PEW) is common in incident hemodialysis patients and linked to poor outcomes. The uric acid/HDL-cholesterol ratio (UHR) and intact parathyroid hormone (iPTH) relate to metabolic, inflammatory, and nutritional disturbances, but their value for predicting PEW in incident hemodialysis is unclear. This retrospective multicenter study included 863 incident hemodialysis patients. PEW was defined according to the International Society of Renal Nutrition and Metabolism criteria. UHR and iPTH were…

Predicting Biomolecular Interactions in the Next Decade: Physics-Based Methods Meet AI-Driven Approaches
Both readings

Predicting Biomolecular Interactions in the Next Decade: Physics-Based Methods Meet AI-Driven Approaches

The quantitative prediction of biomolecular recognition is crucial to molecular science. The challenge is not merely structural determination but the prediction of (thermo)dynamic and kinetic observables arising from high-dimensional molecular ensembles, such as free energies, conformational distributions, and rate processes across different conditions. As the field shifts from structure-centric to ensemble-based descriptions, two complementary modeling strategies have matured: explicit energy-based approaches g…

Science

Machine learning force fields for inorganic crystalline materials: principles, advances, and emerging applications

Machine learning force fields (MLFFs) combine the high accuracy of first-principles methods with the high efficiency of classical force fields, offering new opportunities for atomic-level studies of inorganic crystalline materials. We systematically summarize the research progress on MLFFs, elucidate their fundamental principles and developmental history, and categorically introduce the technical characteristics of representative models and relevant benchmarking platforms. We aim to review the advantages of MLFF…

Machine learning force fields for inorganic crystalline materials: principles, advances, and emerging applications
Media & Arts

Robert Laidlow: Reality Eaters album review

Robert Laidlow is as at home in the realms of science and technology as he is in the world of classical music. As this NMC debut album demonstrates, his intricate, wildly imaginative work is eminently approachable, even if the core concepts are highly complex. Warp, a terse, 12-minute piano concerto, proposes a musical solution to Einstein’s field equations as the intrepid Joseph Havlat boldly goes where no pianist has gone before amid the distorting fabric of orchestral space-time. Strident orchestral lines spi…

Robert Laidlow: Reality Eaters album review
Business

Musk’s SpaceX courts retail investors as it aims for record-breaking stock market flotation

SpaceX will kick off the marketing for its highly anticipated stock exchange debut by hosting an event in June for 1,500 retail investors, as executives set out to convince buyers that the aerospace to artificial intelligence group should be valued at $2tn. In an unusual move, the company has earmarked a large portion of its shares – potentially up to 30% – for non-professional, non-institutional investors, banking on the popularity of its chief executive, Elon Musk, to help it raise $75bn (about £56bn) in what…

Musk’s SpaceX courts retail investors as it aims for record-breaking stock market flotation
Sports

Legal Foundations for the Application OF Artificial Intelligence Technologies in the Sports Industry

The integration of Artificial Intelligence (AI) technologies in the sports industry has brought about significant advancements in performance analysis, fan engagement, and decision-making processes. However, this rapid adoption of AI also raises complex legal challenges that require careful consideration. This article examines the legal foundations and implications of applying AI technologies in the sports industry. Through a comprehensive analysis of existing legal frameworks, case studies, and potential future…

Legal Foundations for the Application OF Artificial Intelligence Technologies in the Sports Industry
Sports

Application of Artificial Intelligence in Real-Time Strategic Decision Support System for Sports Competitions

Various high-tech technologies are used extensively around the world in sports competitions especially in athletic events where scientific real-time decision-making is essential to improve competitive efficiency and match outcomes. This research proposes the introduction of the concept of entropy by applying the ID3 algorithm with the use of the attribute entropy value change as the selection criterion to develop the decision tree model for real-time sports competition data processing. Meanwhile, an enhanced Mon…

Application of Artificial Intelligence in Real-Time Strategic Decision Support System for Sports Competitions