All stories

877 published stories · page 8 of 59

Evidence-backed gain

Dubai launches first accredited Bachelor of Music programme with AI focus; course to begin in September

Dubai is set to offer its first accredited Bachelor of Music programme starting September 2026. This new course focuses on contemporary and electronic music, covering production and business. Students will gain practical experience in professional studios and work on industry projects. Artificial intelligence in music creation will also be a key part of the curriculum. SAE University College Dubai is accepting applications now for this exciting opportunity.

The Economic Times · Education

Dubai launches first accredited Bachelor of Music programme with AI focus; course to begin in September

Evidence-backed problem

Saber’s CEO apologized to former Rideshare “Stimulator” writer Stella Sacco.

After saying to This Week in Videogames that he would have been “happy to replace her with AI,” Sacco says CEO Matthew Karch emailed her and “apologized unreservedly for the awful way he spoke about me in the press.” You can read her full statement below. Sacco, a former lead writer on the recently-announced Rideshare “Stimulator”, had claimed that “Saber replaced me with ChatGPT midway through development.” CEO Matthew Karch denied that Saber “replaced any writers with AI.” The game uses AI for voices, music, a…

Media & Arts

Both readings

Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies

The significant advancements in applying artificial intelligence (AI) to healthcare decision-making, medical diagnosis, and other domains have simultaneously raised concerns about the fairness and bias of AI systems. This is particularly critical in areas like healthcare, employment, criminal justice, credit scoring, and increasingly, in generative AI models (GenAI) that produce synthetic media. Such systems can lead to unfair outcomes and perpetuate existing inequalities, including generative biases that affect…

Health

Evidence-backed problem

A carbon aware job scheduling framework for data center sustainability using deep learning training

Abstract Deep learning workloads have experienced rapid growth which has resulted in higher energy consumption and increased carbon emissions for contemporary data centres. The existing solutions of carbon tracking and static scheduling systems provide insufficient capacity to implement carbon awareness in actual machine learning operational processes. In this paper, we present EcoSchedAI (Eco-...

Climate

Evidence-backed problem

Personalizing Assignments in the AI Era: The Role of Engaged Pedagogy

Generative artificial intelligence (Gen AI) has taken the academic world by storm. This G.I.F.T.S. paper argues that personalizing academic assignments may curb students’ tendency to copy and paste content from Gen AI outputs. I demonstrate how I personalized a written assignment in one of my courses. A total of 81 paper grades were analyzed, and no significant difference was found between stud...

Education

Both readings

CT-based prediction of hematoma expansion and adverse outcomes after intracerebral hemorrhage: evidence appraisal, artificial intelligence translation, and GeroScience perspectives

Spontaneous intracerebral hemorrhage (ICH) is a highly lethal and disabling form of stroke, in which hematoma expansion (HE) is a major and potentially modifiable determinant of early neurological deterioration and poor functional outcome. Computed tomography (CT) remains the first-line imaging modality for acute ICH and provides essential information for early HE risk stratification. However, current evidence is dispersed across conventional CT signs, composite scores, radiomics, machine learning, and deep lear…

Health

Evidence-backed problem

Artificial Intelligence and Nursing: Freedom From the Mundane or Subservience to a New Nobility

Aim To explore the notion of post-humanism and the impact of artificial intelligence (AI) on society, nursing and healthcare. Design Discursive paper. Methods Critical reflection on concepts relating to post-humanism and the impact of AI, sourced from contemporary and established literature. Data sources Information was drawn from a wide range of empirical and theoretical resources, including health services research through to sociology and philosophy, including newspapers, popular science as well as peer revie…

Health

Artificial Intelligence in Nursing Education: A Scoping Review of Academic Perspectives

Aim To examine nursing academics' perceptions and experiences of artificial intelligence (AI) integration in nursing education. Design Scoping review. Data sources MEDLINE, CINAHL, ERIC, Scopus, and Web of Science were searched in August 2025. Methods A scoping review using Joanna Briggs Institute methodology. Peer-reviewed original research and reviews published in English (2019-2025) were included if they examined nursing educators' perspectives, attitudes, or experiences with AI in nursing education across un…

Education

Hybrid ensemble machine learning algorithms for landscape ecological vulnerability assessment to riverbank erosion

Riverbank erosion is a catastrophic geomorphological hazard that poses severe ecological and socio-economic challenges across densely populated floodplains. This study advances a machine learning (ML) framework that integrates individual and bagging-classifier approaches using random forest (RF), multilayer perceptron (MLP) and bagging classifiers to assess landscape ecological vulnerability (LEV) to riverbank erosion. The site-specific environmental, climatic, geomorphological and ecological parameters were sel…

Crime

Integration of single-cell transcriptomics and genomic mutation analysis identifies an immunotherapy-resistant tumor subcluster and validates ARNTL2 as a malignant driver in lung adenocarcinoma

Immunotherapy resistance in lung adenocarcinoma (LUAD) remains a critical clinical challenge, and the mechanisms underlying resistance-associated intratumoral heterogeneity are poorly characterized. We performed single-cell RNA sequencing of LUAD patients receiving neoadjuvant immunotherapy (responders vs. non-responders), integrating inferCNV, GSVA, and differential expression analyses. Cluster-specific genes were validated across seven independent cohorts (TCGA-LUAD, GSE13213, GSE26939, GSE29016, GSE30219, GSE…

Health

Integration of single-cell transcriptomics and genomic mutation analysis identifies an immunotherapy-resistant tumor subcluster and validates ARNTL2 as a malignant driver in lung adenocarcinoma

Application of Artificial Intelligence (AI) in cancer symptom management for adult cancer survivors: a scoping review

Artificial Intelligence (AI) has been increasingly used in cancer survivorship to support symptom management. This scoping review aimed to map existing evidence on AI applications in cancer symptom management for adult cancer survivors, including AI model development, AI-enabled intervention delivery and adoption, symptom targets, key features of the AI approaches used, reported outcomes, influencing factors, and research gaps to inform future priorities. This scoping review was conducted in accordance with the…

Health

Automated diagnostic system for classification of progression stages of osteoarthritis using magnetic resonance imaging

Osteoarthritis (OA) is a degenerative joint disease characterized by cartilage loss, synovial fluid imbalance, and bone structural changes, leading to reduced mobility. Most clinical studies use MRI-derived cartilage characteristics to assess OA progression. To support timely treatment decisions and minimize human error, an automated computer aided system is needed for prediction of OA in the progressive stages. To build the automatic system for classifying progression phases of OA, we present a novel hybrid fra…

Health

Beyond concentration-based analysis: explainable AI identifies environmental settings influencing urban PM 10 variability

This study applies an explainable artificial intelligence framework to investigate PM10 variability using routine regulatory air-quality data from a single monitoring station, targeting data-limited conditions. A four-year dataset (2020-2023) of PM10, PM2.5, NO2, SO2, O3, and meteorological predictors was analyzed using ensemble machine-learning models with metaheuristic hyperparameter optimization. The best-performing model achieved high predictive performance (R2 = 0.913), supporting model-based interpretation…

Climate

Identifying causal pathways and risk-decision rules for nitrous oxide emission hot moments in wastewater treatment plants using probabilistic causal machine learning

Nitrous oxide (N_2O) emissions from biological wastewater treatment represent a significant challenge for climate-responsible operation due to their intermittency and occurrence as short-lived emission hot moments. Effective mitigation therefore requires accurate prediction and systematic identification of causal pathways and operational risk conditions. This study develops a probabilistic causal machine learning framework based on long-term online monitoring data from a full-scale wastewater treatment plant for…

Climate

Identifying causal pathways and risk-decision rules for nitrous oxide emission hot moments in wastewater treatment plants using probabilistic causal machine learning

Automated autism spectrum disorder detection using EEG signals and time-frequency visibility graphs

Early and objective screening of Autism Spectrum Disorder (ASD) remains challenging because conventional diagnosis primarily relies on behavioural assessment and clinical observation. To address this limitation, this study proposes a dual-domain computational framework for automated EEG-based ASD classification by integrating complementary time-frequency analysis with Horizontal Visibility Graph (HVG)-based network modelling. Four time-frequency decomposition techniques, namely the Short-Time Fourier Transform (…

Health

Automated autism spectrum disorder detection using EEG signals and time-frequency visibility graphs