± HEALTH Adequate self-harm surveillance is a key part of suicide prevention. Our previous research demonstrated that…± HEALTH Breast cancer remains the most commonly diagnosed malignancy among women globally, with disproportionately hi…+ HEALTH Background The prevalence of depression is higher among older adults with chronic diseases and cognitive impa…± HEALTH Purpose To compare the information quality, accuracy, and readability of patient-directed responses generated…± HEALTH Objective Comparative evaluations of machine learning (ML) and logistic regression (LR) for clinical predicti…± HEALTH Introduction Alzheimer's disease (AD) is a progressive neurodegenerative disorder for which early diagnosis-p…+ HEALTH Background Accurate identification of ganglionated bowel is essential during laparoscopic pull-through for Hi…+ CLIMATE Accurate prediction of ash fusion temperatures (AFTs) is crucial for ensuring the operational efficiency and…
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Evidence-backed gain

Physics-informed machine learning for universal ash fusion temperatures prediction: A novel categorical chain differential framework

Accurate prediction of ash fusion temperatures (AFTs) is crucial for ensuring the operational efficiency and safety of solid-fuel boilers and gasifiers. However, conventional machine learning methods typically treat each characteristic temperature as an independent prediction target, resulting in temperature inversions that violate the required physical ordering of AFTs. This study aimed to develop a Categorical Chain Differential framework for the coupled and physically consistent prediction of the four AFTs ac…

Bioresource Technology · Climate

Physics-informed machine learning for universal ash fusion temperatures prediction: A novel categorical chain differential framework
Reinforcement Learning for Electric Vehicle Charging Management: Theory and Applications
Evidence-backed gain

Reinforcement Learning for Electric Vehicle Charging Management: Theory and Applications

The growing complexity of electric vehicle charging station (EVCS) operations—driven by grid constraints, renewable integration, user variability, and dynamic pricing—has positioned reinforcement learning (RL) as a promising approach for intelligent, scalable, and adaptive control. After outlining the core theoretical foundations, including RL algorithms, agent architectures, and EVCS classifications, this review presents a structured survey of influential research, highlighting how RL has been applied across va…

Climate
Artificial intelligence -based modeling and comparative evaluation of craniofacial soft tissue and subcutaneous fat thickness using ct imaging for forensic identification in a northwestern indian population
Evidence-backed problem

Artificial intelligence -based modeling and comparative evaluation of craniofacial soft tissue and subcutaneous fat thickness using ct imaging for forensic identification in a northwestern indian population

Background Craniofacial soft tissue thickness (CFSTT) is a critical parameter in forensic facial reconstruction, serving as a link between skeletal morphology and facial appearance. However, most existing studies rely on mean CFSTT values without considering the contribution of subcutaneous fat layer thickness, limiting the accuracy of reconstruction models. Objective This study aims to develop an artificial intelligence (AI)-assisted, CT-based framework for the comparative evaluation of craniofacial soft tissue…

Science
Machine learning insights into band gap properties in halide-based perovskites
Evidence-backed gain

Machine learning insights into band gap properties in halide-based perovskites

Halide perovskites show great promise for applications in optoelectronic devices. The lead-free perovskites are attracting increasing interest due to their low toxicity and motivate the exploration of alternative compositions and structures, including A 2 BX 6 , A 2 BB'X 6 , A 3 B 2 X 9 , and A 4 BX 6 . Accurate predictions of a wide range of band gap energies are important for designing new materials. It is also important to generate a direct relationship between the structural and elemental descriptors and the…

Science
OpenAI claims to have solved maths problem that stumped humans for decades
Both readings

OpenAI claims to have solved maths problem that stumped humans for decades

OpenAI claims to have solved a major mathematics problem that has stumped humans for nearly a century after spending millions of dollars on the artificial intelligence-led endeavour. The company behind ChatGPT said it had cracked the Navier-Stokes problem, one of seven Millennium Prize Problems published by the Clay Mathematics Institute to highlight some of the biggest unsolved puzzles in the field. However, the announcement swiftly became mired in controversy after mathematician Tristan Buckmaster, a professor…

Science
Explainable deep learning improves human mental models of self-driving cars
Evidence-backed problem

Explainable deep learning improves human mental models of self-driving cars

Self-driving cars increasingly rely on deep neural networks to achieve human-like driving 1-3 . The opacity of these black-box planners makes it challenging to accurately anticipate when they will fail 4-6 , with potentially catastrophic consequences 7-9 . Although research into interpreting these systems has surged, most of it is confined to simulations or toy setups because of the difficulty of real-world deployment 10,11 , leaving the practical utility of these techniques unknown. Here, we introduce the Conce…

Science

Explainable AI-Supported Cyber-Physical Collaboration for Sustainable Manufacturing in Industry 5.0

In this study, we present an innovative approach to the sustainable manufacturing of industrial parts using an explainable artificial intelligence (XAI)- based cyber-physical collaboration system for Industry 5.0. Current cyber-physical human systems (CPHSs) have been found to integrate AI only to a limited extent and often lack explainability. Consequently, there is a need to improve their scalability and flexibility, in keeping with the tenets of Industry 5.0: resilience, long-term viability, and human-centric…

Climate
Explainable AI-Supported Cyber-Physical Collaboration for Sustainable Manufacturing in Industry 5.0

Machine learning-assisted nitrogen-doped carbon dots for Fe<sup>3+</sup> detection in aqueous environments

The concentration of iron ions is a crucial indicator for assessing water quality. In this study, nitrogen-doped carbon dots (NCDs) were synthesized using a microwave-assisted method with citric acid and urea as precursors, thereby establishing a fluorescence sensing platform for the detection of alkaline pH and Fe 3+ . During Fe 3+ detection, the fluorescence intensity of NCDs was specifically quenched as the concentration of Fe 3+ increased, demonstrating good linearity across the ranges of 1-10 µM and 10-100…

Climate
Machine learning-assisted nitrogen-doped carbon dots for Fe<sup>3+</sup> detection in aqueous environments

Data-efficient exploration of atomic clusters via uncertainty quantification on complex potential energy surfaces

Machine learning interatomic potentials have become an effective method for exploring complex potential energy surfaces; however, their application to atomic clusters is frequently hindered by the high cost of sampling diverse isomer spaces and the difficulty in ensuring model generalizability across complex energy landscapes. While uncertainty quantification (UQ) offers a pathway to mitigate data scarcity, its efficacy in capturing continuous potential energy surface features and guiding active learning within…

Science
Data-efficient exploration of atomic clusters via uncertainty quantification on complex potential energy surfaces

Forest Kernel Balancing Weights: Outcome-Guided Features for Causal Inference

While balancing covariates between groups is central for observational causal inference, selecting which features to balance remains a challenging problem. Kernel balancing is a promising approach that first estimates a kernel that captures similarity across units and then balances a (possibly low-dimensional) summary of that kernel, indirectly learning important features to balance. In this paper, we propose forest kernel balancing, which leverages the underappreciated fact that tree-based machine learning mode…

Science
Forest Kernel Balancing Weights: Outcome-Guided Features for Causal Inference

Machine learning for monitoring and assessment of potentially toxic elements in soils: a synthesis of spatial validation, explainability, and uncertainty

Potentially toxic elements (PTEs) in soils pose persistent risks to ecosystems, groundwater, and food systems, creating a need for reliable spatial assessment tools. Machine learning (ML) is increasingly used to map PTE concentrations from environmental covariates, but many studies still rely on spatially naive validation, limited interpretation, and incomplete uncertainty reporting. This review synthesizes recent advances (2020-2025) in ML-based PTE mapping with emphasis on four requirements for monitoring-grad…

Climate
Machine learning for monitoring and assessment of potentially toxic elements in soils: a synthesis of spatial validation, explainability, and uncertainty

ARTIFICIAL INTELLIGENCE AND THE FUTURES TURN: an anticipatory infrastructure for qualitative methods

In this article, I focus on artificial intelligence (AI) in a social science futures research agenda. This agenda is proposed in response to a contemporary context where global future uncertainties are generating a futures knowledge market increasingly populated by the promise of faster and scaled-up AI foresight. Acknowledging the possibilities offered by technological and interactional focuses in developing AI methods, I turn to reflexively discuss the “side effects” of using AI methods in qualitative futures-…

Science
ARTIFICIAL INTELLIGENCE AND THE FUTURES TURN: an anticipatory infrastructure for qualitative methods

Quantitative decoupling of source-pathway-receptor driving mechanisms for integrated soil risk via interpretable machine learning

Most existing concentration-based risk assessments of potentially toxic elements (PTEs) in farmland soils tend to overlook source-specific transport pathways and receptor-related risks. In this study, an integrated risk index-machine learning framework based on the source-pathway-receptor concept is developed to comprehensively evaluate PTE risks in a typical mining city. The framework integrates the improved Nemerow index (INI), potential ecological risk index, Monte Carlo simulation-based health risk assessmen…

Climate
Quantitative decoupling of source-pathway-receptor driving mechanisms for integrated soil risk via interpretable machine learning

Development and validation of a generalizable M-protein screening model using routine laboratory indicators: a multicenter retrospective study

Early detection of plasma cell disorders (PCDs) remains challenging due to limited accessibility of gold standard diagnostic methods. This study aimed to develop a simple M-protein screening model using routine laboratory indicators for clinical laboratories. A total of 5217 participants from three Chinese hospitals were enrolled. The derivation cohort (n = 3019) was randomly divided into training and internal validation cohorts. Two external validation cohorts (n = 1747 and n = 451) were included. M-protein pos…

Science
Development and validation of a generalizable M-protein screening model using routine laboratory indicators: a multicenter retrospective study