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Evidence-backed gain

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…

Environmental Pollution · Climate

Evidence-backed gain

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

Evidence-backed problem

Is the environmental impact of datacentres finally cutting through?

Anti-datacentre sentiment is growing from across the political spectrum in the US. More than a dozen states have considered moratoria on datacentres. New York became the first US state to enact a temporary ban last month. Progressive stalwarts Senator Bernie Sanders and Representative Alexandria Ocasio-Cortez have proposed a national moratorium. And even Greg Abbott, the far-right governor of Texas, called for a ban on datacentre development in rural swaths of his state. Fuelling alarm about datacentres are envi…

Climate

Evidence-backed problem

Surprising AI breakthroughs raise soul-searching questions for mathematicians | Letter

I share Kasra Rafi and Bruce Schneier’s impression that recent mathematical breakthroughs by AI consist in clever recombination of existing ideas, not development of truly novel theory (No, AI doesn’t mean the end of mathematics – at least not yet, 25 August). The question is: what happens to mathematics if this changes? Like many mathematicians, I have done much soul-searching in recent weeks, especially since a key problem in my own field of group theory (the existence of non-sofic groups) was solved this mont…

Science

Evidence-backed gain

Separation of coeluted compounds in capillary gas chromatography: customization via machine learning retention time prediction

Gas Chromatography is a versatile separation technique widely used in analytical chemistry for constituent determination. However, gas chromatography compound identification is not directly feasible unless the method is coupled with complementary techniques such as mass spectrometry or with referencing methods like retention indices. Statistical retention time prediction of compounds based on the gas chromatography experimental and instrumental parameters could facilitate the gas chromatography characterization…

Science

Evidence-backed gain

Developing and evaluating automated deep learning and human-in-the-loop vision-language systems for microplastic characterization

Microplastic (MP) pollution poses escalating environmental risks, demanding efficient and reproducible tools for morphological characterization of plastic particles. Traditional manual microscopy is labour-intensive, operator-dependent, and poorly suited to large-scale monitoring. This study presents a comparative evaluation of two distinct artificial intelligence paradigms for the analysis of optical microscope images of microplastics. The first paradigm is a domain-specific, multi-task deep learning (DL) class…

Climate

Evidence-backed problem

US building twice as much gas-fired capacity as China in AI boom, analysis finds

The US has surged ahead of China in the building of new gas-fired power generation, largely to feed a boom in artificial intelligence (AI) that is adding vast amounts of planet-heating emissions, a new analysis has found. For decades, China’s rapid economic growth has seen it outpace the US in the addition of new gas power generation but a recent “frenzy” in datacenter construction for AI has reversed this, said Global Energy Monitor (Gem) in its new report. The US is now building twice as much gas-fired capacit…

Climate

AI-Assisted Thematic Analysis in a Quality Improvement Evaluation: Replicable Findings and Subtle Misrepresentations

Using artificial intelligence to identify themes in interview data from a quality improvement program evaluation produced 4 replicable themes grounded in the data. However, 2 consistently identified themes resulted from subtle misrepresentations and could have easily misled results without thorough data knowledge and output audit by the human research team.

Science

General Atomics and SDSC use AI to advance fusion laser research

Collaboration combines high-performance computing and artificial intelligence to accelerate the design and optimisation of laser systems for inertial confinement fusion research

Science

General Atomics and SDSC use AI to advance fusion laser research

Impact of allometric reference selection on management scale aboveground biomass estimation via Sentinel-2 and machine learning

Accurate estimation of aboveground biomass (AGB) is essential for sustainable forest management, carbon accounting, and climate change mitigation. In remote sensing-based biomass mapping, field-derived AGB values are commonly used as reference data; however, these values are strongly influenced by the selected allometric equation. This study evaluates how alternative allometric reference datasets affect Sentinel-2-based AGB estimation at the forest management scale in Pinus brutia stands. Reference AGB values we…

Climate

Impact of allometric reference selection on management scale aboveground biomass estimation via Sentinel-2 and machine learning

Evaluating machine learning and neural network architectures for forensic sex estimation using mandibular ramus and notch features on panoramic radiographs

Estimating a biological profile, such as sex, is a fundamental step in forensic identification when primary identifiers are unavailable for direct individual comparison. In forensic scenarios involving advanced decay, specific taphonomic alterations, or midfacial blunt force impacts, the mandibular ramus serves as a valuable anatomical marker due to its distinct sexual dimorphism and thick cortical structure, making it more resilient to fragmentation than other, more fragile facial bones. Despite its utility, th…

Science

Optimal Initialization Scale for Neural Networks With Locally Quadratic Loss Landscapes: An SGD Dynamics Perspective

Stochastic gradient descent (SGD), one of the most fundamental optimization algorithms in machine learning (ML), can be recast through a continuous-time approximation as a Fokker-Planck equation for Langevin dynamics, a viewpoint that has motivated many theoretical studies. Within this framework, we study the relationship between the quasi-stationary distribution derived from this equation and the initial distribution through the Kullback-Leibler (KL) divergence. As the quasi-steady-state distribution depends on…

Science

Productivity-based sampling for field-scale soil organic carbon mapping: from regional models to carbon farming needs

This study evaluates an innovative field-scale targeted sampling strategy within a regional hybrid spatial prediction model that combines machine learning and geostatistics. The framework is designed so that newly collected field observations are incorporated only through the local residual kriging step, while the regional Random Forest trend model remains unchanged, allowing field-scale predictions to be refined without full model refitting. The proposed sampling approach integrates a Normalized Difference Vege…

Climate

Productivity-based sampling for field-scale soil organic carbon mapping: from regional models to carbon farming needs

Quantum-Enhanced Transfer Learning for IDR Binding Partner Prediction

Intrinsically Disordered Regions (IDRs) play essential roles in cellular processes through interactions with proteins, nucleic acids, lipids, and metal ions, yet predicting their binding partners remains challenging for understanding protein function and drug discovery. However, current computational methods including protein language models face performance plateaus where traditional approaches to improve accuracy have become ineffective. Here, we present a hybrid quantum-classical machine learning approach tha…

Science

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

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