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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…

Science of The Total Environment · Climate

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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

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Δ -Machine Learning for the Prediction of Metal Complex Properties

The discovery and design of novel transition metal complexes for specific applications heavily rely on computational high-throughput screenings to identify promising candidates for experimental validation. However, traditional computational approaches, such as density functional theory, are often too computationally demanding to be applied on a large scale. Machine learning methods offer a promising alternative due to their excellent computational efficiency, but their accuracy and high data requirements remain…

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Evidence-backed gain

Machine learning force field development and basic physical property studies for molten salt reactor fuel salt LiF-BeF<sub>2</sub>-UF<sub>4</sub>

As one of the most promising technological pathways for Generation IV advanced reactors, molten salt reactors (MSRs) rely on the fuel salt LiF-BeF 2 -UF 4 (FLiBeU), whose microstructural characteristics and fundamental physical properties determine the reactor's thermal-hydraulic behavior and safe operating limits. In response to the experimental challenges posed by the high temperature and high radioactivity of this molten salt system, this study adopts the deep potential molecular dynamics (DPMD) method combin…

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Both readings

Effect of Deep Learning Training Policy on Greenhouse Gas Emissions and Carbon Efficiency for Chest Radiograph Classification

Purpose Environmental sustainability is an emerging priority in radiology, yet the impact of deep learning training policies on greenhouse gas emissions remains poorly characterized. This study quantified the effect of training policy on carbon dioxide equivalent (CO 2 eq) emissions and model performance for chest radiograph classification. Methods Anteroposterior chest radiographs (128 907 training, 24 570 validation, 8282 test) were used to train 3 ImageNet-pretrained convolutional neural networks (ResNet-50,…

Climate

Evidence-backed gain

New tech helps humanoids run, jump and handle tasks with one control

New AI mode, uses teleoperation, video, text and music to control humanoid robots with natural, whole-body movements.

Science

Evidence-backed gain

Generative artificial intelligence in supply chain and operations management: a capability-based framework for analysis and implementation

This research examines the transformative potential of artificial intelligence (AI) in general and Generative AI (GAI) in particular in supply chain and operations management (SCOM).Through the lens of the resource-based view and based on key AI capabilities such as learning, perception, prediction, interaction, adaptation, and reasoning, we explore how AI and GAI can impact 13 distinct SCOM decision-making areas.These areas include but are not limited to demand forecasting, inventory management, supply chain de…

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Assessing scale and predictive diversity in models for single-cell transcriptomics based on Geneformer

Author summary Single-cell analysis helps researchers understand how genes work together inside individual cells, and recent artificial intelligence models have shown strong potential for uncovering these patterns. However, many existing approaches do not fully account for how this data is structured, and often assume that using more training data will always improve performance. In this study, we introduce GFCAB, a model designed to better match the way single-cell data are organized. By reducing repeated predi…

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Assessing scale and predictive diversity in models for single-cell transcriptomics based on Geneformer

Integrating multi-layer perceptron and random forest in an ensemble framework for improved genomic prediction accuracy and SHAP-derived interpretability of residual feed intake in cattle

Background Feed efficiency (FE) is recognized as a vital component of sustainable dairy production, with residual feed intake (RFI) serving as a key metabolic indicator of FE independent of production levels. However, the genetic improvement of this complex trait is limited by the inability of conventional genomic Best Linear Unbiased Prediction (gBLUP) model to capture complex, non-linear genetic architectures and epistatic interactions. To address these limitations, this study aims to compare the predictive pe…

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Integrating multi-layer perceptron and random forest in an ensemble framework for improved genomic prediction accuracy and SHAP-derived interpretability of residual feed intake in cattle

Enhancing Work Productivity through Generative Artificial Intelligence: A Comprehensive Literature Review

In this review, utilizing the PRISMA methodology, a comprehensive analysis of the use of Generative Artificial Intelligence (GAI) across diverse professional sectors is presented, drawing from 159 selected research publications. This study provides an insightful overview of the impact of GAI on enhancing institutional performance and work productivity, with a specific focus on sectors including academia, research, technology, communications, agriculture, government, and business. It highlights the critical role…

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Enhancing Work Productivity through Generative Artificial Intelligence: A Comprehensive Literature Review

Distinguishing compound and cumulative hazards using machine learning and fuzzy logic in multi-hazard susceptibility mapping

This study presents an integrated multi-hazard susceptibility assessment for a mountainous region in northern Iran, focusing on four major hazards: flood, avalanche, rockfall, and landslide. Three machine learning models Artificial Neural Network (ANN), Random Forest (RF), and Support Vector Machine (SVM) were applied to model single-hazard susceptibility using 21 topographic, climatic, geological, land-cover, and proximity-related variables at 30 m spatial resolution. Model performance was evaluated using ROC-A…

Climate

SolenopsisDetector: development of an automatic detection system for fire ants using computer vision and deep learning

Fire ants (Solenopsis spp. Westwood) pose a major ecological and economic threat, mainly due to the invasive potential of certain species. Current identification methods are highly dependent on taxonomic expertise, which can slow down decision-making. The development of an automated detection system could therefore support the identification process. We present SolenopsisDetector (SolenopD), an automated system for identifying Solenopsis ants using computer vision and deep learning. Following taxonomic practice,…

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SolenopsisDetector: development of an automatic detection system for fire ants using computer vision and deep learning

Predicting antifouling paint particle contamination based on 16S rRNA gene sequencing data using random forest-based machine learning

Antifouling paints often contain biocides designed to inhibit biological growth, and antifouling paint particles (APPs) have been previously shown to affect microbial communities in sediment. Given that typical methods for monitoring for APP presence can be specialized and challenging, alternative methods using simple, standardized, and universal approaches, such as 16S rRNA amplicon sequencing, would be highly valuable. This study uses a field-based mesocosm approach to train a random forest-based (supervised)…

Climate

Predicting antifouling paint particle contamination based on 16S rRNA gene sequencing data using random forest-based machine learning

Coupling machine learning with a biophysical model for maturity date prediction of apple fruit across China's apple planting regions

Background Accurate prediction of apple fruit maturity date is essential for optimizing harvest timing, fruit quality and market value under climate change. However, process-based crop models often show limited performance when extrapolated across large spatial scales, whereas machine learning models lack physiological interpretability. To address these limitations, this study has developed a hybrid framework integrating the process-based STICS model with machine learning approaches across China's apple planting…

Climate

Coupling machine learning with a biophysical model for maturity date prediction of apple fruit across China's apple planting regions

Adaptive level modification via player skill classification and large language models

Maintaining player engagement in video games requires a careful balance between challenge and player competence. Static difficulty settings fail to account for individual skill variation, while existing dynamic difficulty adjustment systems are limited to tuning low-level game parameters rather than restructuring level content. This paper presents an adaptive level modification framework that personalizes gameplay by continuously inferring player skill and applying targeted structural modifications to level cont…

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