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

Canadian Association of Radiologists Journal
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
Evidence-backed gain

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…

Enhancing Work Productivity through Generative Artificial Intelligence: A Comprehensive Literature Review
Evidence-backed gain

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…

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

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…

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

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,…

Climate

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)…

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

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…

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

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…

Adaptive level modification via player skill classification and large language models
Climate

The Role of Artificial Intelligence in Revolutionizing Industrial Automation for Municipal Waste Management in Industry 4.0

The topic’s relevance is related to the need to improve the efficiency of municipal waste management in the context of the development of Industry 4.0, where artificial intelligence (AI) can play a key role in optimizing the processes of sorting, collecting, and recycling waste. The purpose of the study is to study the potential of AI to improve environmental and operational indicators in the field of waste management, as well as to test hypotheses regarding the impact of AI on reducing costs, increasing efficie…

The Role of Artificial Intelligence in Revolutionizing Industrial Automation for Municipal Waste Management in Industry 4.0
Climate

Global genomic surveillance of β-lactam resistance in Escherichia coli across human, animal, and environmental reservoirs

Background Escherichia coli poses a global health threat from increasing β-lactam resistance. This study uses genomic and One-Health data to map resistance patterns and enhance antimicrobial resistance (AMR) prediction and management. Methods This study performed a One-Health whole-genome analysis of 30,554 E. coli isolates from human, animal, and environmental sources, spanning from 2000 to 2025. Publicly available genomic data were retrieved from NCBI, encompassing β-lactam resistance genes, including extended…

Global genomic surveillance of β-lactam resistance in Escherichia coli across human, animal, and environmental reservoirs
Science

Anonymized but Useful Synthetic Tabular Health Data for AI based Fall Risk Assessment

Artificial Intelligence (AI) bears potential for improving health care, but this depends on the availability of open-access, realistic, and useful data. To facilitate AI model development in health care we release SynTabFall, a novel synthetic dataset for fall risk assessment. With a total of 745,380 samples and 44 attributes such as demographics, diseases, mobility and cognition related risk factors, this tabular dataset allows for training fall risk prediction models without access to the original patient data…

Anonymized but Useful Synthetic Tabular Health Data for AI based Fall Risk Assessment
Science

EMFF-2025: a general neural network potential for energetic materials with C, H, N, and O elements

The discovery and optimization of high-energy materials (HEMs) face challenges due to the computational expense and slow iteration of traditional methods. Neural network potentials (NNPs) have emerged as an efficient alternative to first-principles simulations. This study presents EMFF-2025, a general NNP model for C, H, N, and O-based HEMs, leveraging transfer learning with minimal data from DFT calculations. The model achieves DFT-level accuracy, predicting the structure, mechanical properties, and decompositi…

EMFF-2025: a general neural network potential for energetic materials with C, H, N, and O elements