Earth & Environmental Sciences

48 items

Nature News Sep 30, 2026
This column-style summary draws on one Nature news story and two papers: a WHOI team broadcast healthy-reef soundscapes onto degraded reefs and found that Porites astreoides larvae settled almost twice as often on average; a second study selectively bred Acropora digitifera for one generation and found adult heat tolerance is heritable (h² about 0.2–0.3), with high-tolerance parents producing offspring that withstood about 1°C-week more heat stress than low-tolerance parents, while no genetic correlation was detected between short- and long-term heat tolerance; a third study compiled 220 global coral restoration projects and found restoration sites tend to be close to human access, more impacted and lower in coral diversity, with 57% of restored sites exposed to at least one bleaching aler
This column-style summary draws on one Nature news story and two papers: a WHOI team broadcast healthy-reef soundscapes onto degraded reefs and found that Porites astreoides larvae settled almost twice as often on average; a second study selectively bred Acropora digitifera for one generation and found adult heat tolerance is heritable (h² about 0.2–0.3), with high-tolerance parents producing offspring that withstood about 1°C-week more heat stress than low-tolerance parents, while no genetic correlation was detected between short- and long-term heat tolerance; a third study compiled 220 global coral restoration projects and found restoration sites tend to be close to human access, more impacted and lower in coral diversity, with 57% of restored sites exposed to at least one bleaching aler
WHOI's healthy-reef soundscapes nearly doubled coral larval settlement, while selective breeding raised adult heat tolerance by about 1°C-week
This column-style summary draws on one Nature news story and two papers: a WHOI team broadcast healthy-reef soundscapes onto degraded reefs and found that Porites astreoides larvae settled almost twice as often on average; a second study selectively bred Acropora digitifera for one generation and found adult heat tolerance is heritable (h² about 0.2–0.3), with high-tolerance parents producing offspring that withstood about 1°C-week more heat stress than low-tolerance parents, while no genetic correlation was detected between short- and long-term heat tolerance; a third study compiled 220 global coral restoration projects and found restoration sites tend to be close to human access, more impacted and lower in coral diversity, with 57% of restored sites exposed to at least one bleaching aler
This column-style summary draws on one Nature news story and two papers: a WHOI team broadcast healthy-reef soundscapes onto degraded reefs and found that Porites astreoides larvae settled almost twice as often on average; a second study selectively bred Acropora digitifera for one generation and found adult heat tolerance is heritable (h² about 0.2–0.3), with high-tolerance parents producing offspring that withstood about 1°C-week more heat stress than low-tolerance parents, while no genetic correlation was detected between short- and long-term heat tolerance; a third study compiled 220 global coral restoration projects and found restoration sites tend to be close to human access, more impacted and lower in coral diversity, with 57% of restored sites exposed to at least one bleaching aler
Nature News Sep 30, 2026
Mineral physicist Alisha Clark used the Z machine at Sandia National Laboratories to send shockwaves through water-infused glass samples, recreating pressures near Earth's core during its formation; earlier experiments showed the wet glass became easier to compress as pressure rose, leading her to propose that molten rock could have locked water inside Earth during its magma-ocean phase rather than losing it all or receiving it later from comets and asteroids.
Mineral physicist Alisha Clark used the Z machine at Sandia National Laboratories to send shockwaves through water-infused glass samples, recreating pressures near Earth's core during its formation; earlier experiments showed the wet glass became easier to compress as pressure rose, leading her to propose that molten rock could have locked water inside Earth during its magma-ocean phase rather than losing it all or receiving it later from comets and asteroids.
Clark fired the Z machine at water-infused glass and found melt may grow easier to compress under deep pressure, offering a clue to how early Earth kept its water
Mineral physicist Alisha Clark used the Z machine at Sandia National Laboratories to send shockwaves through water-infused glass samples, recreating pressures near Earth's core during its formation; earlier experiments showed the wet glass became easier to compress as pressure rose, leading her to propose that molten rock could have locked water inside Earth during its magma-ocean phase rather than losing it all or receiving it later from comets and asteroids.
Mineral physicist Alisha Clark used the Z machine at Sandia National Laboratories to send shockwaves through water-infused glass samples, recreating pressures near Earth's core during its formation; earlier experiments showed the wet glass became easier to compress as pressure rose, leading her to propose that molten rock could have locked water inside Earth during its magma-ocean phase rather than losing it all or receiving it later from comets and asteroids.
bioRxiv Sep 29, 2026
The authors present TRIDENT-2, a multimodal artificial intelligence model trained on 560,780 toxicity assays spanning 82,775 chemicals, 6,793 species, and multiple exposure scenarios to predict chemical toxicity across evolutionarily diverse eukaryotic species, reporting an average median absolute error of 1.76 to 3.80 and remaining accurate across broad chemical and taxonomic distances, which allows toxicity assessment for species and chemicals beyond current experimental evidence.
The authors present TRIDENT-2, a multimodal artificial intelligence model trained on 560,780 toxicity assays spanning 82,775 chemicals, 6,793 species, and multiple exposure scenarios to predict chemical toxicity across evolutionarily diverse eukaryotic species, reporting an average median absolute error of 1.76 to 3.80 and remaining accurate across broad chemical and taxonomic distances, which allows toxicity assessment for species and chemicals beyond current experimental evidence.
TRIDENT-2 predicts chemical toxicity across Eukaryota from 560,780 assays with average median absolute error of 1.76 to 3.80
The authors present TRIDENT-2, a multimodal artificial intelligence model trained on 560,780 toxicity assays spanning 82,775 chemicals, 6,793 species, and multiple exposure scenarios to predict chemical toxicity across evolutionarily diverse eukaryotic species, reporting an average median absolute error of 1.76 to 3.80 and remaining accurate across broad chemical and taxonomic distances, which allows toxicity assessment for species and chemicals beyond current experimental evidence.
The authors present TRIDENT-2, a multimodal artificial intelligence model trained on 560,780 toxicity assays spanning 82,775 chemicals, 6,793 species, and multiple exposure scenarios to predict chemical toxicity across evolutionarily diverse eukaryotic species, reporting an average median absolute error of 1.76 to 3.80 and remaining accurate across broad chemical and taxonomic distances, which allows toxicity assessment for species and chemicals beyond current experimental evidence.
arXiv Sep 29, 2026
The work proposes PyroAdapt, a pretrain–retrieve–rank framework that pretrains a wildfire risk model on historical data, retrieves similar historical locations using unlabeled target-year covariates, and fine-tunes through same-day fire–nonfire cell-pair ranking losses (direct ranking, residual pairwise DPO, and selective ranking); over 666 0.25° California grid cells, the three ranking objectives raise daily average precision from 21.62% under continued focal fine-tuning to 24.35–24.57% and Top5% recall from 18.70% to 22.11–22.79%, selective ranking captures 344 additional positive cell-days under a fixed daily budget of 34 cells (5% of area), and rolling evaluations over Yosemite show the ranking gains persist under temporal distribution shift.
The work proposes PyroAdapt, a pretrain–retrieve–rank framework that pretrains a wildfire risk model on historical data, retrieves similar historical locations using unlabeled target-year covariates, and fine-tunes through same-day fire–nonfire cell-pair ranking losses (direct ranking, residual pairwise DPO, and selective ranking); over 666 0.25° California grid cells, the three ranking objectives raise daily average precision from 21.62% under continued focal fine-tuning to 24.35–24.57% and Top5% recall from 18.70% to 22.11–22.79%, selective ranking captures 344 additional positive cell-days under a fixed daily budget of 34 cells (5% of area), and rolling evaluations over Yosemite show the ranking gains persist under temporal distribution shift.
PyroAdapt lifts daily California wildfire average precision from 21.62% to 24.35–24.57% and captures 344 extra positive cell-days under a fixed 34-cell budget
The work proposes PyroAdapt, a pretrain–retrieve–rank framework that pretrains a wildfire risk model on historical data, retrieves similar historical locations using unlabeled target-year covariates, and fine-tunes through same-day fire–nonfire cell-pair ranking losses (direct ranking, residual pairwise DPO, and selective ranking); over 666 0.25° California grid cells, the three ranking objectives raise daily average precision from 21.62% under continued focal fine-tuning to 24.35–24.57% and Top5% recall from 18.70% to 22.11–22.79%, selective ranking captures 344 additional positive cell-days under a fixed daily budget of 34 cells (5% of area), and rolling evaluations over Yosemite show the ranking gains persist under temporal distribution shift.
The work proposes PyroAdapt, a pretrain–retrieve–rank framework that pretrains a wildfire risk model on historical data, retrieves similar historical locations using unlabeled target-year covariates, and fine-tunes through same-day fire–nonfire cell-pair ranking losses (direct ranking, residual pairwise DPO, and selective ranking); over 666 0.25° California grid cells, the three ranking objectives raise daily average precision from 21.62% under continued focal fine-tuning to 24.35–24.57% and Top5% recall from 18.70% to 22.11–22.79%, selective ranking captures 344 additional positive cell-days under a fixed daily budget of 34 cells (5% of area), and rolling evaluations over Yosemite show the ranking gains persist under temporal distribution shift.
IEEE Spectrum Sep 28, 2026
Written by a Delhi power-engineering professor, this article traces how the city's distribution grid went from losses above 50% and a reliability index of about 70% in 2002 to 5-6% losses and a reliability index above 99.9% in 2026, and distills the path into a combination of technical upgrades, organizational and billing reform, enforcement, and community engagement.
Written by a Delhi power-engineering professor, this article traces how the city's distribution grid went from losses above 50% and a reliability index of about 70% in 2002 to 5-6% losses and a reliability index above 99.9% in 2026, and distills the path into a combination of technical upgrades, organizational and billing reform, enforcement, and community engagement.
Delhi's grid cut electricity losses from over 50% to 5-6% and lifted its reliability index from about 70% to above 99.9%
Written by a Delhi power-engineering professor, this article traces how the city's distribution grid went from losses above 50% and a reliability index of about 70% in 2002 to 5-6% losses and a reliability index above 99.9% in 2026, and distills the path into a combination of technical upgrades, organizational and billing reform, enforcement, and community engagement.
Written by a Delhi power-engineering professor, this article traces how the city's distribution grid went from losses above 50% and a reliability index of about 70% in 2002 to 5-6% losses and a reliability index above 99.9% in 2026, and distills the path into a combination of technical upgrades, organizational and billing reform, enforcement, and community engagement.
Research Square Sep 28, 2026
The authors built a physics-informed screening model for California pesticide exposure: a first-order decay fate index based on soil half-life drives a LightGBM model that predicts weekly county-level pesticide application from 2016-2023 public agricultural, weather, and chemical-property data, and they validated the resulting exposure index against 3,924 U.S. Geological Survey stream measurements at 554 California stations never used in training; agreement was strong and significant for the two most-monitored chemicals (Malathion rho = 0.51, p < 10^-6; Metolachlor rho = 0.28, p < 10^-8), the pooled correlation across all fourteen monitored chemicals was weak, the application model generalized well across space (R2 = 0.64) and time (R2 = 0.
The authors built a physics-informed screening model for California pesticide exposure: a first-order decay fate index based on soil half-life drives a LightGBM model that predicts weekly county-level pesticide application from 2016-2023 public agricultural, weather, and chemical-property data, and they validated the resulting exposure index against 3,924 U.S. Geological Survey stream measurements at 554 California stations never used in training; agreement was strong and significant for the two most-monitored chemicals (Malathion rho = 0.51, p < 10^-6; Metolachlor rho = 0.28, p < 10^-8), the pooled correlation across all fourteen monitored chemicals was weak, the application model generalized well across space (R2 = 0.64) and time (R2 = 0.
A physics-informed machine learning model predicted pesticide exposure in California; against 3,924 stream measurements, the two most-monitored chemicals correlated significantly while the pooled fourteen-chemical correlation was weak
The authors built a physics-informed screening model for California pesticide exposure: a first-order decay fate index based on soil half-life drives a LightGBM model that predicts weekly county-level pesticide application from 2016-2023 public agricultural, weather, and chemical-property data, and they validated the resulting exposure index against 3,924 U.S. Geological Survey stream measurements at 554 California stations never used in training; agreement was strong and significant for the two most-monitored chemicals (Malathion rho = 0.51, p < 10^-6; Metolachlor rho = 0.28, p < 10^-8), the pooled correlation across all fourteen monitored chemicals was weak, the application model generalized well across space (R2 = 0.64) and time (R2 = 0.
The authors built a physics-informed screening model for California pesticide exposure: a first-order decay fate index based on soil half-life drives a LightGBM model that predicts weekly county-level pesticide application from 2016-2023 public agricultural, weather, and chemical-property data, and they validated the resulting exposure index against 3,924 U.S. Geological Survey stream measurements at 554 California stations never used in training; agreement was strong and significant for the two most-monitored chemicals (Malathion rho = 0.51, p < 10^-6; Metolachlor rho = 0.28, p < 10^-8), the pooled correlation across all fourteen monitored chemicals was weak, the application model generalized well across space (R2 = 0.64) and time (R2 = 0.
Journal of Data Science and Intelligent Systems Sep 28, 2026
Using a Multilayer Perceptron Regressor (MLPR) as the base model and a Random Committee hybrid (RC-MLPR), the study predicted the particle Froude number (PFr) in auto-washout drainage systems from five heterogeneous datasets collected from existing literature covering a wide range of hydraulic and sediment conditions, evaluated the models with several performance measures including the agreement index, found that RC-MLPR outperformed other proposed ML models, state-of-the-art ML models, and existing empirical equations, and reported from sensitivity analysis that volumetric sediment concentration (Csed) is the most sensitive variable for PFr prediction by the hybrid RC-MLPR model.
Using a Multilayer Perceptron Regressor (MLPR) as the base model and a Random Committee hybrid (RC-MLPR), the study predicted the particle Froude number (PFr) in auto-washout drainage systems from five heterogeneous datasets collected from existing literature covering a wide range of hydraulic and sediment conditions, evaluated the models with several performance measures including the agreement index, found that RC-MLPR outperformed other proposed ML models, state-of-the-art ML models, and existing empirical equations, and reported from sensitivity analysis that volumetric sediment concentration (Csed) is the most sensitive variable for PFr prediction by the hybrid RC-MLPR model.
A hybrid Random Committee and Multilayer Perceptron Regressor model predicts particle Froude number in auto-washout drainage systems with sedimented beds and identifies volumetric sediment concentration as the most sensitive variable
Using a Multilayer Perceptron Regressor (MLPR) as the base model and a Random Committee hybrid (RC-MLPR), the study predicted the particle Froude number (PFr) in auto-washout drainage systems from five heterogeneous datasets collected from existing literature covering a wide range of hydraulic and sediment conditions, evaluated the models with several performance measures including the agreement index, found that RC-MLPR outperformed other proposed ML models, state-of-the-art ML models, and existing empirical equations, and reported from sensitivity analysis that volumetric sediment concentration (Csed) is the most sensitive variable for PFr prediction by the hybrid RC-MLPR model.
Using a Multilayer Perceptron Regressor (MLPR) as the base model and a Random Committee hybrid (RC-MLPR), the study predicted the particle Froude number (PFr) in auto-washout drainage systems from five heterogeneous datasets collected from existing literature covering a wide range of hydraulic and sediment conditions, evaluated the models with several performance measures including the agreement index, found that RC-MLPR outperformed other proposed ML models, state-of-the-art ML models, and existing empirical equations, and reported from sensitivity analysis that volumetric sediment concentration (Csed) is the most sensitive variable for PFr prediction by the hybrid RC-MLPR model.
Scientific Reports Sep 28, 2026
The study presents a data-driven ecosystem risk assessment framework that treats risk as an emergent property of interacting environmental, anthropogenic, and biological stressors, combining a clustering-based Multi K-means technique with a Variational Autoencoder deep learning model and applying it to 2020 data from the central and western Baltic Sea with abundance information on 145 marine species, commercially relevant species, and cod; it identifies spatially concentrated risk hotspots in species-abundant areas where bottom-oxygen depletion, depth-related constraints, and fishing pressure co-occur, and cross-model concordance analysis shows the two models are both consistent and complementary.
The study presents a data-driven ecosystem risk assessment framework that treats risk as an emergent property of interacting environmental, anthropogenic, and biological stressors, combining a clustering-based Multi K-means technique with a Variational Autoencoder deep learning model and applying it to 2020 data from the central and western Baltic Sea with abundance information on 145 marine species, commercially relevant species, and cod; it identifies spatially concentrated risk hotspots in species-abundant areas where bottom-oxygen depletion, depth-related constraints, and fishing pressure co-occur, and cross-model concordance analysis shows the two models are both consistent and complementary.
Unsupervised models identify Baltic Sea species-rich hotspots threatened jointly by bottom-oxygen depletion and fishing pressure
The study presents a data-driven ecosystem risk assessment framework that treats risk as an emergent property of interacting environmental, anthropogenic, and biological stressors, combining a clustering-based Multi K-means technique with a Variational Autoencoder deep learning model and applying it to 2020 data from the central and western Baltic Sea with abundance information on 145 marine species, commercially relevant species, and cod; it identifies spatially concentrated risk hotspots in species-abundant areas where bottom-oxygen depletion, depth-related constraints, and fishing pressure co-occur, and cross-model concordance analysis shows the two models are both consistent and complementary.
The study presents a data-driven ecosystem risk assessment framework that treats risk as an emergent property of interacting environmental, anthropogenic, and biological stressors, combining a clustering-based Multi K-means technique with a Variational Autoencoder deep learning model and applying it to 2020 data from the central and western Baltic Sea with abundance information on 145 marine species, commercially relevant species, and cod; it identifies spatially concentrated risk hotspots in species-abundant areas where bottom-oxygen depletion, depth-related constraints, and fishing pressure co-occur, and cross-model concordance analysis shows the two models are both consistent and complementary.
bioRxiv Sep 28, 2026
Using 20 years (2003-2023) of dolphin photo-identification data, the study fitted a Bayesian Hidden Markov Model to estimate annual, age-class-specific survival probabilities, abundance and recruitment rate for Indo-Pacific bottlenose dolphins (Tursiops aduncus) in the western (WSB) and eastern (ESB) gulfs of Shark Bay, and related them to seagrass loss following the 2011 marine heatwave, finding adult survival fell from 0.99 (0.98-1.00) pre-MHW to 0.85 (0.79-0.90) post-MHW in WSB and from 0.96 (0.92-0.98) to 0.89 (0.85-0.
Using 20 years (2003-2023) of dolphin photo-identification data, the study fitted a Bayesian Hidden Markov Model to estimate annual, age-class-specific survival probabilities, abundance and recruitment rate for Indo-Pacific bottlenose dolphins (Tursiops aduncus) in the western (WSB) and eastern (ESB) gulfs of Shark Bay, and related them to seagrass loss following the 2011 marine heatwave, finding adult survival fell from 0.99 (0.98-1.00) pre-MHW to 0.85 (0.79-0.90) post-MHW in WSB and from 0.96 (0.92-0.98) to 0.89 (0.85-0.
After a 2011 marine heatwave wiped out seagrass, Shark Bay bottlenose dolphin adult survival fell from 0.99 to 0.85 and the two gulf populations declined by 39% and 36%
Using 20 years (2003-2023) of dolphin photo-identification data, the study fitted a Bayesian Hidden Markov Model to estimate annual, age-class-specific survival probabilities, abundance and recruitment rate for Indo-Pacific bottlenose dolphins (Tursiops aduncus) in the western (WSB) and eastern (ESB) gulfs of Shark Bay, and related them to seagrass loss following the 2011 marine heatwave, finding adult survival fell from 0.99 (0.98-1.00) pre-MHW to 0.85 (0.79-0.90) post-MHW in WSB and from 0.96 (0.92-0.98) to 0.89 (0.85-0.
Using 20 years (2003-2023) of dolphin photo-identification data, the study fitted a Bayesian Hidden Markov Model to estimate annual, age-class-specific survival probabilities, abundance and recruitment rate for Indo-Pacific bottlenose dolphins (Tursiops aduncus) in the western (WSB) and eastern (ESB) gulfs of Shark Bay, and related them to seagrass loss following the 2011 marine heatwave, finding adult survival fell from 0.99 (0.98-1.00) pre-MHW to 0.85 (0.79-0.90) post-MHW in WSB and from 0.96 (0.92-0.98) to 0.89 (0.85-0.
Research Square Sep 28, 2026
This study proposes a hybrid framework at the Sultan Qalawun School Complex in Historic Cairo, Egypt, integrating environmental simulation, machine learning techniques using the Hypar.io platform, and heritage conservation principles; through field data collection, geometric modeling, and AI-driven predictive models it examines the impacts of natural ventilation, vegetation, shading systems, and occupancy patterns on outdoor thermal comfort, evaluates interventions using Predicted Mean Vote (PMV), thermal discomfort hours, indoor environmental quality, and heritage preservation criteria, reports that AI-assisted simulation can reduce computational time by approximately 40% while maintaining high predictive accuracy relative to traditional physics-based simulations, and indicates that conse
This study proposes a hybrid framework at the Sultan Qalawun School Complex in Historic Cairo, Egypt, integrating environmental simulation, machine learning techniques using the Hypar.io platform, and heritage conservation principles; through field data collection, geometric modeling, and AI-driven predictive models it examines the impacts of natural ventilation, vegetation, shading systems, and occupancy patterns on outdoor thermal comfort, evaluates interventions using Predicted Mean Vote (PMV), thermal discomfort hours, indoor environmental quality, and heritage preservation criteria, reports that AI-assisted simulation can reduce computational time by approximately 40% while maintaining high predictive accuracy relative to traditional physics-based simulations, and indicates that conse
Team uses Hypar.io machine-learning microclimate simulation at Cairo's Sultan Qalawun complex, reporting about 40% less computation time with high predictive accuracy
This study proposes a hybrid framework at the Sultan Qalawun School Complex in Historic Cairo, Egypt, integrating environmental simulation, machine learning techniques using the Hypar.io platform, and heritage conservation principles; through field data collection, geometric modeling, and AI-driven predictive models it examines the impacts of natural ventilation, vegetation, shading systems, and occupancy patterns on outdoor thermal comfort, evaluates interventions using Predicted Mean Vote (PMV), thermal discomfort hours, indoor environmental quality, and heritage preservation criteria, reports that AI-assisted simulation can reduce computational time by approximately 40% while maintaining high predictive accuracy relative to traditional physics-based simulations, and indicates that conse
This study proposes a hybrid framework at the Sultan Qalawun School Complex in Historic Cairo, Egypt, integrating environmental simulation, machine learning techniques using the Hypar.io platform, and heritage conservation principles; through field data collection, geometric modeling, and AI-driven predictive models it examines the impacts of natural ventilation, vegetation, shading systems, and occupancy patterns on outdoor thermal comfort, evaluates interventions using Predicted Mean Vote (PMV), thermal discomfort hours, indoor environmental quality, and heritage preservation criteria, reports that AI-assisted simulation can reduce computational time by approximately 40% while maintaining high predictive accuracy relative to traditional physics-based simulations, and indicates that conse

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