Earth & Environmental Sciences

48 items

Research Explorer (The University of Manchester) Oct 1, 2026
Addressing the assumption in European energy poverty research that drivers are separate and additive, this study develops a three-level modelling framework that operationalises intersectionality as a mediating structure linking socio-economic characteristics to energy poverty, and uses regional data for England with combined statistical and machine learning methods to identify underlying patterns, finding that similar levels of energy poverty can emerge from different combinations of conditions: a socio-economic gradient related to labour market position, education, and health plays a dominant role, while additional intersectional patterns capture life-stage differences and energy system characteristics, particularly heating types.
Addressing the assumption in European energy poverty research that drivers are separate and additive, this study develops a three-level modelling framework that operationalises intersectionality as a mediating structure linking socio-economic characteristics to energy poverty, and uses regional data for England with combined statistical and machine learning methods to identify underlying patterns, finding that similar levels of energy poverty can emerge from different combinations of conditions: a socio-economic gradient related to labour market position, education, and health plays a dominant role, while additional intersectional patterns capture life-stage differences and energy system characteristics, particularly heating types.
Modelling regional energy poverty in England: a three-level intersectional framework finds similar poverty levels can arise from different combinations of conditions
Addressing the assumption in European energy poverty research that drivers are separate and additive, this study develops a three-level modelling framework that operationalises intersectionality as a mediating structure linking socio-economic characteristics to energy poverty, and uses regional data for England with combined statistical and machine learning methods to identify underlying patterns, finding that similar levels of energy poverty can emerge from different combinations of conditions: a socio-economic gradient related to labour market position, education, and health plays a dominant role, while additional intersectional patterns capture life-stage differences and energy system characteristics, particularly heating types.
Addressing the assumption in European energy poverty research that drivers are separate and additive, this study develops a three-level modelling framework that operationalises intersectionality as a mediating structure linking socio-economic characteristics to energy poverty, and uses regional data for England with combined statistical and machine learning methods to identify underlying patterns, finding that similar levels of energy poverty can emerge from different combinations of conditions: a socio-economic gradient related to labour market position, education, and health plays a dominant role, while additional intersectional patterns capture life-stage differences and energy system characteristics, particularly heating types.
DOAJ (DOAJ: Directory of Open Access Journals) Oct 1, 2026
This study applies scientometric analysis to literature on intelligent knowledge management in water treatment, organizing the analysis around thematic trends, collaboration networks, research gaps, and future research priorities, drawing on references that span AI-based groundwater quality assessment, machine-learning water quality index prediction, knowledge-graph management of industrial water treatment knowledge, and multiple bibliometric reviews of water and wastewater treatment.
This study applies scientometric analysis to literature on intelligent knowledge management in water treatment, organizing the analysis around thematic trends, collaboration networks, research gaps, and future research priorities, drawing on references that span AI-based groundwater quality assessment, machine-learning water quality index prediction, knowledge-graph management of industrial water treatment knowledge, and multiple bibliometric reviews of water and wastewater treatment.
Scientometric Analysis of Intelligent Knowledge Management in Water Treatment: Thematic Trends, Collaboration Networks, Research Gaps, and Future Priorities
This study applies scientometric analysis to literature on intelligent knowledge management in water treatment, organizing the analysis around thematic trends, collaboration networks, research gaps, and future research priorities, drawing on references that span AI-based groundwater quality assessment, machine-learning water quality index prediction, knowledge-graph management of industrial water treatment knowledge, and multiple bibliometric reviews of water and wastewater treatment.
This study applies scientometric analysis to literature on intelligent knowledge management in water treatment, organizing the analysis around thematic trends, collaboration networks, research gaps, and future research priorities, drawing on references that span AI-based groundwater quality assessment, machine-learning water quality index prediction, knowledge-graph management of industrial water treatment knowledge, and multiple bibliometric reviews of water and wastewater treatment.
发表出处待核验 Oct 1, 2026
Starting from a previously trained temporal convolutional network sperm whale click-train detector, the study compared four transfer approaches across four sources (BAL, CS, ICE, MED)—cross-dataset baseline evaluation, training from scratch on only 500 target recordings, pretraining with random fine-tuning, and pretraining with active (uncertainty-based) fine-tuning—and found that pretrained models dropped in performance on unseen data but that fine-tuning with 500 target recordings effectively mitigated the drop, with active fine-tuning consistently outperforming the other approaches although its gain over random fine-tuning was marginal.
Starting from a previously trained temporal convolutional network sperm whale click-train detector, the study compared four transfer approaches across four sources (BAL, CS, ICE, MED)—cross-dataset baseline evaluation, training from scratch on only 500 target recordings, pretraining with random fine-tuning, and pretraining with active (uncertainty-based) fine-tuning—and found that pretrained models dropped in performance on unseen data but that fine-tuning with 500 target recordings effectively mitigated the drop, with active fine-tuning consistently outperforming the other approaches although its gain over random fine-tuning was marginal.
Pretraining on diverse data and fine-tuning with 500 target recordings lifted a sperm whale click-train detector's AUC from as low as 0.60 to 0.78–0.97 on unseen datasets
Starting from a previously trained temporal convolutional network sperm whale click-train detector, the study compared four transfer approaches across four sources (BAL, CS, ICE, MED)—cross-dataset baseline evaluation, training from scratch on only 500 target recordings, pretraining with random fine-tuning, and pretraining with active (uncertainty-based) fine-tuning—and found that pretrained models dropped in performance on unseen data but that fine-tuning with 500 target recordings effectively mitigated the drop, with active fine-tuning consistently outperforming the other approaches although its gain over random fine-tuning was marginal.
Starting from a previously trained temporal convolutional network sperm whale click-train detector, the study compared four transfer approaches across four sources (BAL, CS, ICE, MED)—cross-dataset baseline evaluation, training from scratch on only 500 target recordings, pretraining with random fine-tuning, and pretraining with active (uncertainty-based) fine-tuning—and found that pretrained models dropped in performance on unseen data but that fine-tuning with 500 target recordings effectively mitigated the drop, with active fine-tuning consistently outperforming the other approaches although its gain over random fine-tuning was marginal.
DOAJ (DOAJ: Directory of Open Access Journals) Oct 1, 2026
Titled "Scientometric Analysis of Research Trends in Smart and Sustainable Architecture in Educational Environments: With an Emphasis on Green Schools and Campuses," the work is, as far as the loaded text shows, a scientometric/bibliometric analysis that gathers and cites literature on smart and sustainable architecture in educational environments, green schools and campuses, spanning green school assessment, sustainable education space design, smart buildings and smart campuses, BIM and IoT, biophilic design, energy efficiency, and net-zero energy buildings, and it cites scientometric and visualization tools such as Bibliometrix, VOSviewer, and CiteSpace along with methodological sources on co-citation and science mapping; however, the loaded text presents only a reference list and contai
Titled "Scientometric Analysis of Research Trends in Smart and Sustainable Architecture in Educational Environments: With an Emphasis on Green Schools and Campuses," the work is, as far as the loaded text shows, a scientometric/bibliometric analysis that gathers and cites literature on smart and sustainable architecture in educational environments, green schools and campuses, spanning green school assessment, sustainable education space design, smart buildings and smart campuses, BIM and IoT, biophilic design, energy efficiency, and net-zero energy buildings, and it cites scientometric and visualization tools such as Bibliometrix, VOSviewer, and CiteSpace along with methodological sources on co-citation and science mapping; however, the loaded text presents only a reference list and contai
This scientometric analysis maps research trends in smart and sustainable architecture in educational environments, with an emphasis on green schools and campuses, and lists the bibliometric tools and representative literature it draws on.
Titled "Scientometric Analysis of Research Trends in Smart and Sustainable Architecture in Educational Environments: With an Emphasis on Green Schools and Campuses," the work is, as far as the loaded text shows, a scientometric/bibliometric analysis that gathers and cites literature on smart and sustainable architecture in educational environments, green schools and campuses, spanning green school assessment, sustainable education space design, smart buildings and smart campuses, BIM and IoT, biophilic design, energy efficiency, and net-zero energy buildings, and it cites scientometric and visualization tools such as Bibliometrix, VOSviewer, and CiteSpace along with methodological sources on co-citation and science mapping; however, the loaded text presents only a reference list and contai
Titled "Scientometric Analysis of Research Trends in Smart and Sustainable Architecture in Educational Environments: With an Emphasis on Green Schools and Campuses," the work is, as far as the loaded text shows, a scientometric/bibliometric analysis that gathers and cites literature on smart and sustainable architecture in educational environments, green schools and campuses, spanning green school assessment, sustainable education space design, smart buildings and smart campuses, BIM and IoT, biophilic design, energy efficiency, and net-zero energy buildings, and it cites scientometric and visualization tools such as Bibliometrix, VOSviewer, and CiteSpace along with methodological sources on co-citation and science mapping; however, the loaded text presents only a reference list and contai
Microsoft Research Oct 1, 2026
Developed during a Microsoft Research summer internship, this end-to-end machine learning pipeline uses solar-wind observations from the L1 Lagrange point, forecasts of the AE and Dst indices, physics-informed constraints, local geological conductivity, and grid-infrastructure data to produce location-specific geomagnetically induced current risk estimates 30-60 minutes ahead for 66,935 substations in the continental United States, detecting nearly 80% of major space-weather events over the 2020-2026 evaluation period, with an AE forecast RMSE of 410.2 nT and a Dst forecast RMSE of 7.2 nT, outperforming the Burton equation on 62.2% of high-activity hours.
Developed during a Microsoft Research summer internship, this end-to-end machine learning pipeline uses solar-wind observations from the L1 Lagrange point, forecasts of the AE and Dst indices, physics-informed constraints, local geological conductivity, and grid-infrastructure data to produce location-specific geomagnetically induced current risk estimates 30-60 minutes ahead for 66,935 substations in the continental United States, detecting nearly 80% of major space-weather events over the 2020-2026 evaluation period, with an AE forecast RMSE of 410.2 nT and a Dst forecast RMSE of 7.2 nT, outperforming the Burton equation on 62.2% of high-activity hours.
Microsoft Research intern builds a machine learning pipeline that gives 30-60 minute space-weather risk warnings for 66,935 U.S. substations, detecting nearly 80% of major events
Developed during a Microsoft Research summer internship, this end-to-end machine learning pipeline uses solar-wind observations from the L1 Lagrange point, forecasts of the AE and Dst indices, physics-informed constraints, local geological conductivity, and grid-infrastructure data to produce location-specific geomagnetically induced current risk estimates 30-60 minutes ahead for 66,935 substations in the continental United States, detecting nearly 80% of major space-weather events over the 2020-2026 evaluation period, with an AE forecast RMSE of 410.2 nT and a Dst forecast RMSE of 7.2 nT, outperforming the Burton equation on 62.2% of high-activity hours.
Developed during a Microsoft Research summer internship, this end-to-end machine learning pipeline uses solar-wind observations from the L1 Lagrange point, forecasts of the AE and Dst indices, physics-informed constraints, local geological conductivity, and grid-infrastructure data to produce location-specific geomagnetically induced current risk estimates 30-60 minutes ahead for 66,935 substations in the continental United States, detecting nearly 80% of major space-weather events over the 2020-2026 evaluation period, with an AE forecast RMSE of 410.2 nT and a Dst forecast RMSE of 7.2 nT, outperforming the Burton equation on 62.2% of high-activity hours.
Nature News Oct 1, 2026
In 2024 the International Union of Geological Sciences upheld the rejection of a proposal to establish the Anthropocene as a formal geological epoch (a stratigrapher subcommittee voted 12 against, 4 in favor, 2 abstaining); two years later, Jürgen Renn and co-authors argue in Earth's Future that unpublished documents show the rejection was illegitimate and its rationale never adequately explained, calling on the IUGS to release key documents and submit the process to independent review, while supporters publish a review in Nature Reviews Earth & Environment compiling multi-proxy evidence from 12 globally distributed stratigraphic records showing mid-twentieth-century Earth system changes are abrupt, globally synchronous and stratigraphically distinct, with a sharp 1952 plutonium increase f
In 2024 the International Union of Geological Sciences upheld the rejection of a proposal to establish the Anthropocene as a formal geological epoch (a stratigrapher subcommittee voted 12 against, 4 in favor, 2 abstaining); two years later, Jürgen Renn and co-authors argue in Earth's Future that unpublished documents show the rejection was illegitimate and its rationale never adequately explained, calling on the IUGS to release key documents and submit the process to independent review, while supporters publish a review in Nature Reviews Earth & Environment compiling multi-proxy evidence from 12 globally distributed stratigraphic records showing mid-twentieth-century Earth system changes are abrupt, globally synchronous and stratigraphically distinct, with a sharp 1952 plutonium increase f
Two years after the Anthropocene proposal was rejected, scholars allege the vote was not transparent and call for the documents to be released, while a review of 12 global stratigraphic records reaffirms 1952 plutonium as a viable boundary marker
In 2024 the International Union of Geological Sciences upheld the rejection of a proposal to establish the Anthropocene as a formal geological epoch (a stratigrapher subcommittee voted 12 against, 4 in favor, 2 abstaining); two years later, Jürgen Renn and co-authors argue in Earth's Future that unpublished documents show the rejection was illegitimate and its rationale never adequately explained, calling on the IUGS to release key documents and submit the process to independent review, while supporters publish a review in Nature Reviews Earth & Environment compiling multi-proxy evidence from 12 globally distributed stratigraphic records showing mid-twentieth-century Earth system changes are abrupt, globally synchronous and stratigraphically distinct, with a sharp 1952 plutonium increase f
In 2024 the International Union of Geological Sciences upheld the rejection of a proposal to establish the Anthropocene as a formal geological epoch (a stratigrapher subcommittee voted 12 against, 4 in favor, 2 abstaining); two years later, Jürgen Renn and co-authors argue in Earth's Future that unpublished documents show the rejection was illegitimate and its rationale never adequately explained, calling on the IUGS to release key documents and submit the process to independent review, while supporters publish a review in Nature Reviews Earth & Environment compiling multi-proxy evidence from 12 globally distributed stratigraphic records showing mid-twentieth-century Earth system changes are abrupt, globally synchronous and stratigraphically distinct, with a sharp 1952 plutonium increase f
Nature News Oct 1, 2026
Nature's photo team rounds up September science images spanning astrophotography (the Shark Nebula, solar active region, lunar occultation of Venus, aurorae), a 38-person high-status burial at Peru's Chan Chan, the first atomic-force micrograph of two DNA double helices 'zipping up', the first new felid described in over a century (Leopardus tilcayo) from Bolivia, a wool-blanket trial on Switzerland's Tsanfleuron Glacier, the Bhotekoshi River mudflow disaster, Starship's first orbital flight, and completion of the Hyper-Kamiokande cavern in Japan.
Nature's photo team rounds up September science images spanning astrophotography (the Shark Nebula, solar active region, lunar occultation of Venus, aurorae), a 38-person high-status burial at Peru's Chan Chan, the first atomic-force micrograph of two DNA double helices 'zipping up', the first new felid described in over a century (Leopardus tilcayo) from Bolivia, a wool-blanket trial on Switzerland's Tsanfleuron Glacier, the Bhotekoshi River mudflow disaster, Starship's first orbital flight, and completion of the Hyper-Kamiokande cavern in Japan.
From the Shark Nebula to a new wildcat: the science behind Nature's September image roundup
Nature's photo team rounds up September science images spanning astrophotography (the Shark Nebula, solar active region, lunar occultation of Venus, aurorae), a 38-person high-status burial at Peru's Chan Chan, the first atomic-force micrograph of two DNA double helices 'zipping up', the first new felid described in over a century (Leopardus tilcayo) from Bolivia, a wool-blanket trial on Switzerland's Tsanfleuron Glacier, the Bhotekoshi River mudflow disaster, Starship's first orbital flight, and completion of the Hyper-Kamiokande cavern in Japan.
Nature's photo team rounds up September science images spanning astrophotography (the Shark Nebula, solar active region, lunar occultation of Venus, aurorae), a 38-person high-status burial at Peru's Chan Chan, the first atomic-force micrograph of two DNA double helices 'zipping up', the first new felid described in over a century (Leopardus tilcayo) from Bolivia, a wool-blanket trial on Switzerland's Tsanfleuron Glacier, the Bhotekoshi River mudflow disaster, Starship's first orbital flight, and completion of the Hyper-Kamiokande cavern in Japan.
Journal of Mining Institute Sep 30, 2026
The study measured the chemical composition and physical properties of seven types of high-temperature process waste from the Ural industrial region (granulated blast-furnace slag, lump blast-furnace slag, steelmaking slag, electric steelmaking slag, copper-smelting slag, ferrochrome production slag, and waste-incinerator slag), selected resource indicators (mass fractions of metallic iron, Cu, Zn, Ni; basicity modulus; crystallinity; particle-size distribution) and environmental indicators (mass fractions of Pb, Cr, S, P; dust fraction proportion; leachability), and after normalization and multicollinearity checks applied fuzzy clustering in Statistica (c=3, m=2, ε=0.
The study measured the chemical composition and physical properties of seven types of high-temperature process waste from the Ural industrial region (granulated blast-furnace slag, lump blast-furnace slag, steelmaking slag, electric steelmaking slag, copper-smelting slag, ferrochrome production slag, and waste-incinerator slag), selected resource indicators (mass fractions of metallic iron, Cu, Zn, Ni; basicity modulus; crystallinity; particle-size distribution) and environmental indicators (mass fractions of Pb, Cr, S, P; dust fraction proportion; leachability), and after normalization and multicollinearity checks applied fuzzy clustering in Statistica (c=3, m=2, ε=0.
Russian team used fuzzy clustering to sort seven high-temperature slags into three groups, finding steelmaking slag resource-valuable but moderately hazardous while copper and incinerator slags are high-hazard
The study measured the chemical composition and physical properties of seven types of high-temperature process waste from the Ural industrial region (granulated blast-furnace slag, lump blast-furnace slag, steelmaking slag, electric steelmaking slag, copper-smelting slag, ferrochrome production slag, and waste-incinerator slag), selected resource indicators (mass fractions of metallic iron, Cu, Zn, Ni; basicity modulus; crystallinity; particle-size distribution) and environmental indicators (mass fractions of Pb, Cr, S, P; dust fraction proportion; leachability), and after normalization and multicollinearity checks applied fuzzy clustering in Statistica (c=3, m=2, ε=0.
The study measured the chemical composition and physical properties of seven types of high-temperature process waste from the Ural industrial region (granulated blast-furnace slag, lump blast-furnace slag, steelmaking slag, electric steelmaking slag, copper-smelting slag, ferrochrome production slag, and waste-incinerator slag), selected resource indicators (mass fractions of metallic iron, Cu, Zn, Ni; basicity modulus; crystallinity; particle-size distribution) and environmental indicators (mass fractions of Pb, Cr, S, P; dust fraction proportion; leachability), and after normalization and multicollinearity checks applied fuzzy clustering in Statistica (c=3, m=2, ε=0.
GEO Knowledge Hub Sep 30, 2026
This OEMC project use case proposes an open workflow that leverages other remote sensing data such as LST and NIRv with a semi-empirical approach combining data-driven methods and physical constraints to enhance the spatial resolution of Sentinel-5P TROPOMI-based SIF estimates from about 5 km to about 1 km, produces a gridded dataset at 0.05 degrees with 8-daily frequency for 2018-2025, ports the tool to the Copernicus Data Space Ecosystem via the OpenEO framework for on-demand downscaling by users, and attempts to match satellite grid cells to eddy covariance flux site GPP ground measurements to assess the effect of spatial heterogeneity.
This OEMC project use case proposes an open workflow that leverages other remote sensing data such as LST and NIRv with a semi-empirical approach combining data-driven methods and physical constraints to enhance the spatial resolution of Sentinel-5P TROPOMI-based SIF estimates from about 5 km to about 1 km, produces a gridded dataset at 0.05 degrees with 8-daily frequency for 2018-2025, ports the tool to the Copernicus Data Space Ecosystem via the OpenEO framework for on-demand downscaling by users, and attempts to match satellite grid cells to eddy covariance flux site GPP ground measurements to assess the effect of spatial heterogeneity.
OEMC use case proposes an open workflow that enhances SIF spatial resolution to about 1 km for better GPP flux estimation
This OEMC project use case proposes an open workflow that leverages other remote sensing data such as LST and NIRv with a semi-empirical approach combining data-driven methods and physical constraints to enhance the spatial resolution of Sentinel-5P TROPOMI-based SIF estimates from about 5 km to about 1 km, produces a gridded dataset at 0.05 degrees with 8-daily frequency for 2018-2025, ports the tool to the Copernicus Data Space Ecosystem via the OpenEO framework for on-demand downscaling by users, and attempts to match satellite grid cells to eddy covariance flux site GPP ground measurements to assess the effect of spatial heterogeneity.
This OEMC project use case proposes an open workflow that leverages other remote sensing data such as LST and NIRv with a semi-empirical approach combining data-driven methods and physical constraints to enhance the spatial resolution of Sentinel-5P TROPOMI-based SIF estimates from about 5 km to about 1 km, produces a gridded dataset at 0.05 degrees with 8-daily frequency for 2018-2025, ports the tool to the Copernicus Data Space Ecosystem via the OpenEO framework for on-demand downscaling by users, and attempts to match satellite grid cells to eddy covariance flux site GPP ground measurements to assess the effect of spatial heterogeneity.
bioRxiv Sep 30, 2026
The study proposes Forest-Weighted S-map (FORWS) and Forest-Weighted Causal Inference (FORWC), which replace the Euclidean metric with adaptive "forest weights" derived from random forest ensembles, and reports comparable or improved forecasting skill relative to conventional tools, substantial resilience to dynamic process noise, mitigation of the curse of dimensionality, and multimodal directed causal inference between high-dimensional acoustic vectors and scalar temperature monitored in a honeybee hive.
The study proposes Forest-Weighted S-map (FORWS) and Forest-Weighted Causal Inference (FORWC), which replace the Euclidean metric with adaptive "forest weights" derived from random forest ensembles, and reports comparable or improved forecasting skill relative to conventional tools, substantial resilience to dynamic process noise, mitigation of the curse of dimensionality, and multimodal directed causal inference between high-dimensional acoustic vectors and scalar temperature monitored in a honeybee hive.
Replacing Euclidean distance with random-forest weights, FORWS and FORWC match or beat conventional tools on noisy high-dimensional series and extract directed interactions between honeybee-hive acoustic vectors and scalar temperature
The study proposes Forest-Weighted S-map (FORWS) and Forest-Weighted Causal Inference (FORWC), which replace the Euclidean metric with adaptive "forest weights" derived from random forest ensembles, and reports comparable or improved forecasting skill relative to conventional tools, substantial resilience to dynamic process noise, mitigation of the curse of dimensionality, and multimodal directed causal inference between high-dimensional acoustic vectors and scalar temperature monitored in a honeybee hive.
The study proposes Forest-Weighted S-map (FORWS) and Forest-Weighted Causal Inference (FORWC), which replace the Euclidean metric with adaptive "forest weights" derived from random forest ensembles, and reports comparable or improved forecasting skill relative to conventional tools, substantial resilience to dynamic process noise, mitigation of the curse of dimensionality, and multimodal directed causal inference between high-dimensional acoustic vectors and scalar temperature monitored in a honeybee hive.

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