Frontiers in Medicine The study builds a mechanism-informed machine learning framework that chains a pharmaceutical-knowledge-driven directed acyclic graph, causal structure discovery (PC, NOTEARS, DirectLiNGAM), XGBoost release prediction, explainable AI (SHAP), ATE/CATE intervention-effect estimation, and counterfactual formulation analysis; on a public dataset of 181 release profiles, 3,783 fractional release measurements, and 43 drug-polymer pairs, XGBoost reached test R² = 0.9774, RMSE = 0.0491, and MAE = 0.0339, and correlation and SHAP importance agreed strongly (r = 0.761) while both agreed poorly with intervention-effect estimates (r = 0.132 and −0.021), indicating that variables useful for prediction differ from those that can be manipulated.
The study builds a mechanism-informed machine learning framework that chains a pharmaceutical-knowledge-driven directed acyclic graph, causal structure discovery (PC, NOTEARS, DirectLiNGAM), XGBoost release prediction, explainable AI (SHAP), ATE/CATE intervention-effect estimation, and counterfactual formulation analysis; on a public dataset of 181 release profiles, 3,783 fractional release measurements, and 43 drug-polymer pairs, XGBoost reached test R² = 0.9774, RMSE = 0.0491, and MAE = 0.0339, and correlation and SHAP importance agreed strongly (r = 0.761) while both agreed poorly with intervention-effect estimates (r = 0.132 and −0.021), indicating that variables useful for prediction differ from those that can be manipulated.
The study builds a mechanism-informed machine learning framework that chains a pharmaceutical-knowledge-driven directed acyclic graph, causal structure discovery (PC, NOTEARS, DirectLiNGAM), XGBoost release prediction, explainable AI (SHAP), ATE/CATE intervention-effect estimation, and counterfactual formulation analysis; on a public dataset of 181 release profiles, 3,783 fractional release measurements, and 43 drug-polymer pairs, XGBoost reached test R² = 0.9774, RMSE = 0.0491, and MAE = 0.0339, and correlation and SHAP importance agreed strongly (r = 0.761) while both agreed poorly with intervention-effect estimates (r = 0.132 and −0.021), indicating that variables useful for prediction differ from those that can be manipulated.
The study builds a mechanism-informed machine learning framework that chains a pharmaceutical-knowledge-driven directed acyclic graph, causal structure discovery (PC, NOTEARS, DirectLiNGAM), XGBoost release prediction, explainable AI (SHAP), ATE/CATE intervention-effect estimation, and counterfactual formulation analysis; on a public dataset of 181 release profiles, 3,783 fractional release measurements, and 43 drug-polymer pairs, XGBoost reached test R² = 0.9774, RMSE = 0.0491, and MAE = 0.0339, and correlation and SHAP importance agreed strongly (r = 0.761) while both agreed poorly with intervention-effect estimates (r = 0.132 and −0.021), indicating that variables useful for prediction differ from those that can be manipulated.
International Journal of Corrosion and Scale Inhibition In a question-and-answer dialogue with ChatGPT, the authors compare normative definitions from GOST 9.106–2021, NACE/ASTM G193-22 and ISO 8044:2024, point to the ISO requirement that an inhibitor be "present in the corrosion system" and to the absence of a distinction between the bulk environment and the near-surface region, and then define an inhibitor through its action on the metal or the corrosion system, offering working definitions of a chemisorbed layer, a conversion coating and a bulk-phase anticorrosion coating, and arriving at a refined definition that excludes substances whose action significantly changes the concentration of corrosive components in the bulk environment, forms a conversion coating, or involves applying a bulk-phase anticorrosion coating.
In a question-and-answer dialogue with ChatGPT, the authors compare normative definitions from GOST 9.106–2021, NACE/ASTM G193-22 and ISO 8044:2024, point to the ISO requirement that an inhibitor be "present in the corrosion system" and to the absence of a distinction between the bulk environment and the near-surface region, and then define an inhibitor through its action on the metal or the corrosion system, offering working definitions of a chemisorbed layer, a conversion coating and a bulk-phase anticorrosion coating, and arriving at a refined definition that excludes substances whose action significantly changes the concentration of corrosive components in the bulk environment, forms a conversion coating, or involves applying a bulk-phase anticorrosion coating.
In a question-and-answer dialogue with ChatGPT, the authors compare normative definitions from GOST 9.106–2021, NACE/ASTM G193-22 and ISO 8044:2024, point to the ISO requirement that an inhibitor be "present in the corrosion system" and to the absence of a distinction between the bulk environment and the near-surface region, and then define an inhibitor through its action on the metal or the corrosion system, offering working definitions of a chemisorbed layer, a conversion coating and a bulk-phase anticorrosion coating, and arriving at a refined definition that excludes substances whose action significantly changes the concentration of corrosive components in the bulk environment, forms a conversion coating, or involves applying a bulk-phase anticorrosion coating.
In a question-and-answer dialogue with ChatGPT, the authors compare normative definitions from GOST 9.106–2021, NACE/ASTM G193-22 and ISO 8044:2024, point to the ISO requirement that an inhibitor be "present in the corrosion system" and to the absence of a distinction between the bulk environment and the near-surface region, and then define an inhibitor through its action on the metal or the corrosion system, offering working definitions of a chemisorbed layer, a conversion coating and a bulk-phase anticorrosion coating, and arriving at a refined definition that excludes substances whose action significantly changes the concentration of corrosive components in the bulk environment, forms a conversion coating, or involves applying a bulk-phase anticorrosion coating.
bioRxiv 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.
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.
Claude 产品博客 NVIDIA announced the Open Agent Safety Platform, an open software platform and reference system design, and collaborated with Anthropic to combine Claude Managed Agents with the open source NVIDIA OpenShell runtime: Managed Agents keeps the passwords and access keys an agent needs in a separate vault so the agent never sees them and adds audit trails plus integration with existing access controls, while OpenShell enforces policies outside the agent for every tool, file, network connection and data access, blocking everything unless a rule allows it and logging every decision it allows or blocks, so teams can start with narrow permissions, review the log, tighten rules toward least access with Claude, and use the policy prover to confirm by mathematical proof what the agent can reach under
NVIDIA announced the Open Agent Safety Platform, an open software platform and reference system design, and collaborated with Anthropic to combine Claude Managed Agents with the open source NVIDIA OpenShell runtime: Managed Agents keeps the passwords and access keys an agent needs in a separate vault so the agent never sees them and adds audit trails plus integration with existing access controls, while OpenShell enforces policies outside the agent for every tool, file, network connection and data access, blocking everything unless a rule allows it and logging every decision it allows or blocks, so teams can start with narrow permissions, review the log, tighten rules toward least access with Claude, and use the policy prover to confirm by mathematical proof what the agent can reach under
NVIDIA announced the Open Agent Safety Platform, an open software platform and reference system design, and collaborated with Anthropic to combine Claude Managed Agents with the open source NVIDIA OpenShell runtime: Managed Agents keeps the passwords and access keys an agent needs in a separate vault so the agent never sees them and adds audit trails plus integration with existing access controls, while OpenShell enforces policies outside the agent for every tool, file, network connection and data access, blocking everything unless a rule allows it and logging every decision it allows or blocks, so teams can start with narrow permissions, review the log, tighten rules toward least access with Claude, and use the policy prover to confirm by mathematical proof what the agent can reach under
NVIDIA announced the Open Agent Safety Platform, an open software platform and reference system design, and collaborated with Anthropic to combine Claude Managed Agents with the open source NVIDIA OpenShell runtime: Managed Agents keeps the passwords and access keys an agent needs in a separate vault so the agent never sees them and adds audit trails plus integration with existing access controls, while OpenShell enforces policies outside the agent for every tool, file, network connection and data access, blocking everything unless a rule allows it and logging every decision it allows or blocks, so teams can start with narrow permissions, review the log, tighten rules toward least access with Claude, and use the policy prover to confirm by mathematical proof what the agent can reach under
Research Square The work proposes a multi-UAV formation control framework called Pure Hamiltonian 3D RK Swarm, embedding an anisotropic vertically-scaled Rimon-Koditschek navigation potential into a pseudo-Hamiltonian dynamical framework, adding an active kinematic preview and deflection layer on the virtual target's trajectory, projecting rigid spatial offsets via a dynamic SO(3) rotation matrix, and using a spatial decay braking force modulated by the normal gradient of the workspace topology to curb overshoot; in numerical simulations with N=9 agents crossing an undulating sinusoidal terrain cluster, the formation satisfies hard safety constraints of 2.5 m buffer, 2.0 m altitude buffer, and 0.9 m inter-drone spacing.
The work proposes a multi-UAV formation control framework called Pure Hamiltonian 3D RK Swarm, embedding an anisotropic vertically-scaled Rimon-Koditschek navigation potential into a pseudo-Hamiltonian dynamical framework, adding an active kinematic preview and deflection layer on the virtual target's trajectory, projecting rigid spatial offsets via a dynamic SO(3) rotation matrix, and using a spatial decay braking force modulated by the normal gradient of the workspace topology to curb overshoot; in numerical simulations with N=9 agents crossing an undulating sinusoidal terrain cluster, the formation satisfies hard safety constraints of 2.5 m buffer, 2.0 m altitude buffer, and 0.9 m inter-drone spacing.
The work proposes a multi-UAV formation control framework called Pure Hamiltonian 3D RK Swarm, embedding an anisotropic vertically-scaled Rimon-Koditschek navigation potential into a pseudo-Hamiltonian dynamical framework, adding an active kinematic preview and deflection layer on the virtual target's trajectory, projecting rigid spatial offsets via a dynamic SO(3) rotation matrix, and using a spatial decay braking force modulated by the normal gradient of the workspace topology to curb overshoot; in numerical simulations with N=9 agents crossing an undulating sinusoidal terrain cluster, the formation satisfies hard safety constraints of 2.5 m buffer, 2.0 m altitude buffer, and 0.9 m inter-drone spacing.
The work proposes a multi-UAV formation control framework called Pure Hamiltonian 3D RK Swarm, embedding an anisotropic vertically-scaled Rimon-Koditschek navigation potential into a pseudo-Hamiltonian dynamical framework, adding an active kinematic preview and deflection layer on the virtual target's trajectory, projecting rigid spatial offsets via a dynamic SO(3) rotation matrix, and using a spatial decay braking force modulated by the normal gradient of the workspace topology to curb overshoot; in numerical simulations with N=9 agents crossing an undulating sinusoidal terrain cluster, the formation satisfies hard safety constraints of 2.5 m buffer, 2.0 m altitude buffer, and 0.9 m inter-drone spacing.
Journal of Bone and Joint Surgery This feasibility study trained a convolutional neural network on pre-reduction injury radiographs and combined its outputs with clinical and demographic data in a random forest model to predict whether a group of fellowship-trained hand surgeons at one institution would recommend operative intervention for distal radial fractures in 1,040 patients (884 training, 156 testing); on the test set the combined model achieved 87.14% accuracy, 97% sensitivity, 73% specificity, an area under the ROC curve of 0.96, and a Brier score of 0.10, with Grad-CAM indicating the CNN focused on clinically relevant features such as fracture displacement and SHAP highlighting age and lateral wrist radiographs as key contributors.
This feasibility study trained a convolutional neural network on pre-reduction injury radiographs and combined its outputs with clinical and demographic data in a random forest model to predict whether a group of fellowship-trained hand surgeons at one institution would recommend operative intervention for distal radial fractures in 1,040 patients (884 training, 156 testing); on the test set the combined model achieved 87.14% accuracy, 97% sensitivity, 73% specificity, an area under the ROC curve of 0.96, and a Brier score of 0.10, with Grad-CAM indicating the CNN focused on clinically relevant features such as fracture displacement and SHAP highlighting age and lateral wrist radiographs as key contributors.
This feasibility study trained a convolutional neural network on pre-reduction injury radiographs and combined its outputs with clinical and demographic data in a random forest model to predict whether a group of fellowship-trained hand surgeons at one institution would recommend operative intervention for distal radial fractures in 1,040 patients (884 training, 156 testing); on the test set the combined model achieved 87.14% accuracy, 97% sensitivity, 73% specificity, an area under the ROC curve of 0.96, and a Brier score of 0.10, with Grad-CAM indicating the CNN focused on clinically relevant features such as fracture displacement and SHAP highlighting age and lateral wrist radiographs as key contributors.
This feasibility study trained a convolutional neural network on pre-reduction injury radiographs and combined its outputs with clinical and demographic data in a random forest model to predict whether a group of fellowship-trained hand surgeons at one institution would recommend operative intervention for distal radial fractures in 1,040 patients (884 training, 156 testing); on the test set the combined model achieved 87.14% accuracy, 97% sensitivity, 73% specificity, an area under the ROC curve of 0.96, and a Brier score of 0.10, with Grad-CAM indicating the CNN focused on clinically relevant features such as fracture displacement and SHAP highlighting age and lateral wrist radiographs as key contributors.
Research Square Using COPD-related datasets from a public database, this study applied differential expression analysis, machine learning, and gene expression analysis to identify APRT and S100A8 as lipotoxicity-related COPD biomarkers (APRT notably lower and S100A8 notably higher in COPD samples), supported by RT-qPCR, built and validated a nomogram for predicting COPD risk (P = 0.395 in the Hosmer-Lemeshow test), found both biomarkers co-enriched in the "focal adhesion" pathway and significantly correlated with neutrophils, predicted 27 drugs targeting APRT (such as alteplase and relaxin) and methotrexate targeting S100A8, and used scRNA-seq to identify macrophages as a key cell type in COPD with dynamic expression patterns of both biomarkers during macrophage differentiation.
Using COPD-related datasets from a public database, this study applied differential expression analysis, machine learning, and gene expression analysis to identify APRT and S100A8 as lipotoxicity-related COPD biomarkers (APRT notably lower and S100A8 notably higher in COPD samples), supported by RT-qPCR, built and validated a nomogram for predicting COPD risk (P = 0.395 in the Hosmer-Lemeshow test), found both biomarkers co-enriched in the "focal adhesion" pathway and significantly correlated with neutrophils, predicted 27 drugs targeting APRT (such as alteplase and relaxin) and methotrexate targeting S100A8, and used scRNA-seq to identify macrophages as a key cell type in COPD with dynamic expression patterns of both biomarkers during macrophage differentiation.
Using COPD-related datasets from a public database, this study applied differential expression analysis, machine learning, and gene expression analysis to identify APRT and S100A8 as lipotoxicity-related COPD biomarkers (APRT notably lower and S100A8 notably higher in COPD samples), supported by RT-qPCR, built and validated a nomogram for predicting COPD risk (P = 0.395 in the Hosmer-Lemeshow test), found both biomarkers co-enriched in the "focal adhesion" pathway and significantly correlated with neutrophils, predicted 27 drugs targeting APRT (such as alteplase and relaxin) and methotrexate targeting S100A8, and used scRNA-seq to identify macrophages as a key cell type in COPD with dynamic expression patterns of both biomarkers during macrophage differentiation.
Using COPD-related datasets from a public database, this study applied differential expression analysis, machine learning, and gene expression analysis to identify APRT and S100A8 as lipotoxicity-related COPD biomarkers (APRT notably lower and S100A8 notably higher in COPD samples), supported by RT-qPCR, built and validated a nomogram for predicting COPD risk (P = 0.395 in the Hosmer-Lemeshow test), found both biomarkers co-enriched in the "focal adhesion" pathway and significantly correlated with neutrophils, predicted 27 drugs targeting APRT (such as alteplase and relaxin) and methotrexate targeting S100A8, and used scRNA-seq to identify macrophages as a key cell type in COPD with dynamic expression patterns of both biomarkers during macrophage differentiation.
Frontiers in Immunology Using network pharmacology to predict shared targets of paeonol and rosacea, then validating in an LL-37-induced rosacea-like BALB/c mouse model, the study found that 150 mg/kg paeonol markedly lowered redness area and score, reduced inflammatory cell and CD4+ T-cell infiltration and angiogenesis, and downregulated S100A9 and p65 phosphorylation, indicating its effects correlate with suppressed S100A9-NF-κB signaling.
Using network pharmacology to predict shared targets of paeonol and rosacea, then validating in an LL-37-induced rosacea-like BALB/c mouse model, the study found that 150 mg/kg paeonol markedly lowered redness area and score, reduced inflammatory cell and CD4+ T-cell infiltration and angiogenesis, and downregulated S100A9 and p65 phosphorylation, indicating its effects correlate with suppressed S100A9-NF-κB signaling.
Using network pharmacology to predict shared targets of paeonol and rosacea, then validating in an LL-37-induced rosacea-like BALB/c mouse model, the study found that 150 mg/kg paeonol markedly lowered redness area and score, reduced inflammatory cell and CD4+ T-cell infiltration and angiogenesis, and downregulated S100A9 and p65 phosphorylation, indicating its effects correlate with suppressed S100A9-NF-κB signaling.
Using network pharmacology to predict shared targets of paeonol and rosacea, then validating in an LL-37-induced rosacea-like BALB/c mouse model, the study found that 150 mg/kg paeonol markedly lowered redness area and score, reduced inflammatory cell and CD4+ T-cell infiltration and angiogenesis, and downregulated S100A9 and p65 phosphorylation, indicating its effects correlate with suppressed S100A9-NF-κB signaling.
医学理论研究 In a general surgery department of a hospital in Wuchuan, Guangdong, 84 elective surgery patients were allocated by random number table to routine ERAS enhanced recovery nursing or to routine ERAS plus an AI intervention covering preoperative intelligent assessment, postoperative intelligent warning, and continuous intelligent follow-up; the AI group showed higher mean disease knowledge (92.38±3.15 vs 68.12±3.54), lower 24-hour postoperative pain (2.76±1.09 vs 4.81±1.42), shorter time to first ambulation (1.48±0.63 vs 2.88±0.93 days), and higher nursing satisfaction (98.19±1.24 vs 89.17±2.70), all with P<0.05.
In a general surgery department of a hospital in Wuchuan, Guangdong, 84 elective surgery patients were allocated by random number table to routine ERAS enhanced recovery nursing or to routine ERAS plus an AI intervention covering preoperative intelligent assessment, postoperative intelligent warning, and continuous intelligent follow-up; the AI group showed higher mean disease knowledge (92.38±3.15 vs 68.12±3.54), lower 24-hour postoperative pain (2.76±1.09 vs 4.81±1.42), shorter time to first ambulation (1.48±0.63 vs 2.88±0.93 days), and higher nursing satisfaction (98.19±1.24 vs 89.17±2.70), all with P<0.05.
In a general surgery department of a hospital in Wuchuan, Guangdong, 84 elective surgery patients were allocated by random number table to routine ERAS enhanced recovery nursing or to routine ERAS plus an AI intervention covering preoperative intelligent assessment, postoperative intelligent warning, and continuous intelligent follow-up; the AI group showed higher mean disease knowledge (92.38±3.15 vs 68.12±3.54), lower 24-hour postoperative pain (2.76±1.09 vs 4.81±1.42), shorter time to first ambulation (1.48±0.63 vs 2.88±0.93 days), and higher nursing satisfaction (98.19±1.24 vs 89.17±2.70), all with P<0.05.
In a general surgery department of a hospital in Wuchuan, Guangdong, 84 elective surgery patients were allocated by random number table to routine ERAS enhanced recovery nursing or to routine ERAS plus an AI intervention covering preoperative intelligent assessment, postoperative intelligent warning, and continuous intelligent follow-up; the AI group showed higher mean disease knowledge (92.38±3.15 vs 68.12±3.54), lower 24-hour postoperative pain (2.76±1.09 vs 4.81±1.42), shorter time to first ambulation (1.48±0.63 vs 2.88±0.93 days), and higher nursing satisfaction (98.19±1.24 vs 89.17±2.70), all with P<0.05.
BJPsych open The protocol describes the design of the 5W-PL study: using the Centre for Victorian Data Linkage's Victorian Linkage Map to link 14 datasets covering mortality, mental health, hospital, emergency, ambulance and human services (child protection, disability, sexual assault, homelessness, alcohol and drug) through deterministic and probabilistic methods, building a cross-sector longitudinal cohort of people born between 1970 and 2010 with service-use records from 2015 to 2025, in order to identify subgroups by care pathway, map geographical patterns of service need, and develop predictive models of service type and intensity for young people aged 12–25.
The protocol describes the design of the 5W-PL study: using the Centre for Victorian Data Linkage's Victorian Linkage Map to link 14 datasets covering mortality, mental health, hospital, emergency, ambulance and human services (child protection, disability, sexual assault, homelessness, alcohol and drug) through deterministic and probabilistic methods, building a cross-sector longitudinal cohort of people born between 1970 and 2010 with service-use records from 2015 to 2025, in order to identify subgroups by care pathway, map geographical patterns of service need, and develop predictive models of service type and intensity for young people aged 12–25.
The protocol describes the design of the 5W-PL study: using the Centre for Victorian Data Linkage's Victorian Linkage Map to link 14 datasets covering mortality, mental health, hospital, emergency, ambulance and human services (child protection, disability, sexual assault, homelessness, alcohol and drug) through deterministic and probabilistic methods, building a cross-sector longitudinal cohort of people born between 1970 and 2010 with service-use records from 2015 to 2025, in order to identify subgroups by care pathway, map geographical patterns of service need, and develop predictive models of service type and intensity for young people aged 12–25.
The protocol describes the design of the 5W-PL study: using the Centre for Victorian Data Linkage's Victorian Linkage Map to link 14 datasets covering mortality, mental health, hospital, emergency, ambulance and human services (child protection, disability, sexual assault, homelessness, alcohol and drug) through deterministic and probabilistic methods, building a cross-sector longitudinal cohort of people born between 1970 and 2010 with service-use records from 2015 to 2025, in order to identify subgroups by care pathway, map geographical patterns of service need, and develop predictive models of service type and intensity for young people aged 12–25.
发表出处待核验 This conference paper abstract examines the development of reflective competence among economics students in AI-mediated English for Specific Purposes (ESP) learning, but the provided text contains only the title, author information, and the beginning of the abstract, lacking specific research methods, data, or conclusions.
This conference paper abstract examines the development of reflective competence among economics students in AI-mediated English for Specific Purposes (ESP) learning, but the provided text contains only the title, author information, and the beginning of the abstract, lacking specific research methods, data, or conclusions.
This conference paper abstract examines the development of reflective competence among economics students in AI-mediated English for Specific Purposes (ESP) learning, but the provided text contains only the title, author information, and the beginning of the abstract, lacking specific research methods, data, or conclusions.
This conference paper abstract examines the development of reflective competence among economics students in AI-mediated English for Specific Purposes (ESP) learning, but the provided text contains only the title, author information, and the beginning of the abstract, lacking specific research methods, data, or conclusions.
Scientific Reports 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.
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.
Anthropic Anthropic released Claude Sonnet 5.5, the second model in the Claude 5.5 family, positioned as a faster and cheaper model for everyday tasks and coding: Terminal-Bench 4.0 rises from 10.3% to 70.6%, GDPval-AA from 1449 to 1844 (near Opus 5.5's 1846), output generation is 30%+ faster, cost per task falls by up to 30% for most work, and it is the first Sonnet model to launch with cyber safeguards and anti-distillation classifiers.
Anthropic released Claude Sonnet 5.5, the second model in the Claude 5.5 family, positioned as a faster and cheaper model for everyday tasks and coding: Terminal-Bench 4.0 rises from 10.3% to 70.6%, GDPval-AA from 1449 to 1844 (near Opus 5.5's 1846), output generation is 30%+ faster, cost per task falls by up to 30% for most work, and it is the first Sonnet model to launch with cyber safeguards and anti-distillation classifiers.
Anthropic released Claude Sonnet 5.5, the second model in the Claude 5.5 family, positioned as a faster and cheaper model for everyday tasks and coding: Terminal-Bench 4.0 rises from 10.3% to 70.6%, GDPval-AA from 1449 to 1844 (near Opus 5.5's 1846), output generation is 30%+ faster, cost per task falls by up to 30% for most work, and it is the first Sonnet model to launch with cyber safeguards and anti-distillation classifiers.
Anthropic released Claude Sonnet 5.5, the second model in the Claude 5.5 family, positioned as a faster and cheaper model for everyday tasks and coding: Terminal-Bench 4.0 rises from 10.3% to 70.6%, GDPval-AA from 1449 to 1844 (near Opus 5.5's 1846), output generation is 30%+ faster, cost per task falls by up to 30% for most work, and it is the first Sonnet model to launch with cyber safeguards and anti-distillation classifiers.
Research Square This work presents an end-to-end comparative medical imaging framework that evaluates ResNet50, EfficientNet-B0, DenseNet121, DeiT-Small, and Swin-Tiny across three heterogeneous tasks—chest X-ray pneumonia classification, brain MRI tumor detection, and dermoscopic skin cancer classification—integrating transfer learning, class-imbalance handling, model calibration, bootstrap confidence intervals, robustness evaluation, failure-case analysis, Grad-CAM explainability, ensemble learning, and extensive performance metrics, together with an LLM-driven reporting component constrained to research-oriented assistance that generates structured model-comparison summaries, explainability interpretations, and decision-support reports; results indicate that CNNs remain highly effective for structured
This work presents an end-to-end comparative medical imaging framework that evaluates ResNet50, EfficientNet-B0, DenseNet121, DeiT-Small, and Swin-Tiny across three heterogeneous tasks—chest X-ray pneumonia classification, brain MRI tumor detection, and dermoscopic skin cancer classification—integrating transfer learning, class-imbalance handling, model calibration, bootstrap confidence intervals, robustness evaluation, failure-case analysis, Grad-CAM explainability, ensemble learning, and extensive performance metrics, together with an LLM-driven reporting component constrained to research-oriented assistance that generates structured model-comparison summaries, explainability interpretations, and decision-support reports; results indicate that CNNs remain highly effective for structured
This work presents an end-to-end comparative medical imaging framework that evaluates ResNet50, EfficientNet-B0, DenseNet121, DeiT-Small, and Swin-Tiny across three heterogeneous tasks—chest X-ray pneumonia classification, brain MRI tumor detection, and dermoscopic skin cancer classification—integrating transfer learning, class-imbalance handling, model calibration, bootstrap confidence intervals, robustness evaluation, failure-case analysis, Grad-CAM explainability, ensemble learning, and extensive performance metrics, together with an LLM-driven reporting component constrained to research-oriented assistance that generates structured model-comparison summaries, explainability interpretations, and decision-support reports; results indicate that CNNs remain highly effective for structured
This work presents an end-to-end comparative medical imaging framework that evaluates ResNet50, EfficientNet-B0, DenseNet121, DeiT-Small, and Swin-Tiny across three heterogeneous tasks—chest X-ray pneumonia classification, brain MRI tumor detection, and dermoscopic skin cancer classification—integrating transfer learning, class-imbalance handling, model calibration, bootstrap confidence intervals, robustness evaluation, failure-case analysis, Grad-CAM explainability, ensemble learning, and extensive performance metrics, together with an LLM-driven reporting component constrained to research-oriented assistance that generates structured model-comparison summaries, explainability interpretations, and decision-support reports; results indicate that CNNs remain highly effective for structured
Journal of Advanced Ceramics This work uses a random forest model to classify the phase composition of multi-rare-earth-principal-component RE2Si2O7 disilicates into single-beta, single-gamma, single-delta/mixed delta+gamma, and separate phase, identifies the average RE3+ cationic radius and the deviation of RE3+ cationic radius as the most influential factors, validates the model by predicting the phase compositions of (Gdx1Hox2Ybx3Lux4)2Si2O7 and (Ndx1Hox2Ybx3Lux4)2Si2O7 systems with experimental characterization of representative compositions, links phase formation through high-throughput DFT calculations to low energy costs for accommodating configurational randomness and rapid convergence of the configurational entropy of mixing with increased excitation energy, and establishes quantitative design criteria for si
This work uses a random forest model to classify the phase composition of multi-rare-earth-principal-component RE2Si2O7 disilicates into single-beta, single-gamma, single-delta/mixed delta+gamma, and separate phase, identifies the average RE3+ cationic radius and the deviation of RE3+ cationic radius as the most influential factors, validates the model by predicting the phase compositions of (Gdx1Hox2Ybx3Lux4)2Si2O7 and (Ndx1Hox2Ybx3Lux4)2Si2O7 systems with experimental characterization of representative compositions, links phase formation through high-throughput DFT calculations to low energy costs for accommodating configurational randomness and rapid convergence of the configurational entropy of mixing with increased excitation energy, and establishes quantitative design criteria for si
This work uses a random forest model to classify the phase composition of multi-rare-earth-principal-component RE2Si2O7 disilicates into single-beta, single-gamma, single-delta/mixed delta+gamma, and separate phase, identifies the average RE3+ cationic radius and the deviation of RE3+ cationic radius as the most influential factors, validates the model by predicting the phase compositions of (Gdx1Hox2Ybx3Lux4)2Si2O7 and (Ndx1Hox2Ybx3Lux4)2Si2O7 systems with experimental characterization of representative compositions, links phase formation through high-throughput DFT calculations to low energy costs for accommodating configurational randomness and rapid convergence of the configurational entropy of mixing with increased excitation energy, and establishes quantitative design criteria for si
This work uses a random forest model to classify the phase composition of multi-rare-earth-principal-component RE2Si2O7 disilicates into single-beta, single-gamma, single-delta/mixed delta+gamma, and separate phase, identifies the average RE3+ cationic radius and the deviation of RE3+ cationic radius as the most influential factors, validates the model by predicting the phase compositions of (Gdx1Hox2Ybx3Lux4)2Si2O7 and (Ndx1Hox2Ybx3Lux4)2Si2O7 systems with experimental characterization of representative compositions, links phase formation through high-throughput DFT calculations to low energy costs for accommodating configurational randomness and rapid convergence of the configurational entropy of mixing with increased excitation energy, and establishes quantitative design criteria for si
Journal of General Education and Humanities The study surveyed 78 students enrolled in the Educational Economics course in the Economic Education Study Program at Universitas Jambi during the 2025/2026 academic year, using total sampling and a 16-item four-point Likert-scale questionnaire across five dimensions (ease of use, knowledge, satisfaction, motivation, activity), and found generally positive perceptions with ease of use scoring highest (M = 3.12), followed by knowledge (M = 3.02), satisfaction (M = 2.94), motivation (M = 2.83), and activity (M = 2.81), while perceptions were comparatively lower for response accuracy, critical thinking, and motivation for academic writing.
The study surveyed 78 students enrolled in the Educational Economics course in the Economic Education Study Program at Universitas Jambi during the 2025/2026 academic year, using total sampling and a 16-item four-point Likert-scale questionnaire across five dimensions (ease of use, knowledge, satisfaction, motivation, activity), and found generally positive perceptions with ease of use scoring highest (M = 3.12), followed by knowledge (M = 3.02), satisfaction (M = 2.94), motivation (M = 2.83), and activity (M = 2.81), while perceptions were comparatively lower for response accuracy, critical thinking, and motivation for academic writing.
The study surveyed 78 students enrolled in the Educational Economics course in the Economic Education Study Program at Universitas Jambi during the 2025/2026 academic year, using total sampling and a 16-item four-point Likert-scale questionnaire across five dimensions (ease of use, knowledge, satisfaction, motivation, activity), and found generally positive perceptions with ease of use scoring highest (M = 3.12), followed by knowledge (M = 3.02), satisfaction (M = 2.94), motivation (M = 2.83), and activity (M = 2.81), while perceptions were comparatively lower for response accuracy, critical thinking, and motivation for academic writing.
The study surveyed 78 students enrolled in the Educational Economics course in the Economic Education Study Program at Universitas Jambi during the 2025/2026 academic year, using total sampling and a 16-item four-point Likert-scale questionnaire across five dimensions (ease of use, knowledge, satisfaction, motivation, activity), and found generally positive perceptions with ease of use scoring highest (M = 3.12), followed by knowledge (M = 3.02), satisfaction (M = 2.94), motivation (M = 2.83), and activity (M = 2.81), while perceptions were comparatively lower for response accuracy, critical thinking, and motivation for academic writing.
CAUCHY Jurnal Matematika Murni dan Aplikasi Under a nested cross-validation framework, this study compared seven hyperparameter tuning strategies for classical KNN over a mixed search space comprising an integer neighborhood size k, a categorical distance metric, and a continuous Minkowski exponent p—grid search, random search, Bayesian optimization, genetic algorithm, surrogate optimization, particle swarm optimization (PSO), and grey wolf optimizer—evaluating them on 15 public classification datasets using accuracy, macro AUC, cross-validation loss, cross-entropy loss, and runtime, and found that PSO achieved the best overall mean rank but held a statistically significant advantage only over random search under Holm-corrected Wilcoxon signed-rank tests.
Under a nested cross-validation framework, this study compared seven hyperparameter tuning strategies for classical KNN over a mixed search space comprising an integer neighborhood size k, a categorical distance metric, and a continuous Minkowski exponent p—grid search, random search, Bayesian optimization, genetic algorithm, surrogate optimization, particle swarm optimization (PSO), and grey wolf optimizer—evaluating them on 15 public classification datasets using accuracy, macro AUC, cross-validation loss, cross-entropy loss, and runtime, and found that PSO achieved the best overall mean rank but held a statistically significant advantage only over random search under Holm-corrected Wilcoxon signed-rank tests.
Under a nested cross-validation framework, this study compared seven hyperparameter tuning strategies for classical KNN over a mixed search space comprising an integer neighborhood size k, a categorical distance metric, and a continuous Minkowski exponent p—grid search, random search, Bayesian optimization, genetic algorithm, surrogate optimization, particle swarm optimization (PSO), and grey wolf optimizer—evaluating them on 15 public classification datasets using accuracy, macro AUC, cross-validation loss, cross-entropy loss, and runtime, and found that PSO achieved the best overall mean rank but held a statistically significant advantage only over random search under Holm-corrected Wilcoxon signed-rank tests.
Under a nested cross-validation framework, this study compared seven hyperparameter tuning strategies for classical KNN over a mixed search space comprising an integer neighborhood size k, a categorical distance metric, and a continuous Minkowski exponent p—grid search, random search, Bayesian optimization, genetic algorithm, surrogate optimization, particle swarm optimization (PSO), and grey wolf optimizer—evaluating them on 15 public classification datasets using accuracy, macro AUC, cross-validation loss, cross-entropy loss, and runtime, and found that PSO achieved the best overall mean rank but held a statistically significant advantage only over random search under Holm-corrected Wilcoxon signed-rank tests.
Research Square The study proposes TLANet, a three-layer attention-enhanced CNN built from three convolutional feature extraction blocks, channel-spatial attention, global average pooling, and a task-specific classification head, evaluated on two independent ISIC tasks: binary benign/malignant classification on ISIC 2016 (900 train / 379 test images), reporting about 88.3% accuracy, and seven-class diagnosis on ISIC 2018/HAM10000 (10,015 images), reporting a macro F1 of about 0.681, alongside standardized preprocessing, augmentation, baseline comparisons against a no-attention CNN, ResNet, EfficientNet, and MobileNet, ablation, and a full metric suite.
The study proposes TLANet, a three-layer attention-enhanced CNN built from three convolutional feature extraction blocks, channel-spatial attention, global average pooling, and a task-specific classification head, evaluated on two independent ISIC tasks: binary benign/malignant classification on ISIC 2016 (900 train / 379 test images), reporting about 88.3% accuracy, and seven-class diagnosis on ISIC 2018/HAM10000 (10,015 images), reporting a macro F1 of about 0.681, alongside standardized preprocessing, augmentation, baseline comparisons against a no-attention CNN, ResNet, EfficientNet, and MobileNet, ablation, and a full metric suite.
The study proposes TLANet, a three-layer attention-enhanced CNN built from three convolutional feature extraction blocks, channel-spatial attention, global average pooling, and a task-specific classification head, evaluated on two independent ISIC tasks: binary benign/malignant classification on ISIC 2016 (900 train / 379 test images), reporting about 88.3% accuracy, and seven-class diagnosis on ISIC 2018/HAM10000 (10,015 images), reporting a macro F1 of about 0.681, alongside standardized preprocessing, augmentation, baseline comparisons against a no-attention CNN, ResNet, EfficientNet, and MobileNet, ablation, and a full metric suite.
The study proposes TLANet, a three-layer attention-enhanced CNN built from three convolutional feature extraction blocks, channel-spatial attention, global average pooling, and a task-specific classification head, evaluated on two independent ISIC tasks: binary benign/malignant classification on ISIC 2016 (900 train / 379 test images), reporting about 88.3% accuracy, and seven-class diagnosis on ISIC 2018/HAM10000 (10,015 images), reporting a macro F1 of about 0.681, alongside standardized preprocessing, augmentation, baseline comparisons against a no-attention CNN, ResNet, EfficientNet, and MobileNet, ablation, and a full metric suite.
Frontiers in Medicine Using data reconstructed from Wang et al.'s supplementary tables, this study built 161 loading-capacity observations with 110 descriptors and 444 cell-viability observations with 25 descriptors, optimized histogram-based gradient boosting regression (HGBR) and partial least squares regression (PLSR) with differential evolution under five-fold grouped cross-validation, and constrained exact duplicate records to the same partition to prevent information leakage; on the duplicate-safe 20% holdout HGBR was strongest for cell viability (R² = 0.774, RMSE = 11.655 percentage points, MAE = 8.153, AARD = 16.69%) while PLSR was best for loading capacity (R² = 0.557, RMSE = 0.345 g/g, MAE = 0.210 g/g), yet holding out entire source publications drove all R² values negative (viability −0.391 and −0.
Using data reconstructed from Wang et al.'s supplementary tables, this study built 161 loading-capacity observations with 110 descriptors and 444 cell-viability observations with 25 descriptors, optimized histogram-based gradient boosting regression (HGBR) and partial least squares regression (PLSR) with differential evolution under five-fold grouped cross-validation, and constrained exact duplicate records to the same partition to prevent information leakage; on the duplicate-safe 20% holdout HGBR was strongest for cell viability (R² = 0.774, RMSE = 11.655 percentage points, MAE = 8.153, AARD = 16.69%) while PLSR was best for loading capacity (R² = 0.557, RMSE = 0.345 g/g, MAE = 0.210 g/g), yet holding out entire source publications drove all R² values negative (viability −0.391 and −0.
Using data reconstructed from Wang et al.'s supplementary tables, this study built 161 loading-capacity observations with 110 descriptors and 444 cell-viability observations with 25 descriptors, optimized histogram-based gradient boosting regression (HGBR) and partial least squares regression (PLSR) with differential evolution under five-fold grouped cross-validation, and constrained exact duplicate records to the same partition to prevent information leakage; on the duplicate-safe 20% holdout HGBR was strongest for cell viability (R² = 0.774, RMSE = 11.655 percentage points, MAE = 8.153, AARD = 16.69%) while PLSR was best for loading capacity (R² = 0.557, RMSE = 0.345 g/g, MAE = 0.210 g/g), yet holding out entire source publications drove all R² values negative (viability −0.391 and −0.
Using data reconstructed from Wang et al.'s supplementary tables, this study built 161 loading-capacity observations with 110 descriptors and 444 cell-viability observations with 25 descriptors, optimized histogram-based gradient boosting regression (HGBR) and partial least squares regression (PLSR) with differential evolution under five-fold grouped cross-validation, and constrained exact duplicate records to the same partition to prevent information leakage; on the duplicate-safe 20% holdout HGBR was strongest for cell viability (R² = 0.774, RMSE = 11.655 percentage points, MAE = 8.153, AARD = 16.69%) while PLSR was best for loading capacity (R² = 0.557, RMSE = 0.345 g/g, MAE = 0.210 g/g), yet holding out entire source publications drove all R² values negative (viability −0.391 and −0.
Studies in Self-Access Learning Journal The study assigned 70 Omani pre-intermediate English learners to two groups of 35, with a control group receiving face-to-face instruction and an experimental group using NotebookLM AI as the main learning source for 80 target words; both groups improved on the posttest but the experimental group scored higher, the control group's scores dropped on the delayed posttest while the experimental group continued to improve, and the experimental group also outperformed the control group on a self-directed learning questionnaire.
The study assigned 70 Omani pre-intermediate English learners to two groups of 35, with a control group receiving face-to-face instruction and an experimental group using NotebookLM AI as the main learning source for 80 target words; both groups improved on the posttest but the experimental group scored higher, the control group's scores dropped on the delayed posttest while the experimental group continued to improve, and the experimental group also outperformed the control group on a self-directed learning questionnaire.
The study assigned 70 Omani pre-intermediate English learners to two groups of 35, with a control group receiving face-to-face instruction and an experimental group using NotebookLM AI as the main learning source for 80 target words; both groups improved on the posttest but the experimental group scored higher, the control group's scores dropped on the delayed posttest while the experimental group continued to improve, and the experimental group also outperformed the control group on a self-directed learning questionnaire.
The study assigned 70 Omani pre-intermediate English learners to two groups of 35, with a control group receiving face-to-face instruction and an experimental group using NotebookLM AI as the main learning source for 80 target words; both groups improved on the posttest but the experimental group scored higher, the control group's scores dropped on the delayed posttest while the experimental group continued to improve, and the experimental group also outperformed the control group on a self-directed learning questionnaire.