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.
Frontiers in Oncology This network meta-analysis pooled 10 phase III randomized controlled trials with over 8,000 patients with unresectable advanced gastric cancer, ranked first-line immune checkpoint inhibitors and targeted agents by SUCRA on time to deterioration across EORTC QLQ-C30, EORTC QLQ-STO22 and EQ-5D domains, and used an exploratory minimum distance criterion to integrate overall survival with health-related quality of life, finding that trastuzumab (HER2-positive population) and tislelizumab (largely biomarker-unselected population) ranked most favorably across most quality-of-life domains, while the composite metric remains exploratory, lacks uncertainty estimates, and should not serve as primary evidence for clinical decision-making.
This network meta-analysis pooled 10 phase III randomized controlled trials with over 8,000 patients with unresectable advanced gastric cancer, ranked first-line immune checkpoint inhibitors and targeted agents by SUCRA on time to deterioration across EORTC QLQ-C30, EORTC QLQ-STO22 and EQ-5D domains, and used an exploratory minimum distance criterion to integrate overall survival with health-related quality of life, finding that trastuzumab (HER2-positive population) and tislelizumab (largely biomarker-unselected population) ranked most favorably across most quality-of-life domains, while the composite metric remains exploratory, lacks uncertainty estimates, and should not serve as primary evidence for clinical decision-making.
This network meta-analysis pooled 10 phase III randomized controlled trials with over 8,000 patients with unresectable advanced gastric cancer, ranked first-line immune checkpoint inhibitors and targeted agents by SUCRA on time to deterioration across EORTC QLQ-C30, EORTC QLQ-STO22 and EQ-5D domains, and used an exploratory minimum distance criterion to integrate overall survival with health-related quality of life, finding that trastuzumab (HER2-positive population) and tislelizumab (largely biomarker-unselected population) ranked most favorably across most quality-of-life domains, while the composite metric remains exploratory, lacks uncertainty estimates, and should not serve as primary evidence for clinical decision-making.
This network meta-analysis pooled 10 phase III randomized controlled trials with over 8,000 patients with unresectable advanced gastric cancer, ranked first-line immune checkpoint inhibitors and targeted agents by SUCRA on time to deterioration across EORTC QLQ-C30, EORTC QLQ-STO22 and EQ-5D domains, and used an exploratory minimum distance criterion to integrate overall survival with health-related quality of life, finding that trastuzumab (HER2-positive population) and tislelizumab (largely biomarker-unselected population) ranked most favorably across most quality-of-life domains, while the composite metric remains exploratory, lacks uncertainty estimates, and should not serve as primary evidence for clinical decision-making.
JOURNAL OF DIGITAL LEARNING AND DISTANCE EDUCATION This study develops a Bidirectional Long Short-Term Memory (BiLSTM) deep learning model that predicts student academic performance and failure risk from weekly interaction patterns in a Learning Management System (LMS), using a dataset of interaction logs from 1,250 distance education students over one semester with features such as material access frequency, forum participation, and assignment submission timing, and reports 91.4% accuracy, 89.2% precision, and 92.6% recall as early as Week 6 of the course, enabling educators to implement timely pedagogical interventions to reduce dropout rates in digital and distance learning environments.
This study develops a Bidirectional Long Short-Term Memory (BiLSTM) deep learning model that predicts student academic performance and failure risk from weekly interaction patterns in a Learning Management System (LMS), using a dataset of interaction logs from 1,250 distance education students over one semester with features such as material access frequency, forum participation, and assignment submission timing, and reports 91.4% accuracy, 89.2% precision, and 92.6% recall as early as Week 6 of the course, enabling educators to implement timely pedagogical interventions to reduce dropout rates in digital and distance learning environments.
This study develops a Bidirectional Long Short-Term Memory (BiLSTM) deep learning model that predicts student academic performance and failure risk from weekly interaction patterns in a Learning Management System (LMS), using a dataset of interaction logs from 1,250 distance education students over one semester with features such as material access frequency, forum participation, and assignment submission timing, and reports 91.4% accuracy, 89.2% precision, and 92.6% recall as early as Week 6 of the course, enabling educators to implement timely pedagogical interventions to reduce dropout rates in digital and distance learning environments.
This study develops a Bidirectional Long Short-Term Memory (BiLSTM) deep learning model that predicts student academic performance and failure risk from weekly interaction patterns in a Learning Management System (LMS), using a dataset of interaction logs from 1,250 distance education students over one semester with features such as material access frequency, forum participation, and assignment submission timing, and reports 91.4% accuracy, 89.2% precision, and 92.6% recall as early as Week 6 of the course, enabling educators to implement timely pedagogical interventions to reduce dropout rates in digital and distance learning environments.
Journal of Data Science and Intelligent Systems 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.
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.
Research Square 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
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
Frontiers in Oncology Using Scopus and Web of Science and a PRISMA workflow that narrowed 4,831 records to 608 peer-reviewed journal articles and reviews from 2020 to 2026, this study applied Bibliometrix and VOSviewer for a task-aware, methodology-centric bibliometric and thematic analysis, finding that diagnosis accounts for 71.22% of studies, mammography for 42.11%, explainable AI shows the strongest burst ratio at 1.00, while treatment-response prediction covers only 2.30% and only about 15-25% of studies explicitly report hyperparameter tuning strategies.
Using Scopus and Web of Science and a PRISMA workflow that narrowed 4,831 records to 608 peer-reviewed journal articles and reviews from 2020 to 2026, this study applied Bibliometrix and VOSviewer for a task-aware, methodology-centric bibliometric and thematic analysis, finding that diagnosis accounts for 71.22% of studies, mammography for 42.11%, explainable AI shows the strongest burst ratio at 1.00, while treatment-response prediction covers only 2.30% and only about 15-25% of studies explicitly report hyperparameter tuning strategies.
Using Scopus and Web of Science and a PRISMA workflow that narrowed 4,831 records to 608 peer-reviewed journal articles and reviews from 2020 to 2026, this study applied Bibliometrix and VOSviewer for a task-aware, methodology-centric bibliometric and thematic analysis, finding that diagnosis accounts for 71.22% of studies, mammography for 42.11%, explainable AI shows the strongest burst ratio at 1.00, while treatment-response prediction covers only 2.30% and only about 15-25% of studies explicitly report hyperparameter tuning strategies.
Using Scopus and Web of Science and a PRISMA workflow that narrowed 4,831 records to 608 peer-reviewed journal articles and reviews from 2020 to 2026, this study applied Bibliometrix and VOSviewer for a task-aware, methodology-centric bibliometric and thematic analysis, finding that diagnosis accounts for 71.22% of studies, mammography for 42.11%, explainable AI shows the strongest burst ratio at 1.00, while treatment-response prediction covers only 2.30% and only about 15-25% of studies explicitly report hyperparameter tuning strategies.
Frontiers in Marine Science Using a threshold of nLw555 = 1.5 mW cm-2 µm-1 sr-1 from MODIS to separate the East China Sea into Clear Water (CW) and Turbid Water (TW), this study reconstructed surface pCO2 for 2003–2023 with a CatBoost model that treats water type as a categorical input alongside multi-spectral satellite variables, achieving independent-test accuracy of R2 = 0.86 and RMSE = 16.55 µatm and cutting TW RMSE by up to about 81% relative to conventional models; the 21-year climatology gives CW 361.2 µatm and TW 415.1 µatm, with TW acting as a weak net source (+0.50 Tg C yr-1) driven by positive non-thermal anomalies (+28.09 µatm), implying conventional models overestimate total ECS carbon uptake by roughly 46% (about 4.04 Tg C yr-1).
Using a threshold of nLw555 = 1.5 mW cm-2 µm-1 sr-1 from MODIS to separate the East China Sea into Clear Water (CW) and Turbid Water (TW), this study reconstructed surface pCO2 for 2003–2023 with a CatBoost model that treats water type as a categorical input alongside multi-spectral satellite variables, achieving independent-test accuracy of R2 = 0.86 and RMSE = 16.55 µatm and cutting TW RMSE by up to about 81% relative to conventional models; the 21-year climatology gives CW 361.2 µatm and TW 415.1 µatm, with TW acting as a weak net source (+0.50 Tg C yr-1) driven by positive non-thermal anomalies (+28.09 µatm), implying conventional models overestimate total ECS carbon uptake by roughly 46% (about 4.04 Tg C yr-1).
Using a threshold of nLw555 = 1.5 mW cm-2 µm-1 sr-1 from MODIS to separate the East China Sea into Clear Water (CW) and Turbid Water (TW), this study reconstructed surface pCO2 for 2003–2023 with a CatBoost model that treats water type as a categorical input alongside multi-spectral satellite variables, achieving independent-test accuracy of R2 = 0.86 and RMSE = 16.55 µatm and cutting TW RMSE by up to about 81% relative to conventional models; the 21-year climatology gives CW 361.2 µatm and TW 415.1 µatm, with TW acting as a weak net source (+0.50 Tg C yr-1) driven by positive non-thermal anomalies (+28.09 µatm), implying conventional models overestimate total ECS carbon uptake by roughly 46% (about 4.04 Tg C yr-1).
Using a threshold of nLw555 = 1.5 mW cm-2 µm-1 sr-1 from MODIS to separate the East China Sea into Clear Water (CW) and Turbid Water (TW), this study reconstructed surface pCO2 for 2003–2023 with a CatBoost model that treats water type as a categorical input alongside multi-spectral satellite variables, achieving independent-test accuracy of R2 = 0.86 and RMSE = 16.55 µatm and cutting TW RMSE by up to about 81% relative to conventional models; the 21-year climatology gives CW 361.2 µatm and TW 415.1 µatm, with TW acting as a weak net source (+0.50 Tg C yr-1) driven by positive non-thermal anomalies (+28.09 µatm), implying conventional models overestimate total ECS carbon uptake by roughly 46% (about 4.04 Tg C yr-1).
NeuroRegulation Addressing AI tools entering neuroregulation practice through multiple simultaneous pathways, including automated qEEG analysis, protocol recommendation systems, AI-assisted documentation, and consumer-facing mental health applications that clients bring directly into the therapeutic relationship, and noting that no ethics code specific to neuroregulation has yet addressed these applications directly, Sherlin and Longo present a conceptual and practical ethical framework grounded in the BCIA Code of Ethics, ISNR Code of Ethics, APA Ethical Principles, ACA Code of Ethics, and the APA (2025) Ethical Guidance for Artificial Intelligence, comprising a risk continuum model organizing AI applications along three dimensions of opacity, clinical consequence, and distance from oversight, four ethic
Addressing AI tools entering neuroregulation practice through multiple simultaneous pathways, including automated qEEG analysis, protocol recommendation systems, AI-assisted documentation, and consumer-facing mental health applications that clients bring directly into the therapeutic relationship, and noting that no ethics code specific to neuroregulation has yet addressed these applications directly, Sherlin and Longo present a conceptual and practical ethical framework grounded in the BCIA Code of Ethics, ISNR Code of Ethics, APA Ethical Principles, ACA Code of Ethics, and the APA (2025) Ethical Guidance for Artificial Intelligence, comprising a risk continuum model organizing AI applications along three dimensions of opacity, clinical consequence, and distance from oversight, four ethic
Addressing AI tools entering neuroregulation practice through multiple simultaneous pathways, including automated qEEG analysis, protocol recommendation systems, AI-assisted documentation, and consumer-facing mental health applications that clients bring directly into the therapeutic relationship, and noting that no ethics code specific to neuroregulation has yet addressed these applications directly, Sherlin and Longo present a conceptual and practical ethical framework grounded in the BCIA Code of Ethics, ISNR Code of Ethics, APA Ethical Principles, ACA Code of Ethics, and the APA (2025) Ethical Guidance for Artificial Intelligence, comprising a risk continuum model organizing AI applications along three dimensions of opacity, clinical consequence, and distance from oversight, four ethic
Addressing AI tools entering neuroregulation practice through multiple simultaneous pathways, including automated qEEG analysis, protocol recommendation systems, AI-assisted documentation, and consumer-facing mental health applications that clients bring directly into the therapeutic relationship, and noting that no ethics code specific to neuroregulation has yet addressed these applications directly, Sherlin and Longo present a conceptual and practical ethical framework grounded in the BCIA Code of Ethics, ISNR Code of Ethics, APA Ethical Principles, ACA Code of Ethics, and the APA (2025) Ethical Guidance for Artificial Intelligence, comprising a risk continuum model organizing AI applications along three dimensions of opacity, clinical consequence, and distance from oversight, four ethic
Frontiers in Applied Mathematics and Statistics The study models two competing hybrid DC pension fund managers as a stochastic differential game in which liability risk follows a jump-diffusion process with truncated exponential jump amplitudes and the exact asset-to-liability ratio formulation is used, and it derives closed-form Nash equilibrium portfolio strategies and value functions via Hamilton–Jacobi–Bellman dynamic programming, finding that equilibrium weights are independent of liability jump parameters, reduce to K=(μ−r)/σ² and are independent of both managers' risk aversion in the symmetric liability-correlation case, that competition strictly amplifies risk-taking relative to the single-agent benchmark, and that a manager's own liability jumps reduce welfare while the competitor's jumps improve it.
The study models two competing hybrid DC pension fund managers as a stochastic differential game in which liability risk follows a jump-diffusion process with truncated exponential jump amplitudes and the exact asset-to-liability ratio formulation is used, and it derives closed-form Nash equilibrium portfolio strategies and value functions via Hamilton–Jacobi–Bellman dynamic programming, finding that equilibrium weights are independent of liability jump parameters, reduce to K=(μ−r)/σ² and are independent of both managers' risk aversion in the symmetric liability-correlation case, that competition strictly amplifies risk-taking relative to the single-agent benchmark, and that a manager's own liability jumps reduce welfare while the competitor's jumps improve it.
The study models two competing hybrid DC pension fund managers as a stochastic differential game in which liability risk follows a jump-diffusion process with truncated exponential jump amplitudes and the exact asset-to-liability ratio formulation is used, and it derives closed-form Nash equilibrium portfolio strategies and value functions via Hamilton–Jacobi–Bellman dynamic programming, finding that equilibrium weights are independent of liability jump parameters, reduce to K=(μ−r)/σ² and are independent of both managers' risk aversion in the symmetric liability-correlation case, that competition strictly amplifies risk-taking relative to the single-agent benchmark, and that a manager's own liability jumps reduce welfare while the competitor's jumps improve it.
The study models two competing hybrid DC pension fund managers as a stochastic differential game in which liability risk follows a jump-diffusion process with truncated exponential jump amplitudes and the exact asset-to-liability ratio formulation is used, and it derives closed-form Nash equilibrium portfolio strategies and value functions via Hamilton–Jacobi–Bellman dynamic programming, finding that equilibrium weights are independent of liability jump parameters, reduce to K=(μ−r)/σ² and are independent of both managers' risk aversion in the symmetric liability-correlation case, that competition strictly amplifies risk-taking relative to the single-agent benchmark, and that a manager's own liability jumps reduce welfare while the competitor's jumps improve it.
Frontiers in Pharmacology In this multicohort retrospective study, K-prototypes clustering of first-24-hour ICU variables in 2,511 patients with HFpEF from MIMIC-IV produced three clinically interpretable but partially overlapping phenotypes—cardiorenal-metabolic, hypertensive-pulmonary, and low-blood-pressure/arrhythmia (K = 2 had a higher mean silhouette width than K = 3, 0.083 versus 0.060, while both showed high median resampling stability, ARI 0.940 versus 0.924)—with a graded 365-day mortality difference in the derivation cohort (38.3%, 31.5%, 23.
In this multicohort retrospective study, K-prototypes clustering of first-24-hour ICU variables in 2,511 patients with HFpEF from MIMIC-IV produced three clinically interpretable but partially overlapping phenotypes—cardiorenal-metabolic, hypertensive-pulmonary, and low-blood-pressure/arrhythmia (K = 2 had a higher mean silhouette width than K = 3, 0.083 versus 0.060, while both showed high median resampling stability, ARI 0.940 versus 0.924)—with a graded 365-day mortality difference in the derivation cohort (38.3%, 31.5%, 23.
In this multicohort retrospective study, K-prototypes clustering of first-24-hour ICU variables in 2,511 patients with HFpEF from MIMIC-IV produced three clinically interpretable but partially overlapping phenotypes—cardiorenal-metabolic, hypertensive-pulmonary, and low-blood-pressure/arrhythmia (K = 2 had a higher mean silhouette width than K = 3, 0.083 versus 0.060, while both showed high median resampling stability, ARI 0.940 versus 0.924)—with a graded 365-day mortality difference in the derivation cohort (38.3%, 31.5%, 23.
In this multicohort retrospective study, K-prototypes clustering of first-24-hour ICU variables in 2,511 patients with HFpEF from MIMIC-IV produced three clinically interpretable but partially overlapping phenotypes—cardiorenal-metabolic, hypertensive-pulmonary, and low-blood-pressure/arrhythmia (K = 2 had a higher mean silhouette width than K = 3, 0.083 versus 0.060, while both showed high median resampling stability, ARI 0.940 versus 0.924)—with a graded 365-day mortality difference in the derivation cohort (38.3%, 31.5%, 23.
Frontiers in Medicine Drawing 2,907 FAERS reports (Q1 2018–Q1 2025) in which methotrexate was the primary suspect drug for pediatric leukemia, the study applied four disproportionality methods (ROR, PRR, BCPNN, MGPS) with sex and five age strata and fitted time-to-onset with a Weibull distribution, finding that nervous system disorders had the largest SOC-level report count (1,691 cases, ROR 2.87, 95% CI 2.69–3.05), that febrile neutropenia led at the PT level (446 reports) followed by neurotoxicity (238) and mucosal inflammation (170), that confusional state, dehydration, and epistaxis showed greater reporting disproportionality in males, that six PTs were detected in all five age strata, and that 681 of 944 evaluable onset reports (72.
Drawing 2,907 FAERS reports (Q1 2018–Q1 2025) in which methotrexate was the primary suspect drug for pediatric leukemia, the study applied four disproportionality methods (ROR, PRR, BCPNN, MGPS) with sex and five age strata and fitted time-to-onset with a Weibull distribution, finding that nervous system disorders had the largest SOC-level report count (1,691 cases, ROR 2.87, 95% CI 2.69–3.05), that febrile neutropenia led at the PT level (446 reports) followed by neurotoxicity (238) and mucosal inflammation (170), that confusional state, dehydration, and epistaxis showed greater reporting disproportionality in males, that six PTs were detected in all five age strata, and that 681 of 944 evaluable onset reports (72.
Drawing 2,907 FAERS reports (Q1 2018–Q1 2025) in which methotrexate was the primary suspect drug for pediatric leukemia, the study applied four disproportionality methods (ROR, PRR, BCPNN, MGPS) with sex and five age strata and fitted time-to-onset with a Weibull distribution, finding that nervous system disorders had the largest SOC-level report count (1,691 cases, ROR 2.87, 95% CI 2.69–3.05), that febrile neutropenia led at the PT level (446 reports) followed by neurotoxicity (238) and mucosal inflammation (170), that confusional state, dehydration, and epistaxis showed greater reporting disproportionality in males, that six PTs were detected in all five age strata, and that 681 of 944 evaluable onset reports (72.
Drawing 2,907 FAERS reports (Q1 2018–Q1 2025) in which methotrexate was the primary suspect drug for pediatric leukemia, the study applied four disproportionality methods (ROR, PRR, BCPNN, MGPS) with sex and five age strata and fitted time-to-onset with a Weibull distribution, finding that nervous system disorders had the largest SOC-level report count (1,691 cases, ROR 2.87, 95% CI 2.69–3.05), that febrile neutropenia led at the PT level (446 reports) followed by neurotoxicity (238) and mucosal inflammation (170), that confusional state, dehydration, and epistaxis showed greater reporting disproportionality in males, that six PTs were detected in all five age strata, and that 681 of 944 evaluable onset reports (72.
Frontiers in Big Data This Perspective addresses LLM agents that accumulate state across sessions, tools, users, and changing environments, and proposes a framework of controlled knowledge updating: treating an update as a decision to select the least invasive substrate sufficient for the claim's scope—among context, external memory, model parameters, activation states, and tool or workflow definitions—including the option not to write persistent state, organized by three principles (substrate proportionality, temporal defeasibility, auditable continuity) and motivating evaluation criteria covering update selection, temporal consistency, interference, reversibility, efficiency, and robustness, together with a four-phase minimal benchmark unit.
This Perspective addresses LLM agents that accumulate state across sessions, tools, users, and changing environments, and proposes a framework of controlled knowledge updating: treating an update as a decision to select the least invasive substrate sufficient for the claim's scope—among context, external memory, model parameters, activation states, and tool or workflow definitions—including the option not to write persistent state, organized by three principles (substrate proportionality, temporal defeasibility, auditable continuity) and motivating evaluation criteria covering update selection, temporal consistency, interference, reversibility, efficiency, and robustness, together with a four-phase minimal benchmark unit.
This Perspective addresses LLM agents that accumulate state across sessions, tools, users, and changing environments, and proposes a framework of controlled knowledge updating: treating an update as a decision to select the least invasive substrate sufficient for the claim's scope—among context, external memory, model parameters, activation states, and tool or workflow definitions—including the option not to write persistent state, organized by three principles (substrate proportionality, temporal defeasibility, auditable continuity) and motivating evaluation criteria covering update selection, temporal consistency, interference, reversibility, efficiency, and robustness, together with a four-phase minimal benchmark unit.
This Perspective addresses LLM agents that accumulate state across sessions, tools, users, and changing environments, and proposes a framework of controlled knowledge updating: treating an update as a decision to select the least invasive substrate sufficient for the claim's scope—among context, external memory, model parameters, activation states, and tool or workflow definitions—including the option not to write persistent state, organized by three principles (substrate proportionality, temporal defeasibility, auditable continuity) and motivating evaluation criteria covering update selection, temporal consistency, interference, reversibility, efficiency, and robustness, together with a four-phase minimal benchmark unit.
Frontiers in Pharmacology The study presents a non-destructive metadata-layer framework (bridge map, typed Intermediate Representation, orchestrator) that re-exposes a legacy clinical reporting library's outputs as machine-readable JSON without modifying validated SAS source, validated on a 558-component, 372,698-line industrial SAS macro library: immediate AI readiness under coexistence mode, an optional 92% reduction in proprietary code, cell-level parity of 80% or above on 11 of 14 report types from internal Phase III study PROT008-SR1 (mean 82.7%, best 99.2%), 100% parity across 5 reports and 4,764 cells on the public CDISC CDISCPilot01 benchmark, and LLM experiments covering table summarization, adverse event anomaly detection, and trial configuration generation.
The study presents a non-destructive metadata-layer framework (bridge map, typed Intermediate Representation, orchestrator) that re-exposes a legacy clinical reporting library's outputs as machine-readable JSON without modifying validated SAS source, validated on a 558-component, 372,698-line industrial SAS macro library: immediate AI readiness under coexistence mode, an optional 92% reduction in proprietary code, cell-level parity of 80% or above on 11 of 14 report types from internal Phase III study PROT008-SR1 (mean 82.7%, best 99.2%), 100% parity across 5 reports and 4,764 cells on the public CDISC CDISCPilot01 benchmark, and LLM experiments covering table summarization, adverse event anomaly detection, and trial configuration generation.
The study presents a non-destructive metadata-layer framework (bridge map, typed Intermediate Representation, orchestrator) that re-exposes a legacy clinical reporting library's outputs as machine-readable JSON without modifying validated SAS source, validated on a 558-component, 372,698-line industrial SAS macro library: immediate AI readiness under coexistence mode, an optional 92% reduction in proprietary code, cell-level parity of 80% or above on 11 of 14 report types from internal Phase III study PROT008-SR1 (mean 82.7%, best 99.2%), 100% parity across 5 reports and 4,764 cells on the public CDISC CDISCPilot01 benchmark, and LLM experiments covering table summarization, adverse event anomaly detection, and trial configuration generation.
The study presents a non-destructive metadata-layer framework (bridge map, typed Intermediate Representation, orchestrator) that re-exposes a legacy clinical reporting library's outputs as machine-readable JSON without modifying validated SAS source, validated on a 558-component, 372,698-line industrial SAS macro library: immediate AI readiness under coexistence mode, an optional 92% reduction in proprietary code, cell-level parity of 80% or above on 11 of 14 report types from internal Phase III study PROT008-SR1 (mean 82.7%, best 99.2%), 100% parity across 5 reports and 4,764 cells on the public CDISC CDISCPilot01 benchmark, and LLM experiments covering table summarization, adverse event anomaly detection, and trial configuration generation.
bioRxiv The authors present RNASeek, a 1.6-billion-parameter generative foundation model built on a DeepSeek architecture and trained on a cross-phyla transcriptomic corpus, using natural-language tokens for conditional prediction and sequence design; it captures species-specific transcript features and intron–exon boundaries in a zero-shot setting, can be fine-tuned to predict ribozyme self-cleavage activity and viral mRNA stability while revealing interpretable features such as loop flexibility, stem stability, and AU-rich motifs, and these functional predictors then serve as reward models for GRPO updates to the generation policy, producing faster-cleaving ribozymes and stability-enhancing 3′ UTRs under user-specified IUPAC constraints, with experimentally validated generated ribozymes reaching
The authors present RNASeek, a 1.6-billion-parameter generative foundation model built on a DeepSeek architecture and trained on a cross-phyla transcriptomic corpus, using natural-language tokens for conditional prediction and sequence design; it captures species-specific transcript features and intron–exon boundaries in a zero-shot setting, can be fine-tuned to predict ribozyme self-cleavage activity and viral mRNA stability while revealing interpretable features such as loop flexibility, stem stability, and AU-rich motifs, and these functional predictors then serve as reward models for GRPO updates to the generation policy, producing faster-cleaving ribozymes and stability-enhancing 3′ UTRs under user-specified IUPAC constraints, with experimentally validated generated ribozymes reaching
The authors present RNASeek, a 1.6-billion-parameter generative foundation model built on a DeepSeek architecture and trained on a cross-phyla transcriptomic corpus, using natural-language tokens for conditional prediction and sequence design; it captures species-specific transcript features and intron–exon boundaries in a zero-shot setting, can be fine-tuned to predict ribozyme self-cleavage activity and viral mRNA stability while revealing interpretable features such as loop flexibility, stem stability, and AU-rich motifs, and these functional predictors then serve as reward models for GRPO updates to the generation policy, producing faster-cleaving ribozymes and stability-enhancing 3′ UTRs under user-specified IUPAC constraints, with experimentally validated generated ribozymes reaching
The authors present RNASeek, a 1.6-billion-parameter generative foundation model built on a DeepSeek architecture and trained on a cross-phyla transcriptomic corpus, using natural-language tokens for conditional prediction and sequence design; it captures species-specific transcript features and intron–exon boundaries in a zero-shot setting, can be fine-tuned to predict ribozyme self-cleavage activity and viral mRNA stability while revealing interpretable features such as loop flexibility, stem stability, and AU-rich motifs, and these functional predictors then serve as reward models for GRPO updates to the generation policy, producing faster-cleaving ribozymes and stability-enhancing 3′ UTRs under user-specified IUPAC constraints, with experimentally validated generated ribozymes reaching
Frontiers in Pharmacology This narrative review traces TDDS evaluation from pre-1975 methodological fragmentation through Franz diffusion cell and IVPT standardization to RHE, full-thickness skin models, ex vivo human skin and iPSC-derived skin organoids (SkOs), reporting that SkOs self-organize stratified epidermis, dermal-like structures, hair follicles and sebaceous glands and that after roughly 4-5 months in culture their transcriptome resembles second-trimester human fetal skin, but that the authors' search identified no clear study using Lee-type hiPSC-derived SkOs as standardized barrier models in Franz diffusion cells or conventional IVPT with systematic measurement of cumulative permeation, steady-state flux, permeability coefficient or skin retention, concluding that SkOs are a frontier candidate rather t
This narrative review traces TDDS evaluation from pre-1975 methodological fragmentation through Franz diffusion cell and IVPT standardization to RHE, full-thickness skin models, ex vivo human skin and iPSC-derived skin organoids (SkOs), reporting that SkOs self-organize stratified epidermis, dermal-like structures, hair follicles and sebaceous glands and that after roughly 4-5 months in culture their transcriptome resembles second-trimester human fetal skin, but that the authors' search identified no clear study using Lee-type hiPSC-derived SkOs as standardized barrier models in Franz diffusion cells or conventional IVPT with systematic measurement of cumulative permeation, steady-state flux, permeability coefficient or skin retention, concluding that SkOs are a frontier candidate rather t
This narrative review traces TDDS evaluation from pre-1975 methodological fragmentation through Franz diffusion cell and IVPT standardization to RHE, full-thickness skin models, ex vivo human skin and iPSC-derived skin organoids (SkOs), reporting that SkOs self-organize stratified epidermis, dermal-like structures, hair follicles and sebaceous glands and that after roughly 4-5 months in culture their transcriptome resembles second-trimester human fetal skin, but that the authors' search identified no clear study using Lee-type hiPSC-derived SkOs as standardized barrier models in Franz diffusion cells or conventional IVPT with systematic measurement of cumulative permeation, steady-state flux, permeability coefficient or skin retention, concluding that SkOs are a frontier candidate rather t
This narrative review traces TDDS evaluation from pre-1975 methodological fragmentation through Franz diffusion cell and IVPT standardization to RHE, full-thickness skin models, ex vivo human skin and iPSC-derived skin organoids (SkOs), reporting that SkOs self-organize stratified epidermis, dermal-like structures, hair follicles and sebaceous glands and that after roughly 4-5 months in culture their transcriptome resembles second-trimester human fetal skin, but that the authors' search identified no clear study using Lee-type hiPSC-derived SkOs as standardized barrier models in Franz diffusion cells or conventional IVPT with systematic measurement of cumulative permeation, steady-state flux, permeability coefficient or skin retention, concluding that SkOs are a frontier candidate rather t
Neuroscience Research Notes Addressing the gap in Speech Recognition Threshold (SRT) materials for Greek-speaking school-aged children (6 to 12 years), this study selected words on four criteria—syllabic structure (trisyllabics), age-appropriate vocabulary familiarity, phonemic differentiation, and homogeneity in audibility—recorded and processed them per ISO 8253-3:2022, had 20 children take part in the word homogeneity evaluation, determined for each word the presentation level needed for 50% correct recognition, and measured recognition rates across intensities to locate the steepest rise of the Performance-Intensity (PI) function between 20% and 80% recognition; it found that words homogeneous in recognition rate are not homogeneous in recognition threshold and vice versa, so only words within +1 Standard Deviati
Addressing the gap in Speech Recognition Threshold (SRT) materials for Greek-speaking school-aged children (6 to 12 years), this study selected words on four criteria—syllabic structure (trisyllabics), age-appropriate vocabulary familiarity, phonemic differentiation, and homogeneity in audibility—recorded and processed them per ISO 8253-3:2022, had 20 children take part in the word homogeneity evaluation, determined for each word the presentation level needed for 50% correct recognition, and measured recognition rates across intensities to locate the steepest rise of the Performance-Intensity (PI) function between 20% and 80% recognition; it found that words homogeneous in recognition rate are not homogeneous in recognition threshold and vice versa, so only words within +1 Standard Deviati
Addressing the gap in Speech Recognition Threshold (SRT) materials for Greek-speaking school-aged children (6 to 12 years), this study selected words on four criteria—syllabic structure (trisyllabics), age-appropriate vocabulary familiarity, phonemic differentiation, and homogeneity in audibility—recorded and processed them per ISO 8253-3:2022, had 20 children take part in the word homogeneity evaluation, determined for each word the presentation level needed for 50% correct recognition, and measured recognition rates across intensities to locate the steepest rise of the Performance-Intensity (PI) function between 20% and 80% recognition; it found that words homogeneous in recognition rate are not homogeneous in recognition threshold and vice versa, so only words within +1 Standard Deviati
Addressing the gap in Speech Recognition Threshold (SRT) materials for Greek-speaking school-aged children (6 to 12 years), this study selected words on four criteria—syllabic structure (trisyllabics), age-appropriate vocabulary familiarity, phonemic differentiation, and homogeneity in audibility—recorded and processed them per ISO 8253-3:2022, had 20 children take part in the word homogeneity evaluation, determined for each word the presentation level needed for 50% correct recognition, and measured recognition rates across intensities to locate the steepest rise of the Performance-Intensity (PI) function between 20% and 80% recognition; it found that words homogeneous in recognition rate are not homogeneous in recognition threshold and vice versa, so only words within +1 Standard Deviati