Frontiers in Endocrinology Using retrospective single-center data from 1,319 patients undergoing their first frozen embryo transfer at Guangdong Provincial Hospital of Chinese Medicine, split by transfer date into a training cohort (n=1,013) and a temporal validation cohort (n=306), the authors used LASSO to select age, induced abortions, cycle type, good-quality embryos, transferred embryos, endometrial change, BMI, antral follicle count, embryo transfer-day endometrial thickness, and retrieval-transfer interval, then developed an XGBoost model and compared it with logistic regression; XGBoost achieved an AUC of 0.854 (95% CI 0.832-0.877) in training and 0.722 (95% CI 0.666-0.779) in temporal validation, with AUPRC values of 0.840 and 0.707, acceptable calibration in validation (Brier score 0.212, intercept 0.
Using retrospective single-center data from 1,319 patients undergoing their first frozen embryo transfer at Guangdong Provincial Hospital of Chinese Medicine, split by transfer date into a training cohort (n=1,013) and a temporal validation cohort (n=306), the authors used LASSO to select age, induced abortions, cycle type, good-quality embryos, transferred embryos, endometrial change, BMI, antral follicle count, embryo transfer-day endometrial thickness, and retrieval-transfer interval, then developed an XGBoost model and compared it with logistic regression; XGBoost achieved an AUC of 0.854 (95% CI 0.832-0.877) in training and 0.722 (95% CI 0.666-0.779) in temporal validation, with AUPRC values of 0.840 and 0.707, acceptable calibration in validation (Brier score 0.212, intercept 0.
Using retrospective single-center data from 1,319 patients undergoing their first frozen embryo transfer at Guangdong Provincial Hospital of Chinese Medicine, split by transfer date into a training cohort (n=1,013) and a temporal validation cohort (n=306), the authors used LASSO to select age, induced abortions, cycle type, good-quality embryos, transferred embryos, endometrial change, BMI, antral follicle count, embryo transfer-day endometrial thickness, and retrieval-transfer interval, then developed an XGBoost model and compared it with logistic regression; XGBoost achieved an AUC of 0.854 (95% CI 0.832-0.877) in training and 0.722 (95% CI 0.666-0.779) in temporal validation, with AUPRC values of 0.840 and 0.707, acceptable calibration in validation (Brier score 0.212, intercept 0.
Using retrospective single-center data from 1,319 patients undergoing their first frozen embryo transfer at Guangdong Provincial Hospital of Chinese Medicine, split by transfer date into a training cohort (n=1,013) and a temporal validation cohort (n=306), the authors used LASSO to select age, induced abortions, cycle type, good-quality embryos, transferred embryos, endometrial change, BMI, antral follicle count, embryo transfer-day endometrial thickness, and retrieval-transfer interval, then developed an XGBoost model and compared it with logistic regression; XGBoost achieved an AUC of 0.854 (95% CI 0.832-0.877) in training and 0.722 (95% CI 0.666-0.779) in temporal validation, with AUPRC values of 0.840 and 0.707, acceptable calibration in validation (Brier score 0.212, intercept 0.
Frontiers in Endocrinology This retrospective study of 232 patients who underwent thyroid surgery at Tongling People's Hospital used LASSO to select six features from 32 candidates (BRAF mutation status, tumor size, capsular invasion, extrathyroidal invasion, multifocality, and TSH), compared seven machine learning algorithms, found the Support Vector Machine best in the validation cohort (AUC 0.849, 95% CI 0.756–0.934; accuracy 0.768), identified TSH and tumor size as the top SHAP contributors, and showed in an ablation analysis that removing BRAF lowered validation AUC from 0.849 to 0.843 (P = 0.671).
This retrospective study of 232 patients who underwent thyroid surgery at Tongling People's Hospital used LASSO to select six features from 32 candidates (BRAF mutation status, tumor size, capsular invasion, extrathyroidal invasion, multifocality, and TSH), compared seven machine learning algorithms, found the Support Vector Machine best in the validation cohort (AUC 0.849, 95% CI 0.756–0.934; accuracy 0.768), identified TSH and tumor size as the top SHAP contributors, and showed in an ablation analysis that removing BRAF lowered validation AUC from 0.849 to 0.843 (P = 0.671).
This retrospective study of 232 patients who underwent thyroid surgery at Tongling People's Hospital used LASSO to select six features from 32 candidates (BRAF mutation status, tumor size, capsular invasion, extrathyroidal invasion, multifocality, and TSH), compared seven machine learning algorithms, found the Support Vector Machine best in the validation cohort (AUC 0.849, 95% CI 0.756–0.934; accuracy 0.768), identified TSH and tumor size as the top SHAP contributors, and showed in an ablation analysis that removing BRAF lowered validation AUC from 0.849 to 0.843 (P = 0.671).
This retrospective study of 232 patients who underwent thyroid surgery at Tongling People's Hospital used LASSO to select six features from 32 candidates (BRAF mutation status, tumor size, capsular invasion, extrathyroidal invasion, multifocality, and TSH), compared seven machine learning algorithms, found the Support Vector Machine best in the validation cohort (AUC 0.849, 95% CI 0.756–0.934; accuracy 0.768), identified TSH and tumor size as the top SHAP contributors, and showed in an ablation analysis that removing BRAF lowered validation AUC from 0.849 to 0.843 (P = 0.671).
Studies in Self-Access Learning Journal This editorial introduction presents the September 2026 issue of Studies in Self-Access Learning Journal (Vol. 17, No. 3, pp. 268–272), with authors based in Indonesia, Japan, Oman, Thailand, the United Kingdom, and Vietnam, organizing nine papers into four themes: self-access connections with wider learning spaces and communities (Kashiwa; Martinho et al.), the lives and well-being of people within self-access communities (Phelps; Pemberton et al.), self-regulation and learning processes in self-access (Nakanishi et al.; Anggoro et al.), and the growing role of generative AI in self-access language learning (Ubukata & Marzin; Andersson; Al Ghaithi & Behforouz), plus two book reviews on self-regulated and self-directed learning (Nguyen; Tomiyama).
This editorial introduction presents the September 2026 issue of Studies in Self-Access Learning Journal (Vol. 17, No. 3, pp. 268–272), with authors based in Indonesia, Japan, Oman, Thailand, the United Kingdom, and Vietnam, organizing nine papers into four themes: self-access connections with wider learning spaces and communities (Kashiwa; Martinho et al.), the lives and well-being of people within self-access communities (Phelps; Pemberton et al.), self-regulation and learning processes in self-access (Nakanishi et al.; Anggoro et al.), and the growing role of generative AI in self-access language learning (Ubukata & Marzin; Andersson; Al Ghaithi & Behforouz), plus two book reviews on self-regulated and self-directed learning (Nguyen; Tomiyama).
This editorial introduction presents the September 2026 issue of Studies in Self-Access Learning Journal (Vol. 17, No. 3, pp. 268–272), with authors based in Indonesia, Japan, Oman, Thailand, the United Kingdom, and Vietnam, organizing nine papers into four themes: self-access connections with wider learning spaces and communities (Kashiwa; Martinho et al.), the lives and well-being of people within self-access communities (Phelps; Pemberton et al.), self-regulation and learning processes in self-access (Nakanishi et al.; Anggoro et al.), and the growing role of generative AI in self-access language learning (Ubukata & Marzin; Andersson; Al Ghaithi & Behforouz), plus two book reviews on self-regulated and self-directed learning (Nguyen; Tomiyama).
This editorial introduction presents the September 2026 issue of Studies in Self-Access Learning Journal (Vol. 17, No. 3, pp. 268–272), with authors based in Indonesia, Japan, Oman, Thailand, the United Kingdom, and Vietnam, organizing nine papers into four themes: self-access connections with wider learning spaces and communities (Kashiwa; Martinho et al.), the lives and well-being of people within self-access communities (Phelps; Pemberton et al.), self-regulation and learning processes in self-access (Nakanishi et al.; Anggoro et al.), and the growing role of generative AI in self-access language learning (Ubukata & Marzin; Andersson; Al Ghaithi & Behforouz), plus two book reviews on self-regulated and self-directed learning (Nguyen; Tomiyama).
Research Square Using retrospective data from 100 patients who underwent transsphenoidal endoscopic pituitary tumor resection between 2016 and 2025, the study extracted 22 preoperative variables and developed and evaluated an artificial neural network and a random forest model against R², MAE, RMSE, and clinical accuracy thresholds of ±30 and ±45 minutes; the ANN outperformed random forest with a test-set MAE of 0.636 hours (about 38 minutes), 63.3% of predictions fell within ±30 minutes and 76.7% within ±45 minutes, and key predictors included tumor recurrence, distance from the third ventricle floor, and the cinch sign, which the authors present as objective evidence for nursing scheduling and operating room planning.
Using retrospective data from 100 patients who underwent transsphenoidal endoscopic pituitary tumor resection between 2016 and 2025, the study extracted 22 preoperative variables and developed and evaluated an artificial neural network and a random forest model against R², MAE, RMSE, and clinical accuracy thresholds of ±30 and ±45 minutes; the ANN outperformed random forest with a test-set MAE of 0.636 hours (about 38 minutes), 63.3% of predictions fell within ±30 minutes and 76.7% within ±45 minutes, and key predictors included tumor recurrence, distance from the third ventricle floor, and the cinch sign, which the authors present as objective evidence for nursing scheduling and operating room planning.
Using retrospective data from 100 patients who underwent transsphenoidal endoscopic pituitary tumor resection between 2016 and 2025, the study extracted 22 preoperative variables and developed and evaluated an artificial neural network and a random forest model against R², MAE, RMSE, and clinical accuracy thresholds of ±30 and ±45 minutes; the ANN outperformed random forest with a test-set MAE of 0.636 hours (about 38 minutes), 63.3% of predictions fell within ±30 minutes and 76.7% within ±45 minutes, and key predictors included tumor recurrence, distance from the third ventricle floor, and the cinch sign, which the authors present as objective evidence for nursing scheduling and operating room planning.
Using retrospective data from 100 patients who underwent transsphenoidal endoscopic pituitary tumor resection between 2016 and 2025, the study extracted 22 preoperative variables and developed and evaluated an artificial neural network and a random forest model against R², MAE, RMSE, and clinical accuracy thresholds of ±30 and ±45 minutes; the ANN outperformed random forest with a test-set MAE of 0.636 hours (about 38 minutes), 63.3% of predictions fell within ±30 minutes and 76.7% within ±45 minutes, and key predictors included tumor recurrence, distance from the third ventricle floor, and the cinch sign, which the authors present as objective evidence for nursing scheduling and operating room planning.
ChemPhysChem Using tensiometry, dynamic light scattering, small-angle neutron scattering, and steady-state and time-resolved fluorescence at 30 °C, this study examined four Gemini surfactants (8-4-8, 10-4-10, 12-4-12, 10-8-10) at 100 mM with and without 100 mM NaBr or NaSal, and found that longer alkyl chains lower the CMC (about 55.4 mM to about 1.2 mM) and enlarge micelles with higher aggregation numbers (13 to 39), that increasing the spacer from 4 to 8 carbons shrinks micelles (D_h about 2.6 nm to about 1.1 nm), that NaBr swells micelles by electrostatic screening (D_h about 4.8 nm) while NaSal elongates them markedly (D_h about 15.
Using tensiometry, dynamic light scattering, small-angle neutron scattering, and steady-state and time-resolved fluorescence at 30 °C, this study examined four Gemini surfactants (8-4-8, 10-4-10, 12-4-12, 10-8-10) at 100 mM with and without 100 mM NaBr or NaSal, and found that longer alkyl chains lower the CMC (about 55.4 mM to about 1.2 mM) and enlarge micelles with higher aggregation numbers (13 to 39), that increasing the spacer from 4 to 8 carbons shrinks micelles (D_h about 2.6 nm to about 1.1 nm), that NaBr swells micelles by electrostatic screening (D_h about 4.8 nm) while NaSal elongates them markedly (D_h about 15.
Using tensiometry, dynamic light scattering, small-angle neutron scattering, and steady-state and time-resolved fluorescence at 30 °C, this study examined four Gemini surfactants (8-4-8, 10-4-10, 12-4-12, 10-8-10) at 100 mM with and without 100 mM NaBr or NaSal, and found that longer alkyl chains lower the CMC (about 55.4 mM to about 1.2 mM) and enlarge micelles with higher aggregation numbers (13 to 39), that increasing the spacer from 4 to 8 carbons shrinks micelles (D_h about 2.6 nm to about 1.1 nm), that NaBr swells micelles by electrostatic screening (D_h about 4.8 nm) while NaSal elongates them markedly (D_h about 15.
Using tensiometry, dynamic light scattering, small-angle neutron scattering, and steady-state and time-resolved fluorescence at 30 °C, this study examined four Gemini surfactants (8-4-8, 10-4-10, 12-4-12, 10-8-10) at 100 mM with and without 100 mM NaBr or NaSal, and found that longer alkyl chains lower the CMC (about 55.4 mM to about 1.2 mM) and enlarge micelles with higher aggregation numbers (13 to 39), that increasing the spacer from 4 to 8 carbons shrinks micelles (D_h about 2.6 nm to about 1.1 nm), that NaBr swells micelles by electrostatic screening (D_h about 4.8 nm) while NaSal elongates them markedly (D_h about 15.
Journal of Intelligent Media Computing This study used Mask R-CNN to segment goats from two-dimensional images and extract heart girth and body length, combined these with age and sex metadata, trained XGBoost, Random Forest and CatBoost and integrated them with weights of 0.5/0.3/0.2; on a validation set split by Animal ID (37 goats) XGBoost reached R²=0.8514 (RMSE=4.26 kg, MAE=3.47 kg, MAPE=16.27%), and on a completely unseen test set (37 goats) XGBoost performed best with R²=0.7955 (MSE=29.15 kg², RMSE=5.40 kg, MAE=4.01 kg, MAPE=16.38%) while the weighted ensemble recorded R²=0.7713 (MSE=32.60 kg², RMSE=5.71 kg, MAE=4.13 kg, MAPE=16.60%), indicating that combining visual and morphometric information under strict group-based evaluation provides reliable non-contact body weight estimation on unseen livestock.
This study used Mask R-CNN to segment goats from two-dimensional images and extract heart girth and body length, combined these with age and sex metadata, trained XGBoost, Random Forest and CatBoost and integrated them with weights of 0.5/0.3/0.2; on a validation set split by Animal ID (37 goats) XGBoost reached R²=0.8514 (RMSE=4.26 kg, MAE=3.47 kg, MAPE=16.27%), and on a completely unseen test set (37 goats) XGBoost performed best with R²=0.7955 (MSE=29.15 kg², RMSE=5.40 kg, MAE=4.01 kg, MAPE=16.38%) while the weighted ensemble recorded R²=0.7713 (MSE=32.60 kg², RMSE=5.71 kg, MAE=4.13 kg, MAPE=16.60%), indicating that combining visual and morphometric information under strict group-based evaluation provides reliable non-contact body weight estimation on unseen livestock.
This study used Mask R-CNN to segment goats from two-dimensional images and extract heart girth and body length, combined these with age and sex metadata, trained XGBoost, Random Forest and CatBoost and integrated them with weights of 0.5/0.3/0.2; on a validation set split by Animal ID (37 goats) XGBoost reached R²=0.8514 (RMSE=4.26 kg, MAE=3.47 kg, MAPE=16.27%), and on a completely unseen test set (37 goats) XGBoost performed best with R²=0.7955 (MSE=29.15 kg², RMSE=5.40 kg, MAE=4.01 kg, MAPE=16.38%) while the weighted ensemble recorded R²=0.7713 (MSE=32.60 kg², RMSE=5.71 kg, MAE=4.13 kg, MAPE=16.60%), indicating that combining visual and morphometric information under strict group-based evaluation provides reliable non-contact body weight estimation on unseen livestock.
This study used Mask R-CNN to segment goats from two-dimensional images and extract heart girth and body length, combined these with age and sex metadata, trained XGBoost, Random Forest and CatBoost and integrated them with weights of 0.5/0.3/0.2; on a validation set split by Animal ID (37 goats) XGBoost reached R²=0.8514 (RMSE=4.26 kg, MAE=3.47 kg, MAPE=16.27%), and on a completely unseen test set (37 goats) XGBoost performed best with R²=0.7955 (MSE=29.15 kg², RMSE=5.40 kg, MAE=4.01 kg, MAPE=16.38%) while the weighted ensemble recorded R²=0.7713 (MSE=32.60 kg², RMSE=5.71 kg, MAE=4.13 kg, MAPE=16.60%), indicating that combining visual and morphometric information under strict group-based evaluation provides reliable non-contact body weight estimation on unseen livestock.
CAUCHY Jurnal Matematika Murni dan Aplikasi Using 2024 data for 38 Indonesian provinces on stunting prevalence and nine determinants (low birth weight, safe drinking water, Human Development Index, mean years of schooling, poverty rate, exclusive breastfeeding coverage, mean maternal age at first marriage, antenatal care coverage, and pediatric health service coverage), the study jointly fitted direct and cross semivariograms under a linear model of coregionalization, selected LMC = Nug + Sph(600km), and found moderate spatial dependence of stunting (SDP = 68.
Using 2024 data for 38 Indonesian provinces on stunting prevalence and nine determinants (low birth weight, safe drinking water, Human Development Index, mean years of schooling, poverty rate, exclusive breastfeeding coverage, mean maternal age at first marriage, antenatal care coverage, and pediatric health service coverage), the study jointly fitted direct and cross semivariograms under a linear model of coregionalization, selected LMC = Nug + Sph(600km), and found moderate spatial dependence of stunting (SDP = 68.
Using 2024 data for 38 Indonesian provinces on stunting prevalence and nine determinants (low birth weight, safe drinking water, Human Development Index, mean years of schooling, poverty rate, exclusive breastfeeding coverage, mean maternal age at first marriage, antenatal care coverage, and pediatric health service coverage), the study jointly fitted direct and cross semivariograms under a linear model of coregionalization, selected LMC = Nug + Sph(600km), and found moderate spatial dependence of stunting (SDP = 68.
Using 2024 data for 38 Indonesian provinces on stunting prevalence and nine determinants (low birth weight, safe drinking water, Human Development Index, mean years of schooling, poverty rate, exclusive breastfeeding coverage, mean maternal age at first marriage, antenatal care coverage, and pediatric health service coverage), the study jointly fitted direct and cross semivariograms under a linear model of coregionalization, selected LMC = Nug + Sph(600km), and found moderate spatial dependence of stunting (SDP = 68.
Frontiers in Toxicology A collaborative working group reviewed the principles, strengths, and limits of large language models (LLMs) in toxicological risk assessment (TRA) and then used an exemplar case study, the derivation of the oral Permitted Daily Exposure (PDE) for acetyl tributyl citrate (ATBC, CAS 77-90-7) under the draft ICH Q3E guideline, testing zero-shot end-to-end prompts, structured step-guided prompts, and a document-constrained configuration with ChatGPT (GPT-5 and later GPT-5.
A collaborative working group reviewed the principles, strengths, and limits of large language models (LLMs) in toxicological risk assessment (TRA) and then used an exemplar case study, the derivation of the oral Permitted Daily Exposure (PDE) for acetyl tributyl citrate (ATBC, CAS 77-90-7) under the draft ICH Q3E guideline, testing zero-shot end-to-end prompts, structured step-guided prompts, and a document-constrained configuration with ChatGPT (GPT-5 and later GPT-5.
A collaborative working group reviewed the principles, strengths, and limits of large language models (LLMs) in toxicological risk assessment (TRA) and then used an exemplar case study, the derivation of the oral Permitted Daily Exposure (PDE) for acetyl tributyl citrate (ATBC, CAS 77-90-7) under the draft ICH Q3E guideline, testing zero-shot end-to-end prompts, structured step-guided prompts, and a document-constrained configuration with ChatGPT (GPT-5 and later GPT-5.
A collaborative working group reviewed the principles, strengths, and limits of large language models (LLMs) in toxicological risk assessment (TRA) and then used an exemplar case study, the derivation of the oral Permitted Daily Exposure (PDE) for acetyl tributyl citrate (ATBC, CAS 77-90-7) under the draft ICH Q3E guideline, testing zero-shot end-to-end prompts, structured step-guided prompts, and a document-constrained configuration with ChatGPT (GPT-5 and later GPT-5.
bioRxiv The study trained a 3D residual channel attention network (RCAN) for 2x super-resolution of 4D flow MRI on a synthetic pulmonary-artery benchmark and found that adding biomarker supervision reduced pulsatility-index (PI) error under both regularization conditions (in the original primary comparison, 12.76 +/- 2.34% vs. 17.12 +/- 1.23%; paired difference -4.35 percentage points [95% CI -5.58, -3.09]; p = 0.0015), while PI improvement was not mirrored by uniformly improved reconstruction metrics (PSNR changes were not significant; SSIM decreased by 0.009 and 0.007) and only adjacent-frame temporal-difference, not divergence, regularization was associated with reproducible PI deterioration.
The study trained a 3D residual channel attention network (RCAN) for 2x super-resolution of 4D flow MRI on a synthetic pulmonary-artery benchmark and found that adding biomarker supervision reduced pulsatility-index (PI) error under both regularization conditions (in the original primary comparison, 12.76 +/- 2.34% vs. 17.12 +/- 1.23%; paired difference -4.35 percentage points [95% CI -5.58, -3.09]; p = 0.0015), while PI improvement was not mirrored by uniformly improved reconstruction metrics (PSNR changes were not significant; SSIM decreased by 0.009 and 0.007) and only adjacent-frame temporal-difference, not divergence, regularization was associated with reproducible PI deterioration.
The study trained a 3D residual channel attention network (RCAN) for 2x super-resolution of 4D flow MRI on a synthetic pulmonary-artery benchmark and found that adding biomarker supervision reduced pulsatility-index (PI) error under both regularization conditions (in the original primary comparison, 12.76 +/- 2.34% vs. 17.12 +/- 1.23%; paired difference -4.35 percentage points [95% CI -5.58, -3.09]; p = 0.0015), while PI improvement was not mirrored by uniformly improved reconstruction metrics (PSNR changes were not significant; SSIM decreased by 0.009 and 0.007) and only adjacent-frame temporal-difference, not divergence, regularization was associated with reproducible PI deterioration.
The study trained a 3D residual channel attention network (RCAN) for 2x super-resolution of 4D flow MRI on a synthetic pulmonary-artery benchmark and found that adding biomarker supervision reduced pulsatility-index (PI) error under both regularization conditions (in the original primary comparison, 12.76 +/- 2.34% vs. 17.12 +/- 1.23%; paired difference -4.35 percentage points [95% CI -5.58, -3.09]; p = 0.0015), while PI improvement was not mirrored by uniformly improved reconstruction metrics (PSNR changes were not significant; SSIM decreased by 0.009 and 0.007) and only adjacent-frame temporal-difference, not divergence, regularization was associated with reproducible PI deterioration.
Research Square The authors built a physics-informed screening model for California pesticide exposure: a first-order decay fate index based on soil half-life drives a LightGBM model that predicts weekly county-level pesticide application from 2016-2023 public agricultural, weather, and chemical-property data, and they validated the resulting exposure index against 3,924 U.S. Geological Survey stream measurements at 554 California stations never used in training; agreement was strong and significant for the two most-monitored chemicals (Malathion rho = 0.51, p < 10^-6; Metolachlor rho = 0.28, p < 10^-8), the pooled correlation across all fourteen monitored chemicals was weak, the application model generalized well across space (R2 = 0.64) and time (R2 = 0.
The authors built a physics-informed screening model for California pesticide exposure: a first-order decay fate index based on soil half-life drives a LightGBM model that predicts weekly county-level pesticide application from 2016-2023 public agricultural, weather, and chemical-property data, and they validated the resulting exposure index against 3,924 U.S. Geological Survey stream measurements at 554 California stations never used in training; agreement was strong and significant for the two most-monitored chemicals (Malathion rho = 0.51, p < 10^-6; Metolachlor rho = 0.28, p < 10^-8), the pooled correlation across all fourteen monitored chemicals was weak, the application model generalized well across space (R2 = 0.64) and time (R2 = 0.
The authors built a physics-informed screening model for California pesticide exposure: a first-order decay fate index based on soil half-life drives a LightGBM model that predicts weekly county-level pesticide application from 2016-2023 public agricultural, weather, and chemical-property data, and they validated the resulting exposure index against 3,924 U.S. Geological Survey stream measurements at 554 California stations never used in training; agreement was strong and significant for the two most-monitored chemicals (Malathion rho = 0.51, p < 10^-6; Metolachlor rho = 0.28, p < 10^-8), the pooled correlation across all fourteen monitored chemicals was weak, the application model generalized well across space (R2 = 0.64) and time (R2 = 0.
The authors built a physics-informed screening model for California pesticide exposure: a first-order decay fate index based on soil half-life drives a LightGBM model that predicts weekly county-level pesticide application from 2016-2023 public agricultural, weather, and chemical-property data, and they validated the resulting exposure index against 3,924 U.S. Geological Survey stream measurements at 554 California stations never used in training; agreement was strong and significant for the two most-monitored chemicals (Malathion rho = 0.51, p < 10^-6; Metolachlor rho = 0.28, p < 10^-8), the pooled correlation across all fourteen monitored chemicals was weak, the application model generalized well across space (R2 = 0.64) and time (R2 = 0.
Jurnal Riset Teknik Komputer The article proposes a computer-vision pipeline that combines color-based segmentation with a convolutional neural network to detect healthy, blue-stain, and fungal regions on Sengon wood surfaces: RGB is transformed into HSV and YCrCb to exploit hue, saturation, and chrominance differences, thresholding and morphological operations yield candidate regions, these are cropped into fixed-size patches and classified by a lightweight CNN, and precision, recall, F1-score, accuracy, IoU, and Dice coefficient form the evaluation design; the article states it is a research design, so empirical result values are not yet presented and will be reported after the target dataset is tested.
The article proposes a computer-vision pipeline that combines color-based segmentation with a convolutional neural network to detect healthy, blue-stain, and fungal regions on Sengon wood surfaces: RGB is transformed into HSV and YCrCb to exploit hue, saturation, and chrominance differences, thresholding and morphological operations yield candidate regions, these are cropped into fixed-size patches and classified by a lightweight CNN, and precision, recall, F1-score, accuracy, IoU, and Dice coefficient form the evaluation design; the article states it is a research design, so empirical result values are not yet presented and will be reported after the target dataset is tested.
The article proposes a computer-vision pipeline that combines color-based segmentation with a convolutional neural network to detect healthy, blue-stain, and fungal regions on Sengon wood surfaces: RGB is transformed into HSV and YCrCb to exploit hue, saturation, and chrominance differences, thresholding and morphological operations yield candidate regions, these are cropped into fixed-size patches and classified by a lightweight CNN, and precision, recall, F1-score, accuracy, IoU, and Dice coefficient form the evaluation design; the article states it is a research design, so empirical result values are not yet presented and will be reported after the target dataset is tested.
The article proposes a computer-vision pipeline that combines color-based segmentation with a convolutional neural network to detect healthy, blue-stain, and fungal regions on Sengon wood surfaces: RGB is transformed into HSV and YCrCb to exploit hue, saturation, and chrominance differences, thresholding and morphological operations yield candidate regions, these are cropped into fixed-size patches and classified by a lightweight CNN, and precision, recall, F1-score, accuracy, IoU, and Dice coefficient form the evaluation design; the article states it is a research design, so empirical result values are not yet presented and will be reported after the target dataset is tested.
Research Square The work proposes a UAV control scheme that combines a Recursive Gaussian Process (RGP) with a feedback linearization controller, using a purpose-designed Kalman-filter-based tuning algorithm to compensate online for the nonlinear dynamics left uncanceled by feedback linearization, implements the entire RGP pipeline directly onboard a real quadrotor (the ANT-X Lab drone), and validates it for single-axis horizontal position control in indoor experiments: across repetitive and non-repetitive sinusoidal references, the geometric mean root mean square error improves by 38% to 75% relative to the same controller without RGP, with geometric mean tracking errors between 4.5 cm (constant-amplitude sinusoid at omega=1 rad/s) and 9.85 cm (sweep sinusoid).
The work proposes a UAV control scheme that combines a Recursive Gaussian Process (RGP) with a feedback linearization controller, using a purpose-designed Kalman-filter-based tuning algorithm to compensate online for the nonlinear dynamics left uncanceled by feedback linearization, implements the entire RGP pipeline directly onboard a real quadrotor (the ANT-X Lab drone), and validates it for single-axis horizontal position control in indoor experiments: across repetitive and non-repetitive sinusoidal references, the geometric mean root mean square error improves by 38% to 75% relative to the same controller without RGP, with geometric mean tracking errors between 4.5 cm (constant-amplitude sinusoid at omega=1 rad/s) and 9.85 cm (sweep sinusoid).
The work proposes a UAV control scheme that combines a Recursive Gaussian Process (RGP) with a feedback linearization controller, using a purpose-designed Kalman-filter-based tuning algorithm to compensate online for the nonlinear dynamics left uncanceled by feedback linearization, implements the entire RGP pipeline directly onboard a real quadrotor (the ANT-X Lab drone), and validates it for single-axis horizontal position control in indoor experiments: across repetitive and non-repetitive sinusoidal references, the geometric mean root mean square error improves by 38% to 75% relative to the same controller without RGP, with geometric mean tracking errors between 4.5 cm (constant-amplitude sinusoid at omega=1 rad/s) and 9.85 cm (sweep sinusoid).
The work proposes a UAV control scheme that combines a Recursive Gaussian Process (RGP) with a feedback linearization controller, using a purpose-designed Kalman-filter-based tuning algorithm to compensate online for the nonlinear dynamics left uncanceled by feedback linearization, implements the entire RGP pipeline directly onboard a real quadrotor (the ANT-X Lab drone), and validates it for single-axis horizontal position control in indoor experiments: across repetitive and non-repetitive sinusoidal references, the geometric mean root mean square error improves by 38% to 75% relative to the same controller without RGP, with geometric mean tracking errors between 4.5 cm (constant-amplitude sinusoid at omega=1 rad/s) and 9.85 cm (sweep sinusoid).
Terence Tao blog RSS Using a hypothetical scenario discussed at her department's September 17 faculty retreat—an OpenAI five-sigma dark-energy detection—University of Washington Astronomy chair Jess Werk argues that generative AI makes productivity easy to manufacture while academic incentives have long rewarded output over understanding, and proposes three sets of department-level measures (PhD processes, faculty reward systems, and department community) that together form a "cognitive sanctuary" to protect doctoral training and human judgment.
Using a hypothetical scenario discussed at her department's September 17 faculty retreat—an OpenAI five-sigma dark-energy detection—University of Washington Astronomy chair Jess Werk argues that generative AI makes productivity easy to manufacture while academic incentives have long rewarded output over understanding, and proposes three sets of department-level measures (PhD processes, faculty reward systems, and department community) that together form a "cognitive sanctuary" to protect doctoral training and human judgment.
Using a hypothetical scenario discussed at her department's September 17 faculty retreat—an OpenAI five-sigma dark-energy detection—University of Washington Astronomy chair Jess Werk argues that generative AI makes productivity easy to manufacture while academic incentives have long rewarded output over understanding, and proposes three sets of department-level measures (PhD processes, faculty reward systems, and department community) that together form a "cognitive sanctuary" to protect doctoral training and human judgment.
Using a hypothetical scenario discussed at her department's September 17 faculty retreat—an OpenAI five-sigma dark-energy detection—University of Washington Astronomy chair Jess Werk argues that generative AI makes productivity easy to manufacture while academic incentives have long rewarded output over understanding, and proposes three sets of department-level measures (PhD processes, faculty reward systems, and department community) that together form a "cognitive sanctuary" to protect doctoral training and human judgment.
arXiv The authors built AgentWorld, a benchmark of 100 human-annotated tasks (plus 100 LLM-augmented variants) on the open-source MMORPG engine Kaetram, requiring 3–20 agents with asymmetric roles to coordinate over 25–55 rounds under a blackbox setting, and proposed Causal Collaboration Effectiveness (CCE), a graph-based metric over causal action graphs; testing Gemini 3 Flash, Claude Haiku 4.5, GPT-5 Mini, and DeepSeek R1-70B, the best model reaches only 52.0% task success and a CCE of 0.320, with failure modes centered on communication breakdowns, role confusion, and inability to maintain shared plans across rounds.
The authors built AgentWorld, a benchmark of 100 human-annotated tasks (plus 100 LLM-augmented variants) on the open-source MMORPG engine Kaetram, requiring 3–20 agents with asymmetric roles to coordinate over 25–55 rounds under a blackbox setting, and proposed Causal Collaboration Effectiveness (CCE), a graph-based metric over causal action graphs; testing Gemini 3 Flash, Claude Haiku 4.5, GPT-5 Mini, and DeepSeek R1-70B, the best model reaches only 52.0% task success and a CCE of 0.320, with failure modes centered on communication breakdowns, role confusion, and inability to maintain shared plans across rounds.
The authors built AgentWorld, a benchmark of 100 human-annotated tasks (plus 100 LLM-augmented variants) on the open-source MMORPG engine Kaetram, requiring 3–20 agents with asymmetric roles to coordinate over 25–55 rounds under a blackbox setting, and proposed Causal Collaboration Effectiveness (CCE), a graph-based metric over causal action graphs; testing Gemini 3 Flash, Claude Haiku 4.5, GPT-5 Mini, and DeepSeek R1-70B, the best model reaches only 52.0% task success and a CCE of 0.320, with failure modes centered on communication breakdowns, role confusion, and inability to maintain shared plans across rounds.
The authors built AgentWorld, a benchmark of 100 human-annotated tasks (plus 100 LLM-augmented variants) on the open-source MMORPG engine Kaetram, requiring 3–20 agents with asymmetric roles to coordinate over 25–55 rounds under a blackbox setting, and proposed Causal Collaboration Effectiveness (CCE), a graph-based metric over causal action graphs; testing Gemini 3 Flash, Claude Haiku 4.5, GPT-5 Mini, and DeepSeek R1-70B, the best model reaches only 52.0% task success and a CCE of 0.320, with failure modes centered on communication breakdowns, role confusion, and inability to maintain shared plans across rounds.
arXiv This technical report introduces Kaggle Game Arena, an open head-to-head evaluation platform in which ten frontier models play large-scale round-robin matches in Chess, Poker and Werewolf through a uniform text harness (900,000 poker hands, 180,000 per model), ranking them by objective outcomes such as wins, chip counts and role-level decomposition rather than subjective judgment, and reports sharp within-game stratification alongside rankings that do not agree across games.
This technical report introduces Kaggle Game Arena, an open head-to-head evaluation platform in which ten frontier models play large-scale round-robin matches in Chess, Poker and Werewolf through a uniform text harness (900,000 poker hands, 180,000 per model), ranking them by objective outcomes such as wins, chip counts and role-level decomposition rather than subjective judgment, and reports sharp within-game stratification alongside rankings that do not agree across games.
This technical report introduces Kaggle Game Arena, an open head-to-head evaluation platform in which ten frontier models play large-scale round-robin matches in Chess, Poker and Werewolf through a uniform text harness (900,000 poker hands, 180,000 per model), ranking them by objective outcomes such as wins, chip counts and role-level decomposition rather than subjective judgment, and reports sharp within-game stratification alongside rankings that do not agree across games.
This technical report introduces Kaggle Game Arena, an open head-to-head evaluation platform in which ten frontier models play large-scale round-robin matches in Chess, Poker and Werewolf through a uniform text harness (900,000 poker hands, 180,000 per model), ranking them by objective outcomes such as wins, chip counts and role-level decomposition rather than subjective judgment, and reports sharp within-game stratification alongside rankings that do not agree across games.
arXiv The work introduces LightMIS, a family of ultra-lightweight convolutional networks that aligns the outputs of a five-level encoder to a common resolution with Scale-Aligned Projection blocks, aggregates them once, and refines the fused representation with an Adaptive Fusion Cascade, thereby removing the learned stage-wise decoder; evaluated by five-fold cross-validation under a common nnU-Net v2.3.1 protocol on DRIVE, Kvasir-SEG, DSB18, BUSI, ISIC-2017, and ISIC-2018, the full model reaches 86.71% modality-macro Dice and 78.99% IoU with 0.131M parameters and 0.575 GFLOPs, within 0.04 and 0.08 percentage points of Mobile U-ViT's 86.75% and 79.07% while using 90.58-99.61% fewer parameters and 82.54-96.
The work introduces LightMIS, a family of ultra-lightweight convolutional networks that aligns the outputs of a five-level encoder to a common resolution with Scale-Aligned Projection blocks, aggregates them once, and refines the fused representation with an Adaptive Fusion Cascade, thereby removing the learned stage-wise decoder; evaluated by five-fold cross-validation under a common nnU-Net v2.3.1 protocol on DRIVE, Kvasir-SEG, DSB18, BUSI, ISIC-2017, and ISIC-2018, the full model reaches 86.71% modality-macro Dice and 78.99% IoU with 0.131M parameters and 0.575 GFLOPs, within 0.04 and 0.08 percentage points of Mobile U-ViT's 86.75% and 79.07% while using 90.58-99.61% fewer parameters and 82.54-96.
The work introduces LightMIS, a family of ultra-lightweight convolutional networks that aligns the outputs of a five-level encoder to a common resolution with Scale-Aligned Projection blocks, aggregates them once, and refines the fused representation with an Adaptive Fusion Cascade, thereby removing the learned stage-wise decoder; evaluated by five-fold cross-validation under a common nnU-Net v2.3.1 protocol on DRIVE, Kvasir-SEG, DSB18, BUSI, ISIC-2017, and ISIC-2018, the full model reaches 86.71% modality-macro Dice and 78.99% IoU with 0.131M parameters and 0.575 GFLOPs, within 0.04 and 0.08 percentage points of Mobile U-ViT's 86.75% and 79.07% while using 90.58-99.61% fewer parameters and 82.54-96.
The work introduces LightMIS, a family of ultra-lightweight convolutional networks that aligns the outputs of a five-level encoder to a common resolution with Scale-Aligned Projection blocks, aggregates them once, and refines the fused representation with an Adaptive Fusion Cascade, thereby removing the learned stage-wise decoder; evaluated by five-fold cross-validation under a common nnU-Net v2.3.1 protocol on DRIVE, Kvasir-SEG, DSB18, BUSI, ISIC-2017, and ISIC-2018, the full model reaches 86.71% modality-macro Dice and 78.99% IoU with 0.131M parameters and 0.575 GFLOPs, within 0.04 and 0.08 percentage points of Mobile U-ViT's 86.75% and 79.07% while using 90.58-99.61% fewer parameters and 82.54-96.
arXiv BoundInk is a writer-conditioned online handwriting generation framework that treats inter-character boundaries (cursive joins, spacing, alignment) as explicit generation targets, modeling predecessor-to-current local transitions with a bigram-aware sliding-window Transformer while injecting sentence context through gated fusion, and it introduces Connectivity and Spacing Metrics (CSM) to directly assess cursive continuity and character/word spacing; across three benchmark-matched settings it improves all applicable boundary-quality measures, reduces normalized DTW by 17.6–47.8%, and is preferred in 78.0–82.6% of valid criterion-wise judgments in blind human evaluation.
BoundInk is a writer-conditioned online handwriting generation framework that treats inter-character boundaries (cursive joins, spacing, alignment) as explicit generation targets, modeling predecessor-to-current local transitions with a bigram-aware sliding-window Transformer while injecting sentence context through gated fusion, and it introduces Connectivity and Spacing Metrics (CSM) to directly assess cursive continuity and character/word spacing; across three benchmark-matched settings it improves all applicable boundary-quality measures, reduces normalized DTW by 17.6–47.8%, and is preferred in 78.0–82.6% of valid criterion-wise judgments in blind human evaluation.
BoundInk is a writer-conditioned online handwriting generation framework that treats inter-character boundaries (cursive joins, spacing, alignment) as explicit generation targets, modeling predecessor-to-current local transitions with a bigram-aware sliding-window Transformer while injecting sentence context through gated fusion, and it introduces Connectivity and Spacing Metrics (CSM) to directly assess cursive continuity and character/word spacing; across three benchmark-matched settings it improves all applicable boundary-quality measures, reduces normalized DTW by 17.6–47.8%, and is preferred in 78.0–82.6% of valid criterion-wise judgments in blind human evaluation.
BoundInk is a writer-conditioned online handwriting generation framework that treats inter-character boundaries (cursive joins, spacing, alignment) as explicit generation targets, modeling predecessor-to-current local transitions with a bigram-aware sliding-window Transformer while injecting sentence context through gated fusion, and it introduces Connectivity and Spacing Metrics (CSM) to directly assess cursive continuity and character/word spacing; across three benchmark-matched settings it improves all applicable boundary-quality measures, reduces normalized DTW by 17.6–47.8%, and is preferred in 78.0–82.6% of valid criterion-wise judgments in blind human evaluation.
Nature News Nature reports that 82% of the 140 in vivo CAR-T cell drugs being tested in animals and people are being developed by groups in China, which has become a fast mover to the clinic through investigator-initiated trials; a Nature study in the same evidence bundle shows that PHGDH-deficient pancreatic cancer cells reprogram neighbouring hepatocytes through a CXCL5–CXCR2–PI3K–AKT–FOXO3A axis, driving hepatocyte PHGDH expression and serine secretion that supports liver metastatic outgrowth.
Nature reports that 82% of the 140 in vivo CAR-T cell drugs being tested in animals and people are being developed by groups in China, which has become a fast mover to the clinic through investigator-initiated trials; a Nature study in the same evidence bundle shows that PHGDH-deficient pancreatic cancer cells reprogram neighbouring hepatocytes through a CXCL5–CXCR2–PI3K–AKT–FOXO3A axis, driving hepatocyte PHGDH expression and serine secretion that supports liver metastatic outgrowth.
Nature reports that 82% of the 140 in vivo CAR-T cell drugs being tested in animals and people are being developed by groups in China, which has become a fast mover to the clinic through investigator-initiated trials; a Nature study in the same evidence bundle shows that PHGDH-deficient pancreatic cancer cells reprogram neighbouring hepatocytes through a CXCL5–CXCR2–PI3K–AKT–FOXO3A axis, driving hepatocyte PHGDH expression and serine secretion that supports liver metastatic outgrowth.
Nature reports that 82% of the 140 in vivo CAR-T cell drugs being tested in animals and people are being developed by groups in China, which has become a fast mover to the clinic through investigator-initiated trials; a Nature study in the same evidence bundle shows that PHGDH-deficient pancreatic cancer cells reprogram neighbouring hepatocytes through a CXCL5–CXCR2–PI3K–AKT–FOXO3A axis, driving hepatocyte PHGDH expression and serine secretion that supports liver metastatic outgrowth.
arXiv CARD introduces a hierarchical framework for personalized text generation: it clusters users by shared stylistic patterns and trains group-specific LoRA adapters, derives lightweight user preference vectors through implicit preference learning that contrasts user-authored text with cluster-level generations, and injects personalization at inference only via low-rank logit corrections, ranking first in 10 of 12 settings across six LaMP and LongLaMP tasks and two metrics while remaining stable for low-resource users, across model scales, and in storage efficiency.
CARD introduces a hierarchical framework for personalized text generation: it clusters users by shared stylistic patterns and trains group-specific LoRA adapters, derives lightweight user preference vectors through implicit preference learning that contrasts user-authored text with cluster-level generations, and injects personalization at inference only via low-rank logit corrections, ranking first in 10 of 12 settings across six LaMP and LongLaMP tasks and two metrics while remaining stable for low-resource users, across model scales, and in storage efficiency.
CARD introduces a hierarchical framework for personalized text generation: it clusters users by shared stylistic patterns and trains group-specific LoRA adapters, derives lightweight user preference vectors through implicit preference learning that contrasts user-authored text with cluster-level generations, and injects personalization at inference only via low-rank logit corrections, ranking first in 10 of 12 settings across six LaMP and LongLaMP tasks and two metrics while remaining stable for low-resource users, across model scales, and in storage efficiency.
CARD introduces a hierarchical framework for personalized text generation: it clusters users by shared stylistic patterns and trains group-specific LoRA adapters, derives lightweight user preference vectors through implicit preference learning that contrasts user-authored text with cluster-level generations, and injects personalization at inference only via low-rank logit corrections, ranking first in 10 of 12 settings across six LaMP and LongLaMP tasks and two metrics while remaining stable for low-resource users, across model scales, and in storage efficiency.
arXiv The work introduces MOPD-Router, a multi-teacher on-policy distillation framework that needs neither domain labels nor a separately trained routing model, selecting and weighting the full teacher pool at every token, and proposes ExpertAlign, a metric that scores each teacher by the positive cosine alignment between the specialization it acquired relative to the shared pre-RL base and the teaching direction it would apply to the current student; across unlabeled and domain-labeled training mixtures under strong-to-weak and same-size distillation, ExpertAlign achieves the strongest overall performance in all four settings, improving the overall score by 5.88 points (+12.3%) over Mean aggregation on unlabeled data and by 3.95 points (+7.
The work introduces MOPD-Router, a multi-teacher on-policy distillation framework that needs neither domain labels nor a separately trained routing model, selecting and weighting the full teacher pool at every token, and proposes ExpertAlign, a metric that scores each teacher by the positive cosine alignment between the specialization it acquired relative to the shared pre-RL base and the teaching direction it would apply to the current student; across unlabeled and domain-labeled training mixtures under strong-to-weak and same-size distillation, ExpertAlign achieves the strongest overall performance in all four settings, improving the overall score by 5.88 points (+12.3%) over Mean aggregation on unlabeled data and by 3.95 points (+7.
The work introduces MOPD-Router, a multi-teacher on-policy distillation framework that needs neither domain labels nor a separately trained routing model, selecting and weighting the full teacher pool at every token, and proposes ExpertAlign, a metric that scores each teacher by the positive cosine alignment between the specialization it acquired relative to the shared pre-RL base and the teaching direction it would apply to the current student; across unlabeled and domain-labeled training mixtures under strong-to-weak and same-size distillation, ExpertAlign achieves the strongest overall performance in all four settings, improving the overall score by 5.88 points (+12.3%) over Mean aggregation on unlabeled data and by 3.95 points (+7.
The work introduces MOPD-Router, a multi-teacher on-policy distillation framework that needs neither domain labels nor a separately trained routing model, selecting and weighting the full teacher pool at every token, and proposes ExpertAlign, a metric that scores each teacher by the positive cosine alignment between the specialization it acquired relative to the shared pre-RL base and the teaching direction it would apply to the current student; across unlabeled and domain-labeled training mixtures under strong-to-weak and same-size distillation, ExpertAlign achieves the strongest overall performance in all four settings, improving the overall score by 5.88 points (+12.3%) over Mean aggregation on unlabeled data and by 3.95 points (+7.