PLOS ONE Alerts: New Articles

1. Correction: Nonlinear dose-response of mind-body exercise in older adults with mild cognitive impairment: A systematic review and meta-analysis

   by Yan-Fang Peng, Xi Chen, Guo-Qing Zhu, Guang-Cai Qu

2. YOLO-SOD: Color probability map-based fuzzy framework using deep models for face detection

   by Aniqa Saba, Muhammad Faizan Shirazi, Yawar Rehman, Muhammad Mubashir Khan

   Detecting small faces remains challenging due to scale variation and limited discriminative cues at low resolutions. We address this with a two-pronged approach. First, we modify YOLOv5 by adding extra convolutional layers and small-object–focused detection heads that operate on larger, higher-resolution backbone feature maps, improving sensitivity to tiny targets. Second, we introduce a dynamic, image adaptive patching strategy for scale selection. We compute Cr channel color probability maps and measure the number and density of pixel clusters; a Fuzzy Inference System (FIS) uses these cues to select the most suitable patch size for near and far faces. On the WIDER FACE benchmark, our method attains 93.07% mAP@50 (Easy), 91.25% mAP@50 (Medium), and 84.45% mAP@50 (Hard). On our proposed local dataset, the proposed model achieves 49.36% mAP@50 for different size faces. Beyond faces, the approach generalizes to small object detection on VisDrone-DET2019, reaching 46.76 mAP@50. These results demonstrate that coupling small-object-aware architecture design with color-guided, fuzzy patch selection yields competitive mAP@50 for small and distant targets.
3. The impact of perceived social support on college athletes’ academic burnout: The moderating role of cognitive reappraisal within a serial mediation model

   by Guihua Xu, Xirui Yang, Qiyong Zhang

   College athletes face substantial challenges from the combined demands of academic study and athletic training, placing them at elevated risk for academic burnout (AB). This study examined how perceived social support (PSS) is associated with AB by testing the mediating roles of mental toughness (MT) and athlete burnout (ATHB) and the moderating effect of cognitive reappraisal (CR). Using a cross-sectional design, 1,918 college athletes from over 20 Chinese universities completed standardized questionnaires assessing PSS, MT, ATHB, CR, and AB. Serial and moderated serial mediation were analyzed with the PROCESS macro (Models 6 and 89) while controlling for demographic and sport-related variables. PSS was directly and negatively associated with AB (β = −0.122, p < 0.001), with significant indirect associations separately through MT and ATHB and sequentially through MT and ATHB. CR showed path-specific moderation: at higher levels of CR, the negative PSS–AB association was stronger, but so was the positive ATHB–AB association, whereas the MT–AB association did not differ significantly across CR levels. Thus, CR should not be interpreted as having a uniformly buffering role. These findings may inform future multicomponent support approaches aimed at strengthening PSS and MT and identifying and addressing ATHB, while applying CR-related training cautiously and with attention to the specific pathway involved. The effectiveness and boundary conditions of such approaches require longitudinal or experimental testing.
4. Expression of Concern: The agricultural carbon reduction effect of digital rural construction under the dual carbon target

   by The PLOS One Editors

5. Predicting under-five child mortality in Zambia: Machine learning versus parametric survival models

   by Kassaye Getaneh Arge

   Background

   In sub-Saharan Africa, child mortality remains a critical public health challenge, with Zambia exhibiting some of the highest rates in the region. While survival analysis has identified key risk factors, these methods often fail to capture complex non-linear interactions and the dynamic, time-varying nature of longitudinal markers like birth intervals. This study aimed to develop and compare predictive survival models to improve risk stratification for under-five mortality using the 2024 Zambia Demographic and Health Survey (ZDHS).

   Methods

   A cohort of 35,610 live births was initially identified, with a final analytic sample of 9,362 singleton births and 1,236 mortality events (13.2%) after applying exclusion criteria for maternal age and data completeness. We compared four modeling frameworks for predictive comparison: Cox Proportional Hazards (Cox PH), Lasso Cox regression, Random Survival Forests (RSF), and Gradient Boosting Machines (GBM), alongside a Bayesian Joint Model for inference on the birth interval and mortality association. The proportional hazards assumption was assessed using Schoenfeld residuals, and stratified Cox models were employed to address time-varying effects. Model performance was evaluated using nested cross-validation with mother-level outer folds and time-dependent Concordance Indices (C-index) and Area Under the Curve (AUC) metrics across a 48-month follow-up period.

   Results

   Under nested cross-validation, the four survival models demonstrated comparable predictive performance, with C-indices ranging from 0.745 (Cox PH) to 0.750 (Gradient Boosting Machine) and Integrated Brier Scores ranging from 0.095 to 0.096; pairwise comparisons across models were not statistically significant (DeLong’s test: all p > 0.05). The Bayesian Joint Model revealed a significant association between the longitudinal birth interval process and the hazard of death (α=0.035, 95% CI: 0.023–0.048, p < 0.001), supporting the maternal depletion hypothesis. Variable importance analysis identified birth interval and maternal age as the most influential predictors of child survival, followed by previous child deaths. Stratified analysis of the Cox model revealed significant effects for maternal education (Secondary: HR = 0.426, 95% CI: 0.318–0.570) and previous child deaths (HR = 1.648, 95% CI: 1.529–1.777).

   Conclusion

   Advanced survival modeling using joint modeling techniques provides deeper biological insights into child mortality dynamics, and machine learning and traditional survival models offered equivalent predictive discrimination and calibration in this analysis. These findings support targeted public health interventions focusing on birth spacing, maternal age, and enhanced monitoring for high-risk families as critical strategies for reducing under-five mortality in Zambia.