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Shallow Hole Trapping and Intrinsic Defect Tolerance in Visible-Light Photocatalysts: The Role of Lattice Relaxation and Electronic Screening
Multi-session smart night charging of EVs for accelerated decarbonization of electric mobility
The majority of electric vehicles (EVs) are charged domestically overnight, when the precise timing of power allocation is not important to the user, thus providing a source of flexibility that can be leveraged by charging control algorithms. In this paper, we show that by keeping the EV connected to a standard domestic smart plug every night, it is possible to achieve an approximate 37% annual reduction in carbon intensity compared to uncontrolled charging (based on 2022 UK National Grid data) without compromising the EV owners’ charging demand. For this purpose, we use Model Predictive Control techniques to schedule power delivery with the objective of minimizing grid-average CO 2 emissions over forecast windows of multiple sessions (up to seven days ahead). We find that flexibility on the part of EV owners—both their willingness to keep the EV connected at night, and to indicate a precautionary (i.e., upper bound) estimate of their required state-of-charge in the morning—allows for significant emissions reduction and accelerated decarbonization of the mobility sector.
Alkyne Cyclizations through Vinyl Cations and Stereoselective Allene Formation by Terpene Synthases
Editorial Note: Cohort profile: The Endometriosis pain QUality aftEr Surgical Treatment (EndoQUEST) Study
Gas-Phase and Condensed-Phase Synergy in a Nonflammable Electrolyte for Highly Stable Sodium-Ion Batteries
Exploring the key points and key shots in table tennis matches based on survival analysis
This study aimed to investigate the Key Points ( KP ) and Key Shots ( KS ) in singles matches for elite table tennis players, enhancing the knowledge of winning patterns in table tennis. A total of 60 men’s singles and 64 women’s singles matches were analyzed, all data sourced from events such as the World Championships, World Cup, Olympic Games, and WTT Champions. Methods: Survival analysis was introduced, in which points scored in a match and shot counts in a rally were treated as “survival time,” while losing a point was defined as the “event.” The Kaplan-Meier method was employed to estimate cumulative survival probabilities, which reflect the likelihood of players enhancing their winning potential at specific scores or shot numbers. The point or shot number at which the cumulative survival probability drops to 50% is defined as the KP or KS , respectively. Additionally, the Log-rank (Mantel-Cox) non-parametric test was applied to determine whether significant differences existed between the survival curves of different groups. Results: KP in table tennis singles demonstrate a stable pattern of “ 7 for women, 8 for men,” while Winning Points ( WP ) are predominantly concentrated at the 9th point. The KS positions are consistent in both men’s and women’s singles, specifically the 4th shot in the total rally, the 3rd shot in the serving round, and the 2nd shot in the receiving round. Winning Shots ( WS ) consistently occur at the 6th shot in the total rally, as well as the 3rd shot in both the serving and receiving rounds. Conclusion: These findings advance our understanding of winning patterns in table tennis, providing theoretical foundations and practical references for designing contextualized training programs, precisely regulating athletes’ competitive psychology, and optimizing future competition rules.
Dual-Targeting Multivalent Aptamer-Drug Hybrids for Synergistic Cancer Immunotherapy
EconoGNN: A graph neural network framework for temporal economic resilience insights
Global economic shocks such as the 2008 financial crisis or recent trade escalations between the United States and China have exposed the complexity of interdependent economies and the need for systemic, multi-agent analysis. However, most regional economic resilience (RER) studies remain limited by localized datasets, inconsistent definitions, and static modeling approaches, restricting their ability to generalize insights across space and time. We introduce EconoGNN, a Graph Neural Network framework that integrates complexity theory, economic modeling, and machine learning to predict and explain regional economic resilience across 183 countries over 25 years. By combining over 81 million trade records (UN COMTRADE) and 500,000 macroeconomic observations (Penn World Table), and adopting an official resilience metric from the World Bank, our approach enables reproducible and interpretable global-scale analysis. EconoGNN achieves F1-scores of 0.750 with the temporal GNN architecture GConvGRU, AUC-ROC of 0.792, and PR-AUC of 0.757, demonstrating robust performance across different recovery threshold settings ( τ = 0.90–1.00) with F1-scores ranging from 0.730 to 0.771, and yielding statistically significant improvements (p-value ≤0.05) over baselines. GNNExplainer validation confirms explanation reliability (Fidelity+ = 0.827, Characterization = 0.913), enabling country-customized interpretability of resilience drivers. Moreover the EconoGNN framework integrates key structural and welfare indicators to model both national and cross-border economic interactions, reducing omitted-variable bias and implicitly accounting for political, institutional, and cultural differences.
Comprehensive analysis of immune-related genes reveals diagnostic biomarkers and molecular subtypes in diabetic retinopathy
Diabetic retinopathy (DR) is a common microvascular complication of diabetes and one of the primary causes of vision loss; however, its pathogenic mechanisms remain largely unresolved. In recent years, immune dysregulation has been increasingly recognized to be closely associated with DR progression. In the present study, we integrated two GEO datasets to identify immune-related differentially expressed genes (DEGs) associated with DR. After batch effect correction and differential expression analysis, a total of 123 immune-related DEGs were identified. Functional enrichment analysis demonstrated that these genes are primarily associated with immune activation pathways, such as T-cell receptor signaling, natural killer cell–mediated cytotoxicity, and IL-17 signaling. Using least absolute shrinkage and selection operator (LASSO) regression, five key genes (IL10RA, PLAUR, PLAU, VTN, and VGF) were identified and used to construct a diagnostic model with excellent predictive performance. Protein–protein interaction and immune infiltration analyses revealed that these genes are closely associated with immune cell activity, particularly the increased infiltration of resting CD4 memory T cells and M2 macrophages in the DR retina. To validate these findings, an STZ-induced diabetic mouse model was used, with key genes further validated at the protein level via Western blot. In addition, consensus clustering was used to stratify patients with DR into two molecular subtypes with distinct immune characteristics and pathway enrichment patterns. Overall, our study elucidates the immune-related mechanisms underlying DR and highlights PLAUR, PLAU, and VGF as potential immune-associated biomarkers, providing a theoretical basis for precision diagnosis and development of targeted immunotherapeutic strategies for DR.
Controlling Electrode–Electrolyte Interactions to Enhance Capacitance
RMETNet: A cross-subject motor imagery EEG signal classification model based on TSLANet and riemannian geometry features
Motor imagery electroencephalogram (MI-EEG) analysis is essential for natural interaction and autonomous control in brain-computer interfaces (BCIs). However, deep learning models often struggle with inter-subject variability, which limits their ability to generalize across subjects. This study proposes RMETNet, a novel framework that integrates TSLANet, a spatio-temporal convolution module, and a multi-scale Riemannian geometry feature module. TSLANet suppresses noise and captures complex temporal patterns for preliminary signal decoding, while the spatio-temporal convolution module extracts higher-order representations. The Riemannian branch learns geometry-based distribution features across subjects, and the fused features are used for classification. To address inter-subject distribution shifts, RMETNet incorporates Maximum Mean Discrepancy (MMD) loss for domain adaptation, aligning feature distributions between source and target domains. Experiments show that on the four-class BCI Competition IV 2a (BCICIV2a) dataset, RMETNet achieved accuracies of 71.39% in the cross-subject setting and 80.71% in the subject-dependent setting; on the two-class BCI Competition IV 2b (BCICIV2b) dataset, it achieved 80.93% and 86.76%, respectively. The model consistently outperformed baseline algorithms. Ablation and visualization analyses further validated its effectiveness in reducing inter-subject feature distribution disparities and enhancing MI-EEG decoding. The code is available at: https://github.com/rokanfeermecer486/RMETNet .