Search Results (417)

Lithium-ion battery fires are governed by continuous heat release during thermal runaway, flammable gas venting, and thermal coupling between adjacent cells. Even after visible flames are extinguished, post-extinguishment temperature rise, thermal runaway propagation, and reignition may still occur. This review establishes a full-process control framework linking flame suppression, sustained cooling, thermal runaway propagation mitigation, and reignition control. Within this framework, water-based agents, clean gaseous agents, dry powder agents, foams, cryogenic media, and hybrid suppression methods are not only compared by their flame extinguishment performance but also by their cooling capability, thermal runaway propagation/reignition control, scenario applicability, and environmental impacts. Evaluation metrics and standardization requirements are further integrated to support cross-study comparison and practical suppressant selection. Existing studies indicate that a single suppressant is generally unable to achieve both rapid flame extinguishment and post-extinguishment thermal stability. Multi-mechanism synergy, realistic scenario validation, and standardized evaluation protocols are therefore essential for improving the full-process control of lithium-ion battery fires. Full article

Accurate detection of forced oscillations is important for the stable operation of power systems. The method based on prior knowledge relies on manual feature extraction, which has limited ability to characterize non-stationary signals. While deep learning (DL) methods can automatically learn features, they may overlook the physical mechanisms of power systems, potentially leading to misjudgments. We propose a Hybrid Knowledge-DL network (HKD-SVM) that utilizes Support Vector Machine (SVM) as the classifier. In our method, Discrete Wavelet Transform (DWT) is used to represent the time–frequency structure of the input signals, and DL features are extracted by Convolutional Neural Network (CNN) from this time–frequency representation. These learned features are subsequently fused with prior knowledge features that carry explicit physical interpretations, thereby constructing a more discriminative feature representation space. Finally, SVM is adopted as the classifier, making the network well-suited for nonlinear, high-dimensional classification scenarios with limited training samples, which are common in power system applications. Experiments on both simulated and real-world phasor measurement unit (PMU) data demonstrate that HKD-SVM outperforms purely data-driven and purely knowledge-driven methods. The proposed method provides an effective solution for power system oscillation detection. Full article

In the single-depot multi-traveling salesman problem, traditional depot location methods often overlook task balance among traveling salesmen, leading to excessive load on certain units and compromising overall operational efficiency. To address this issue, this paper proposes an optimized depot location method based on clustering and multi-task balancing. The core contribution lies in the design of a multi-weight adaptive depot optimization method. This approach clusters city nodes into multiple groups through cluster analysis and dynamically synthesizes direction vectors using information such as the number of samples within each cluster and the convex perimeter. It iteratively optimizes depot locations, minimizing the total path length while enhancing workload balance across all traveling salesman routes. Additionally, a “divide-and-conquer” strategy decomposes the complex MTSP into multiple parallel TSP subproblems, which are then efficiently solved using Or-Tools. A comprehensive evaluation framework is introduced, incorporating Total-Sum distance, Min-Max distance, Workload Balance, Cluster separability, Robustness, and Running time. Experimental results on the TSPLIB standard dataset demonstrate that the proposed method exhibits significant advantages over various traditional clustering algorithms in both route optimization and route balancing, validating its effectiveness and practicality. The method’s robust performance provides a reliable solution for real-world applications such as logistics distribution, further highlighting its practical value. Experimental results show that the proposed method reduces the total travel distance and improves workload balance on multiple TSPLIB instances compared with conventional depot selection baselines. Full article

Landslide-generated impulse waves in deeply incised gorge regions pose significant risks to hydropower infrastructure, particularly under fluctuating reservoir water levels. This study investigates the potential global instability sliding of the Sejiang deforming slope on the left bank of the Bala Hydropower Station, Sichuan Province of China. A three-dimensional numerical simulation of the landslide–water entry, wave generation, and propagation processes conducted using computational fluid dynamics (CFD) shows a maximum wave height of 5.57 m on the opposite bank. Moreover, the CFD results were compared with those derived from Pan′s empirical formula method. In contrast, Pan′s empirical formula only provides conservative predictions for water-entry velocity and wave height, with a maximum wave height of 13.60 m on the opposite bank. Notably, the CFD numerical simulations precisely characterize complex topographic effects, such as the approximately 156% wave height amplification at the spoil disposal bend due to reflection and superposition, as well as the localized energy convergence in front of the dam. Furthermore, the impulse wave destructive potential is positively correlated with reservoir water levels. While the assessment confirms no risk of dam overtopping under the current scenarios, it highlights the necessity for differentiated protection strategies targeting three critical zones, i.e., the initial wave impact zone, the multi-directional superposition zone at the spoil disposal area, and the localized energy convergence zone near the dam. This study provides a reliable quantitative basis for refined hazard assessment and disaster mitigation in complex reservoir topographies. Full article

Emilia sonchifolia (L.) DC is a medicinal and edible herb of Asteraceae with Lingnan characteristics. Flavonoids are its core pharmacodynamic substances, but the molecular regulation mechanism of differential accumulation of flavonoids in different organs of this species is still unclear. In this study, the molecular basis of tissue-specific synthesis of flavonoids was analyzed by integrating UPLC-MS broad-target metabolome and Illumina high-throughput transcriptome with four tissues of Emilia sonchifolia: root, stem, leaf, and flower. The results showed that a total of 73 flavonoid metabolites were identified in the metabolome, including naringenin chalcone, luteolin, quercitrin, and other pharmacologically active substances. Multi-omics joint analysis showed that the floral organ was the core tissue for the synthesis and enrichment of flavonoids, and there were specific characteristic flavonoid subtypes in different tissues. A total of 211 differentially expressed genes related to the flavonoid synthesis pathway were screened by transcriptome analysis, including 16 flavonol synthases, five cinnamic acid 4-hydroxylases, and five chalcone synthases. The WGCNA and gene–metabolite association network showed that the transcription levels of key enzyme genes such as CHS, C4H, F3′H, and F3H were highly positively correlated with the accumulation of downstream flavonols. The qRT-PCR quantitative verification showed that the expression patterns of CHS1, CHI4, F3′H5, F3H, FLS4, and GT in the four tissues were highly consistent with the transcriptome sequencing results, which confirmed that the transcriptome data were reliable. For the first time, this study revealed the molecular regulatory network of tissue-specific accumulation of flavonoids in Emilia sonchifolia, and clarified that the flower organ was the optimal medicinal harvesting site of flavonoids. It provided key theoretical support for the breeding of high-efficacy Emilia sonchifolia germplasm, the development of flavonoid active ingredients, and the study of secondary metabolic evolution of Compositae plants. Full article

Despite advances in systemic therapies, metastatic colorectal cancer (mCRC) remains largely incurable, underscoring persistent gaps in our understanding of metastatic progression and therapeutic resistance. Emerging evidence suggests that gut and tumor-associated microbial communities may contribute to metastatic progression by shaping organ-specific niches, disrupting intestinal and vascular barriers, remodeling immune and stromal microenvironments, and altering host–microbial metabolism. This review synthesizes current evidence on the involvement of gut and intratumoral microbial communities in colorectal cancer metastasis, with emphasis on liver, lung, lymphatic, and peritoneal metastatic patterns; microbial translocation and barrier dysfunction; microbiome–tumor microenvironment interactions; and metabolic pathways such as bile acid, short-chain fatty acid, and tryptophan metabolism. Furthermore, distinct microbial signatures have been associated with responses to chemotherapy, radiotherapy, immunotherapy, and targeted therapies, supporting their potential value as candidate biomarkers for treatment stratification and prognosis, particularly when interpreted alongside treatment exposure and longitudinal microbiome dynamics. Finally, we discuss microbiome-targeted interventions as emerging adjunctive strategies that may help modulate treatment response, while emphasizing the need for standardized, longitudinal, and mechanistically validated studies before clinical translation in mCRC. Full article

High levels of antimony (Sb) adversely affect plant growth and development. We aimed to uncover the harmful effects of different forms of Sb on root morphology, physiology and expression profiles of genes encoding functions associated with roots. Rice plants grown in ½ Hoagland nutrient solution were exposed to Sb(III) and Sb(V) at concentrations of 10 and 20 mgL⁻¹ for one week. Results demonstrated that higher concentrations of Sb(III) significantly impaired root morphological traits, with high toxicity observed at 20 mgL⁻¹ Sb(III) and 10 mgL⁻¹ Sb(V). The application of Sb(III) led to reduced uronic acid levels in hemicellulose-II (HCII) with cell organelle and cytosol displaying substantial accumulation of Sb(III). Enzymatic activity revealed that high levels of Sb(III) disrupted the activity of cellulase (CE) and pectin methylesterase (PME), while augmenting the activity of Xyloglucan endotransglycosylase hydrolase (XTH). Additionally, polygalacturonase (PG) was significantly reduced under Sb(V) 10 mgL⁻¹ exposure. Pearson’s correlation coefficient was used for continuous data to draw a linear trend between studied parameters. Shoot biomass displayed a negative correlation with root and shoot Sb. Shoot XTH had a positive correlation with shoot Sb. Furthermore, root and shoot Sb showed a positive association with root diameter and XTH. Hemicellulose-1 (HCI) was negatively associated with PME, PG and pectin, suggesting that HCI was a suitable binding site for uronic acid in the context of Sb contamination. Additionally, to elucidate the molecular mechanisms underlying root structural alterations, the expression profiles of key cell wall-related genes—Expansin, Cellulase synthase, XTH8 (xyloglucan endotransglucosylase/hydrolases) and Pectinesterase—were systematically analyzed using qRT-PCR. All genes were up-regulated in response to different forms of Sb exhibiting resistance. Xylanase was down-regulated, showing its role in the containment of Sb in roots. Further studies are advised for elucidating the mechanism of action of Sb on different tissues of rice plants. Full article

Increasing evidence shows that epistasis, defined as interactive effects between genetic loci, may contribute to the missing heritability of cancer. However, systematic genome-wide epistasis identification in cancer remains challenging. Here, by leveraging genotype and clinical data from 380,983 samples in the UK Biobank, we identified 202,032 candidate epistatic single nucleotide polymorphism (epiSNP) pairs associated with cancer risk across 16 cancer types. Notably, multivariable Cox regression identified 123 epiSNP pairs with significant interaction effects on overall survival, suggesting that interaction-level genetic signals can provide prognostic information beyond individual SNP effects. Through functional analysis of the 202,032 candidate epiSNP pairs, we identified 7152 pairs supported by gene co-expression data and 12,326 pairs with protein–protein interaction (PPI) evidence. By mapping epiSNP pairs to corresponding gene pairs and then linking these gene pairs to drug–target databases, we identified 1040 epistatic gene pairs with FDA-approved drug–target records. Additionally, through KM survival analysis of the candidate epiSNP pairs, we detected 7068 pairs significantly associated with patient overall survival. Finally, we constructed an open-access database, EpiSNPdb, to facilitate cancer epistasis research. Full article

Magnesium-ion batteries have attracted extensive attention due to their abundance, high safety, and superior volumetric energy density. However, their development remains constrained by the sluggish diffusion of Mg²⁺ ions and the limited magnesium storage capacity of electrode materials. In this work, we systematically investigate the Mg storage performance and underlying mechanisms of monolayer MoS₂ anodes substitutionally doped with Cu, Ag, and Au at a concentration of 4% on the Mo sublattice. The results reveal that all three dopants maintain the structural integrity of the two-dimensional MoS₂ framework after optimization while introducing localized electronic states near the Fermi level, thereby enhancing Mg adsorption. Compared with intrinsic MoS₂, the doped systems exhibit markedly improved Mg binding. Sequential adsorption calculations indicate that, at the investigated 4% substitution concentration, Cu-MoS₂, Ag-MoS₂, and Au-MoS₂ can stably accommodate up to five Mg atoms, elevating the theoretical specific capacity to ~67 mAh/g while maintaining low open-circuit voltages during magnesium intercalation. Diffusion kinetics analysis further shows that Cu-MoS₂ possesses the lowest Mg migration barrier (0.25 eV), surpassing Ag-MoS₂ (0.38 eV) and Au-MoS₂ (0.61 eV). Considering both thermodynamic stability and ion transport kinetics, Cu-MoS₂ achieves an optimal balance among storage capacity, operating voltage, and diffusion performance, highlighting its promise as a high-performance anode material for magnesium-ion batteries. Full article

Objectives: Hypospadias is one of the most common congenital malformations of the male genitourinary system, and postoperative complications remain a major concern affecting surgical outcomes and patients‘ quality of life. Whether machine learning models can effectively predict complication risk using routinely available clinical variables remains unclear. Methods: A retrospective analysis was performed on 671 hypospadias patients who underwent urethroplasty at the Department of Urology, Capital Children’s Medical Center, between December 2015 and September 2024. The final dataset included 671 patients (training set: 536; validation set: 135). The median follow-up duration was 48 months (range: 19 to 72 months). Least absolute shrinkage and selection operator (LASSO) regression with nested cross-validation within the training set was used for feature selection, followed by the development of five machine learning models (Random Forest, XGBoost, LightGBM, Logistic Regression, and Support Vector Machine). Model performance was evaluated using AUC, calibration curves, Brier score, and decision curve analysis. Feature importance was assessed using SHapley Additive exPlanations (SHAP). Results: LASSO retained four features for model development: hypospadias type, surgical technique, surgeon experience, and patient age. The overall complication rate was 22.9% (154/671). Among the models evaluated, the Support Vector Machine (SVM) showed the most balanced performance in the validation set, achieving an AUC of 0.810 and a Brier score of 0.157. LightGBM demonstrated comparable performance (AUC: 0.802). SHAP analysis identified surgical technique as the most influential predictor, followed by surgeon volume and hypospadias type, though these findings should be interpreted with caution given the confounding between surgical complexity and disease severity. Conclusions: An interpretable SVM-based prediction model was developed and internally validated to stratify risk for postoperative complications after hypospadias repair using routinely available clinical variables. SHAP provided clinicians with visual insights into key risk-associated factors. However, given the single-center retrospective design and lack of external validation, further multicenter prospective studies are warranted to confirm the generalizability of these findings before clinical implementation. Full article

Organophosphate esters (OPEs), characterized by diverse chemical substituents, have emerged as widely used flame retardants and plasticizers, replacing polybrominated diphenyl ethers (PBDEs) under global regulatory actions. However, due to their environmental persistence, bioaccumulation potential, and multiple toxic effects, OPEs are now recognized as emerging pollutants and have attracted extensive research attention. This review systematically compares structurally distinct OPEs in terms of their environmental occurrence, physicochemical properties, mobility, and biodegradation fate. We place special emphasis on recent advances in microbial degradation and enzymatic transformation pathways under both aerobic and anaerobic conditions, with a focus on key degrading strains, metabolic intermediates, and underlying mechanisms. Furthermore, factors influencing biodegradation rates (including compound structure, microbial community composition, and environmental variables) are comprehensively examined. By identifying critical research gaps and proposing future directions, this review aims to provide a scientific foundation for sustainable management and effective risk assessment of OPEs in the environment. Full article

Background: Within the fresh food e-commerce supply chain, government effort-contingent subsidies and logistics partnerships critically impact order pricing, preservation, and profitability. This study aims to investigate how different logistics cooperation modes interact with effort-contingent subsidies to optimize supply chain performance under preservation effort and random demand. Methods: We developed three Stackelberg game models: an independent logistics model, a logistics-supplier cooperation model without subsidies and a logistics-supplier cooperative model with government effort-contingent subsidies. Equilibrium strategies were derived theoretically and analyzed through numerical simulations to examine preservation efforts and pricing. Results: The findings show that logistics cooperation combined with effort-contingent subsidies not only optimizes the decision-making behaviors, but also effectively shares preservation costs and mitigates double marginalization. This synergy incentivizes higher preservation efforts and lowers wholesale prices, ultimately reducing retail prices and expanding market demand. Furthermore, the analysis reveals that excessively optimistic demand forecasts (over-ordering) sharply increase systemic costs, causing a reverse drop in actual demand. Conclusions: Government subsidies are an effective macro-policy tool for alleviating cold-chain costs when integrated with logistics cooperation. To build a sustainable supply chain, e-commerce platforms must adopt prudent ordering strategies, while policymakers should structurally design subsidies to incentivize preservation investments rather than purely subsidizing profits. Full article

Straw incorporation is an important agronomic practice for sustainable cropland management, improving soil structure and enhancing soil organic carbon (SOC) sequestration. In this study, a systematic review was conducted using the Web of Science (WoS) database, and a random-effects meta-analysis was performed on 63 field experiments in mainland China, including 846 treatment–control comparisons, to evaluate the effects of straw application rate and duration on soil aggregates, aggregate-associated carbon, and SOC. Across all studies, pooled effect sizes indicated that straw incorporation significantly increased Mean Weight Diameter (MWD) (22%), macroaggregate content (17%), and SOC (25%), while enhancing aggregate-associated carbon in macroaggregates (35%), microaggregates (30%), and silt–clay fractions (22%). It also reduced silt–clay fraction content by 17%, indicating improved aggregate redistribution. Interaction analysis suggested that short-term incorporation (<5 years) at 5–8 t ha⁻¹ and long-term incorporation (≥5 years) at 10–15 t ha⁻¹ were more favorable for macroaggregate formation and carbon sequestration. Precipitation negatively affected microaggregate carbon, highlighting environmental regulation of carbon stability. Overall, straw incorporation strengthens soil structural stability, promotes SOC accumulation, and provides evidence-based guidance for sustainable residue management and climate-smart agriculture in mainland China. Full article

Nuclei segmentation is a key task in digital histopathology, highlighting important aspects of nuclear morphology and topology in many cancer-related evaluations and studies. Variability in nuclear appearance both within and across different organs, stain heterogeneity, and inconsistencies in acquisition procedures contribute to the complexity of the task. The existing nuclei segmentation methods apply deep learning to address these challenges, using models with millions of parameters, thereby significantly increasing computational complexity. They also face limitations in generalizing to unseen organs and slide preparations. In this paper, we propose a transparent and lightweight Green U-Shaped Learning model for nuclei segmentation (NS-GUSL). NS-GUSL features a multi-scale architecture for coarse-to-fine refinement of probability maps, which are subsequently binarized using a novel low-confidence sample binarization (LCSB) technique. The model features a modular, feed-forward feature learning scheme with unsupervised representation learning and supervised feature selection and generation. A final morphological post-processing step refines the segmentation maps to improve instance separation while preserving nuclei convexity. The model was trained and tested on the MoNuSeg dataset and compared against other deep learning baselines for segmentation performance. In addition, external validation experiments were conducted to evaluate the proposed model’s generalizability to unseen organs and staining procedures. NS-GUSL exhibits the best panoptic segmentation performance and competitive detection quality across all datasets. Moreover, our model is shown to be compact, low in computational complexity, and to have a minimal carbon footprint, compared to other deep learning models, making it a suitable choice for deployment on edge devices. Full article

The spacecraft reachable domain has become increasingly important for orbital game analysis due to growing on-orbit activities such as servicing, debris removal, and space situational awareness. This paper provides a comprehensive review of reachable domain theory and its applications in orbital games. A unified mathematical framework is established through three complementary classification dimensions: spatial attributes that distinguish absolute from relative reachable domains, temporal attributes that differentiate free-time from fixed-time reachable domains, and informational attributes that contrast deterministic and predictive reachable domains. Solution methods are systematically reviewed according to this taxonomy, covering analytical and semi-analytical methods, numerical optimization approaches, and geometric and sampling methods for spatial-scale reachable domains, as well as linearized ellipsoidal approximation, exact envelope determination, and fast analytical approximation for time-scale reachable domains. Applications are examined through three representative scenarios: one-on-one pursuit-evasion games, multi-agent cooperative games, and threat-avoidance and defense games. Key limitations of existing approaches are identified, including modeling fidelity, computational efficiency, and scalability under uncertainty. Future research directions are outlined to address these challenges. Full article