Search Results (445)

The persistent challenge of fracture-driven interference (FDI) during large-scale hydraulic fracturing in the southern Sichuan Basin has severely compromised shale gas productivity, while the existing research has inadequately addressed both FDI risk reductions and the optimization of reservoir stimulation. To bridge this gap, this study developed a mechanistic model of the competitive multi-cluster fracture propagation under non-uniform perforation conditions and established a perforation-based design methodology for the mitigation of horizontal well interference. The results demonstrate that spindle-shaped perforations enhance the uniformity of fracture propagation by 20.3% and 35.1% compared to that under uniform and trapezoidal perforations, respectively, with the perforation quantity (48) and diameter (10 mm) identified as the dominant control parameters for balancing multi-cluster growth. Through a systematic evaluation of the fracture communication mechanisms, three distinct inter-well types of FDI were identified: Type I (natural fracture–stress anisotropy synergy), Type II (natural-fracture-dominated), and Type III (stress-anisotropy-dominated). To mitigate these, customized perforation schemes coupled with geometry-optimized fracture layouts were developed. The surveillance data for the offset well show that the pressure interference decreased from 14.95 MPa and 6.23 MPa before its application to 0.7 MPa and 0 MPa, achieving an approximately 95.3% reduction in the pressure interference in the application wells. The expansion morphology of the inter-well fractures confirmed effective fluid redistribution across clusters and containment of the overextension of planar fractures, demonstrating this methodology’s dual capability to enhance the effectiveness of stimulation while resolving FDI challenges in deep shale reservoirs, thereby advancing both productivity and operational sustainability in complex fracturing operations. Full article

With the acceleration of modernization, villages in Southwest China are experiencing spatial fragmentation and homogenization, leading to the loss of traditional identity. Addressing how to balance scientific planning with cultural and spatial continuity has become a key challenge in rural governance. This study takes Xuyong County in Luzhou City as a case and develops a three-tier analytical framework—“genome–spatial factors–specific indicators”—based on the space gene theory to identify, classify, and map spatial patterns in marginal villages of southern Sichuan. Through cluster analysis, common and distinctive spatial genes are extracted. Common genes—such as medium surface roughness (GeneN-2-b), medium building dispersion (GeneA-3-b), and low intelligibility (GeneT-2-b)—are prevalent across multiple village types, reflecting shared adaptive strategies to complex terrains, ecological constraints, and historical development. In contrast, distinctive genes—such as high building dispersion (GeneA-3-a) and linear boundaries (GeneB-1-c)—highlight unique spatial responses that are shaped by local cultural and environmental conditions. The results contribute to a deeper understanding of spatial morphology and adaptive mechanisms in rural settlements. This research offers a theoretical and methodological basis for village classification, conservation zoning, and spatial optimization, providing practical guidance for rural revitalization efforts focusing on both development and heritage protection. Full article

Substation equipment defect detection is a critical aspect of ensuring the reliability and stability of modern power grids. However, existing deep-learning-based detection methods often face significant challenges in real-world deployment, primarily due to low detection accuracy and inconsistent anomaly definitions across different substation environments. To address these limitations, this paper proposes the Language-Guided Enhanced Anomaly Power Equipment Detection Network (LEAD-Net), a novel framework that leverages text-guided learning during training to significantly improve defect detection performance. Unlike traditional methods, LEAD-Net integrates textual descriptions of defects, such as historical maintenance records or inspection reports, as auxiliary guidance during training. A key innovation is the Language-Guided Anomaly Feature Enhancement Module (LAFEM), which refines channel attention using these text features. Crucially, LEAD-Net operates solely on image data during inference, ensuring practical applicability. Experiments on a real-world substation dataset, comprising 8307 image–text pairs and encompassing a diverse range of defect categories encountered in operational substation environments, demonstrate that LEAD-Net significantly outperforms state-of-the-art object detection methods (Faster R-CNN, YOLOv9, DETR, and Deformable DETR), achieving a mean Average Precision (mAP) of 79.51%. Ablation studies confirm the contributions of both LAFEM and the training-time text guidance. The results highlight the effectiveness and novelty of using training-time defect descriptions to enhance visual anomaly detection without requiring text input at inference. Full article

Lithium–sulfur (Li-S) batteries are hindered by the sluggish electrochemical kinetics and poor reversibility of lithium polysulfides (LiPSs), which limits their practical energy density and cycle life. In order to address this issue, a novel Ni₃N/Nb₄N₅ heterostructure was synthesized via electrospinning and nitridation as a functional coating for polypropylene (PP) separators. Adsorption experiments were conducted in order to ascertain the heterostructure’s superior affinity for LiPSs, thereby effectively mitigating their shuttling. Studies of Li₂S nucleation demonstrated the catalytic role of the substance in accelerating the deposition kinetics of Li₂S. Consequently, Li-S cells that employed the Ni₃N/Nb₄N₅-modified separator were found to achieve significantly enhanced electrochemical performance, with the cells delivering an initial discharge capacity of 1294.4 mAh g⁻¹ at 0.2 C. The results demonstrate that, after 150 cycles, the cells retained a discharge capacity of 796.2 mAh g⁻¹, corresponding to a low capacity decay rate of only 0.25% per cycle. In addition, the rate capability of the cells was found to be improved in comparison to control cells with NiNb₂O₆-modified or pristine separators. Full article

Carbapenem-resistant hypervirulent Klebsiella pneumoniae (CR-hvKp), a pathogen causing severe nosocomial infections and high mortality rates, is increasingly becoming a serious global public health threat. Capsular polysaccharide (CPS), a major virulence factor of hvKp, can be enzymatically degraded by bacteriophage-derived depolymerases. However, to our knowledge, depolymerases targeting K. pneumoniae K54-type strains have rarely been identified. Here, we identified and characterized three novel capsule depolymerases, Dep_C, Dep_Y, and Dep_Z, derived from three different K. pneumoniae phages, which retained robust activity across a broad pH range (pH 3.0–12.0) and demonstrated thermal stability up to 50 °C. These depolymerases could efficiently digest the CPS of K. pneumoniae K54-serotype strains, significantly inhibit biofilm formation, and remove their mature biofilms. Although no bactericidal activity was detected, these depolymerases rendered host bacteria susceptible to serum complement-mediated killing. We further demonstrate that Dep_C, Dep_Y, and Dep_Z can effectively and significantly prolong the survival time of mice in a pneumonia model infected with K54-type K. pneumoniae and reduce the colonization and virulence of the bacteria in the mice. These findings indicate that depolymerases Dep_C, Dep_Y, and Dep_Z could increase bacterial susceptibility to host immune responses of hvKp to the host through their degradation effect on the CPS. In conclusion, our study demonstrates that the three capsule depolymerases are promising antivirulent agents to combat CR-hvKp infections. Full article

Probiotics demonstrate the ability to maintain intestinal homeostasis and promote gut health. However, their effects on gut microbiota in adults over 60 years old with chronic metabolic disease (CMD) or cardio-cerebrovascular disease (CCD) remain poorly understood. This study analyzed 1586 stool samples from 1377 adults (CMD, CCD, and healthy controls) using 16S rRNA sequencing. Cohort 1 (n = 1168) was used for cross-sectional analysis, while cohort 2 (n = 209) underwent longitudinal assessment over approximately 13 months. The results demonstrated that probiotics promoted significant gut microbiota alterations across both cohorts. Probiotic supplementation significantly increased lactobacilli in the CMD, CCD, and H groups. In both cohorts, probiotic supplementation enhanced Butyricicoccus, Clostridium sensu stricto 1, and Coprococcus in H groups, enhanced Anaerostipes and Fusicatenibacter in CMD groups, and reduced Haemophilus and Lachnospira in CCD groups. Notably, long-term supplementation not only elevated Dorea, Eubacterium hallii group, and Blautia in all groups but also suppressed Klebsiella and Bilophila in the CMD and CCD groups. Enterotype analysis revealed that probiotics increased the proportion of enterotype 1 and transition probabilities from enterotype 2 to 1 in the CMD and CCD groups, demonstrating that CCD/CMD gut microbiota exhibited greater responsiveness to probiotic modulation. Overall, this study suggests probiotics’ role in modulating adult gut microbiota and their potential benefits in chronic metabolic and cardio-cerebrovascular diseases. Full article

Growing evidence underscores the pivotal roles of both in situ-resident and -non-resident cardiac cells in the repair mechanisms following myocardial infarction (MI). MI continues to be a predominant cause of death and disability, posing a significant threat to global health and well-being. Despite advances in medical care, current therapies remain insufficient in preventing ventricular remodeling and heart failure post-MI. We seek to clarify the underlying regenerative mechanisms by which distinct cell types contribute to the repair of MI injury and to systematically assess the translational potential and therapeutic efficacy of these cell-based approaches in clinical applications. This review conducts a comprehensive analysis of recent research progress on the roles of non-cardiac stem cells in situ and cardiac cells derived from explants in MI repair. These cells contribute to the repair process through multiple mechanisms, including cell proliferation and differentiation, angiogenesis, paracrine signaling, immune regulation and fibrosis modulation. Our analysis reveals the intricate mechanisms of MI repair and highlights the necessity for developing age-specific therapeutic strategies for certain cell types. This review offers novel insights into cell-based treatment for MI and provides a scientific foundation for future clinical trials of cardiac regenerative medicine. Full article

VIRMA (also known as KIAA1429), as a core regulatory subunit of the m6A methyltransferase complex, plays a key role in tumorigenesis and progression by dynamically regulating RNA methylation modifications. Studies have shown that VIRMA is aberrantly overexpressed in more than 20 types of malignant tumors, including liver cancer, breast cancer, and lung cancer, and is significantly associated with chromosome 8q amplification and poor prognosis. Its mechanism of action involves regulating the expression of tumor-associated genes through both m6A-dependent and m6A-independent pathways, thereby promoting tumor proliferation, metastasis, and drug resistance. These findings suggest that VIRMA has the potential to serve as a pan-cancer diagnostic and prognostic biomarker. This review summarizes the role of VIRMA in malignant tumors from multiple perspectives and explores its potential applications in clinical diagnosis and treatment. Full article

Severe acute respiratory syndrome coronavirus 2 has undergone several mutations since 2020, and novel variants continue to emerge to this day. The immune escape ability of the emerging mutants is enhanced and results in robust transmissibility. The neutralizing ability of the antibodies produced in the human body during previous infections is decreased against some of these mutants, which poses a severe challenge to the preventive and therapeutic effectiveness of vaccines and antibody drugs. The nucleocapsid protein is one of the main structural proteins of the coronavirus and plays an important role in the life cycle of the novel coronavirus. This protein is one of the key targets for drug development, and the first major step in drug development is to obtain pure nucleocapsid proteins. However, since nucleocapsid proteins have a nucleic acid-binding function and automatically undergo liquid–liquid phase separation and agglomeration, the purification of full-length nucleocapsids is challenging. In this context, a set of easy-to-operate processes was developed in this study for the purification of nucleocapsid proteins. Finally, a pure full-length nucleocapsid protein without nucleic acid contamination was obtained, which exhibited significantly enhanced accessibility for structural and functional virological studies, vaccine development, and related research applications. Further, the nucleic acid-binding domain of the nucleocapsid protein was targeted, and potential severe acute respiratory syndrome coronavirus 2 inhibitors were identified using virtual screening and biolayer interferometry technology. Notably, the eukaryotically expressed nucleocapsid protein demonstrated a significantly greater binding affinity for Light Green SF Yellowish (K_D = 119.7 nM) compared to that demonstrated by its prokaryotic counterpart (K_D = 19.9 × 10³ nM). The findings of this study suggest the importance of considering both protein source and post-translational modifications of the target proteins to be used in drug screening workflows. Therefore, this compound not only represents a novel therapeutic candidate for COVID-19 but also a critical tool for elucidating antiviral mechanisms. Full article

To address the limitations of traditional methods in modeling complex nonlinear relationships in horizontal in situ stress prediction for shale reservoirs, this study proposes an integrated framework that combines well logging interpretation with machine learning to accurately predict horizontal in situ stress in shale reservoirs. Based on the logging data from five wells in the Luzhou Block of the Sichuan Basin (16,000 samples), Recursive Feature Elimination (RF-RFE) was used to identify nine key factors, including Stoneley wave slowness and caliper, from 30 feature parameters. Bayesian optimization was employed to fine-tune the hyperparameters of the XGBoost model globally. Results indicate that the XGBoost model performs optimally in predicting maximum horizontal principal stress (SHmax) and minimum horizontal principal stress (SHmin). It achieves R² values of 0.978 and 0.959, respectively, on the test set. The error metrics (MAE, MSE, RMSE) of the XGBoost model are significantly lower than those of SVM and Random Forest, demonstrating its precise capture of the nonlinear relationships between logging parameters and in situ stress. This framework enhances the model’s adaptability to complex geological conditions through multi-well data training and eliminating redundant features, providing a reliable tool for hydraulic fracturing design and wellbore stability assessment in shale gas development. Full article

Background: Health inequality is seen as a challenge for implementing the Healthy China Strategy. This study analyzes the income-related health inequality among urban–rural resident basic medical insurance (URRBMI) participants. Methods: This study utilized data from the 2019 China Household Finance Survey (CHFS), and the concentration index (CI) was employed to estimate the effects of income-related health inequality on participants. Results: Our findings provide clear evidence that health inequality among participants has fluctuated—narrowing, widening, and then narrowing again—in the areas of the contribution, medical treatment, and reimbursement of URRBMI, respectively. Overall, the analysis indicates a widening of health inequality post-reimbursement, with results remaining consistent. A heterogeneity analysis shows that health inequality is most pronounced among women and those with less than a middle school education. Finally, our study reveals a pro-rich trend in the actual utilization of medical services among participants, with persistent disparities in outpatient and inpatient service usage even after standardization, further exacerbating income-related health inequality. Conclusions: We recommend that the URRBMI design take participants’ income levels into account, with policies favoring disadvantaged individuals to enhance their medical security, improve access to healthcare services, and ultimately reduce health inequality. Full article

This study numerically investigates the solid–liquid mixing characteristics in solid–liquid stirred tanks with solid volume fraction as high as 35%, focusing on the effect of impeller and baffle configurations on solid and liquid flow behaviors. Three stirred tanks with different capacities and impellers were analyzed to evaluate liquid flow field, solid suspension, and free surface profiles. It has demonstrated superior shear rate uniformity in the multi-impeller systems compared to the single-impeller, attributed to the enhanced fluid circulation. Multi-impeller systems can achieve near-complete off-bottom suspension, while the single-impeller configuration exhibited band-shaped particle accumulation above the impeller. Free surface vortices, significantly deeper in the 6 m³ multi-impeller tank due to high blade tip velocities, were mitigated through the integration of four circumferentially arranged triangular baffles. The existence of baffles can suppress surface turbulence, promote axial flow patterns, and eliminate particle accumulation at the tank bottom, improving shear rate and solid concentration homogeneity. These findings provide a beneficial guideline for the optimization of solid–liquid mixing efficiency the similar flow system or processes. Full article

The development of shale gas relies on hydraulic fracturing technology and requires the injection of a large amount of fracturing fluid. The well shut-off period after fracturing can promote water infiltration and suction. Optimizing the well shut-off time is crucial for enhancing the recovery rate. Among existing methods, the dimensionless time model is widely used, but it has limitations because it does not represent the length of on-site scale features. In this study, we focused on the shut-in time for a deep shale gas well (Lu-A) in Luzhou and a medium-deep shale gas well (Wei-B) in Weiyuan. By integrating the spontaneous seepage and aspiration experiments in the laboratory and the post-pressure backflow data (including mineralization degree, liquid volume recovery rate, etc.), a multi-scale well shutdown time prediction model considering the characteristic length was established. The experimental results show that the spontaneous resorption characteristic times of Lu-A and Wei-B are 3 h and 22 h, respectively. Based on the inversion of crack monitoring data, the key parameters such as the weighted average crack width (1.73/1.30 mm) and crack spacing (0.20/0.32 m) of Lu-A and Wei-B were obtained. Through the scale upgrade calculation of the feature length (0.10/0.16 m), the system determined that the optimal well shutdown times for the two wells were 14.5 days and 16.7 days, respectively. The optimization method based on a multi-parameter analysis of backflow fluid proposed in this study not only solves the limitations of the traditional dimensionless time model in characterizing the feature length but also provides a theoretical basis for the formulation of the well shutdown system and nozzle control strategy of shale gas wells. Full article

With the advancement of oil and gas exploration and development, tight sandstone gas has become a major current exploration field. However, the effective development of tight sandstone gas faces significant challenges due to the strong heterogeneity of tight sandstone reservoirs, diverse reservoir types, complex pore structures, and unclear understanding of reservoir formation mechanisms, which brings great difficulties. Clarifying the types and formation mechanisms of tight sandstone reservoirs is vital for guiding oil and gas exploration and development. This study investigates the characteristics, types, and formation mechanisms of tight sandstone gas reservoirs in the Xujiahe Formation (T₃X₂) of the Anyue area using core observation, cast thin-section identification, scanning electron microscopy, high pressure mercury intrusion, nuclear magnetic resonance, and other experimental methods. It defines the physical property lower limit of T₃X₂ reservoirs in Anyue, classifies reservoir types, elaborates on the basic characteristics of each type, and analyzes their genetic mechanisms. The results show that T₃X₂ reservoirs in the Anyue area can be divided into four types. Sedimentary, diagenetic, and tectonic processes are identified as the primary factors controlling reservoir quality, governing the formation mechanisms of different reservoir types. Based on these findings, a reservoir formation mechanism model for T₃X₂ reservoirs in the Anyue area is established, providing an important basis for subsequent oil and gas exploration and development in the region. Full article

Nipah virus (NiV) is a highly pathogenic paramyxovirus characterized by zoonotic infection, high mortality, and a lack of effective treatment, posing a serious threat to global public health security. Currently, it still lacks specific treatments or approved vaccines, and is listed as a potential pandemic threat pathogen by the World Health Organization. This paper systematically reviews the core progress and challenges of NiV investigation, with a focus on the development of animal models, vaccine development strategies, treatment strategy, and bottlenecks in translational medicine. Additionally, we discuss the strengths and limitations of existing animal models, including ferrets, hamsters, mice, and non-human primates (NHPs), and assess advances in vaccine platforms such as viral vectors, subunit vaccines, and mRNA-based vaccine candidates. The paper critically reviews the challenges facing translational research, conservation correlates, and outbreak preparedness, while also providing future research directions for pandemic preparedness and public health security strategies. Full article