Search Results (1,857)

Aggressive behaviour in pigs poses significant challenges to animal welfare, production efficiency, and economic performance in the pork industry. This review explores the multifaceted causes of pig aggression, focusing on genetic, environmental, and physiological factors. Aggression in pigs is categorized into social, maternal, fear-induced, play, and redirected aggression, with early-life hierarchies and environmental stressors playing critical roles. Physiological markers, such as elevated cortisol and reduced serotonin levels, are closely linked to aggressive behaviour, while dietary interventions, including tryptophan supplementation, have shown promise in mitigating aggression. Environmental factors, such as overcrowding, noise, and heat stress, exacerbate aggressive tendencies, whereas enrichment strategies, like music and improved housing conditions, can reduce stress and aggression. Genome-wide analyses have pinpointed specific polymorphisms in neurotransmitter genes (DRD2, SLC6A4, MAOA) and stress-response loci (RYR1) as significant predictors of porcine aggression. Advances in genomic technologies, including genome-wide association studies (GWASs) and transcriptomic analyses, have further elucidated the genetic and epigenetic underpinnings of aggressive behaviour. Practical application in breeding programmes remains challenging due to aggression polygenic nature and industry hesitancy toward genomic approaches. Future research should focus on integrating genetic markers into breeding programmes, developing multitrait selection indices, and exploring epigenetic modifications to improve animal welfare and production efficiency. By addressing these challenges, the pork industry can enhance both the well-being of pigs and the sustainability of production systems. Full article

The rapid expansion of economic globalization and trade has led to a sharp increase in the shipping investment risks currently faced by cities around the world. This study aims to explore the risk warning mechanism of shipping input under public health events to establish an effective risk warning method. This would enable the rapid identification of potential risk inputs and the implementation of targeted prevention and control measures to ensure public health and safety. This study investigates the mechanisms of both intra-regional and cross-border risk transmission within shipping networks. It establishes a transmission dynamics model (termed the SEIR-SEI model) incorporating climatic, economic, and health factors to analyze the potential inherent risks of regional shipping nodes. It uses the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) model, modified by the entropy weight method, to calculate the importance of nodes in the shipping network. This comprehensive approach considers the network’s clustering coefficient, betweenness centrality, closeness centrality, degree centrality, and eigenvector centrality. To validate the practicality of the model, this study selects shipping data with Shanghai, China, as the destination node to conduct simulation computations of different risk propagation chains. The findings demonstrate that overall risk transmission is determined by the joint influence of a node’s inherent risks and propagation probabilities. This study not only clarifies the process of cross-border transmission of public health events through the shipping network between cities of different countries, but also provides insights for the application of shipping input risk assessment systems, enriching the academic research on logistics network propagation. Full article

This study investigates the integration of Artificial Intelligence (AI) in Religious Education (RE), a field traditionally rooted in spiritual formation and human interaction. Amid increasing digital transformation in education, theological institutions are exploring AI tools for teaching, assessment, and pastoral engagement. Using a critical literature review and analysis of institutional case studies, the paper examines the historical development of AI in education, current applications in general and theological contexts, and the ethical challenges it introduces, especially regarding decision making, data privacy, and bias as well as didactically grounded opportunities such as AI-mediated dialogic simulations. The study identifies both the pedagogical advantages of AI, such as personalization and administrative efficiency, and the risks of theological distortion, overreliance, and epistemic conformity. It presents a range of real-world implementations from institutions like Harvard Divinity School and the Oxford Centre for Digital Theology, highlighting best practices and cautionary approaches. The findings suggest that AI can enrich RE when deployed thoughtfully and ethically, but it must not replace the relational and formational aspects central to RE. The paper concludes by recommending policy development, ethical oversight, and interdisciplinary collaboration to guide responsible integration. This research contributes to the growing discourse on how AI can be aligned with the spiritual and intellectual goals of RE in a rapidly evolving digital age. Full article

Background: Lung cancer is among the most prevalent and fatal cancers worldwide. Traditional diagnostic methods, such as computed tomography, are not ideal for screening due to their high cost and radiation exposure. In contrast, blood-based diagnostics, as non-invasive approaches, are expected to reduce patient burden, thereby increasing screening participation and ultimately improving survival rates. However, conventional tumor markers have shown limited effectiveness in early detection. Methods: We recruited 199 patients with lung cancer and 590 healthy volunteers. Nine tumor markers (CEA, CA19-9, CYFRA, AFP, PSA, CA125, CA15-3, SCC antigen, and NCC-ST439) were analyzed, along with enriched glycopeptides (EGPs) derived from serum proteins using liquid chromatography–mass spectrometry. Machine learning models, including decision trees and deep learning approaches, were employed to develop a predictive model for accurately distinguishing lung cancer from healthy controls based on tumor markers and EGP profiles. Results: We found that α1-antitrypsin with fully sialylated biantennary glycan, attached to asparagine 271 (AT271-FSG), and α2-macroglobulin with fully sialylated biantennary glycan, attached to asparagine 70 (MG70-FSG), could significantly distinguish between patients with lung cancer and healthy individuals. Comprehensive Serum Glycopeptide Spectra Analysis (CSGSA), integrating nine conventional tumor markers and 1688 EGPs using a machine learning model, enhanced diagnostic accuracy and achieved an ROC-AUC score of 0.935. It also identified stage I cases with an ROC-AUC of 0.914, indicating the possibility of early-stage detection. The PPV reached 2.8%, which was sufficient for practical application. Conclusions: This method represents a significant advancement in cancer diagnostics, combining multiple biomarkers with cutting-edge machine learning to improve the early detection of lung cancer. Full article

Fraxin is a bioactive compound derived from Cortex Fraxini. It is known for its diverse biological activities and numerous benefits, including anti-inflammatory, antioxidant, analgesic, antimicrobial, antiviral, and immunomodulatory effects. Despite growing interest in natural compounds for cardiovascular diseases Fraxin’s atheroprotective properties and molecular targets have not yet been fully elucidated. To address this gap, our research employed an integrated approach combining network pharmacology, molecular docking simulations, and in vitro biological validation to systematically unravel Fraxin’s therapeutic mechanisms against atherosclerosis (AS). The results showed that 84 potential targets for Fraxin against AS were predicted through public databases, and the key target TLR4 was identified by protein–protein interaction and molecular docking analysis. GO enrichment and KEGG pathway analysis revealed that these potential targets were significantly enriched in the PI3K-Akt and oxidative stress responses pathways. Subsequently conducted in vitro studies validated that Fraxin modulates the TLR4/PI3K/Akt signaling pathway to suppress reactive oxygen species generation and downregulate pro-inflammatory cytokines including Il1b, Il6, and Tnf thereby slowing atherosclerotic disease advancement. This investigation methodically delineates Fraxin’s therapeutic targets and underlying molecular mechanisms in AS management, establishing a scientific foundation for its potential translation into clinical practice. Full article

Carbon black (CB) serves as the most crucial reinforcing filler in natural rubber (NR) applications. However, conventional CB production relies on petroleum or coal resources, raising concerns about non-renewability and unsustainable resource consumption. Although biomass-derived carbon materials have been explored as alternatives for natural rubber reinforcement, their practical application remains constrained by inherent limitations such as large particle size and low graphitic structure, which compromise reinforcement efficiency. This study presents a novel walnut shell biochar (WSB) for natural rubber enhancement. The biochar was prepared via conventional pyrolysis and subsequently subjected to an environmentally friendly physical ball-milling process. This treatment effectively increased graphitized domains while enriching surface functional groups. Systematic investigations were conducted on the effects of ball-milling duration and biochar loading on rubber reinforcement performance. Results demonstrate that the biochar-reinforced vulcanizates achieved a 22% improvement in tensile strength compared to unfilled rubber. Notably, at 10 phr loading, the tensile strength of biochar-filled vulcanizates reached 98% of that achieved by CB(N330)-filled counterparts. The study further revealed that biochar incorporation effectively reduced hysteresis loss and enhanced elastic recovery in rubber composites. This work proposes a facile method to develop sustainable biochar-based reinforcing agents with significant potential for natural rubber applications. Full article

The complex market environment places unprecedented pressure on business decision-making processes. Effectively utilizing existing social resources to establish risk prevention mechanisms and accurately assess an enterprise’s risk-taking capacity has become a core issue for corporate survival and development. This paper examines 1810 listed companies on the Shanghai and Shenzhen A-shares markets from 2010 to 2022, constructing comprehensive social networks based on multiple corporate governance entities. It investigates the influence and transmission mechanisms of corporate social networks on risk-taking levels. The results reveal that (1) enhanced corporate social network centrality, structural holes, and connectivity significantly and positively affect corporate risk-taking levels; (2) information transparency and corporate governance quality serve as important mediating mechanisms through which social networks influence corporate risk-taking; (3) significant heterogeneity exists regarding executives’ backgrounds and industry attributes—specifically, in firms with executives possessing financial backgrounds and in high-tech industry enterprises, network characteristics play a more pronounced role in promoting risk-taking. This research not only enriches the literature on factors influencing enterprise risk-taking but also provides theoretical foundations and practical insights for improving corporate risk management capabilities through optimized social network structures. Full article

This study introduces an innovative and scalable approach for automated road surface assessment by integrating Mobile Mapping System (MMS)-based LiDAR data analysis with an open-source WebGIS platform. In a U.S.-based case study, over 20 datasets were collected along Interstate I-65 in West Lafayette, Indiana, using the Purdue Wheel-based Mobile Mapping System—Ultra High Accuracy (PWMMS-UHA), following Indiana Department of Transportation (INDOT) guidelines. Preprocessing included noise removal, resolution reduction to 2 cm, and ground/non-ground separation using the Cloth Simulation Filter (CSF), resulting in Bare Earth (BE), Digital Terrain Model (DTM), and Above Ground (AG) point clouds. The optimized BE layer, enriched with intensity and color information, enabled crack detection through Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Random Forest (RF) classification, with and without intensity normalization. DBSCAN parameter tuning was guided by silhouette scores, while model performance was evaluated using precision, recall, F1-score, and the Jaccard Index, benchmarked against reference data. Results demonstrate that RF consistently outperformed DBSCAN, particularly under intensity normalization, achieving Jaccard Index values of 94% for longitudinal and 88% for transverse cracks. A key contribution of this work is the integration of geospatial analytics into an interactive, open-source WebGIS environment—developed using Blender, QGIS, and Lizmap—to support predictive maintenance planning. Moreover, intervention thresholds were defined based on crack surface area, aligned with the Pavement Condition Index (PCI) and FHWA standards, offering a data-driven framework for infrastructure monitoring. This study emphasizes the practical advantages of comparing clustering and machine learning techniques on 3D LiDAR point clouds, both with and without intensity normalization, and proposes a replicable, computationally efficient alternative to deep learning methods, which often require extensive training datasets and high computational resources. Full article

In polymer electrolyte membrane fuel cells (PEMFCs), substantial efforts have been made to focus on Pt and Pt alloy catalysts to enhance their catalytic performance. However, these catalysts still fail to meet practical requirements and existing PtCo catalysts face durability issues due to structural limitations. In this study, carbon-supported hybrid PtCo alloy catalysts (H-PtCo) with improved activity and durability are synthesized by reducing Co precursors onto pre-formed colloidal Pt nanoparticles. Elemental mapping via transmission electron microscopy reveals that the H-PtCo catalysts exhibit a high concentration of Co atoms near the sub-surface. This Co enrichment results from the conformal deposition of Co atoms onto Pt nanoparticles, followed by high-temperature treatment. Electrochemical characterizations, including linear sweep voltammetry (LSV) and accelerated durability test (ADT), demonstrate that the H-PtCo catalysts outperform conventional PtCo alloys (C-PtCo), synthesized via the co-reduction method of Pt and Co, in terms of oxygen reduction reaction (ORR) activity and stability. Furthermore, single-cell tests reveal that the H-PtCo catalysts significantly enhance both performance and durability compared to C-PtCo and Pt catalysts. These findings emphasize the critical role of Co atom distribution within PtCo nanoparticles in improving catalytic efficiency and long-term stability. Full article

The advantages of mHealth applications have been widely recognized. However, the existing literature rarely explores how user interface (UI) design and individual cultural values influence elderly users’ mHealth application stickiness, particularly among the young elderly. This study examines how two UI design attributes (usability and aesthetics) and individual uncertainty avoidance (from Hofstede’s cultural dimensions) influence elderly users’ stickiness to mHealth applications. The study used PLS-SEM to analyze survey data from 492 elderly people in China. The research results indicate that uncertainty avoidance cultural values are negatively correlated with psychological engagement. The UI design attributes (aesthetic and usability) are positively correlated with psychological engagement, with learnability (usability sub-attributes) having the most significant impact. Furthermore, the study also reveals the serial mediation role of psychological engagement and user internal experiences (satisfaction and attachment). Notably, this study enriches the current literature on user behavior regarding mHealth applications by elucidating the process of user stickiness, incorporating UI design attributes and individual uncertainty avoidance cultural values. These findings offer valuable theoretical and practical insights. Full article

Years of employing the “one-shot” fertilization practice have led to low nutrient utilization efficiency and the degradation of soil structure in the black soil region during crop cultivation. Replacing a portion of chemical fertilizers with microbial agents can effectively solve these issues. In this study, we conducted a field plot experiment comparing eight different treatment combinations to investigate the effects of combining microbial agents with varying amounts of chemical fertilizers on black soil nutrients, soil ecology, and maize yield. The high-throughput absolute quantification 16S rRNA sequencing method was utilized to further investigate the effect of the various treatments on soil bacterial community structure and elucidate the interactions between environmental factors and microbial communities. The results showed that MC80 increased maize yield by 5.76% compared to RC, with an input–output ratio of 1:1.58. Additionally, soil nutrient levels in MC80 were higher than those in RC, increasing nutrient utilization efficiency, activating soil nutrients, and enhancing soil fertility. Meanwhile, the absolute quantification data of bacteria also indicated the highest bacterial abundance and diversity in MC80 samples. Among these, Acidobacteria was the main contributor to the changes in the bacterial community, showing significant enrichment in MC80. RDA and Spearman correlation analyses indicated that soil nutrients are the key factors influencing the bacterial community in this ecosystem, while the microbial community plays a crucial role in nutrient transformation processes. Principal component analysis (PCA) was used for comprehensive evaluation and ranking. Overall, the soil under the MC80 treatment was most conducive to microbial survival and maize growth. This study provides a high-yield and sustainable fertilization method for maize and offers a theoretical basis for applying microbial agents in sustainable agriculture. Full article

In this paper, a systematical study on the influence of strengthening parameters on the flexural performance of RC beams using the NSM application was carried out. Experimental results consist of two reference beams and 25 beams divided into two groups using NSM systems with various embedded bars and strengthening configurations were presented. Additionally, theoretical analysis was conducted to enrich the research on the parameters affecting the strength and failure mode of the beams. The accuracy of the theoretical formulas has been verified through experimental results, and the average value of the ratio between the theoretical and experimental values is approximately 0.9. Results indicated that NSM technology is an effective approach for strengthening RC structures. Compared with the control specimens, the maximum load-bearing capacity of the beams with the NSM system experiences a remarkable enhancement of nearly 140%. The flexural behavior of the beams strengthened by the NSM system are closely related to the material properties (steel bar, NSM bars, concrete, and filler), location of the cutoff points, external confinement, and prestress level. The NSM bars characterized by high strength and high elasticity prove to be far more advantageous in enhancing the strength of the strengthened specimens. The research findings can provide theoretical support for the practical engineering applications of the NSM technology in strengthening reinforced concrete structures. Full article

During the breeding process of Macrobrachium rosenbergii, a male-to-female ratio of 1:3 or higher is typically adopted, so as a result, the quality of the male broodstock significantly influences the quality of the offspring. We observed that overused males exhibited notable changes in body color, particularly in the tail fan region, which turned orange or red due to the excessive accumulation of astaxanthin in the muscles and exoskeleton. Frequent mating also led to a significant decrease in male body weight, with histological analysis revealing disorganized muscle fiber patterns and increased tissue damage. To investigate the molecular mechanisms underlying these physiological changes, we performed transcriptomic and metabolomic analyses of muscle tissues. A total of 1069 differentially expressed genes (DEGs), 540 differentially expressed proteins (DEPs), and 385 differentially expressed metabolites (DEMs) were identified. Pathway analysis revealed that the DEGs were significantly enriched in pathways related to energy metabolism and degenerative diseases, while the DEMs were notably associated with cancer metabolism, signal transduction, substance transport, energy metabolism, nucleic acid metabolism, neurotransmission, immune response, and metabolic diseases. Proteome analysis showed that proteins and lipids were involved in muscle energy supply. These findings suggest that male M. rosenbergii upregulate energy metabolism in muscles to cope with frequent mating stress, but this adaptation leads to physiological damage. This study provides valuable insights for optimizing male broodstock selection and mating frequency in M. rosenbergii breeding practices. Full article

Biological soil crusts (BSCs) play a pivotal role in maintaining ecosystem stability and soil fertility in arid and semi-arid regions. However, the biogeographical differences in soil functional composition between cyanobacterial BSCs (C-BSCs) and moss BSCs (M-BSCs), particularly how environmental changes affect nutrient cycling strategies and microbial community functions, remain poorly understood. In this study, we investigated BSCs across aridity gradients (semi-humid, semi-arid, and arid regions) in China, focusing on carbon and nitrogen cycling pathways, enzyme activities, and nutrient acquisition strategies. It was found that aridity and BSC type had significant effects on the functional characteristics of microorganisms. This was demonstrated by significant differences in various soil microbial activities including enzyme activities and carbon and nitrogen nutrient cycling. With increasing aridity, C-BSCs exhibited reduced carbon cycling activity but enhanced nitrogen cycling processes, whereas M-BSCs displayed diminished activity in both carbon and nitrogen cycling. These divergent strategies were linked to soil properties such as pH and organic carbon content, with C-BSCs adapting through nitrogen-related processes (e.g., nifH, amoA) and M-BSCs relying on C fixation and degradation. These findings provide novel insights into the functional gene diversity of BSCs across different regions, offering valuable references for ecological restoration in arid areas. Specifically, our study highlights the potential of BSC inoculation for carbon and nitrogen enrichment in arid regions, with implications for climate-resilient restoration practices. Full article

Legal text summarization presents distinct challenges due to the intricate and domain-specific nature of legal language. This paper introduces a novel framework integrating dynamic Retrieval-Augmented Generation (RAG) with domain-specific adaptation to enhance the accuracy and contextual relevance of legal document summaries. The proposed Dynamic Legal RAG system achieves a vital form of symmetry between information retrieval and content generation, ensuring that retrieved legal knowledge is both comprehensive and precise. Using the BM25 retriever with top-3 chunk selection, the system optimizes relevance and efficiency, minimizing redundancy while maximizing legally pertinent content. with top-3 chunk selection, the system optimizes relevance and efficiency, minimizing redundancy while maximizing legally pertinent content. A key design feature is the compression ratio constraint (0.05 to 0.5), maintaining structural symmetry between the original judgment and its summary by balancing representation and information density. Extensive evaluations establish BM25 as the most effective retriever, striking an optimal balance between precision and recall. A comparative analysis of transformer-based (Decoder-only) models—DeepSeek-7B, LLaMA 2-7B, and LLaMA 3.1-8B—demonstrates that LLaMA 3.1-8B, enriched with Legal Named Entity Recognition (NER) and the Dynamic RAG system, achieves superior performance with a BERTScore of 0.89. This study lays a strong foundation for future research in hybrid retrieval models, adaptive chunking strategies, and legal-specific evaluation metrics, with practical implications for case law analysis and automated legal drafting. Full article