Search Results (2,145)

Equine gastric ulcer syndrome (EGUS) refers to mucosal gastric disease in horses, including equine squamous gastric disease (ESGD) and equine glandular gastric disease (EGGD), which present as two distinct disease entities differing in pathophysiology and approach to disease management. Both diseases are a source of pain in affected horses, partly explaining why EGUS continues to receive substantial attention in the equine medical, welfare and equitation research sectors. There is a complex interplay between EGUS and a variety of physical and psychological stressors. Horses with EGUS are often presented to veterinarians with a history of problem behaviors, some of which resolve following gastroprotectant therapy. However, problem behaviors persist in some cases, despite gastroscopic resolution of disease. Some of these horses have pain-related learnt, anticipatory behavior, even after the original source of pain has resolved. Such cases, as well as chronic or refractory EGUS cases, can benefit from a behavioral medicine approach. This includes the management of any underlying diseases, environmental modification, behavior modification, and, in select cases, behavior-modifying medication. This commentary, based on the authors’ clinical experiences and current literature, explores how behavioral medicine can be integrated with traditional pharmacologic, nutraceutical, and husbandry strategies for the multi-modal management of EGUS, with a focus on managing the horse’s experience to improve case outcome. Full article

Computational task offloading is a key technology in the field of vehicle-to-everything (V2X) communication, where security issues represent a core challenge throughout the offloading process. We must ensure the legitimacy of both the offloading entity (requesting vehicle) and the offloader (edge server or assisting vehicle), as well as the confidentiality and integrity of task data during transmission and processing. To this end, we propose a security authentication scheme for the V2X computational task offloading environment. We conducted rigorous formal and informal analyses of the scheme, supplemented by verification using the formal security verification tool AVISPA. This demonstrates that the proposed scheme possesses fundamental security properties in the V2X environment, capable of resisting various threats and attacks. Furthermore, compared to other related authentication schemes, our proposed solution exhibits favorable performance in terms of computational and communication overhead. Finally, we conducted network simulations using NS-3 to evaluate the scheme’s performance at the network layer. Overall, the proposed scheme provides reliable and scalable security guarantees tailored to the requirements of computing task offloading in V2X environments. Full article

This study examines cost and schedule deviations in secondary road infrastructure projects in Colombia, with a focus on the influence of public procurement characteristics. Despite the construction sector’s importance to national development, limited research has explored how procurement-related variables affect project performance. To address this gap, 149 completed road projects were analyzed using data from Colombia’s open procurement database, which provides publicly accessible, standardized information on contracting processes. A four-stage methodology was applied: data collection, exploratory analysis, bivariate analysis (including correlation and Kruskal–Wallis tests), and multivariate analysis using Random Forest and Bayesian networks. Schedule and cost deviations were used as dependent variables, with 17 independent variables. Results show that 81.9% of projects experienced some form of deviation, with a positive correlation between schedule and cost overruns. Significant factors were identified across different stages of the project life cycle. Variables significant for both deviations include the number of bidders, the number of valid bidders, the estimated cost, the final cost, the project intensity, and the type of award process. The findings provide data-driven arguments to improve award processes and support more informed planning of future projects, helping public entities reduce deviations and enhance the outcome of their infrastructure. Full article

Seismic facies recognition constitutes a fundamental task in seismic data interpretation, playing an essential role in characterizing subsurface geological structures, sedimentary environments, and hydrocarbon reservoir distributions. Conventional approaches primarily depend on expert interpretation, which often introduces substantial subjectivity and operational inefficiency. Although deep learning-based methods have been introduced, most rely solely on unimodal data—namely, seismic images—and encounter challenges such as limited annotated samples and inadequate generalization capability. To overcome these limitations, this study proposes a multimodal seismic facies recognition framework named GAT-UKAN, which integrates a U-shaped Kolmogorov–Arnold Network (U-KAN) with a Graph Attention Network (GAT). This model is designed to accept dual-modality inputs. By fusing visual features with knowledge embeddings at intermediate network layers, the model achieves knowledge-guided feature refinement. This approach effectively mitigates issues related to limited samples and poor generalization inherent in single-modality frameworks. Experiments were conducted on the F3 block dataset from the North Sea. A knowledge graph comprising 47 entities and 12 relation types was constructed to incorporate expert knowledge. The results indicate that GAT-UKAN achieved a Pixel Accuracy of 89.7% and a Mean Intersection over Union of 70.6%, surpassing the performance of both U-Net and U-KAN. Furthermore, the model was transferred to the Parihaka field in New Zealand via transfer learning. After fine-tuning, the predictions exhibited strong alignment with seismic profiles, demonstrating the model’s robustness under complex geological conditions. Although the proposed model demonstrates excellent performance in accuracy and robustness, it has so far been validated only on 2D seismic profiles. Its capability to characterize continuous 3D geological features therefore remains limited. Full article

Inequalities in enrollment in STEM persist for those entering higher education as first-generation college students, underserved racial and ethnic groups, female and nonbinary individuals, and those from lower socioeconomic backgrounds. The current study aims to better understand the relationship students have with graduate school success factors by redistributing the Graduate Student Success Survey+ (GSSS+) at an R2 institution in the southeastern United States. Exploratory factor analysis was used to test the survey’s validity, with 242 participants. A 7-factor, 40-item model was developed, comprising the following subscales: mentor support, peer support, imposter phenomenon, financial support, microaggressions (related to race and gender), access and opportunity (for research, writing, and presentations), and resilience. Item analysis identified perceived barriers (e.g., microaggressions, imposter phenomenon, and financial stress) for underserved students (i.e., females, underserved racial and ethnic groups, and part-time students). Regression analysis on resilience revealed a positive relationship with mentor support, peer support, and financial support. A negative relationship with resilience was associated with a greater perception of imposter phenomenon. Findings from this study underscore the need for additional support from mentors and other university entities to foster a stronger sense of resilience in students, along with increased opportunities for participation in research, academic writing, and publication. Full article

Molecular docking has emerged as a pivotal computational approach in agri-food research, offering a rapid and targeted means to discover bioactive molecules for crop protection and food safety. Its ability to predict and visualize interactions between natural or synthetic compounds and specific biological targets provides valuable opportunities to address urgent agricultural challenges, including climate change and the rise in resistant crop pathogens. By enabling the in silico screening of diverse chemical entities, this technique facilitates the identification of molecules with antimicrobial and antifungal properties, specifically designed to interact with critical enzymatic pathways in plant pathogens. Recent advancements, such as the integration of molecular dynamics simulations and artificial intelligence-enhanced scoring functions, have significantly improved docking accuracy by addressing limitations like protein flexibility and solvent effects. These technological improvements have accelerated the discovery of eco-friendly biopesticides and multifunctional nutraceutical agents. Promising developments include nanoparticle-based delivery systems that enhance the stability and efficacy of bioactive molecules. Despite its potential, molecular docking still faces challenges related to incomplete protein structures, variability in scoring algorithms, and limited experimental validation in agricultural contexts. This work highlights these limitations while outlining current trends and future prospects to guide its effective application in agri-food biotechnology. Full article

This paper introduces Contextual Object Grouping (COG), a specific computer vision framework that enables automatic interpretation of technical security diagrams through dynamic legend learning for intelligent sensing applications. Unlike traditional object detection approaches that rely on post-processing heuristics to establish relationships between the detected elements, COG embeds contextual understanding directly into the detection process by treating spatially and functionally related objects as unified semantic entities. We demonstrate this approach in the context of Cyber-Physical Security Systems (CPPS) assessment, where the same symbol may represent different security devices across different designers and projects. Our proof-of-concept implementation using YOLOv8 achieves robust detection of legend components (mAP50 ≈ 0.99, mAP50–95 ≈ 0.81) and successfully establishes symbol–label relationships for automated security asset identification. The framework introduces a new ontological class—the contextual COG class that bridges atomic object detection and semantic interpretation, enabling intelligent sensing systems to perceive context rather than infer it through post-processing reasoning. This proof-of-concept appears to validate the COG hypothesis and suggests new research directions for structured visual understanding in smart sensing environments, with applications potentially extending to building automation and cyber-physical security assessment. Full article

The sustainability of a port is directly related to the time spent by ships in terminals and depends on the terminal, the technologies used in it, and external conditions. Currently used sustainable port terminal technologies allow a significant increase in the intensity of ship loading operations and, at the same time, shorten the time spent by ships at the quays. Since port construction processes take a lot of time, many ports have many quays every day that are not moored by ships. Ports try to attract passenger and cargo flows, but they are also not infinite. In individual port terminals, for example, container and Ro–Ro terminals, most of the time is spent on cargo processing inside the terminal, and only part of the time is spent on ship loading operations. Probabilistic assessment of ship mooring at quays allows an understanding of not only the optimal need for quays and modernization of their equipment, but at the same time for a more purposeful assessment of the possibilities of using quays, accepting diversification options and, therefore, optimizing the ports themselves as a sustainable port entity. The article presents a methodology for assessing berth occupancy focused on the development of a sustainable port based on probabilistic methods that would allow calculating potential berth occupancy. The developed methodology, compared to existing methodologies and models, allows for a more realistic assessment of the expected berth occupancy, using actual port and ship data. The presented theoretical and experimental research results confirm the suitability of the developed methodology for the development of a sustainable port and the possibilities of applying the developed methodology in any port, adapting it to specific port conditions. Full article

The rapid dissemination of health misinformation on the Internet and social media has become a growing challenge for public health, particularly in terms of health information credibility. Promising efforts have been made to detect misinformation using generative AI and large language models (LLMs). However, such tools still lack domain-specific knowledge that limits their performance. In this study, we examine the use of predefined knowledge data structures in the forms of debunk lists to augment existing LLMs’ capabilities. We evaluate five different LLMs, including Llama-3.1-8B-instruct, Mistral-large, GPT-4o-mini, Claude-3.5-haiku, and Gemini-1.5-flash, under three experimental settings: zero-shot and debunk-augmented (50 and 100 entities). Results show that external knowledge, in the form of debunk lists, can notably improve LLMs’ performance in detecting misinformation. While Llama shows minimal benefit, the F1 score improvement ranges from 2.63% (GPT-4o) to 11% (Claude). In addition, analysis of model justifications shows that frequent use of debunk lists does not necessarily relate to accurate predictions. This highlights the importance of a model’s ability in effectively using the debunk list rather than reporting superficial integration of external knowledge. Moreover, the proposed framework is generalizable to other misinformation domains and provides key insights for applying external knowledge and evaluating LLMs’ reasoning reliability. Full article

Rice, as one of the world’s four major staple crops, is frequently threatened by pests and diseases during its growth. With the rapid expansion of agricultural information data, the effective management and utilization of such data have become crucial for the development of agricultural informatization. Named entity recognition technology offers precise support for the early prevention and control of crop pests and diseases. However, entity recognition for rice pests and diseases faces challenges such as structural complexity and prevalent nesting issues. Inspired by biological visual mechanisms, we propose a deep learning model capable of extracting multi-granularity features. Text representations are encoded using BERT, and the model enhances its ability to capture nested boundary information through multi-granularity convolutional neural networks (CNNs). Finally, sequence modeling and labeling are performed using a bidirectional long short-term memory network (BiLSTM) combined with a conditional random field (CRF). Experimental results demonstrate that the proposed model effectively identifies entities related to rice diseases and pests, achieving an F1 score of 91.74% on a self-constructed dataset. Full article

Metatropic dysplasia is an autosomal dominant skeletal disorder characterized by progressive kyphoscoliosis, severe platyspondyly, pronounced metaphyseal enlargement, and shortening of the long bones. This condition is caused by pathogenic variants in the TRPV4 (Transient Receptor Potential Vanilloid 4) gene, which encodes a non-selective calcium channel involved in bone homeostasis. Variants in TRPV4 have been associated with two major disease groups: skeletal dysplasias and neuropathies, with recent findings indicating an overlap in their clinical features. We report three patients with metatropic dysplasia, each presenting a distinct severity profile. All exhibited a bell-shaped thorax, significant platyspondyly, and shortened long bones with broad metaphyses. Notably, patients 1 and 3 had more complex clinical courses, including seizures and global developmental delay. Genetic analysis revealed two different TRPV4 variants: p.Asn796del (patient 1) and p.Pro799Leu (patients 2 and 3). These cases illustrate variability in extra-skeletal manifestations, complications, and prognosis. In our patients with TRPV4-related disorders, the co-occurrence of neurological symptoms and skeletal abnormalities suggests a clinically heterogeneous spectrum consistent with a single disease rather than distinct entities. A comprehensive, multidisciplinary approach is essential to optimize management and improve the quality of life for patients. Full article

(1) Background: Marine eutrophication represents a formidable challenge to sustainable global aquaculture, posing a severe threat to marine ecosystems and impeding the achievement of UN Sustainable Development Goal 14. Current methodologies for identifying eutrophication events and tracing their drivers from vast, heterogeneous text data rely on manual analysis and thus have significant limitations. (2) Methods: To address this issue, we developed a novel automated attribution analysis framework. We first pre-trained a domain-specific model (Aquaculture-BERT) on a 210-million-word corpus, which is the foundation for constructing a comprehensive Aquaculture Eutrophication Knowledge Graph (AEKG) with 3.2 million entities and 8.5 million relations. (3) Results: Aquaculture-BERT achieved an F1-score of 92.1% in key information extraction, significantly outperforming generic models. The framework successfully analyzed complex cases, such as Xiamen harmful algal bloom, generating association reports congruent with established scientific conclusions and elucidating latent pollution pathways (e.g., pond aquaculture–nitrogen input–Phaeocystis bloom). (4) Conclusions: This study delivers an AI-driven framework that enables the intelligent and efficient analysis of aquaculture-induced eutrophication, propelling a paradigm shift toward the deep integration of data-driven discovery with hypothesis-driven inquiry. The framework provides a robust tool for quantifying the environmental impacts of aquaculture and identifying pollution sources, contributing to sustainable management and achieving SDG 14 targets. Full article

The purpose of few-shot relation extraction (RE) is to recognize the relationship between specific entity pairs in text when there are a limited number of labeled samples. A few-shot RE method based on a prototype network, which constructs relation prototypes by relying on the support set to assign labels to query samples, inherently leverages the symmetry between support and query processing. Although these methods have achieved remarkable results, they still face challenges such as the misjudging of noisy samples or outliers, as well as distinguishing semantic similarity relations. To address the aforementioned challenges, we propose a novel semantic enhanced prototype network, which can integrate the semantic information of relations more effectively to promote more expressive representations of instances and relation prototypes, so as to improve the performance of the few-shot RE. Firstly, we design a prompt encoder to uniformly process different prompt templates for instance and relation information, and then utilize the powerful semantic understanding and generation capabilities of large language models (LLMs) to obtain precise semantic representations of instances, their prototypes, and conceptual prototypes. Secondly, graph attention learning techniques are introduced to effectively extract specific-relation features between conceptual prototypes and isomorphic instances while maintaining structural symmetry. Meanwhile, a prototype-level contrastive learning strategy with bidirectional feature symmetry is proposed to predict query instances by integrating the interpretable features of conceptual prototypes and the intra-class shared features captured by instance prototypes. In addition, a clustering loss function was designed to guide the model to learn a discriminative metric space with improved relational symmetry, effectively improving the accuracy of the model’s relationship recognition. Finally, the experimental results on the FewRel1.0 and FewRel2.0 datasets show that the proposed approach delivers improved performance compared to existing advanced models in the task of few-shot RE. Full article

Relation extraction in the equipment domain often exhibits asymmetric patterns, where entities participate in multiple overlapping relations that break the expected structural symmetry of semantic associations. Such asymmetry increases task complexity and reduces extraction accuracy in conventional approaches. To address this issue, we propose a symmetry- and asymmetry-aware dual-path retrieval and in-context learning-based large language model. Specifically, the BGE-M3 embedding model is fine-tuned for domain-specific adaptation, and a multi-level retrieval database is constructed to capture both global semantic symmetry at the sentence level and local asymmetric interactions at the relation level. A dual-path retrieval strategy, combined with Reciprocal Rank Fusion, integrates these complementary perspectives, while task-specific prompt templates further enhance extraction accuracy. Experimental results demonstrate that our method not only mitigates the challenges posed by overlapping and asymmetric relations but also leverages the latent symmetry of semantic structures to improve performance. Experimental results show that our approach effectively mitigates challenges from overlapping and asymmetric relations while exploiting latent semantic symmetry, achieving an F1-score of 88.53%, a 1.86% improvement over the strongest baseline (GPT-RE). Full article

Introduction: Primary glomerulopathies are immune-driven kidney diseases. IgA nephropathy (IgAN) and membranoproliferative glomerulonephritis (MPGN) are prevalent entities with a risk of chronic progression. Immune checkpoints, such as PD-1/PD-L1, CTLA-4/CD86, and CD200R/CD200, regulate activation and tolerance in T, B, and NK cells, and also exist in soluble forms, reflecting systemic immune balance. Objective: To compare immune checkpoint profiles in IgAN and MPGN versus healthy volunteers (HV) through surface expression, soluble serum levels, and PBMC transcripts, with attention to sex-related differences and diagnostic value assessed by ROC curves. Materials and Methods: Ninety age-matched subjects were studied: IgAN (n = 30), MPGN (n = 30), HV (n = 30). Flow cytometry evaluated checkpoint expression on CD4+/CD8+ T cells, CD19+ B cells, and NK cells. ELISA quantified sPD-1, sPD-L1, sCTLA-4, sCD86, sCD200, sCD200R; PBMC transcript levels were assessed. Group comparisons, sex stratification, and ROC analyses were performed. Results: Lymphocyte distributions were preserved, but IgAN patients showed anemia and impaired renal function, while MPGN patients had greater proteinuria and dyslipidemia. GN patients displayed increased PD-1/PD-L1 and CD200R/CD200, with reduced CTLA-4/CD86, compared to HV. Serum analysis revealed elevated sPD-1, sPD-L1, sCD200, sCD200R and decreased sCTLA-4, sCD86. PBMC transcripts paralleled these trends, with PD-1/PD-L1 mainly increased in MPGN. Sex had minimal impact. ROC analyses showed strong GN vs. HV discrimination by CD19⁺CTLA-4⁺, PD-1/PD-L1, and CD200/CD200R, but limited ability to separate IgAN from MPGN. Conclusions: IgAN and MPGN share a sex-independent checkpoint signature: PD-1/PD-L1 and CD200R/CD200 upregulation with CTLA-4/CD86 downregulation. CD19⁺, CTLA-4⁺, and soluble PD-1/PD-L1/CD200(R) emerge as promising biomarkers requiring further validation. Full article