Search Results (2,279)

Temporal knowledge graphs aim to enhance the dynamic and evolutionary representation of knowledge while enabling time-based reasoning. However, the reasoning based on temporal knowledge graphs in real geographic environments suffers from low accuracy due to the difficulty in effectively utilizing complex spatio-temporal information. Spatial attributes within entities typically encompass both relative and absolute spatial information types. However, during spatio-temporal reasoning, the deep coupling between the quadruple $(entities, relations, timestamp)$ and these two spatial information types is frequently overlooked, as they remain unintegrated in inference predictions. This paper proposes a novel Multi-Task Spatial Recurrent Evolution Graph Convolutional Network (MTS-RE-GCN) framework to enable temporal knowledge graph methods to better reason about spatial entities under time-varying conditions. Experiments on the spatio-temporal dataset and the benchmark dataset (i.e., ICEWS14s, ICEWS18) with spatio-temporal features demonstrate that MTS-RE-GCN significantly outperforms the baseline models (e.g., RE-GCN, TiRGN). For entity prediction tasks, MTS-RE-GCN achieves mean reciprocal rank (MRR) scores of 0.848, 0.739, 0.566, representing improvements of 9.00%, 6.03%, 3.28%, correspondingly. This provides a comprehensive and efficient solution for spatio-temporal entity prediction in temporal knowledge graphs, holding significant implications for spatio-temporal data analysis, event prediction, and related fields. Full article

Recent GraphRAG methods based on knowledge graphs (KGs) primarily rely on either under-reasoning or a structural path-level retriever, which prevents them from jointly capturing fine-grained semantic relevance and explicit multi-hop reasoning paths. This separation often results in semantic mismatch—where logical links are missing—or structural over-constraint in reasoning— where rigid dependencies limit flexible reasoning—thereby degrading both answer accuracy and the reliability of evidence in complex KGQA tasks. To address these issues, we propose HybRAG, a hybrid retrieval framework that synergistically integrates a semantic node-level retriever and structural path-level retriever. HybRAG constructs a hybrid subgraph that jointly reflects the semantic proximity of entities and the relational structures encoded in the KG. Furthermore, we incorporate retrieval-augmented fine-tuning, which enables the model to internalize advanced reasoning strategies for interpreting disparate semantic and structural signals, rather than merely memorizing domain facts. Through extensive experiments on the WebQSP and CWQ benchmarks, we demonstrate that HybRAG effectively bridges the gap between LLM-centric semantic approaches and GNN-centric structural approaches, outperforming single-retriever baselines. Our findings, including detailed sensitivity and ablation analyses, provide empirical evidence that the systematic alignment of semantic and structural signals is essential for ensuring the reasoning reliability and scalability of next-generation GraphRAG systems. Full article

Background and Objectives: Pelvic ring injuries with symphyseal disruption are classically associated with high-energy mechanisms such as motor vehicle collisions. Recently, waterslides have emerged as an underrecognized but distinct source of severe pelvic trauma. Waterslide-related pelvic trauma represents a distinct biomechanical entity characterized by a supine or semi-supine body position at splashdown, extreme forced hip abduction, asymmetric lower-limb positioning, and abrupt hydrodynamic deceleration. The high descent velocity, abrupt hydrodynamic deceleration, and forced hip abduction at water entry may combine to generate open-book-type pelvic injuries. Evidence guiding diagnosis and surgical management in this setting remains scarce. Materials and Methods: We retrospectively analyzed a consecutive series of adult patients sustaining waterslide-related anterior–posterior compression type II (APC2) pelvic ring injuries. Demographic data and the body mass index (BMI), fracture classification, surgical strategy, complications, and functional outcomes were reviewed. Only patients with complete imaging, operative records, and follow-up were included. Results: Four patients (38–72 years) met the inclusion criteria. All sustained rotationally unstable open-book pelvic injuries and were classified as APC2; three were AO/OTA 61B2.3 and one 61B3.3. All patients were overweight or obese (BMI 27.2–31.2). Pelvic binders provided an effective acute reduction in symphyseal diastasis; however, in one bilateral injury, CT imaging obtained with the binder in situ masked posterior ligamentous instability. Definitive surgical fixation was performed in all cases. Early mechanical failure occurred in two patients treated with short anterior symphyseal plate constructs. In the bilateral injury, isolated anterior fixation failed repeatedly until posterior sacroiliac stabilization was added. No deep infections or thromboembolic events occurred. Although two patients required short observational ICU stays, none were admitted for hemodynamic instability or pelvic bleeding. Conclusions: At 12-month follow-up, three patients achieved pain-free ambulation without assistive devices, while one patient required intermittent use of a single crutch; all patients regained independence in daily activities. Waterslide accidents represent a high-energy injury mechanism capable of producing severe APC2 pelvic disruptions, particularly in patients with an elevated BMI. Awareness of this mechanism and meticulous assessment of posterior stability are essential to avoid under-treatment and mechanical failure. Full article

Resilience is frequently mobilized in heritage discourse as a systemic capacity for stability and recovery. This article critically interrogates resilience as a managerial rationality imported into archaeological and heritage practice, often without sufficient attention to its epistemological and political implications. Drawing on assemblage theory and the concept of the event, it reframes resilience archaeologically as a material effect of relations among people, things, practices, and landscapes. Rather than evaluating the persistence of bounded entities, resilience is approached through material reconfigurations, ruptures, and continuities that leave durable traces in the archaeological record. This perspective clarifies how processes commonly described as collapse, reorganization, or emergence become archaeologically legible, and why not all disturbances constitute events. The article then examines heritage as the afterlife of events, conceptualized as an assemblage that stabilizes rupture through practices of conservation, commemoration, and care. Heritage endurance is neither automatic nor neutral, but contingent on ongoing work and embedded in relations of power. The article concludes by reflecting on the ethical and political limits of resilience in contexts of crisis and inequality, arguing for a reflexive, assemblage-based understanding of heritage focused on processes of reorganization rather than managerial equilibrium. Full article

When extracting entities, relations, and their associated words from scientific literature, it is imperative to consider the supporting role of feature words on the extraction results. These feature words can provide local semantic information and be combined with the global feature representation of the sentence, improving the accuracy of information extraction. However, existing methods, when fusing local semantic feature words with global features, due to ineffective distinction between the influence of feature words and non-feature words, result in limited enhancement on model performance. To solve this problem, we propose a feature words adversarial scheme (FWAS) with dual pointer method. This method implements a dynamic filtering mechanism for feature words through feature pointers, in order to semantically enhance the encoding of the original text. Simultaneously, an inverse feature pointer is designed to establish a negative weight decay mechanism, weakening interference of non-key vocabulary. During joint training, annotation information for entity relations is introduced to supervise the dual feature selection mechanism. Experimental results on three public scientific information extraction datasets demonstrate that our method consistently outperforms strong baselines, achieving up to 4.9% improvement in F1-score. This method offers a new perspective for information extraction tasks in scientific and technical literature and provides scalable optimization directions for subsequent research Full article

Graph Neural Networks (GNNs) have become a central methodology for modelling biological systems where entities and their interactions form inherently non-Euclidean structures. From protein interaction networks and gene regulatory circuits to molecular graphs and multi-omics integration, the relational nature of biological data makes GNNs particularly well-suited for capturing complex dependencies that traditional deep learning methods fail to represent. Despite their rapid adoption, the effectiveness of GNNs in bioinformatics depends not only on model design but also on how biological graphs are constructed, parameterised and trained. In this review, we provide a structured framework for understanding and applying GNNs in bioinformatics, organised around three key dimensions: (1) graph construction and representation, including strategies for deriving biological networks from heterogeneous sources and selecting biologically meaningful node and edge features; (2) GNN architectures, covering spectral and spatial formulations, representative models such as Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), Graph Sample and AggregatE (GraphSAGE) and Graph Isomorphism Network (GIN), and recent advances including transformer-based and self-supervised paradigms; and (3) applications in biomedical domains, spanning disease–gene association prediction, drug discovery, protein structure and function analysis, multi-omics integration and biomedical knowledge graphs. We further examine training considerations, including optimisation techniques, regularisation strategies and challenges posed by data sparsity and noise in biological settings. By synthesising methodological foundations with domain-specific applications, this review clarifies how graph quality, architectural choice and training dynamics jointly influence model performance. We also highlight emerging challenges such as modelling temporal biological processes, improving interpretability, and enabling robust multimodal fusion that will shape the next generation of GNNs in computational biology. Full article

This work is axiomatic and structural in nature and is not intended as a phenomenological physical theory, but as a framework clarifying minimal informational primitives from which geometric and dynamical descriptions may emerge. We present a background-independent framework in which physical geometry, interaction-like forces, and spacetime arise as effective descriptions of constrained relational information rather than as fundamental entities. The only primitive structure is a network of degrees of freedom linked by admissible informational relations, each subject to quantifiable constraints on accessibility or flow. The motivation is to identify whether a single minimal relational primitive can account jointly for the emergence of geometry, forces, and spacetime, without presupposing a manifold, fields, or fundamental interactions. The framework is formalized using weighted relational graphs in which constraint weights encode limitations on information flow between degrees of freedom. Effective geometry is defined operationally through minimal constraint cost along relational paths, yielding an emergent metric without assuming spatial embedding. Relational evolution is modeled via a minimal configuration-space dynamics defined by local rewrite moves, and a statistical description is introduced through an informational action that governs coarse-grained response rather than serving as a fundamental dynamical law. Curvature-like observables are defined using transport-based comparisons of local accessibility structure. Within this setting, metric structure emerges from constrained relational accessibility, while curvature-like behavior arises from heterogeneity in constraint structure. Effective forces appear as entropic or informational action gradients with respect to coarse-grained control parameters that modulate relational constraints, and are interpreted as emergent responses rather than primitive interactions. A finite worked example explicitly demonstrates the emergence of nontrivial distance, curvature proxies, and an effective force via geodesic switching under constraint variation, without assuming fundamental spacetime, fields, or particles. The results support an interpretation in which geometry, forces, and spacetime are representational features of constrained information flow rather than fundamental elements of physical law. The framework clarifies conceptual distinctions and points of compatibility with existing approaches to emergent spacetime, and it outlines qualitative expectations for regimes in which smooth geometric descriptions are expected to break down. The work delineates the scope and limits of geometric description without proposing a complete phenomenological theory. Full article

Phages, the most abundant entities on Earth, exhibit a complex interplay with bacteria, especially within environmental biofilms, resulting in an ecological arms race. This study investigates the interaction between phages and bacteria in the drains of beef-processing plants using high-throughput sequencing and metagenomic analysis. Metagenomic data collected from 75 drain samples from beef-processing plants were analyzed to investigate phage–bacterial interactions. First, assembled contigs were screened to identify viral sequences, which were then taxonomically annotated to determine the viral composition, including phages. Functional annotation of these viral sequences provided information about the viral genes and their roles in bacterial interactions specifically associated with attack and counterattack of bacteria. In parallel, bacterial contigs were examined to identify genes associated with antiphage defense systems, providing insights into the strategies adapted by bacteria to resist phage infection. Taxonomic annotation of viral sequences from the bulk metagenomic data revealed the presence of phages targeting Pseudomonas, Klebsiella, and Enterococcus. The higher abundance of Pseudomonas phages aligns with our previous study, where Pseudomonas was identified as the dominant bacterial genus, suggesting potential copersistence of phages and their hosts. Functional annotation of phage contigs revealed infective and lysis-related genes, highlighting their potential role in bacterial attack. Conversely, bacterial contigs encoded antiphage defense systems, including CRISPR-Cas, restriction–modification, and other defense-related genes. The study also uncovered the presence of anti-CRISPR proteins in phages, suggesting a counterattack on the bacterial defense. These findings provide evidence for phage attack, bacterial defense, and phage counterattack and may showcase the ongoing coevolutionary arms race between phages and bacteria. While this evidence looks promising, these results remain preliminary and further studies are needed to validate these findings. Still, this study provides a foundational understanding of bacteria–phage coexistence in beef-processing plant drains and paves the way for further explorations of these intricate interactions and their possible applications in controlling pathogenic microorganisms within biofilms. Full article

Bridge maintenance decision-making is challenged by the “data-rich but knowledge-poor” nature of unstructured inspection and maintenance reports. A bridge maintenance knowledge graph (BMKG) construction framework is proposed, developed from a corpus of 275 inspection reports, to enable structured representation of engineering knowledge and decision support. A standards-aligned domain ontology provides semantic constraints for downstream information extraction and organization. Building on this ontology, a RoBERTa–BiGRU–CRF named entity recognition (NER) model is developed, achieving a precision of 90.8%, recall of 93.8%, and a micro-averaged F1-score (micro-F1) of 92.3%. Inter-annotator agreement for the NER annotations was quantified using Cohen’s kappa, yielding κ = 0.86. To avoid the cost of large-scale relation annotation, relations are constructed using interpretable, rule-based constraints. Through manual verification audit of randomly sampled relationship instances under a strict exact-match criterion (i.e., requiring exact matches for entity boundaries, entity types, and relationship types), an overall manual verification rate of 93.67% was obtained. Unlike existing KG methods that rely heavily on annotated data, the BMKG framework integrates ontological constraints with a rule-driven approach, prioritizing interpretability and reducing dependency on large-scale relation labeling. Consequently, the resulting knowledge graph supports semantic retrieval and visual exploration, enabling efficient disease-to-recommendation queries for refined bridge maintenance management. Full article

Muscle degeneration is the hallmark of muscular dystrophies—genetically heterogeneous disorders traditionally approached through the lens of molecular pathogenesis or symptomatic management in isolation. Here, we present a deliberately interdisciplinary synthesis that bridges molecular genetics, clinical phenotyping, and evidence-based orthopedic decision-making to address a significant critical gap: the lack of genotype-informed, function-oriented frameworks for musculoskeletal complications. We re-evaluate disease entities—not only by their molecular etiology (e.g., DMD, LMNA, DUX4 dysregulation), but through the prism of orthopedic manifestations as diagnostic gateways and therapeutic milestones. For instance, early rigid spine in LMNA-related dystrophy is not merely a sign of contracture, but a red flag demanding cardiac risk stratification before surgical planning, in alignment with current consensus. Similarly, scoliosis management in Duchenne muscular dystrophy is discussed through quantitative decision thresholds (Cobb angle ≥ 20–30°, FVC ≥ 30–35%) derived from long-term outcome studies, rather than general clinical recommendations. Critically, we confront challenges posed by disease-modifying therapies: patients now survive into their 30s and 40s, yet develop novel, therapy-exacerbated orthopedic phenotypes (e.g., steroid-induced osteoporosis, atypical spinal rigidity). Therefore, we argue that precision orthopedics—tailored surveillance, genotype-stratified intervention timing (e.g., D4Z4 repeat-guided monitoring in FSHD, and realistic functional goal-setting (e.g., scapular arthrodesis for overhead function)—should become the gold standard of care. For example, desminopathies may show marked phenotypic variability even within the same mutation. Our review thus serves not only as a molecular overview, but as a practical roadmap for neurologists, geneticists, orthopedic surgeons, and rehabilitation specialists seeking to translate genomic insights into durable functional outcomes. Full article

Hosts generating unsolicited network traffic increasingly operate in a coordinated manner rather than in isolation. Scanning and exploitation activities are often distributed across multiple hosts that share common infrastructure, toolchains, and behavioral patterns, forming loosely coupled yet persistently aligned sender groups. Accurately attributing such groups is critical for understanding organized activities and strengthening network defense capabilities. However, existing attribution approaches face notable limitations. Methods that rely on threat intelligence suffer from delayed updates and limited coverage. Static feature-based approaches ignore temporal ordering and therefore fail to capture multi-stage behavioral evolution. Although dynamic sequence models incorporate temporal patterns, they typically overlook the collaborative structural relationships among coordinated senders. In this paper, we propose SentinelGraph, a temporal graph reasoning framework for sender group attribution from honeypot traffic. SentinelGraph constructs a temporal knowledge graph and integrates a recurrent graph evolution module to jointly model coordination structures and their temporal dynamics. A structure enhancement module further exploits contextual information available at the target time, while an auxiliary relation loss encourages the learning of enriched entity representations. This design enables accurate attribution even for previously unseen senders by leveraging information from their observed neighbors. Experiments on real-world honeypot data demonstrate that SentinelGraph substantially outperforms state-of-the-art methods in modeling coordinated network behaviors. Full article

Extracting structured threat intelligence from unstructured cybersecurity texts requires accurate identification of entities together with their underlying semantic relations. However, threat reports often exhibit intricate sentence structures, long-range contextual dependencies, and tightly coupled entity–relation patterns, which pose substantial challenges for existing extraction approaches. To address these challenges, this study investigates joint entity–relation extraction from the perspective of semantic dependency modeling and develops HDIM-JER, a unified framework that captures structured interactions among heterogeneous linguistic features. HDIM-JER integrates character-level cues, contextual representations, and higher-order semantic dependency evidence to enhance structural awareness during joint inference, where different second-order dependency configurations provide an interpretable perspective on structurally symmetric and hierarchically asymmetric interaction patterns among entity–relation instances. By incorporating multi-level dependency interactions, HDIM-JER effectively alleviates error propagation associated with pipeline-based architectures and improves the modeling of complex relational dependencies. Extensive experiments on a threat intelligence corpus and a public benchmark dataset demonstrate consistent performance improvements over representative state-of-the-art methods in both entity recognition and relation extraction, confirming the effectiveness of higher-order semantic dependency interaction modeling for threat intelligence analysis. Full article

Temporal knowledge graphs (TKGs) effectively represent dynamic facts by incorporating a temporal dimension, yet they frequently encounter data incompleteness issues that constrain downstream applications. Concurrently, TKG prediction tasks, which enable reasoning about future events, have garnered significant attention. Existing TKG completion methods often neglect semantic information, underexploit event information from subsequent timestamps, and fail to leverage the structural symmetries inherent in temporal data. To address these limitations, this paper proposes a synergistic approach comprising two models: SiSe for completion and DL-CompGCN for prediction. SiSe integrates semantic and structural embeddings by employing entity text descriptions as semantic signals, utilizing symmetric cross-attention for bidirectional feature fusion and leveraging bidirectional gated recurrent units to capture symmetric temporal influences from both past and future events. On ICEWS14, ICEWS05-15, and GDELT completion datasets, the MRR improves by 1.2, 1.4, and 0.8 percentage points, respectively. DL-CompGCN addresses the accuracy–interpretability trade-off in prediction tasks through a time-aware graph convolutional encoder and a dual-decoder framework that combines bilinear scoring with first-order logical rules to generate interpretable paths while preserving the symmetric properties of temporal relations. It achieves state-of-the-art performance on ICEWS14, ICEWS05-15, and ICEWS18 prediction datasets. The proposed models explicitly incorporate symmetric principles in their architectural design; SiSe employs symmetric bidirectional temporal modeling, while DL-CompGCN maintains symmetry in its graph propagation and rule inference mechanisms. The experimental results demonstrate that both models significantly outperform baseline methods, offering a comprehensive solution for temporal knowledge graph reasoning that respects and exploits the symmetric structures inherent in temporal data. Full article

This study examines the Laozi Zhigui—a key text of Han dynasty Huang-Lao thought—and reconstructs the categorical status of qi to reassess received primordial qi-centered cosmological interpretations and clarify the text’s distinctive worldview. The Laozi Zhigui explains the relation between Dao and the myriad entities through four stages of wu (nothingness)—Dao, De, Spirit-Illumination, and Great Harmony—and previous studies, working within inherited qi-centered cosmological frameworks, have generally assimilated these stages to qi. A contextual reading of key passages on cosmology, mind–nature, and self-cultivation clarifies that in the Laozi Zhigui, qi does not belong to the same ontological category as these four stages of wu. Instead, it functions as a mediating substance through which the order of wu is carried over into you (somethingness). Furthermore, the four stages of wu are likewise not as the internal differentiation of qi but as a non-substantialist account of the “generation of order.” On this basis, the worldview of the Laozi Zhigui can be reconstructed as a triadic schema of wu–qi–you (nothingness–qi–somethingness), which yields a distinctive model of qi cosmology that, unlike Han dynasty primordial qi-centered accounts, does not presuppose the generation and fission of a single primordial qi. Full article

In the current paradigm of adapted sport in Spain, national sports federations play a crucial role. This study aims to map and characterize the public visibility of the current situation of Spanish sports federations in relation to the integration and development of Sports for People with Disabilities on their official websites, and to interpret these publicly reported indicators in relation to federation-level integration practices discussed in the international literature and legislative changes promoted by Sports Law 39/2022. To this end, through an exploratory and descriptive cross-sectional study, a systematic survey of the published digital resources of the 61 national single-sport federations recognized by the Higher Sports Council has been carried out. The results show that federations present initiatives related to the integration of people with disabilities in sport: 21 have a Paralympic category, 42 present themselves as inclusive entities, 13 of the federative regulations specifically address the issue, and in 38 cases, specialized personnel can be found or linked to sport for people with disabilities. In conclusion, this research shows the degree of integration and development of Spanish sports federations in relation to sport for people with disabilities. Full article