Search Results (118)

Cross-document relation extraction (CodRE) aims to predict the semantic relationships between target entities located in different documents, a critical capability for comprehensive knowledge graph construction and multi-source intelligence analysis. Existing approaches primarily rely on bridge entities to capture interdependencies between target entities across documents. However, these models face two potential limitations: they employ entity-centered context filters that overlook relation-specific information, and they fail to account for varying semantic distances between document paths. To address these challenges, we propose CLEAR (Cross-document Link-Enhanced Attention for Relations), a novel framework integrating three key components: (1) the Relation-aware Context Filter that incorporates relation type descriptions to preserve critical relation-specific evidence; (2) the Path Distance-Weighted Attention mechanism that dynamically adjusts attention weights based on semantic distances between document paths; and (3) a cross-path entity matrix that leverages inner- and inter-path relations to enrich target entity representations. Experimental results on the CodRED benchmark demonstrate that CLEAR outperforms all competitive baselines, achieving state-of-the-art performance, with 68.78% AUC and 68.42% F1 scores, confirming the effectiveness of our framework. Full article

Artificial intelligence and bioacoustics represent a paradigm shift in non-invasive poultry welfare monitoring through advanced vocalization analysis. This comprehensive systematic review critically examines the transformative evolution from traditional acoustic feature extraction—including Mel-Frequency Cepstral Coefficients (MFCCs), spectral entropy, and spectrograms—to cutting-edge deep learning architectures encompassing Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, attention mechanisms, and groundbreaking self-supervised models such as wav2vec2 and Whisper. The investigation reveals compelling evidence for edge computing deployment via TinyML frameworks, addressing critical scalability challenges in commercial poultry environments characterized by acoustic complexity and computational constraints. Advanced applications spanning emotion recognition, disease detection, and behavioral phenotyping demonstrate unprecedented potential for real-time welfare assessment. Through rigorous bibliometric co-occurrence mapping and thematic clustering analysis, this review exposes persistent methodological bottlenecks: dataset standardization deficits, evaluation protocol inconsistencies, and algorithmic interpretability limitations. Critical knowledge gaps emerge in cross-species domain generalization and contextual acoustic adaptation, demanding urgent research prioritization. The findings underscore explainable AI integration as essential for establishing stakeholder trust and regulatory compliance in automated welfare monitoring systems. This synthesis positions acoustic AI as a cornerstone technology enabling ethical, transparent, and scientifically robust precision livestock farming, bridging computational innovation with biological relevance for sustainable poultry production systems. Future research directions emphasize multi-modal sensor integration, standardized evaluation frameworks, and domain-adaptive models capable of generalizing across diverse poultry breeds, housing conditions, and environmental contexts while maintaining interpretability for practical farm deployment. Full article

Depressive disorder (DD) in children and adolescents is a growing public health concern with a complex and multifactorial etiology. While most biomarker research has focused on adults, increasing attention is being paid to age-specific molecular mechanisms. This narrative review provides a comprehensive overview of current knowledge on potential biomarkers of DD, including genetic, neurotransmitter, hormonal, inflammatory, lipid, and oxidative stress markers, in youth compared to adult populations. Special emphasis is given to findings from the DEPOXIN project (Molecular basis of depressive disorder in children and adolescents, the influence of omega-3 fatty acids and oxidative stress), a multicenter study investigating biological markers in children and adolescents with DD. The project identified significantly increased oxidative stress markers (8-isoprostanes, advanced oxidation protein products, nitrotyrosine) and decreased antioxidant enzyme activity (glutathione peroxidase). Moreover, HDL (high density lipoproteins) cholesterol and its subfractions were negatively correlated with depression severity. At the same time, thromboxane B2, omega-6/omega-3 fatty acid ratios, and salivary cortisol levels showed strong positive correlations with depressive symptoms and biochemical markers of inflammation. These results suggest a distinct molecular profile of depression in paediatric populations, emphasizing the importance of developmental context in biomarker research. The review aims to synthesize existing evidence, compare findings across age groups, and highlight the need for personalized, age-appropriate strategies in the diagnosis and treatment of depressive disorders. Full article

Landslides are characterized by their suddenness and destructive power, making rapid and accurate identification crucial for emergency rescue and disaster assessment in affected areas. To address the challenges of limited landslide samples and data complexity, a landslide identification sample library was constructed using high-resolution remote sensing imagery combined with field validation. An innovative Dual-Coded Segmentation Network (DS Net), which realizes dynamic alignment and deep fusion of local details and global context, image features and domain knowledge through the multi-attention mechanism of Prior Knowledge Integration (PKI) module and Cross-Feature Aggregation (CFA) module, significantly improves the landslide detection accuracy and reliability. To objectively evaluate the performance of the DS Net model, four efficient semantic segmentation models—SegFormer, SegNeXt, FeedFormer, and U-MixFormer—were selected for comparison. The results demonstrate that DS Net achieves superior performance (overall accuracy = 0.926, precision = 0.884, recall = 0.879, and F1-score = 0.882), with metrics that are 3.5–7.1% higher than the other models. These findings confirm that DS Net effectively improves the accuracy and efficiency of landslide identification, providing a critical scientific basis for landslide prevention and mitigation. Full article

In the context of the clean and low-carbon transformation of power systems, addressing the challenge of day-ahead electricity market price prediction issues triggered by the strong stochastic volatility of power supply output due to high-penetration renewable energy integration, as well as problems such as limited dataset scales and short market cycles in test sets associated with existing electricity price prediction methods, this paper introduced an innovative prediction approach based on a multi-modal feature fusion and BiGRUSA-ResSE-KAN deep learning model. In the data preprocessing stage, maximum–minimum normalization techniques are employed to process raw electricity price data and exogenous variable data; the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and variational mode decomposition (VMD) methods are utilized for multi-modal decomposition of electricity price data to construct a multi-scale electricity price component matrix; and a sliding window mechanism is applied to segment time-series data, forming a three-dimensional input structure for the model. In the feature extraction and prediction stage, the BiGRUSA-ResSE-KAN multi-branch integrated network leverages the synergistic effects of gated recurrent units combined with residual structures and attention mechanisms to achieve deep feature fusion of multi-source heterogeneous data and model complex nonlinear relationships, while further exploring complex coupling patterns in electricity price fluctuations through the knowledge-adaptive network (KAN) module, ultimately outputting 24 h day-ahead electricity price predictions. Finally, verification experiments conducted using test sets spanning two years from five major electricity markets demonstrate that the introduced method effectively enhances the accuracy of day-ahead electricity price prediction, exhibits good applicability across different national electricity markets, and provides robust support for electricity market decision making. Full article

Global agricultural productivity and ecosystem sustainability face escalating threats from multiple abiotic stresses, particularly heavy metal contamination, drought, and soil salinization. In this context, developing effective strategies to enhance plant stress tolerance has emerged as a critical research frontier. Phosphate-solubilizing microorganisms (PSMs) have garnered significant scientific attention due to their capacity to convert insoluble soil phosphorus into plant-available forms through metabolite production, and concurrently exhibiting multifaceted plant growth-promoting traits. Notably, PSMs demonstrate remarkable potential in enhancing plant resilience and productivity under multiple stress conditions. This review article systematically examines current applications of PSMs in typical abiotic stress environments, including heavy metal-polluted soils, arid ecosystems, and saline–alkaline lands. We comprehensively analyze the stress-alleviation effects of PSMs and elucidate their underlying mechanisms. Furthermore, we identify key knowledge gaps and propose future research directions in microbial-assisted phytoremediation and stress-mitigation strategies, offering novel insights for developing next-generation bioinoculants and advancing sustainable agricultural practices in challenging environments. Full article

Nuclear factor erythroid 2–related factor 2 (Nrf2) is a transcription factor that plays a central role in regulating cellular responses to oxidative stress. It governs the expression of a broad range of genes involved in antioxidant defense, detoxification, metabolism, and other cytoprotective pathways. In normal cells, the transient activation of Nrf2 serves as a protective mechanism to maintain redox homeostasis. However, the persistent or aberrant activation of Nrf2 in cancer cells has been implicated in tumor progression, metabolic reprogramming, and resistance to chemotherapy and radiotherapy. These dual roles underscore the complexity of Nrf2 signaling and its potential as a therapeutic target. A deeper understanding of Nrf2 regulation in both normal and malignant contexts is essential for the development of effective Nrf2-targeted therapies. This review provides a comprehensive overview of Nrf2 regulation and function, highlighting its unique features in cancer biology, particularly its role in metabolic adaptation and drug resistance. Special attention is given to the current knowledge of Nrf2′s involvement in leukemia and emerging strategies for its therapeutic modulation. Full article

Speech lie detection is a technique that analyzes speech signals in detail to determine whether a speaker is lying. It has significant application value and has attracted attention from various fields. However, existing speech lie detection algorithms still have certain limitations. These algorithms fail to fully explore manually extracted features based on prior knowledge and also neglect the dynamic characteristics of speech as well as the impact of temporal context, resulting in reduced detection accuracy and generalization. To address these issues, this paper proposes a multi-feature speech lie detection algorithm based on the dual-stream deep architecture (DDA-MSLD).This algorithm employs a dual-stream structure to learn different types of features simultaneously. Firstly, it combines a gated recurrent unit (GRU) network with the attention mechanism. This combination enables the network to more comprehensively capture the context of speech signals and focus on the parts that are more critical for lie detection. It can perform in-depth sequence pattern analysis on manually extracted static prosodic features and nonlinear dynamic features, obtaining high-order dynamic features related to lies. Secondly, the encoder part of the transformer is used to simultaneously capture the macroscopic structure and microscopic details of speech signals, specifically for high-precision feature extraction of Mel spectrogram features of speech signals, obtaining deep features related to lies. This dual-stream structure processes various features of speech simultaneously, describing the subjective state of speech signals from different perspectives and thereby improving detection accuracy and generalization. Experiments were conducted on the multi-person scenario lie detection dataset CSC, and the results show that this algorithm outperformed existing state-of-the-art algorithms in detection performance. Considering the significant differences in lie speech in different lying scenarios, and to further evaluate the algorithm’s generalization performance, a single-person scenario Chinese lie speech dataset Local was constructed, and experiments were conducted on it. The results indicate that the algorithm has a strong generalization ability in different scenarios. Full article

Text summarization plays a crucial role in processing extensive textual data, particularly in low-resource languages such as Korean. However, abstractive summarization faces persistent challenges, including semantic distortion and inconsistency. This study addresses these limitations by proposing a multi-knowledge-enhanced abstractive summarization model tailored for Korean texts. The model integrates internal knowledge, specifically keywords and topics that are extracted using a context-aware BERT-based approach. Unlike traditional statistical extraction methods, our approach utilizes the semantic context to ensure that the internal knowledge is both diverse and representative. By employing a multi-head attention mechanism, the proposed model effectively integrates multiple types of internal knowledge with the original document embeddings. Experimental evaluations on Korean datasets (news and legal texts) demonstrate that our model significantly outperforms baseline methods, achieving notable improvements in lexical overlap, semantic consistency, and structural coherence, as evidenced by higher ROUGE and BERTScore metrics. Furthermore, the method maintains information consistency across diverse categories, including dates, quantities, and organizational details. These findings highlight the potential of context-aware multi-knowledge integration in enhancing Korean abstractive summarization and suggest promising directions for future research into broader knowledge-incorporation strategies. Full article

This study examines adaptation strategies to mitigate the risks posed by Glacial Lake Outburst Floods (GLOFs) in the Hindu Kush Himalayan (HKH) region, encompassing Pakistan, India, Nepal, Bhutan, and Afghanistan. GLOFs occur when water is suddenly released from glacial lakes and they present significant threats to communities, infrastructure, and ecosystems in high-altitude regions, particularly as climate change intensifies their frequencies and severity. While there are many studies on the changes in glacial lakes, studies on adaptation to GLOF risks are scant. Also, these studies tend to focus on case-specific scenarios, leaving a gap in comprehensive, region-wide analyses. This review article aims to fill that gap by synthesizing the adaptation strategies adopted across the HKH region. We conducted a literature review following several inclusion and exclusion criteria and reviewed 23 scholarly sources on GLOF adaptation. We qualitatively synthesized the data and categorized the adaptation strategies into two main types: structural and non-structural. Structural measures include engineering solutions such as lake-level control, channel modifications, and flood defense infrastructure, designed to reduce the physical damage caused by GLOFs. Non-structural measures include community-based practices, economic diversification, awareness programs, and improvements in institutional governance, addressing social and economic vulnerabilities. We found that Afghanistan remains underrepresented in GLOF-related studies, with only one article that specifically focuses on GLOFs, while Nepal and Pakistan receive greater attention in research. The findings underscore the need for a holistic, context-specific approach that integrates both structural and non-structural measures to enhance resilience across the HKH region. Policy-makers should prioritize the development of sustainable mechanisms to support long-term adaptation efforts, foster cross-border collaborations for data sharing and coordinated risk management, and ensure that adaptation strategies are inclusive of vulnerable communities. Practitioners should focus on strengthening early warning systems, expanding community-based adaptation initiatives, and integrating traditional knowledge with modern scientific approaches to enhance local resilience. By adopting a collaborative and regionally coordinated approach, stakeholders can improve GLOF risk preparedness, mitigate socioeconomic impacts, and build long-term resilience in South Asia’s high-altitude regions. Full article

Hand–object occlusion poses a significant challenge in 3D pose estimation. During hand–object interactions, parts of the hand or object are frequently occluded by the other, making it difficult to extract discriminative features for accurate pose estimation. Traditional methods typically extract features for both the hand and object from a single image using a shared backbone network. However, this approach often results in feature contamination, where hand and object features are mixed, especially in occluded regions. To address these issues, we propose a novel 3D hand–object pose estimation framework that explicitly tackles the problem of occlusion through two key innovations. While existing methods rely on a single backbone for feature extraction, our framework introduces a feature decoupling strategy that shares low-level features (using ResNet-50) to capture interaction contexts, while separating high-level features into two independent branches. This design ensures that hand-specific features and object-specific features are processed separately, reducing feature contamination and improving pose estimation accuracy under occlusion. Recognizing the correlation between the hand’s occluded regions and the object’s geometry, we introduce the Hand–Object Cross-Attention Transformer (HOCAT) module. Unlike traditional attention mechanisms that focus solely on feature correlations, the HOCAT leverages the geometric stability of the object as prior knowledge to guide the reconstruction of occluded hand regions. Specifically, the object features (key/value) provide contextual information to enhance the hand features (query), enabling the model to infer the positions of occluded hand joints based on the object’s known structure. This approach significantly improves the model’s ability to handle complex occlusion scenarios. The experimental results demonstrate that our method achieves significant improvements in hand–object pose estimation tasks on publicly available datasets such as HO3D V2 and Dex-YCB. On the HO3D V2 dataset, the PAMPJPE reaches 9.1 mm, the PAMPVPE is 9.0 mm, and the F-score reaches 95.8%. Full article

Background: Distant supervision employs external knowledge bases to automatically match with text, allowing for the automatic annotation of sentences. Although this method effectively tackles the challenge of manual labeling, it inevitably introduces noisy labels. Traditional approaches typically employ sentence-level attention mechanisms, assigning lower weights to noisy sentences to mitigate their impact. But this approach overlooks the critical importance of information flow between sentences. Additionally, previous approaches treated an entire bag as a single classification unit, giving equal importance to all features within the bag. However, they failed to recognize that different dimensions of features have varying levels of significance. Method: To overcome these challenges, this study introduces a novel network that incorporates sentence interaction and a bag-level feature enhancement (ESI-EBF) mechanism. We concatenate sentences within a bag into a continuous context, allowing information to flow freely between them during encoding. At the bag level, we partition the features into multiple groups based on dimensions, assigning an importance coefficient to each sub-feature within a group. This enhances critical features while diminishing the influence of less important ones. In the end, the enhanced features are utilized to construct high-quality bag representations, facilitating more accurate classification by the classification module. Result: The experimental findings from the New York Times (NYT) and Wiki-20m datasets confirm the efficacy of our suggested encoding approach and feature improvement module. Our method also outperforms state-of-the-art techniques on these datasets, achieving superior relation extraction accuracy. Full article

To address the issues of fragmented species information, low knowledge extraction efficiency, and insufficient utilization in the aquaculture domain, the main objective of this study is to construct the first knowledge graph for the Golden Pomfret aquaculture field and optimize the named entity recognition (NER) methods used in the construction process. The dataset contains challenges such as long text processing, strong local context dependencies, and entity sample imbalance, which result in low information extraction efficiency, recognition errors or omissions, and weak model generalization. This paper proposes a novel named entity recognition model, LA-CANER (Local Attention-Category Awareness NER), which combines local attention mechanisms with category awareness to improve both the accuracy and speed of NER. The constructed knowledge graph provides significant scientific knowledge support to Golden Pomfret aquaculture workers. First, by integrating and standardizing multi-source information, the knowledge graph offers comprehensive and accurate data, supporting decision-making for aquaculture management. The graph enables precise reasoning based on disease symptoms, environmental factors, and historical production data, helping workers identify potential risks early and take preventive actions. Furthermore, the knowledge graph can be integrated with large models like GPT-4 and DeepSeek-R1. By providing structured knowledge and rules, the graph enhances the reasoning and decision-making capabilities of these models. This promotes the application of smart aquaculture technologies and enables precision farming, ultimately increasing overall industry efficiency. Full article

Suicide in Italy represents a serious public health problem, with significant data highlighting the urgency for prevention interventions. According to the epidemiological data, in the two-year period 2020–2021, 7422 suicides were recorded, representing an increase compared to previous years. Suicide is the most extreme self-harm. The contributing factors that surround this event are multiple, typically in conditions of serious distress or psychological distress, in particular in people suffering from serious psychiatric and/or mental disorders, such as depression. The role of the forensic pathologist in the context of suicide is crucial for ascertaining the contributing factors of death and for understanding the circumstances that lead to the suicidal act. Forensic medicine plays a crucial role in the analysis and understanding of suicides, addressing both the legal and medical implications. The aim of this study was to accurately describe the phenomenon of suicide in Italy. The accuracy of the review was very important in paying attention to the large difference in how the phenomenon manifests itself in the male population compared to the female population. The different ages at which suicide is committed were highlighted. The geographical difference between the North and the South and between the more urbanized areas compared to the rural areas where suicide is committed was analyzed. This scientific work also aimed to explore how forensic pathologists contribute to the resolution of complex forensic investigations. Psychological autopsy is an investigative method used primarily in cases of violent or questionable death, with the aim of understanding the psychological and social circumstances that led to an individual’s death. This practice is distinct from forensic autopsy, which focuses on the physical analysis of the body to determine the cause of death. The role of forensic pathologists in investigating suicide cases is crucial, as they not only determine the cause of death but also analyze the psychological implications that may have led to the extreme act. The main objective of a forensic pathologist in these cases is to gather and interpret evidence that can help understand the psychological and social context that influenced the decision to commit suicide, identifying any warning signs and underlying motivations and factors that may have contributed to the suicide. This approach provides valuable information for prevention, enhancing the understanding of the psychological mechanisms behind suicide and supporting targeted interventions in the future. The manuscripts also have an explanatory purpose and may have a therapeutic role in helping surviving relatives understand suicide. Knowledge of the messages contained in suicide notes could be useful for suicide prevention programs. Full article

Entity alignment in knowledge graphs plays a crucial role in ensuring the consistency and integration of data across different domains. For example, in power topology, accurate entity matching is essential for optimizing system design and control. However, traditional approaches to entity alignment often rely heavily on language models to extract general features, which can overlook important logical aspects such as temporal and event-centric relationships that are crucial for precise alignment.To address this issue, we propose EAL (Entity Alignment with Logical Capturing), a novel and lightweight RNN-based framework designed to enhance logical feature learning in entity alignment tasks. EAL introduces a logical paradigm learning module that effectively models complex-event relationships, capturing structured and context-aware logical patterns that are essential for alignment. This module encodes logical dependencies between entities to dynamically capture both local and global temporal-event interactions. Additionally, we integrate an adaptive logical attention mechanism that prioritizes influential logical features based on task-specific contexts, ensuring the extracted features are both relevant and discriminative. EAL also incorporates a key feature alignment framework that emphasizes critical event-centric logical structures. This framework employs a hierarchical feature aggregation strategy combining low-level information on temporal events with high-level semantic patterns, enabling robust entity matching while maintaining computational efficiency. By leveraging a multi-stage alignment process, EAL iteratively refines alignment predictions, optimizing both precision and recall. Experimental results on benchmark datasets demonstrate the effectiveness and robustness of EAL, which not only achieves superior performance in entity alignment tasks but also provides a lightweight yet powerful solution that reduces reliance on large language models. Full article