Search Results (72)

Geopolitical instability poses a significant threat to food systems by disrupting production, trade, and market access, thereby undermining both food security and long-term sustainability. Unlike peacetime food insecurity driven by poverty or climate change, conflict-related crises often involve blockades, agricultural destruction, and deliberate famine. This paper conducts a bibliometric review of the academic literature from 2010 to 2024, and partially 2025, to examine how food security and resilience under the influence of conflict have been conceptualized, focusing on their intersections with war, global food systems, and sustainability. We used the Web of Science database and tools such as VOSviewer version 1.6.18, Microsoft Excel and Bibliomagika version 2.10.0, to map thematic clusters, identify influential authors, publishers, and academic partnerships and trace the evolution of scholarly attention on this topic. Our findings reveal a growing recognition of using food as a tool of war, the increasing politicization of food aid, and heightened awareness of the fragility of agricultural systems under conflict. At the same time, significant gaps still persist, particularly in the study of “unconventional” food systems such as black markets and informal supply chains, which often sustain communities during crises but remain underexplored in mainstream scholarship. By identifying these gaps, this review outlines research priorities for developing inclusive and resilient policies, ultimately enhancing the capacity of global food systems to withstand the pressures of conflict and geopolitical instability. Full article

Weakly supervised video anomaly detection (WSVAD) aims to identify unusual events using only video-level labels. However, current methods face several key challenges, including ineffective modelling of complex temporal dependencies, indistinct feature boundaries between visually similar normal and abnormal events, and high false alarm rates caused by an inability to distinguish salient events from complex background noise. This paper proposes a novel method that systematically enhances feature representation and discrimination to address these challenges. The proposed method first builds robust temporal representations by employing a hierarchical multi-scale temporal encoder and a position-aware global relation network to capture both local and long-range dependencies. The core of this method is the dual-stream attention-enhanced memory network, which achieves precise discrimination by learning distinct normal and abnormal patterns via dual memory banks, while utilising bidirectional spatial attention to mitigate background noise and focus on salient events before memory querying. The models underwent a comprehensive evaluation utilising solely RGB features on two demanding public datasets, UCF-Crime and XD-Violence. The experimental findings indicate that the proposed method attains state-of-the-art performance, achieving 87.43% AUC on UCF-Crime and 85.51% AP on XD-Violence. This result demonstrates that the proposed “attention-guided prototype matching” paradigm effectively resolves the aforementioned challenges, enabling robust and precise anomaly detection. Full article

Monocular depth estimation (MDE) is a cornerstone of computer vision and is applied to diverse practical areas such as autonomous vehicles, robotics, etc., yet even the latest methods suffer substantial errors in high-dynamic-range (HDR) scenes where over- or under-exposure erases critical texture. To address this challenge in real-world autonomous driving scenarios, we propose the Exposure-Aware Single-Step Diffusion Framework for Monocular Depth Estimation (EASD). EASD leverages a pre-trained Stable Diffusion variational auto-encoder, freezing its encoder to extract exposure-robust latent RGB and depth representations. A single-step diffusion process then predicts the clean depth latent vector, eliminating iterative error accumulation and enabling real-time inference suitable for autonomous vehicle perception pipelines. To further enhance robustness under extreme lighting conditions, EASD introduces an Exposure-Aware Feature Fusion (EAF) module—an attention-based pyramid that dynamically modulates multi-scale features according to global brightness statistics. This mechanism suppresses bias in saturated regions while restoring detail in under-exposed areas. Furthermore, an Exposure-Balanced Loss (EBL) jointly optimises global depth accuracy, local gradient coherence and reliability in exposure-extreme regions—key metrics for safety-critical perception tasks such as obstacle detection and path planning. Experimental results on NYU-v2, KITTI, and related benchmarks demonstrate that EASD reduces absolute relative error by an average of 20% under extreme illumination, using only 60,000 labelled images. The framework achieves real-time performance (<50 ms per frame) and strikes a superior balance between accuracy, computational efficiency, and data efficiency, offering a promising solution for robust monocular depth estimation in challenging automotive lighting conditions such as tunnel transitions, night driving and sun glare. Full article

Knowledge Graph Reasoning (KGR) aims to deduce missing or novel knowledge by learning structured information and semantic relationships within Knowledge Graphs (KGs). Despite significant advances achieved by deep neural networks in recent years, existing models typically extract non-linear representations from explicit features in a relatively simplistic manner and fail to fully exploit semantic heterogeneity of relation types and entity co-occurrence frequencies. Consequently, these models struggle to capture critical predictive cues embedded in various entities and relations. To address these limitations, this paper proposes a relation aware spectral decoupling attention network for KGR (RASD). First, a spectral decoupling attention network module projects joint embeddings of entities and relations into the frequency domain, extracting features across different frequency bands and adaptively allocating attention at the global level to model frequency specific information. Next, a relation-aware learning module employs relation aware filters and an augmentation mechanism to preserve distinct relational properties and suppress redundant features, thereby enhancing representation of heterogeneous relations. Experimental results demonstrate that RASD achieves significant and consistent improvements over multiple leading baseline models on link prediction tasks across five public benchmark datasets. Full article

Self-medication is a subject of global importance. If practiced responsibly, self-medication represents a part of self-care or positive care of an individual or a community in promoting their own health. However, today’s practices of self-medication are often inappropriate and irresponsible, and as such appear all over the world. Inappropriate self-medication can be connected with possible serious health risks and consequences. Therefore, it represents a global health issue. It can even generate additional health problems, which will eventually become a burden to healthcare systems and can induce significant costs, which also raises socioeconomic concerns. Hence, self-medication attracts the attention of researchers and practitioners globally in efforts to clarify the current status and define feasible measures that should be implemented to address this issue. This narrative review aims to give an overview of the situation in the field of self-medication globally, including current practices and attitudes, as well as implications for actions needed to improve this problem. A PubMed/MEDLINE search was conducted for articles published in the period from 1995 up to March 2025 using keywords “self-medication” or “selfmedication” alone or in combinations with terms related to specific subthemes related to self-medication, such as COVID-19, antimicrobials, healthcare professionals, and storing habits of medicines at home. Studies were included if self-medication was their main focus. Publications that only mentioned self-medication in different contexts, but not as their main focus, were excluded. Considering the outcomes of research on self-medication in various contexts, increasing awareness of responsible self-medication through education and informing, together with surveillance of particular medicines and populations, could lead to more appropriate and beneficial self-medication in the future. Full article

Background: Mental health issues among medical students have gained increasing attention globally, with studies indicating a high prevalence of psychological disorders within this population. The use of antidepressants and anti-anxiety medications has become a common response to these mental health challenges. However, it is crucial to understand the extent of their usage and associated effects on students’ mental health and academic performance. This cross-sectional study explored the use of antidepressants and anti-anxiety drugs and their impact on the mental health of medical students in Saudi Arabia. Methods: A cross-sectional survey of 561 medical students from 34 universities was conducted between March and July 2024. An anonymous online questionnaire was used to collect sociodemographic, mental health, and medication usage-related information. Results: Most of the participants were female (71.5%) and aged 21–25 years (62.7%). Approximately 23.8% of them used antidepressants, 5.6% reported using anti-anxiety medications, and 14.0% used both types of medication. Among the medication users, 71.7% were using selective serotonin reuptake inhibitors (SSRIs), and 28.3% were using other medications. Adverse drug reactions were reported by 58.8% of the participants, and 39.6% changed drugs with inadequate efficacy. Notably, 49.0% of the respondents who have ever used medications discontinued their medication without consulting a healthcare professional. Despite these challenges, 62.0% of the participants felt that their medications had a positive impact on their academic performance, 73.4% believed that the benefits outweighed the drawbacks, and 76.2% expressed a willingness to continue taking their medication. In particular, 77.6% agreed that treatment with these drugs could prevent mental breakdowns. Sleep duration, physical activity, and family history of psychiatric disorders were significantly associated with medication use, with p values of 0.002, 0.014, and 0.042, respectively. Conclusions: These results shed light on the need to understand the prescribing practices of antidepressant and anti-anxiety drugs among medical students while promoting the appropriate use of these medications among the students. There is a need to incorporate mental health interventions into counseling services and awareness programs to support students. Future longitudinal studies are needed to explore long-term trends. Full article

As Advanced Persistent Threats (APTs) continue to evolve, constructing a dynamic cybersecurity knowledge graph requires precise extraction of entity–relationship triples from unstructured threat intelligence. Existing approaches, however, face significant challenges in modeling low-frequency threat associations, extracting multi-relational entities, and resolving overlapping entity scenarios. To overcome these limitations, we propose the Symmetry-Aware Prototype Contrastive Learning (SAPCL) framework for joint entity and relation extraction. By explicitly modeling syntactic symmetry in attack-chain dependency structures and its interaction with asymmetric adversarial semantics, SAPCL integrates dependency relation types with contextual features using a type-enhanced Graph Attention Network. This symmetry–asymmetry fusion facilitates a more effective extraction of multi-relational triples. Furthermore, we introduce a triple prototype contrastive learning mechanism that enhances the robustness of low-frequency relations through hierarchical semantic alignment and adaptive prototype updates. A non-autoregressive decoding architecture is also employed to globally generate multi-relational triples while mitigating semantic ambiguities. SAPCL was evaluated on three publicly available CTI datasets: HACKER, ACTI, and LADDER. It achieved F1-scores of 56.63%, 60.21%, and 53.65%, respectively. Notably, SAPCL demonstrated a substantial improvement of 14.5 percentage points on the HACKER dataset, validating its effectiveness in real-world cyber threat extraction scenarios. By synergizing syntactic–semantic multi-feature fusion with symmetry-driven dynamic representation learning, SAPCL establishes a symmetry–asymmetry adaptive paradigm for cybersecurity knowledge graph construction, thus enhancing APT attack tracing, threat hunting, and proactive cyber defense. Full article

Background/Objectives: Alzheimer’s disease (AD) is the leading cause of dementia and is characterized by progressive neurodegeneration, resulting in cognitive impairment and structural brain changes. Although no curative treatment exists, pharmacological therapies like cholinesterase inhibitors and NMDA receptor antagonists may deliver symptomatic relief and modestly delay disease progression. Structural magnetic resonance imaging (sMRI) is a commonly utilized modality for the diagnosis of brain neurological diseases and may indicate abnormalities. However, improving the recognition of discriminative characteristics is the primary difficulty in diagnosis utilizing sMRI. Methods: To tackle this problem, the Fuzzy Optimized Attention Network with Multi-Instance Deep Learning (FOA-MIDL) system is presented for the prodromal phase of mild cognitive impairment (MCI) and the initial detection of AD. Results: An attention technique to estimate the weight of every case is presented: the fuzzy salp swarm algorithm (FSSA). The swarming actions of salps in oceans serve as the inspiration for the FSSA. When moving, the nutrient gradients influence the movement of leading salps during global search exploration, while the followers fully explore their local environment to adjust the classifiers’ parameters. To balance the relative contributions of every patch and produce a global distinct weighted image for the entire brain framework, the attention multi-instance learning (MIL) pooling procedure is developed. Attention-aware global classifiers are presented to improve the understanding of the integral characteristics and form judgments for AD-related categorization. The Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the Australian Imaging, Biomarker, and Lifestyle Flagship Study on Ageing (AIBL) provided the two datasets (ADNI and AIBL) utilized in this work. Conclusions: Compared to many cutting-edge techniques, the findings demonstrate that the FOA-MIDL system may determine discriminative pathological areas and offer improved classification efficacy in terms of sensitivity (SEN), specificity (SPE), and accuracy. Full article

Knowledge graph-enhanced recommendation has gained increasing attention for its ability to provide structured semantic context. However, most existing approaches struggle with two critical challenges: the sparsity of long-tail relations in knowledge graphs and the lack of adaptability to users’ dynamic preferences. In this paper, we propose GLARA, a novel recommendation framework that combines semantic abstraction and behavioral adaptation through a two-stage modeling process. First, a Virtual Relational Knowledge Graph (VRKG) is constructed by clustering semantically similar relations into higher-level virtual groups, which alleviates relation sparsity and enhances generalization. Then, a global Local Weighted Smoothing (LWS) module and a local Graph Attention Network (GAT) are integrated to jointly refine item and user representations: LWS propagates information within each virtual relation subgraph to improve semantic consistency, while GAT dynamically adjusts neighbor importance based on recent interaction signals. Extensive experiments on Last.FM and MovieLens-1M demonstrate that GLARA outperforms state-of-the-art methods, achieving up to 5.8% improvements in NDCG@20, especially in long-tail and cold-start scenarios. Additionally, case studies confirm the model’s interpretability by tracing recommendation paths through clustered semantic relations. This work offers a flexible and interpretable solution for robust recommendation under sparse and dynamic conditions. Full article

(1) Background: With the deep integration of e-commerce and video technology, live-streaming marketing has emerged globally and maintained rapid growth. However, most of the current research on live-streaming e-commerce marketing focuses on merchants’ sales strategies and consumers’ purchase intentions, and there is relatively little research related to the risks of live-streaming e-commerce marketing. Nevertheless, with the development of live-streaming e-commerce marketing and its integration with technologies such as artificial intelligence and virtual reality (VR), live-streaming e-commerce marketing still faces challenges such as unclear subject responsibility, difficulty in verifying the authenticity of marketing information, and uneven product quality. It also harbors problems such as the ethical misbehavior of AI anchors and the excessive beautification of products by VR technology. (2) Methods: This study systematically analyzes the scenarios of live-streaming marketing to elucidate the mechanisms of risk formation. Utilizing fault tree analysis (FTA) and risk checklist methods, risks are identified based on the three core elements of live-streaming marketing: “people–products–scenes”. Subsequently, the Delphi method is employed to refine the initial risk indicator system, resulting in the construction of a comprehensive risk indicator system comprising three first-level indicators, six second-level indicators, and 16 third-level indicators. A hesitant fuzzy multi-attribute group decision-making method (HFMGDM) is then applied to calculate the weights of the risk indicators and comprehensively assess the live-streaming marketing risks in live broadcast rooms of three prominent celebrity anchors in China. Furthermore, a detailed analysis is conducted on the risks associated with the six secondary indicators. Based on the risk evaluation results, targeted recommendations are proposed. This study aims to enhance consumers’ awareness of risk prevention when conducting live-streaming transactions and pay attention to related risks, thereby safeguarding consumer rights and fostering the healthy and sustainable development of the live-streaming marketing industry. (3) Conclusions: The results show that the top five risk indicators in terms of weight ranking are: Ethical Risk of the AI Anchor (A4), VR Technology Promotion Risk (F3), Anchor Reputation (A1), Product Quality (D1), and Logistics Distribution Service Quality (D2). The comprehensive live-streaming marketing risk of each live broadcast room is Y > L > D. Based on the analysis results, targeted recommendations are provided for anchors, MCN institutions, merchants, supply chains, and live-streaming platforms to improve consumer satisfaction and promote sustainable development of the live-streaming marketing industry. Full article

Background: Dementia is a global condition affecting over 55 million people. Since there is no treatment, non-pharmacological interventions aim to delay its progression in a safe and cost-effective way. The extant literature suggests that Positive Psychology Interventions (PPIs) can probably be effective for this purpose. The systematic review aims to assess the effectiveness of PPIs as non-pharmacological interventions for mild cognitive decline related to dementia by evaluating their effectiveness in cognitive functions and brain functioning in people with Subjective Cognitive Decline (SCD), Mild Cognitive Impairment (MCI), and mild Alzheimer’s disease dementia (AD). Methods: A comprehensive search conducted in the databases Scopus, PubMed, ScienceDirect and PsychINFO (December 2024–March 2025) published between 2015 and 2025 to identify records that met inclusion criteria: studies included patients with SCD, MCI and mild AD dementia, implemented PPIs, Randomized controlled trials (RCTs) and pre–post intervention studies with measurable outcomes, assess at least one of the following: cognitive functions and brain functioning. Results: The systematic review included 12 studies (N = 669 participants) that can answer the research question. Only mindfulness interventions were identified. Findings suggest that different types of mindfulness interventions, such as the Mindfulness Awareness Program (MAP) and Mindfulness Training (MT), may be efficient for improving specific cognitive functions (e.g., working memory and attention) and influencing biological pathways related to cognitive decline. However, long-term efficacy has not been demonstrated, and results are mixed and unclear. Conclusions: Μindfulness interventions seem promising for enhancing cognition and brain functioning in older adults with cognitive decline, although the data is limited. However, limitations such as the heterogeneity of the studies and the diversity of the interventions make it necessary for more systematic and organized research to be conducted on the implementation of such interventions. At the same time, it is proposed to examine the effectiveness of other constructs of positive psychology, such as character strengths (CS). Full article

Background/Objective: Besides the common risks associated with agriculture, recently, there has been growing concern about the impact of agriculture on farmers’ mental health, due to high stress levels, depression, anxiety, and increasing rates of suicide, especially complex considering that many of these farmers are older people. The potential of the practice of mindfulness to minimize mental health problems and improve people’s sense of well-being has been studied in recent decades, although there is a dearth of literature related to farmer populations. This study aimed to correlate the presence of mindfulness traits with general life quality and well-being and assess the levels of mindfulness and life satisfaction among family farmers, as well as to evaluate which characteristics might be associated with them. Method: The sample was composed of 30 farmers from the region of Viseu—Portugal, who were randomly selected for a survey consisting of an adaptation of the Mindful Attention Awareness Scale (MAAS) and the Satisfaction with Life Scale (SWLS), with some new items specific to the context of agriculture. A proposed Farmers’ Mindfulness and Life Satisfaction Scale (FMLSS) was validated through factor analysis and internal reliability analysis. Result: The results showed a relatively high average score for the 10 items of the mindfulness scale (4.23 ± 0.56) and the global sum of scores for the 5 items of the life satisfaction scale (26.67 ± 4.76). Factor analysis revealed six factors, globally explaining 77% of the variance, with values of alpha varying from 0.640 to 0.874. The FMLSS was validated with 19 items of the 20 initially considered (α = 0.672). Cluster analysis revealed two typologies of participants, “Pleased” and “Accommodated” family farmers. These two clusters had global values for the FMLSS of 5.19 ± 0.51 and 4.37 ± 0.59, with the higher value obtained for the “Pleased” family farmers, who were mostly of male gender and worked more hours per week and whose agricultural activities had higher significance for their family income. Conclusions: Overall, we observed a relatively high level of mindfulness and satisfaction with life among family farmers. This suggests the importance of future research on mental health among family farmers. Full article

Gait recognition, a long-range biometric technique based on walking posture, the fact that they do not require the cooperation of the subject and are non-invasive has made them highly sought after in recent years.Although existing methods have achieved impressive results in laboratory environments, the recognition performance is still deficient in real-world applications, especially when confronted with complex and dynamic scenarios. The major challenges in gait recognition include changes in viewing angle, occlusion, clothing changes, and significant differences in gait characteristics under different walking conditions. To slove these issues, we propose a gait recognition method based on relational-aware global attention. Specifically, we introduce a Relational-aware Global Attention (RGA) module, which captures global structural information within gait sequences to enable more precise attention learning. Unlike traditional gait recognition methods that rely solely on local convolutions, we stack pairwise associations between each feature position in the gait silhouette and all other feature positions, along with the features themselves, using a shallow convolutional model to learn attention. This approach is particularly effective in gait recognition due to the physical constraints on human walking postures, allowing the structural information embedded in the global relationships to aid in inferring the semantics and focus areas of various body parts, thereby improving the differentiation of gait features across individuals. Our experimental results on multiple datasets (Grew, Gait3D, SUSTech1k) demonstrate that GaitRGA achieves significant performance improvements, especially in real-world scenarios. Full article

Accurate forecasting of multivariate traffic flow poses formidable challenges, primarily due to the ever-evolving spatio-temporal dynamics and intricate spatial heterogeneity, where the heterogeneity signifies that the correlations among locations are not just related to distance. However, few of the existing models are designed to fully and effectively integrate the above-mentioned features. To address these complexities head-on, this paper introduces a novel solution in the form of Dynamic Pattern-aware Spatio-Temporal Convolutional Networks (DPSTCN). Temporally, the model introduces a novel temporal module, containing a temporal convolutional network (TCN) enriched with an enhanced pattern-aware self-attention mechanism, adept at capturing temporal patterns, including local/global dependencies, dynamics, and periodicity. Spatially, the model constructs static and dynamic pattern-aware convolutions, leveraging geographical and area-functional information to effectively capture intricate spatial patterns, including dynamics and heterogeneity. Evaluations across four distinct traffic benchmark datasets consistently demonstrate the state-of-the-art capacity of our model compared to the existing eleven approaches, especially great improvements in RMSE (Root Mean Squared Error) value. Full article

Background: The misuse of antibiotics in both humans and food-producing animals poses significant risks to human health and contributes to the rise of antibiotic resistance. Raising public awareness is crucial to managing antibiotic resistance, particularly among university students, as they represent a future force in tackling this global issue. Methods: A cross-sectional study was conducted from July 2022 to May 2024 in seven cities in Southern and Central China to assess university students’ knowledge, attitude, and practice regarding antibiotic use in humans and food-producing animals. Binary logistic regression was used to identify associated factors. Results: A total of 6357 students from 72 universities participated. Less than half of the students answered the knowledge items appropriately. Only 21.47% to 29.98% had a proper understanding of basic antibiotic concepts and their use in humans and food-producing animals. Respectively, 21.49% and 28.50% students paid attention to antibiotic content in food from food-producing animals and refused to buy food containing antibiotics. Factors associated with higher knowledge, attitude, and practice total scores included being male, being of older age, having a postgraduate education, majoring in the medical science discipline, studying at a double-first-class university, having a higher family monthly income, having parents in the medical area, and using antibiotics in the past year (p < 0.001). Conclusions: Given students’ insufficient knowledge—particularly in identifying antibiotics and understanding their functions—and inappropriate practices related to purchasing food from food-producing animals, targeted education programs are suggested. These programs should address the fundamental concepts of antibiotic use in both humans and food-producing animals while providing practical guidance on individual behaviors to help mitigate antibiotic resistance. Full article