Search Results (21,297)

Existing methods for identifying Tactics, Techniques, and Procedures (TTPs) from complex cyber-attack descriptions face three core challenges: (1) severe semantic asymmetry between unstructured attack narratives and standardized TTP definitions; (2) continuously distributed semantic relations that cannot be fully captured by hard-label supervision; and (3) an open, long-tailed TTP taxonomy that impairs model generalization. To address these limitations, we introduce DTGBI-TM, a lightweight dual-tower semantic matching framework that integrates soft-label supervision, hierarchical hard-negative sampling, and gated binary interaction modeling. The model separately encodes attack descriptions and TTP definitions and employs a gated interaction module to adaptively fuse shared and divergent semantics, enabling fine-grained asymmetric alignment. A confidence-guided soft–hard collaborative supervision mechanism unifies weighted classification, semantic regression, and contrastive consistency into a multi-objective loss, dynamically rebalancing gradients to mitigate long-tail effects. Leveraging ATT & CK hierarchical priors, the model further performs in-tactic and cross-tactic hard-negative sampling to enhance semantic discrimination. Experiments on a real-world corpus demonstrate that DTGBI-TM achieves 98.53% F1 in semantic modeling and 79.77% Top-1 accuracy in open-set TTP prediction, while maintaining high inference efficiency and scalability in deployment. Full article

Background/Objectives: The relationship between household tobacco expenditure and child health has attracted considerable attention from both academic and policy communities, as tobacco expenditure can influence children’s health, nutrition, and overall well-being in multiple ways, particularly in rural and low-income settings. This study examines the causal impact of household tobacco expenditure on child health outcomes in a transitional economy. Methods: Using nationally representative microdata from the most recent Household Living Standards Survey, the authors employ Ordinary Least Squares (OLS), Random Effects (RE), and Instrumental Variable (IV) estimations to identify the effects of tobacco spending on children’s healthcare utilization and health status. Results: The results consistently show that higher household tobacco expenditure significantly increases the likelihood of hospitalization among Vietnamese children, with the effects being most pronounced for those under six years of age. Moreover, the authors uncover substantial heterogeneity across gender, maternal age at childbirth, and regional contexts, highlighting persistent socioeconomic inequalities in health outcomes. Conclusions: This study provides compelling evidence of the adverse effects of household tobacco expenditure on children’s health in Vietnam. Theoretically, the study contributes to the literature on the economics of health and intra-household resource allocation by providing micro-level causal evidence from a transitional setting. From a policy perspective, the findings underscore the need for targeted fiscal and public health interventions to mitigate tobacco-related welfare losses and to promote equitable access to healthcare among vulnerable populations. Full article

This study aimed to understand the contributing factors to cohesion in women’s refugee resettlement networks in Northern Ireland and the Republic of Ireland. In 2022, forty women across four networks participated in semi-structured, in-depth interviews in an exploratory study into a social phenomenon of community-building with refugee women resettling on the island of Ireland. We identified four themes through reflexive thematic analysis. Motherhood and Strength of Relationships related to the networks’ interactions and effect on members’ lives, whereas Leadership Dynamics and Goal Setting and Problem Solving related to the networks’ structure and practices. This study offers evidence of how members viewed their networks as having a transformative power in the process of resettling their lives in Ireland for themselves and their families. Full article

Many countries around the world require some or all schools to develop and implement a school improvement plan (SIP), which is a tool intended to guide the identification of school-specific needs for improvement along with a series of priorities, goals, and actions to help address those needs. Yet, the literature on this topic remains rather sparse. In this article, we conducted a systematic review of the international literature on SIPs published from 2010 through 2025, identifying 62 relevant articles for analysis. We organized this review’s findings around six areas related to SIP development and implementation: assessing current conditions, determining needs, setting direction, organizing resources, taking action, and evaluating progress. Findings suggest that while divergences exist between contexts with respect to these six areas, there are considerable convergences in how educators and others conceptualize and interact with SIPs. We close with recommendations for future research that both strengthens and broadens the extant literature on SIPs. Full article

The advent of digital transformation, social learning, and the increasing use of artificial intelligence is driving requisite changes in the development of data centres, which are buildings designed to process and store data. Green innovation is an integral component of the sustainable development of data centre units. Solutions utilising green and blue infrastructure in data centres are being currently introduced with the objective of optimising energy consumption and reducing energy demand. The primary aim of the research is to analyse the utilisation of biomass production and blue–green infrastructure in data centres. The article provides a consolidated set of key performance indicators (KPIs): energy efficiency, water use, waste heat utilisation, renewable energy integration, hourly carbon-free matching, embodied carbon, and land use impacts, that can be used to compare different data centre designs. Traditional PUE-centric evaluations are broadened by added metrics such as biodiversity/green area, intensity, and 24/7 CFE, reflecting the broader, multi-dimensional sustainability challenges highlighted in the current literature. Twelve international case studies described in the literature were compared and the feasibility of the Polish pilot project in Michalowo was assessed to illustrate specific cases related to energy-saving solutions and the use of renewable energy sources in data centres. Full article

This article presents research on developing a synthetic measure to assess the readiness of individual EU countries to store energy from renewable energy sources. The authors developed individual measures that describe both the technical aspects of energy storage and the systemic and strategic aspects related to energy security and energy transition. These measures enabled the development of a synthetic measure, the Energy Storage Readiness Index (ESRI-BESS), and scenarios for the development of energy storage facilities in the European Union. TOPSIS and Monte Carlo methods were used. In the research presented, the authors focused their analyses on how the system interacts with storage facilities, rather than on what is installed. A quantitative set of indicators was constructed, embedded in the 4A energy security model. The resulting indicator measures not only whether storage facilities exist but also whether the system is prepared to ensure the country’s energy security. The results obtained indicate the need to build a flexible regulatory framework adapted to the growing role of storage facilities, that is, to facilitate and accelerate the process of connecting storage facilities to the grid. In the context of 4A, it is important to note that energy storage facilities can strengthen all four pillars of energy security when infrastructure development is paralleled by reforms and grid integration. The ability to store and flexibly manage energy is becoming a new dimension of energy transformation. Full article

In high-mountain canyon areas, complex geological environments lead to frequent cascading disasters with unclear triggering mechanisms, posing severe threats to human life and property. Existing knowledge graph research in geology predominantly focuses on single-hazard types or general geological entities, lacking structured modeling and specialized datasets for cascading disaster processes, particularly the evolutionary chains in high-mountain canyon settings. To address this gap, this study proposes a method for constructing a knowledge graph tailored to cascading disasters in high-mountain canyon regions. First, a three-layer schema framework—comprising concept, relation, and instance layers—was designed to systematically characterize the knowledge elements and evolutionary relationships of disaster chains. To address the lack of a knowledge dataset for cascade disasters, this paper integrates multi-source heterogeneous data to construct a high-mountain canyon cascading disasters entity–relation dataset (DCER-MC), providing a reliable benchmark for related tasks. Based on this dataset, we implemented the knowledge graph and conducted disaster chain analysis. Experiments and applications demonstrate that the constructed knowledge graph effectively supports structured storage, centralized management, and scenario-based application of regional cascading disaster information. The main contributions of this work are (1) proposing a targeted schema framework for cascading-disaster knowledge graphs; (2) releasing a specialized dataset for cascading disasters in high-mountain canyon regions; and (3) establishing a complete pipeline from data to knowledge to scenario-based services, offering a novel knowledge-driven paradigm for disaster chain risk identification, inference prediction, and emergency decision-making in these areas. Full article

Bolter miners have been widely used in coal mining or excavation industries. Its efficiency is closely related to the performance of its cutting reducer, which is literally determined by the thermal behavior of the planetary gear set. Thus, this study conducts experimental investigation on the thermal behavior of a cutting reducer (produced by Zhengzhou Machinery Research Institute Transmission Technology Co., Ltd., rated input power 170 kW, transmission ratio 3.06), where the results show the high temperature rise around the intermediate shaft for unloaded condition and significant influence of the torque for loaded conditions. Then, the Finite Element Method (FEM) is used to analyze the temperature field and thermal–structural coupling of the planetary gear set. The thermal stress and deformation increase by 11.5% and 38.4%, respectively, indicating high risk of gear damage. Moreover, the load spectrum imitating the actual industrial condition is added to the KISSsoft to evaluate the reliability and contact of the planetary gear set. The findings including low safety factors of the sun gear tooth surface and planetary gear root, slipping during the sun gear and planetary gear meshing, and uneven contact fluctuations can benefit planetary gear set optimization. Full article

Integrating environmental, social, and governance (ESG) reporting with the Sustainable Development Goals (SDGs) is important for achieving corporate sustainability. The rapid evolution of regulations like the Corporate Sustainability Reporting Directive (CSRD), and the fragmented research landscape create uncertainty for strategic planning. This paper addresses the critical gap related to the lack of predictive data into future research trends at the ESG-SDG nexus. The research begins with a bibliometric analysis using two software programs R-Biblioshiny 5.2.0 and VOSviewer 1.6.20, to process data extracted from the Web of Science (Clarivate). Selected key terms regarding sustainability reporting concepts and reporting standards, as well as the engagements of auditors were used to filter the database information. Starting from the bibliometric analysis of 361 publications completed during January 2015–September 2025, the study performs further a quantitative measurement bibliometrics using RStudio 4.5.2 and provides a novel ensemble forecasting model (AutoRegressive Integrated Moving Average, Error, Trend, Seasonal Components, and Linear regression with SDG factors) that cartograph the alignment of the current research field and forecast its evolution. The results reveal that terms regarding reporting “CSRD” and sustainability assurance, “ISSA 5000” are the most dominant research fronts, strongly aligned with SDG 12, 13 and 17. The forecasting model predicts sustained growth in this area. The study contributes by providing a forward-thinking strategic map for researchers, policymakers and businesses, transforming sustainability integration from a compliance task into systematic, data-driven approach for priority setting strategy. Full article

Aging and institutionalization, associated with functional and psychological decline, justify studying how physical performance is linked to mental health in older adults. Objectives: To analyze the relationship between physical performance and anxiety, depression, sleep quality, and perceived stress in institutionalized older adults. Methods: Multicenter, observational, cross-sectional study in eight nursing homes (N = 105; ≥65 years, M = 80.78 SD ± 7.91). Instruments: SPPB, HADS, PSQI, PSS-10. Descriptives and exploratory bivariate tests. Primary analysis: single multivariable linear regression with SPPB as outcome and HADS-A, HADS-D, PSQI, PSS-10 entered simultaneously, adjusted for age and sex. Robustness: GLM with robust SEs, influence sensitivity excluding Cook’s D > 4/n or leverage > 2 p/n, and a proportional-odds model for SPPB. All statistical tests were two-sided, with α set at 0.05. Results: Mean SPPB was 6.94 ± 3.17; 77.1% of participants showed poor physical performance. Bivariate: All mental health constructs showed significant associations with physical performance. Multivariable model: adjusted R² = 0.198; F (6,98) = 5.28, p < 0.001. Depression B = −0.230 (95% CI −0.398 to −0.061), p = 0.008; sleep quality (higher = worse) B = −0.187 (95% CI −0.351 to −0.024), p = 0.025; age B = −0.087 (95% CI −0.158 to −0.017), p = 0.016. Anxiety showed a positive adjusted association B = +0.224 (95% CI 0.038 to 0.410), p = 0.019 (consistent with suppression); perceived stress B = −0.062, p = 0.275; sex B = −0.144, p = 0.812. Robust SEs left inferences unchanged. Influence sensitivity (n = 97) preserved directions with the PSQI association attenuating to non-significance. Ordinal results were directionally consistent. Conclusions: After adjusting for age and sex, depression and sleep quality independently relate to physical function, while age is inversely associated; anxiety and perceived stress show no independent effects. Full article

Background/Objectives: Population aging requires scalable approaches for early identification of cognitive decline, particularly mild cognitive impairment (MCI). Although the full 11-task BrainOK smartphone assessment showed excellent discrimination (AUC = 0.941), its administration time constrains large-scale use. To develop and validate a brief Short-Form BrainOK (SF-BrainOK) that preserves diagnostic performance while substantially reducing testing time. Methods: We enrolled 168 community-dwelling older adults (≥60 years). MCI was defined using the Montreal Cognitive Assessment (MoCA; score ≤ 23) as the reference standard. Candidate tasks were selected from the original BrainOK via LASSO-based preselection. To maximize data utilization given the limited sample size, model performance was evaluated using Leave-One-Out Cross-Validation (LOOCV). The cut-off value was determined by maximizing Youden’s J. Results: The final two-task model combined executive function task (Rule-based Drumming I) and memory task (Password Memory I). On the independent test set, discrimination was robust (AUC = 0.783), with sensitivity = 0.75 (95% CI: 0.63–0.85, specificity = 0.71 (95% CI: 0.62–0.80, and accuracy = 0.765 (95% CI: 0.65–0.79) at the optimal cutoff. Conclusions: SF-BrainOK provides a brief, two-task digital screen that markedly reduces administration time while maintaining effective diagnostic performance. By targeting executive function and memory—domains repeatedly shown to be sensitive to early MCI-related change—SF-BrainOK supports scalable, opportunistic screening and the timely identification of at-risk individuals in resource-constrained settings. Full article

Background: Emotions play a central role in how employees respond to workplace bullying, influencing both their well-being and organizational outcomes. The purpose of the current study was to examine how workplace bullying and turnover intention are related to negative emotions and workplace unfairness. Methods: The research involved collecting data from 269 boundary-spanning bank workers (call center workers, frontline office staff, and customer service representatives) who experienced bullying. A moderated mediation was tested using Model 7 of the Process macro. The relationship between workplace bullying and turnover intention was analyzed, emphasizing the moderating effect of workplace unfairness and the mediating role of negative emotions. Results: The results validated the model, showing that an increase in negative emotions and workplace unfairness promotes the link between workplace bullying and the intention to leave. Increased negative emotions and perceived workplace unfairness amplified the relationship between workplace bullying and turnover intention. Conclusions: The findings underscored the cumulative risk of bullying environments for employee well-being and retention, providing practical recommendations for HRM and leadership strategies to cultivate healthier, more inclusive workplace settings. This study adds to the bullying–turnover literature by examining the joint role of negative emotions and workplace unfairness in a moderated mediation framework. The study connects these findings to sustainable labor management, emphasizing both theoretical and practical implications for organizations. Full article

Tectonic deformation can substantially change the pore characteristics and the resulting methane adsorption capacity of shales; thus, it strongly influences shale gas exploration and development in structurally complex areas of southern China. Two sets of shales with identical lithofacies that were derived from either structurally stable or deformed regions were collected at Fuling Field to evaluate the response of their pore properties and methane adsorption behavior to tectonic deformation through field emission scanning electron microscopy (FE-SEM), low-pressure gas (CO₂/N₂) adsorption, and high-pressure methane adsorption experiments. Three primary shale lithofacies were identified in each set of shales: organic-lean (OL) siliceous-rich argillaceous (CM-1) shale lithofacies, organic-moderate (OM) argillaceous/siliceous mixed (M-2) shale lithofacies, and organic-rich (OR) argillaceous-rich siliceous (S-3) shale lithofacies. In the stable region, organic matter (OM) pores dominated the pore types of OR S-3 shales, whereas the primary pore types of OL CM-1 shales were clay cleavage micro-fractures. OM M-2 shales exhibited a composite type of OM pores and clay cleavage micro-fractures. Compared with structurally stable shales, the original OM-hosted and clay-related pores in structurally deformed shales were extensively compacted or even closed due to tectonic compression during structural deformation. Despite pore collapse, two new types of tectonic micro-fractures were generated and found to be well developed in deformed shales through the rupture of brittle minerals in OR S-3 shales and the deformation of clay minerals in OL CM-1 shales. Simultaneously, organic matter–clay aggregates that formed during tectonic compression constituted a distinctive structure in deformed OM M-2 shales. As a result, the deformed shales displayed a decrease in their micropore and mesopore volumes, as well as a decrease in their pore surface areas, because of strong tectonic compression accompanied by an increase in the macropore volume due to the development of tectonic micro-fractures. Furthermore, the large pore surface areas in structurally stable shales could supply abundant adsorption sites and facilitate the enrichment of adsorbed gas. The expanded macropore volumes in structurally deformed shales could provide more storage spaces that are favorable for the accumulation of free gas. Full article

Background/Objectives: Pregnancy represents a unique physiological state marked by extensive metabolic adaptations, particularly in lipid pathways essential for maternal adjustments, fetal development, and postpartum recovery. This study aimed to explore these changes through untargeted lipidomic profiling. Methods: This observational, comparative, non-interventional clinical study included 107 women, of which 65 were in the third trimester of pregnancy (mean age 27.9 ± 5 years) and 42 were in the early postpartum period (≤7 days, mean age 28.9 ± 5.9 years). Inclusion criteria were singleton, term pregnancies (37–41 weeks) with neonates weighing > 2500 g and no associated pregnancy-related pathologies; exclusion criteria included multiple gestation, use of lipid-altering medications, maternal age > 40 years, or diagnosed pregnancy complications. Plasma samples were analyzed using High-Performance Liquid Chromatography–Quadrupole Time-Of-Flight–Electrospray Ionization (positive mode)–Mass Spectrometry, data were processed with MetaboAnalyst 6.0 using multivariate and univariate analyses (Partial Least Squares–Discriminant Analysis, Volcano Plot, Random Forest, Receiver Operating Characteristic analysis), with statistical significance set at p < 0.05. Results: Multivariate analysis demonstrated a clear separation between groups with high predictive accuracy as reflected by strong classification metrics (Accuracy = 0.90, R² = 0.75, Q² = 0.68). Several discriminative lipids were consistently identified across statistical models, including 2-Methoxyestrone (AUC = 0.861), Eicosanedioic acid (AUC = 0.854), and Pregnenolone sulfate (AUC = 0.843). These biomarkers were further categorized into five major lipid classes: steroid hormones, long-chain fatty acids, lysophospholipids, ceramides/sphingolipids, and glycerolipids. Conclusions: Untargeted lipidomic profiling revealed distinct metabolic signatures that differentiate late pregnancy from early post-partum states. The identification of robust lipid biomarkers with high discriminative performance highlights their potential utility in maternal health monitoring, obstetric risk assessment, and postpartum recovery surveillance. Full article

Early age of onset is a major predictor of poor disease course in Bipolar Disorder (BD) and Schizophrenia (SCZ), often associated with greater symptom severity, cognitive decline, and worse outcomes. However, the biological mechanisms that shape age- and sex-specific vulnerability remain unclear, limiting progress toward early identification and intervention. To address this gap, we conducted an integrative transcriptomic study of 369 postmortem dorsolateral prefrontal cortex samples from the CommonMind Consortium. Differential gene expression, Weighted Gene Co-Expression Network Analysis, and gene set enrichment analysis were applied to identify pathways associated with age of onset, complemented by sex-stratified models and cellular deconvolution. To assess predictive signals, we applied a rigorous two-stage machine-learning framework using nested cross-validation, with Lasso feature selection followed by L2-regularized logistic classification. Performance was evaluated solely on held-out test folds. Genes and modules linked to earlier onset showed consistent enrichment for calcium signaling, with downregulation of CACNA1C and multiple adenylate-cyclase-related transcripts, while female-specific analyses revealed selective dysregulation of cyclase-associated pathways. Network analysis identified a calcium-enriched module associated with onset and sex, and diagnosis-specific modeling highlighted MAP2K7 in early-onset BD. The predictive model achieved an AUC of 0.63, and the top 50 machine-learning features were significantly enriched in calcium signaling pathway. These findings converge on calcium–cAMP signaling networks as key drivers of early psychiatric vulnerability and suggest biomarkers for precision-targeted interventions. Full article