Search Results (6,779)

Background/Objectives: Collaboration in mental health care is essential for implementing a model oriented towards the psychosocial rehabilitation of people based on multifaceted interventions involving different actors and sectors of society to respond to demands. Despite the benefits presented by the scientific evidence, there are still many barriers to collaborative care, and professionals continue to struggle in reorienting their conduct. The current situation demands organization and the framing of well-founded action plans to overcome challenges, which in turn requires a detailed understanding of collaborative practices in mental health care and their conceptual boundaries. A concept analysis was undertaken to propose a working definition of collaborative practices in mental health care (CPMHC). Methods: This paper used the Walker and Avant concept analysis method. This includes identifying the defining concept attributes, antecedents, consequences, and empirical referents. A literature search was carried out from November 2024 to February 2025 in three databases (Medline, CINAHL, and LILACS), considering studies published between 2010 and 2024. Results: The final sample of literature investigated consisted of 30 studies. The key attributes were effective communication, building bonds, co-responsibility for care, hierarchical flexibility, articulation between services, providers and community, monitoring and evaluating of care processes, and attention to the plurality of sociocultural contexts. Conclusions: This comprehensive analysis contributes to guiding future research and policy development of collaborative practices in mental health, considering the individual, relational, institutional, and social levels. Further research is possible to deepen the understanding of the production of collaborative practices in mental health in the face of the complexity of social relations and structural inequities. Full article

Background/Objectives: Evidence-based nursing practice (EBNP) is essential in high-acuity settings such as intensive care units (ICUs) and emergency departments (EDs), where nurses are frequently required to make time-critical, high-stakes clinical decisions that directly influence patient safety and outcomes. Despite its recognized importance, the implementation of EBNP remains inconsistent, with frontline nurses often facing barriers to accessing and applying current evidence. Methods: This descriptive, cross-sectional study systematically mapped and prioritized clinical questions generated by ICU and ED nurses at a tertiary hospital in South Korea. Using open-ended questionnaires, 204 clinical questions were collected from 112 nurses. Each question was coded and classified according to the Nursing Interventions Classification (NIC) taxonomy (8th edition) through a structured cross-mapping methodology. Inter-rater reliability was assessed using Cohen’s kappa coefficient. Results: The majority of clinical questions (56.9%) were mapped to the Physiological: Complex domain, with infection control, ventilator management, and tissue perfusion management identified as the most frequent areas of inquiry. Patient safety was the second most common domain (21.6%). Notably, no clinical questions were mapped to the Family or Community domains, highlighting a gap in holistic and transitional care considerations. The mapping process demonstrated high inter-rater reliability (κ = 0.85, 95% CI: 0.80–0.89). Conclusions: Frontline nurses in high-acuity environments predominantly seek evidence related to complex physiological interventions and patient safety, while holistic and community-oriented care remain underrepresented in clinical inquiry. Utilizing the NIC taxonomy for systematic mapping establishes a reliable framework to identify evidence gaps and support targeted interventions in nursing practice. Regular protocol evaluation, alignment of continuing education with empirically identified priorities, and the integration of concise evidence summaries into clinical workflows are recommended to enhance EBNP implementation. Future research should expand to multicenter and interdisciplinary settings, incorporate advanced technologies such as artificial intelligence for automated mapping, and assess the long-term impact of evidence-based interventions on patient outcomes. Full article

The construction industry has been troubled by a shortage of skilled labor and safety accidents in recent years. Therefore, more and more robots are introduced to undertake dangerous and repetitive jobs, so that human workers can concentrate on higher-value and creative problem-solving tasks. Nevertheless, although human–robot collaboration (HRC) shows great potential, most existing evaluation methods still focus on the single performance of either the human or robot, and systematic indicators for a whole HRC team remain insufficient. To fill this research gap, the present study constructs a comprehensive evaluation framework for HRC team performance in construction projects. Firstly, a detailed literature review is carried out, and three theories are integrated to build 33 indicators preliminarily. Afterwards, an expert questionnaire survey (N = 15) is adopted to revise and verify the model empirically. The survey yielded a Cronbach’s alpha of 0.916, indicating excellent internal consistency. The indicators rated highest in importance were task completion time (µ = 4.53) and dynamic separation distance (µ = 4.47) on a 5-point scale. Eight indicators were excluded due to mean importance ratings falling below the 3.0 threshold. The framework is formed with five main dimensions and 25 concrete indicators. Finally, an AHP-TOPSIS method is used to evaluate the HRC team performance. The AHP analysis reveals that Safety (weight = 0.2708) is prioritized over Productivity (weight = 0.2327) by experts, establishing a safety-first principle for successful HRC deployment. The framework is demonstrated through a case study of a human–robot plastering team, whose team performance scored as fair. This shows that the framework can help practitioners find out the advantages and disadvantages of HRC team performance and provide targeted improvement strategies. Furthermore, the framework offers construction managers a scientific basis for deciding robot deployment and team assignment, thus promoting safer, more efficient, and more creative HRC in construction projects. Full article

Grounding systems are critical for ensuring electrical safety, fault current dissipation, and electromagnetic compatibility in power installations across different voltage levels. This research presents a comparative study on the optimization of grounding configurations for 400 V, 10 kV, and 35 kV electrical installations, focusing on key performance parameters such as grounding resistance, step and touch voltages, and fault current dissipation efficiency. The study employs computational simulations using the finite element method (FEM) alongside empirical field measurements to evaluate the influence of soil resistivity, electrode materials, and grounding configurations, including rod electrodes, grids, deep-driven rods, and hybrid grounding systems. Results indicate that soil resistivity significantly affects grounding efficiency, with deep-driven rods providing superior performance in high-resistivity conditions, while grounding grids demonstrate enhanced fault current dissipation in substations. The integration of conductive backfill materials, such as bentonite and conductive concrete, further reduces grounding resistance and enhances system reliability. This study provides engineering insights into optimizing grounding systems based on installation voltage levels, cost considerations, and compliance with IEEE Std 80-2013 and IEC 60364-5-54. The findings contribute to the development of more resilient and cost-effective grounding strategies for electrical installations. Full article

The description and assessment of braided river architecture are usually limited by the paucity of real geological datasets from field observations; due to the complexity and diversity of rivers, traditional evaluation models are difficult to apply to braided river systems in different climatic and tectonic settings. This study aims to establish an architectural model suitable for the study area setting by introducing a hierarchical analysis approach through well-exposed three-dimensional outcrops along the Second Songhua River. A micro–macro four-level hierarchical framework is adopted to obtain a detailed anatomy of sedimentary outcrops: lithofacies, elements, element associations, and archetypes. Fourteen lithofacies are identified: three conglomerates, seven sandstones, and four mudstones. Five elements provide the basic components of the river system framework: fluvial channel, laterally accreting bar, downstream accreting bar, abandoned channel, and floodplain. Four combinations of adjacent elements are determined: fluvial channel and downstream accreting bar, fluvial channel and laterally accreting bar, erosionally based fluvial channel and laterally accreting bar, and abandoned channel and floodplain. Considering the sedimentary evolution process, the braided river prototype, which is an element-based channel filling unit, is established by documenting three contact combinations between different elements and six types of fine-grained deposits’ preservation positions in the elements. Empirical relationships are developed among the bankfull channel depth, mean bankfull channel depth, and bankfull channel width. For the braided river systems, the establishment of the model promotes understanding of the architecture and evolution, and the application of the hierarchical analysis approach provides a basis for outcrop, underground reservoir, and tank experiments. Full article

Large, high-quality patient datasets are essential for applications like economic modeling and patient simulation. However, real-world data is often inaccessible or incomplete. Synthetic patient data offers an alternative, and current methods often fail to preserve clinical plausibility, real-world correlations, and logical consistency. This study presents a patient cohort generator designed to produce realistic, statistically valid synthetic datasets. The generator uses predefined probability distributions and Cholesky decomposition to reflect real-world correlations. A dependency matrix handles variable relationships in the right order. Hard limits block unrealistic values, and binary variables are set using percentiles to match expected rates. Validation used two datasets, NHANES (2021–2023) and the Framingham Heart Study, evaluating cohort diversity (general, cardiac, low-dimensional), data sparsity (five correlation scenarios), and model performance (MSE, RMSE, R², SSE, correlation plots). Results demonstrated strong alignment with real-world data in central tendency, dispersion, and correlation structures. Scenario A (empirical correlations) performed best (R² = 86.8–99.6%, lowest SSE and MAE). Scenario B (physician-estimated correlations) also performed well, especially in a low-dimensions population (R² = 80.7%). Scenario E (no correlation) performed worst. Overall, the proposed model provides a scalable, customizable solution for generating synthetic patient cohorts, supporting reliable simulations and research when real-world data is limited. While deep learning approaches have been proposed for this task, they require access to large-scale real datasets and offer limited control over statistical dependencies or clinical logic. Our approach addresses this gap. Full article

Neural networks (NNs) achieve breakthroughs in computer vision and natural language processing,yet their “black box” nature persists. Traditional methods prioritise parameter optimisation and loss design, overlooking NNs’ fundamental structure as topologically organised nonlinear computational systems. This work proposes a complex network theory framework decoding structure–function coupling by mapping convolutional layers, fully connected layers, and Dropout modules into graph representations. To overcome limitations of heuristic compression techniques, we develop a topology-sensitive adaptive pruning algorithm that evaluates critical paths via node strength centrality, preserving structural–functional integrity. On CIFAR-10, our method achieves 55.5% parameter reduction with only 7.8% accuracy degradation—significantly outperforming traditional approaches. Crucially, retrained pruned networks exceed original model accuracy by up to 2.63%, demonstrating that topology optimisation unlocks latent model potential. This research establishes a paradigm shift from empirical to topologically rationalised neural architecture design, providing theoretical foundations for deep learning optimisation dynamics. Full article

Occupational heat stress represents an increasing challenge to safety and operational performance in underground mining, where elevated temperatures, humidity, and limited ventilation are common. This study proposes an integrated framework to analyze the systemic impact of heat stress on human reliability in mining operations. We conducted a systematic literature review to identify empirical studies addressing thermal exposure, extracting key operational functions for modeling. These functions were structured using the Functional Resonance Analysis Method (FRAM) to reveal interdependencies and performance variability. Human reliability was evaluated using Fuzzy CREAM, which quantified the degree of contextual control associated with each function. Finally, we applied the Gaussian Analytic Hierarchy Process (AHP) to prioritize functions based on thermal impact, contextual reliability, and systemic connectivity. The results showed that functions involving subjective or complex judgment, such as assessing thermal stress or identifying psychophysiological indicators, exhibited lower reliability and higher vulnerability. In contrast, monitoring and control functions based on standardized procedures were more stable and resilient. This combined approach identified critical points of systemic fragility and offers a robust decision-support tool for prioritizing thermal risk mitigation. The findings contribute to advancing the scientific understanding of heat stress impacts in mining and support the development of targeted interventions to enhance human performance and safety in extreme environments. Full article

Thailand’s southern border provinces need youth-focused multicultural leadership programs integrating local religious–cultural elements, community involvement, and long-term evaluation to enhance social cohesion and sustainable development. This study aimed to develop and evaluate a program to foster multicultural leadership among youth in Thailand’s three southern border provinces. The research was conducted in two phases. The first phase involved synthesizing key multicultural leadership characteristics, designing a structured program and assessing its relevance and coherence through expert evaluation. The second phase focused on empirical validation by implementing the program with 22 selected youth participants, employing repeated-measures analysis of variance to assess its effectiveness. Additionally, experts evaluated the program’s validity, appropriateness, cost-effectiveness, utility, and feasibility. The resulting program, “EARCA”, comprises five core components: Experiential Exposure, Active Exploration & Engagement, Reflective Thinking & Analysis, Concept Integration & Synthesis, and Application & Extension. Expert assessments confirmed its appropriateness at the highest level, with a consistency index ranging from 0.8 to 1.0. Statistical analyses demonstrated significant improvements in all dimensions of multicultural leadership among participants. Furthermore, the program was rated highly accurate, appropriate, cost-effective, practical, and feasible for real-world implementation. These findings offer valuable insights for policymakers and practitioners seeking to enhance multicultural leadership development through structured, evidence-based interventions. Full article

Solving competitive facility location problems can optimize market share or operational efficiency in environments where multiple firms compete for customer attention. In such contexts, facility attractiveness is shaped not only by geographic proximity but also by customer preference characteristics. This study presents a novel heuristic framework that integrates multi-view K-means clustering with customer behavior modeling reinforced by a co-regularization mechanism to align clustering results across heterogeneous data views. By jointly exploiting spatial and behavioral information, the framework clusters customers and facilities into meaningful market segments. Within each segment, a bilevel optimization model is applied to represent the sequential decision-making of competing entities—where a leader first selects facility locations, followed by a reactive follower. An empirical evaluation on a real-world dataset from San Francisco demonstrates that the proposed approach, using optimal co-regularization parameters, achieves a total runtime of approximately 4.00 s—representing a 99.34% reduction compared to the full CFLBP-CB model (608.58 s) and a 99.32% reduction compared to a genetic algorithm (585.20 s). Concurrently, it yields an overall profit of 16,104.17, which is an approximate 0.72% increase over the Direct CFLBP-CB profit of 15,988.27 and is only 0.21% lower than the genetic algorithm’s highest profit of 16,137.75. Moreover, comparative analysis reveals that the proposed multi-view clustering with co-regularization outperforms all single-view baselines, including K-means, spectral, and hierarchical methods. This superiority is evidenced by an approximate 5.21% increase in overall profit and a simultaneous reduction in optimization time, thereby demonstrating its effectiveness in capturing complementary spatial and behavioral structures for competitive facility location. Notably, the proposed two-stage approach achieves high-quality solutions with significantly shorter computation times, making it suitable for large-scale or time-sensitive competitive facility planning tasks. Full article

A numerical method is proposed for evaluating the axial stress–strain relationship of FRP-confined concrete. In this method, empirical formulae for the compressive strength and strain at peak stress of confined concrete are obtained by fitting experimental data collected from the literature. It is then assumed that when FRP-confined concrete and actively confined concrete are subjected to the same lateral strain and confining pressure at a specific loading stage, their axial stress–strain relationships are identical at that stage. Based on this assumption, a numerical method for the axial stress–strain relationship of FRP-confined concrete is developed by combining the stress–strain model of actively confined concrete with the axial–lateral strain correlation. Finally, the validity of this numerical method is verified with experimental data with various geometric and material parameters, demonstrating a reasonable agreement between predicted stress–strain curves and measured ones. A parametric analysis is conducted to reveal that the stress–strain curve is independent of the specimen length for strong FRP confinement with small failure strains, while the specimen length exhibits a significant effect on the softening branch for weak FRP confinement. Therefore, for weakly FRP-confined concrete, it is recommended to consider the specimen length effect in evaluating the axial stress–strain relationship. Full article

Slab-replacement operations are crucial for restoring deteriorated CRTS I slab ballastless tracks to operational standards. This study investigates the structural implications of the operation by evaluating the strength characteristics and material properties of track components both prior to and following replacement. Apparent strength was measured using rebound hammer tests on three categories of slabs: retained, deteriorated, and newly installed track slabs. In addition, samples of old and new filling resins were collected and tested to determine their elastic moduli. These empirical data were subsequently used to develop a refined finite-element model that captures both pre- and post-replacement conditions. Under varying temperature loads, disparities in component performance were found to significantly affect stress distribution. Specifically, before replacement, deteriorated track slabs exhibited 10.74% lower strength compared to adjacent retained slabs, whereas, after replacement, new slabs showed a 25.26% increase in strength over retained ones. The elastic modulus of old filling resin was measured at 5.19 kN/mm, 35.13% below the minimum design requirement, while the new resin reached 10.48 kN/mm, exceeding the minimum by 31.00%. Although the slab-replacement operation enhances safety by addressing structural deficiencies, it may also create new weak points in adjacent areas, where insufficient stiffness results in stress concentrations and potential damage. This study offers critical insights for optimizing maintenance strategies and improving the long-term performance of ballastless track systems. Full article

Objective: The impact of a febrile neutropenia (FN) emergency department (ED) triage screening tool and protocol on time to antibiotic administration (TTA) and patient outcomes was evaluated. Methods: This was a retrospective, quasi-experimental study of adult FN patients admitted through the ED from April 2014 to April 2017. In March 2016 a triage screening tool and protocol were implemented. In patients who screened positive, nursing initiated a protocol that included laboratory diagnostics and a pharmacy consult for empiric antibiotics prior to evaluation by a provider. Patients were evaluated pre- and post-protocol for TTA, 30-day mortality, ED length of stay (LOS), and hospital LOS. Results: A total of 130 patients were included in the study, 77 pre-protocol and 53 post-protocol. Median TTA was longer in the pre-protocol group at 174 min (interquartile range [IQR] 105–224) vs. 109 min (IQR 71–214) post-protocol, p = 0.04. Thirty-day mortality was greater at 18.8% pre-protocol vs. 7.5% post-protocol, p = 0.12. There was no difference in hospital LOS. Pre-protocol patients compared to post-protocol patients who had a pharmacy consult demonstrated a further reduction in TTA (174 min [IQR 105–224] vs. 87.5 min [IQR 61.5–135], p < 0.01) and a reduced mortality (18% vs. 0%, p = 0.04). Conclusions: To our knowledge, this is the first report of a protocol for febrile neutropenia that allows pharmacists to order antibiotics based on a nurse triage assessment. Evaluation of the protocol demonstrated a significant reduction in TTA and trend toward improved mortality. Full article

As the global energy transition accelerates, the integration of solar power and artificial intelligence (AI) technologies offers new pathways for sustainable local development. This study examines four Serbian municipalities—Šabac, Sombor, Pirot, and Čačak—to assess how AI-enabled solar power systems can enhance energy resilience, reduce emissions, and support community-level sustainability goals. Using a mixed-method approach combining spatial analysis, predictive modeling, and stakeholder interviews, this research study evaluates the performance and institutional readiness of local governments in terms of implementing intelligent solar infrastructure. Key AI applications included solar potential mapping, demand-side management, and predictive maintenance of photovoltaic (PV) systems. Quantitative results show an improvement >60% in forecasting accuracy, a 64% reduction in system downtime, and a 9.7% increase in energy cost savings. These technical gains were accompanied by positive trends in SDG-aligned indicators, such as improved electricity access and local job creation in the green economy. Despite challenges related to data infrastructure, regulatory gaps, and limited AI literacy, this study finds that institutional coordination and leadership commitment are decisive for successful implementation. The proposed AI–Solar Integration for Local Sustainability (AISILS) framework offers a replicable model for emerging economies. Policy recommendations include investing in foundational digital infrastructure, promoting low-code AI platforms, and aligning AI–solar projects with SDG targets to attract EU and national funding. This study contributes new empirical evidence on the digital–renewable energy nexus in Southeast Europe and underscores the strategic role of AI in accelerating inclusive, data-driven energy transitions at the municipal level. Full article

Climate warming poses significant challenges for the sustainable management of natural water resources, making efficient planning and usage essential. This study evaluates the water requirements, irrigation demand, and rainfall deficits for two key vegetable crops, carrot and white cabbage, under projected climate scenarios RCP 4.5 and RCP 8.5 for the period 2031–2100. The analysis was conducted for Kraków and Rzeszów Counties in southern Poland using projected monthly temperature and precipitation data from the Klimada 2.0 portal. Potential evapotranspiration (ETp) during the growing season (May–October) was estimated using Treder’s empirical model and the crop coefficient method adapted for Polish conditions. The reference period for comparison was 1951–2020. The results reveal a significant upward trend in water demand for both crops, with the highest increases under the RCP 8.5 scenario–seasonal ETp values reaching up to 517 mm for cabbage and 497 mm for carrot. Rainfall deficits are projected to intensify, especially during July and August, with greater shortages in Rzeszów County compared to Kraków County. Irrigation demand varies depending on soil type and drought severity, becoming critical in medium and very dry years. These findings underscore the necessity of adapting irrigation strategies and water resource management to ensure sustainable vegetable production under changing climate conditions. The data provide valuable guidance for farmers, advisors, and policymakers in planning effective irrigation infrastructure and optimizing water-use efficiency in southern Poland. Full article