Search Results (5,050)

Background: Conflicts are an inevitable part of interpersonal relationships, and personality traits influence how they are resolved. In the nursing work environment, conflicts often arise from poor communication and stress, negatively impacting nurses’ well-being and quality of care. The “Big Five” personality model highlights how traits such as extraversion, agreeableness, and emotional stability shape conflict approaches. Understanding these traits aids in developing effective conflict management strategies. This study investigates intragroup conflicts among nurses by identifying their types and examining how sociodemographic factors and personality traits predict their occurrence. The aim is to provide insights that support targeted interventions and improve team dynamics in nursing practice. Methods: The study was conducted as a cross-sectional analysis within the University Hospital Centre Osijek from March to August 2024, involving nurses and technicians. Data was collected using structured questionnaires with clearly defined inclusion and exclusion criteria. The questionnaire included the Process Conflict Scale, the Big Five Inventory, and a Demographic questionnaire. Appropriate statistical analyses were conducted, including descriptive statistics, normality testing with the Kolmogorov–Smirnov test, non-parametric Spearman and Point-Biserial correlations, and linear regression to examine predictors of intragroup conflicts. All assumptions for regression were met, with significance set at p < 0.05, and analyses were performed using JASP software version 0.17.2.1. Results: The research reveals significant differences among various types of team conflicts, where personality traits such as neuroticism increase, while conscientiousness decreases conflicts. The professional competence of respondents also positively correlates with logistical conflicts, and personality explains the variance in conflicts among nurses. Conclusions: Intragroup conflicts among nurses, particularly task-related, stem from communication issues and high care standards. Neuroticism negatively affects team dynamics, while conscientiousness can reduce conflicts but may also lead to disagreements if expectations are unmet. Education on conflict management and clearly defined roles can improve teamwork and quality of care. Full article

Climate change is fundamentally transforming global water systems, affecting the availability, quality, and ecological dynamics of water resources. This review synthesizes current scientific understanding of climate change impacts on hydrological systems, with a focus on freshwater ecosystems, and regional water availability. Rising global temperatures are disrupting thermal regimes in rivers, lakes, and ponds; intensifying the frequency and severity of extreme weather events; and altering precipitation and snowmelt patterns. These changes place mounting stress on aquatic ecosystems, threaten water security, and challenge conventional water management practices. The paper also identifies key vulnerabilities across diverse geographic regions and evaluates adaptation strategies such as integrated water resource management (IWRM), the water, energy and food (WEF) nexus, ecosystem-based approaches (EbA), the role of advanced technology and infrastructure enhancements. By adopting these strategies, stakeholders can strengthen the resilience of water systems and safeguard critical resources for both ecosystems and human well-being. Full article

This exploratory proof-of-concept study investigated the simultaneous deployment of autonomous, persona-driven Artificial Intelligence (AI) agents across multiple social media platforms using the ElizaOS framework. We developed three platform-specific agents with seven-layer character architectures and deployed them on Twitter/X, Discord, and Telegram for 18 days. The system processed 5389 interactions while gathering feedback from 28 volunteer participants. Addressing three research questions, we found that: (1) automation effectiveness was platform-dependent, with direct support platforms (Telegram, Discord) rated more useful than broadcast-oriented Twitter/X; (2) character design impact depended primarily on platform-persona alignment rather than architectural sophistication; and (3) technical performance showed platform-specific patterns, with median storage times ranging from 9.0 milliseconds (Twitter/X) to 61.5 milliseconds (Telegram) and high variability across all platforms. A notable finding was what we term the “Discord Paradox”—high quality ratings (4.05/5) but lowest preference (8.7%), suggesting platform familiarity and accessibility influence adoption more than agent quality. While the deployment demonstrated technical feasibility and revealed distinct user dynamics across platforms, the findings indicate that platform-specific optimization may be more effective than universal approaches. This exploratory study advances understanding of multi-platform agent deployment for marketing automation, identifying behavioral patterns and platform-specific dynamics that offer testable hypotheses for future systematic research. Full article

This paper addresses the optimization of traffic signal timing at urban intersections by introducing a dynamic green ratio allocation framework based on cycle-based delay classification. Conventional methods such as the Webster delay model often fail to capture the asymmetric delay characteristics and the impact of fluctuating flows across multiple cycles. We propose a novel approach that classifies cycles into undersaturated and oversaturated states and develops dedicated optimization models for each type. For undersaturated cycles, a new delay function is derived to accurately capture the interaction between queue dissipation and green time allocation, enabling multi-period minimization of total vehicle delay. For oversaturated cycles, queue minimization at the end of each phase is adopted to accelerate congestion dissipation. The framework is validated through simulation and compared with existing methods, demonstrating superior performance in congestion clearance and delay minimization. The results show improved adaptability to changing traffic conditions and enhanced practicality for real-time signal control in smart transportation systems. Full article

Accurate prediction of the ultimate bearing capacity (UBC) of single piles is essential for safe and economical foundation design, as it directly impacts construction safety and resource efficiency. This study aims to develop a hybrid prediction framework integrating Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to optimize a Backpropagation Neural Network (BPNN). GA performs global exploration to generate diverse initial solutions, while PSO accelerates convergence through adaptive parameter updates, balancing exploration and exploitation. The primary objective of this study is to enhance the accuracy and reliability of UBC prediction, which is crucial for informed decision-making in geotechnical engineering. A dataset consisting of 282 high-strain dynamic load tests was employed to assess the performance of the proposed GA-PSO-BPNN model in comparison with CNN, XGBoost, and traditional dynamic formulas (Hiley, Danish, and Winkler). The GA-PSO-BPNN achieved an R² of 0.951 and an RMSE of 660.13, outperforming other AI models and traditional approaches. Furthermore, SHAP (SHapley Additive exPlanations) analysis was conducted to evaluate the relative importance of input variables, where SHAP values were used to explain the contribution of each feature to the model’s predictions. The findings indicate that the GA-PSO-BPNN model provides a robust, cost-efficient, and interpretable approach for UBC prediction, which aligns with current sustainability goals by optimizing resource usage in foundation design. This model shows significant potential for practical use across various geotechnical settings, contributing to safer, more sustainable infrastructure projects. Full article

The fracture behavior of the Al₄Cu₉ intermetallic compound at the interface of impact-welded Cu/Al joints remains insufficiently explored through integrated multiscale modeling and experimental validation. In this study, molecular dynamic (MD) simulations, finite element (FE) analysis implemented in ABAQUS (version 2020) and a cohesive zone model (CZM) were combined with optical microscopy (OM) and scanning electron microscopy (SEM) observations of the interface and crack initiation zones in impact-welded Cu/Al specimens to investigate crack propagation mechanisms under different defect configurations. The experimental specimens consisted of 1060 aluminum (Al) and oxygen-free high-conductivity (OFHC) copper, fabricated via impact welding and subsequently annealed at 250 °C for 100 h. The interfacial morphology and crack initiation features obtained from OM and SEM provided direct validation for the traction–separation (T-S) parameters extracted from MD and mapped into the FE model. The results indicate that composite defects (blunt crack + void) cause a significantly greater reduction in fracture energy and stress intensity factor than single defects and that defect effects outweigh temperature effects within the range of 200–500 K. The experimentally observed crack initiation locations were in strong agreement with simulation predictions. This integrated simulation–experiment approach not only elucidates the multiscale fracture mechanisms of the Al₄Cu₉ interface but also provides a physically validated basis for the reliability assessment and optimization of aerospace Cu/Al welded structures. Full article

This study investigates the dynamic relationship between key macroeconomic indicators, specifically inflation (CPI), the Federal Funds Rate, GDP growth, unemployment, and money supply, and U.S. stock market returns, represented by the S&P 500 index, over the period January 2015 to June 2025. The objective is to understand how inflation and monetary policy affect market performance in both the short and long run. Using an Autoregressive Distributed Lag (ARDL) modeling framework and Error Correction Model (ECM), the study examines monthly S&P 500 returns alongside macroeconomic variables, accounting for lagged effects and potential cointegration. The model captures both immediate and delayed impacts, employing the Bounds Testing approach to confirm long-run equilibrium relationships. Results show significant mean-reversion in stock returns, a delayed negative impact of inflation and interest rate increases, and a positive contemporaneous response to GDP growth. Unemployment exhibits a counterintuitive positive effect on returns, suggesting forward-looking investor expectations. The money supply also positively influences equity prices, supporting liquidity-based asset pricing theories. This paper provides updated empirical evidence on macro-finance linkages and highlights the complex interplay of monetary policy, inflation, and market expectations in shaping U.S. equity returns. Full article

In the context of increasing resource scarcity and pervasive uncertainty, informed economic decision-making requires access to timely and accurate information. Real-time sustainability monitoring tools, such as sensor- or RFID-based systems, have become essential to capture dynamic changes in production environments. Given the growing importance of sustainability, evaluating the environmental impact of production systems through Life Cycle Assessment (LCA) is critical, while economic dimensions are typically addressed via Life Cycle Costing (LCC). However, conventional LCA and LCC approaches often rely on static or outdated data, limiting their applicability in dynamic environments. This paper presents an integrated framework for the real-time assessment of Life Cycle Assessment (LCA) and Life Cycle Costing (LCC), utilizing Radio Frequency Identification (RFID) technology to facilitate continuous data collection throughout the production chain. By combining environmental and economic assessments with real-time data streams, the proposed framework supports more adaptive, transparent, and sustainable decision-making in resource-constrained industrial contexts. Full article

Hepatocellular carcinoma (HCC) is characterized by a complex disruption of hemostatic balance, increasing the risk of both thrombotic and hemorrhagic events. Thrombotic complications, most notably portal vein thrombosis (PVT) and venous thromboembolism (VTE), have a significant impact on clinical outcomes and therapeutic strategies. Cirrhosis contributes to the precarious equilibrium between pro- and anticoagulant forces through impaired synthesis of coagulation factors, endothelial dysfunction, and systemic inflammation. In the presence of HCC tumor-driven mechanisms, such as tissue factor expression, extracellular vesicle release, platelet activation, and suppression of fibrinolysis exacerbate this prothrombotic state. In this scenario, advanced diagnostic tools such as thrombin generation assay (TGA) and rotational thromboelastometry (ROTEM) offer a more accurate assessment of coagulation dynamics than conventional tests, enabling better risk stratification especially for therapeutic purposes. Anticoagulant therapy has demonstrated clinical benefit in selected cases of non-malignant PVT and VTE, particularly when liver function is preserved. While prophylactic strategies are still under investigation, data suggest they may be safely implemented in selected surgical patients. In the setting of immunotherapy, especially regimens involving anti-VEGF agents, anticoagulation may be considered with careful management of bleeding risk due to portal hypertension. An individualized approach to anticoagulation, supported by functional coagulation testing, is gaining acceptance as a means to safely reduce thrombotic burden and potentially improve outcomes in patients with HCC. Full article

As carbon cycling and global environmental protection gain increasing attention, forest disturbance research has intensified worldwide. Constrained by limited data availability, existing frameworks often rely on extracting individual spectral bands for simple binary disturbance detection, lacking systematic approaches to visualize and classify causes of disturbance over large areas. Accurately identifying disturbance types is critical because different disturbances (e.g., fires, logging, pests) exhibit vastly different impacts on forest structure, successional pathways and, consequently, forest carbon sequestration and storage capacities. This study proposes an integrated remote sensing and deep learning (DL) method for forest disturbance type identification, enabling high-precision monitoring in Northeast China from 1992 to 2023. Leveraging the Google Earth Engine platform, we integrated Landsat time-series data (30 m resolution), Global Forest Change data, and other multi-source datasets. We extracted four key vegetation indices (NDVI, EVI, NBR, NDMI) to construct long-term forest disturbance feature series. A comparative analysis showed that the proposed convolutional neural network (CNN) model with six feature bands achieved 5.16% higher overall accuracy and a 6.92% higher Kappa coefficient than a random forest (RF) algorithm. Remarkably, even with only six features, the CNN model outperformed the RF model trained on fifteen features, achieving a 0.4% higher overall accuracy and a 0.58% higher Kappa coefficient, while utilizing 60% fewer parameters. The CNN model accurately classified forest disturbances—including fires, pests, logging, and geological disasters—achieving a 92.26% overall accuracy and an 89.04% Kappa coefficient. This surpasses the 81.4% accuracy of the Global Forest Change product. The method significantly improves the spatiotemporal accuracy of regional-scale forest monitoring, offering a robust framework for tracking ecosystem dynamics. Full article

Vulnerability to hazards is a critical global issue, as it not only depends on the magnitude of natural hazards but also on the underlying social and economic conditions of communities. Understanding these factors is essential for designing effective risk reduction strategies and informed policy decisions. The objective of this research is to define a social vulnerability index (SoVI) and to analyse its distribution at the provincial and urban levels by applying different aggregation methods. This study provides a novel approach by examining the sensitivity of the index to different weighting methodologies, addressing a gap in the literature regarding the robustness of social vulnerability measures. An alternative approach is provided to determine the sensitivity of the SoVI in regions, in addition to understanding the dynamics of the socioeconomic characteristics considered in the territory and contributing to the theoretical and normative discussion of the construction of the index. To meet the objective, a sensitivity analysis is provided through different methods of weighting the vulnerability dimensions. The results indicate that the distribution of the SoVI in the provinces of Ecuador is heterogeneous, highlighting the importance of considering local socioeconomic contexts in vulnerability assessments. Additionally, the study shows that the values of the constructed index are sensitive to the weighting methods of the dimensions, which underscores the need for a careful selection of aggregation techniques to ensure reliable policy implications. It was also possible to identify that when social vulnerability is analysed at the city level, these show higher values than the corresponding provinces, challenging the common assumption that urban areas inherently provide better living conditions. This finding contributes to the ongoing debate on the impacts of rapid urbanization on social vulnerability. Full article

Urban Heat Islands (UHIs) are intensifying under climate change, exacerbating thermal exposure risks for socially vulnerable populations. While the role of urban environmental features in shaping UHI patterns is well recognized, their differential impacts on diverse social groups remain underexplored—limiting the development of equitable, context-sensitive mitigation strategies. To address this challenge, we employ an interpretable ensemble machine learning framework to quantify how vegetation, water proximity, and built form influence UHI exposure across social strata and simulate the outcomes of alternative urban interventions. Drawing on data from 1660Dissemination Areas in Vancouver, we model UHI across seasonal and diurnal contexts, integrating environmental variables with socio-demographic indicators to evaluate both thermal and equity outcomes. Our ensemble AutoML framework demonstrates strong predictive accuracy across these contexts (R² up to 0.79), providing reliable estimates of UHI dynamics. Results reveal that increasing vegetation cover consistently delivers the strongest cooling benefits (up to 2.95 °C) while advancing social equity, though fairness improvements become consistent only when vegetation intensity exceeds 1.3 times the baseline level. Water-related features yield additional cooling of approximately 1.15–1.5 °C, whereas built-form interventions yield trade-offs between cooling efficacy and fairness. Notably, modest reductions in building coverage or road density can meaningfully enhance distributional justice with limited thermal compromise. These findings underscore the importance of tailoring mitigation strategies not only for climatic impact but also for social equity. Our study offers a scalable analytical approach for designing just and effective urban climate adaptations, advancing both environmental sustainability and inclusive urban resilience in the face of intensifying heat risks. Full article

With the increasing global demand for energy, the development of unconventional resources has become a focal point of research. Among these, shale gas has drawn considerable attention due to its abundant reserves. However, its low permeability and complex fracture networks present substantial challenges. This study investigates the composite fracturing technology combining supercritical CO₂ and slickwater for shale gas extraction, elucidating the mechanisms by which it influences shale fracture roughness and conductivity through an integrated approach of theory, experiments, and numerical modeling. Experimental results demonstrate that the surface roughness of shale fractures increases markedly after supercritical CO₂–slickwater treatment. Moreover, the dynamic evolution of permeability and porosity is governed by roughness strain, adsorption expansion, and corrosion compression strain. Based on fluid–solid coupling theory, a mathematical model was developed and validated via numerical simulations. Sensitivity analysis reveals that fracture density and permeability have a pronounced impact on shale gas field productivity, whereas fracture dip angle exerts a comparatively minor effect. The findings provide a theoretical basis for optimizing composite fracturing technology, thereby enhancing shale gas extraction efficiency and promoting effective resource utilization. Full article

Soil organic carbon (SOC) plays a critical role in the global carbon cycle and serves as a sensitive indicator of climate change impacts, with its dynamics significantly influencing regional ecological security and sustainable development. This study focuses on the Songnen Plain in Northeast China—a key black soil agricultural region increasingly affected by water erosion, primarily through surface runoff and rill formation on gently sloping cultivated land. We aim to investigate the spatiotemporal dynamics of SOC stocks across different cultivated land erosion zones under projected future climate change scenarios. To quantify current and future SOC stocks, we applied a boosted regression tree (BRT) model based on 130 topsoil samples (0–30 cm) and eight environmental variables representing topographic and climatic conditions. The model demonstrated strong predictive performance through 10-fold cross-validation, yielding high R² and Lin’s concordance correlation coefficient (LCCC) values, as well as low mean absolute error (MAE) and root mean square error (RMSE). Key drivers of SOC stock spatial variation were identified as mean annual temperature, elevation, and slope aspect. Using a space-for-time substitution approach, we projected SOC stocks under the SSP245 and SSP585 climate scenarios for the 2050s and 2090s. Results indicate a decline of 177.66 Tg C (SSP245) and 186.44 Tg C (SSP585) by the 2050s relative to 2023 levels. By the 2090s, SOC losses under SSP245 and SSP585 are projected to reach 2.84% and 1.41%, respectively, highlighting divergent carbon dynamics under varying emission pathways. Spatially, SOC stocks were predominantly located in areas of slight (67%) and light (22%) water erosion, underscoring the linkage between erosion intensity and carbon distribution. This study underscores the importance of incorporating both climate and anthropogenic influences in SOC assessments. The resulting high-resolution SOC distribution map provides a scientific basis for targeted ecological restoration, black soil conservation, and sustainable land management in the Songnen Plain, thereby supporting regional climate resilience and China’s “dual carbon” goals. These insights also contribute to global efforts in enhancing soil carbon sequestration and achieving carbon neutrality goals. Full article

Turbulence and wind gusts pose significant risks to the safety and efficiency of UAVs (uncrewed aerial vehicles) in urban environments. In these settings, wind dynamics are strongly influenced by interactions with buildings and terrain, giving rise to small-scale phenomena such as vortex shedding and gusts. These wind speed oscillations generate unsteady forces that can destabilise UAV flight, particularly for small vehicles. Additionally, predicting their formation requires high-resolution Computational Fluid Dynamics (CFD) models, as current weather forecasting tools lack the resolution to capture these phenomena. However, such models require 3D representations of study areas with high geometric consistency and detail, which are not available for most cities. To address this issue, this work introduces an automated methodology for urban CFD mesh generation using open-source data. The proposed method generates error-free meshes compatible with OpenFOAM and includes tools for geometry modification, enhancing solver convergence and enabling adjustments to mesh complexity based on computational resources. Using this approach, CFD simulations are conducted for the city of Ourense, followed by an analysis of their impact on UAV operations and the integration of the system into a trajectory optimisation framework. The CFD model is also validated using experimental anemometer measurements. Full article