Search Results (54,087)

(1) Background: Mobile populations living in disadvantaged circumstances often face disrupted continuity of care due to incomplete or inaccessible health records. This feasibility study explored the perceived usefulness of Electronic Personal Health Records (EPHRs) in enhancing access to and continuity of care for mobile populations across Denmark, Ghana, Kenya, and The Netherlands. (2) Methods: A qualitative study using ninety semi-structured interviews, with multi-level stakeholders ranging from policymakers to mobile individuals, recruited through purposive and convenience sampling. Interview guides and analysis were informed by the Technology Acceptance Model (TAM), and analysis by the Unified Theory of Acceptance and Use of Technology (UTAUT). (3) Results: Stakeholders highlighted the value of improved medical data sharing and ownership and considered EPHRs promising for enhancing care continuity and efficiency. Key concerns included limited digital and health literacy, and data security and privacy, underscoring the need for education and safeguards against inappropriate data sharing. Due to differences in digital readiness and privacy guidelines, a one-size-fits-all EPHR is unlikely to succeed. (4) Conclusions: EPHRs are considered valuable tools to enhance care continuity and increase patient ownership, but they face technical, structural, and social challenges, including data security and varying levels of digital (health) literacy. Successful implementation requires context-sensitive, co-created solutions supported by strong policy frameworks. Full article

This study systematically investigates the influence of varying corrosion severity on the bearing capacity of K-shaped circular-section joints, with explicit consideration of weld line positioning. Four full-scale circular-section joint specimens with clearance gaps were designed to simulate localized corrosion through artificially introduced perforations, and axial static loading tests were performed to assess the degradation of structural performance. Experimental results indicate that the predominant failure mode of corroded K-joints manifests as brittle fracture in the weld-affected zone, attributable to the combined effects of material weakening and stress concentration. The enlargement of corrosion pit dimensions induces progressive deterioration in joint stiffness and ultimate bearing capacity, accompanied by increased displacement at failure. A refined finite element model was established using ABAQUS. The obtained load–displacement curve from the simulation was compared with the experimental data to verify the validity of the model. Subsequently, a parametric analysis was conducted to investigate the influence of multiple variables on the residual bearing capacity of the nodes. Numerical investigations indicate that the severity of corrosion exhibits a positive correlation with the reduction in bearing capacity, whereas web-chord members with smaller inclination angles demonstrate enhanced corrosion resistance, when θ is equal to 30 degrees, K_s decreases from approximately 0.983 to around 0.894. Thin-walled joints exhibit accelerated performance deterioration compared to thick-walled configurations under equivalent corrosion conditions. Furthermore, increased pipe diameter ratios exacerbate corrosion-induced reductions in structural efficiency, when the corrosion rate is 0.10, β = 0.4 corresponds to K_s = 0.98, and when β = 0.7, it is approximately 0.965. and distributed micro-pitting results in less severe capacity degradation than concentrated macro-pitting over the same corrosion areas. Full article

This study evaluates the trade-offs and environmental impacts of transitioning from coal to natural gas (NG) for electricity generation in San Juan County, with a focus on greenhouse gas emissions and water consumption. It addresses key questions, including how water use and emissions change as the county shifts from coal to natural gas. The research analyzes water usage and emissions of CO₂, NO_x, and SO₂ during both the extraction and combustion phases of coal and natural gas. Specifically, it compares water consumption and direct emissions from coal-fired and natural gas-fired power plants. The analysis utilizes ten years of combustion-phase data from the Four Corners (coal-fired) and Afton (natural gas-fired) power plants in New Mexico. Linear regression was applied to the historical data, and four transition scenarios were modeled: (1) 100% coal-generated electricity, (2) a 20% reduction in coal with a corresponding increase in NG, (3) a 50% reduction in coal with a corresponding increase in NG, and (4) a complete transition to NG. Regression analysis and scenario calculations indicate that switching to NG results in significant water savings and reduced emissions. Water savings in the combustion phase decrease by up to 2750 gallons per MWh, valued at USD 0.743 per MWh when electricity is generated 100% from NG. CO₂ emissions are substantially reduced, with the largest decrease being 0.6127 metric tons per MWh, valued at USD 61.26 per MWh. NO_x emissions in the combustion phase decline by 0.0018 metric tons per MWh, with an economic valuation of USD 14.61 per MWh, while SO₂ emissions decrease by 0.0006 metric tons per MWh, valued at USD 11.91 per MWh when electricity generation is 100% NG-based. The results highlight the environmental and economic advantages of transitioning from coal to NG. The findings underscore the environmental and economic advantages of transitioning from coal to natural gas. Water conservation is particularly vital in San Juan County’s semi-arid climate. Additionally, lower emissions support climate change mitigation, enhance air quality, and improve public health. The economic valuation of emissions reductions further highlights the financial benefits of this transition, positioning natural gas as a more sustainable and economically viable energy source for the region. Ultimately, this study emphasizes the need to adopt cleaner energy sources such as renewable energy to achieve long-term environmental sustainability and economic efficiency. Full article

Background: Efficient and safe discharge is critical in pediatric dental procedures performed under deep sedation in non-operating room anesthesia (NORA) settings. Traditional institutional criteria may delay discharge due to subjectivity. Objective: This study compared the Modified Aldrete Recovery Score (MAS) and institutional discharge criteria to determine which provides faster and reliable discharge decisions. Methods: In this prospective observational study, 100 children (ages 2–10, ASA I–III) undergoing deep sedation for dental treatment were evaluated. Two nurse anesthetists independently assessed discharge readiness every five minutes using either MAS or institutional criteria. Demographic data, BMI percentile, ASA class, anesthesia duration, and propofol dose were recorded. Discharge times were compared using Wilcoxon signed-rank and subgroup analyses and correlation tests. Results: MAS allowed significantly earlier discharge than institutional criteria (24.75 ± 7.33 vs. 36.79 ± 8.59 min, p = 0.01). The agreement between methods was poor (ICC = 0.06). Discharge time varied significantly by BMI percentile (p = 0.01); obese children had shorter recovery times, while time differences were greater in overweight children. No adverse events or readmissions occurred. Conclusions: MAS provides a quicker and equally safe discharge assessment in pediatric dental sedation. Its use may enhance workflow efficiency and standardize recovery decisions in NORA settings lacking formal PACUs. Full article

Solar greenhouses are an important component of modern facility agriculture, and the dynamic changes in their internal environment directly affect crop growth and yield. Among these factors, crop transpiration releases water vapor through transpiration, directly altering the indoor humidity balance and forming a dynamic coupling with factors such as temperature and light. The environment of solar greenhouses exhibits highly nonlinear and multivariate coupling characteristics, leading to insufficient prediction accuracy in existing models. However, accurate predictions are crucial for regulating crop growth and yield. However, current mainstream greenhouse environmental prediction models still have obvious limitations when dealing with such complexity: traditional machine learning models and single-variable-driven models have issues such as insufficient accuracy (average MAE is 15–20% higher than in this study) and weak adaptability to nonlinear environmental changes in multi-environmental factor coupling predictions, making it difficult to meet the needs of precision farming. A review of relevant research over the past five years shows that while LSTM-based models perform well in time series prediction, they ignore the spatial correlations between environmental factors. Models incorporating attention mechanisms can capture key variables but suffer from high computational costs. To address these issues, this study proposes a prediction model based on multi-strategy optimization and gradient-boosting (GBDT) algorithms. By introducing a multi-scale feature fusion module, it addresses the accuracy issues in multi-factor coupling prediction. Additionally, it employs a lightweight network design to balance prediction performance and computational efficiency, filling the gap in existing research applications under complex greenhouse environments. The model optimizes data preprocessing and model parameters through Sobol sequence initialization, adaptive t-distribution perturbation strategies, and Gaussian–Cauchy mixture mutation strategies and combines CatBoost for modeling to enhance prediction accuracy. Experimental results show that the MSCSO–CatBoost model performs excellently in temperature prediction, with the mean absolute error (MAE) and root mean square error (RMSE) reduced by 22.5% (2.34 °C) and 24.4% (3.12 °C), respectively, and the coefficient of determination (R²) improved to 0.91, significantly outperforming traditional regression methods and combinations of other optimization algorithms. Additionally, the model demonstrates good generalization capability in predicting multiple environmental variables such as temperature, humidity, and light intensity, adapting to environmental fluctuations under different climatic conditions. This study confirms that combining multi-strategy optimization with gradient-boosting algorithms can significantly improve the prediction accuracy of solar greenhouse environments, providing reliable support for precision agricultural management. Future research could further explore the model’s adaptive optimization in complex climatic regions. Full article

Despite significant progress, precipitation forecasting in West Africa remains challenging due to the complexity of atmospheric processes and the region’s climatic variability. This study aims to identify optimal configurations of the WRF model to improve precipitation forecasting. To evaluate the sensitivity of the model’s physical parameterizations, 15 configurations were tested by combining various cumulus parameterization schemes (CPSs) and planetary boundary layer (PBL) schemes. The analysis examines two contrasting rainfall events in Senegal: one characterized by widespread intense precipitation and another featuring localized moderate rainfall. Simulated rainfall, temperature, and humidity were validated against rain gauges, satellite products (ENACTS, ARC, CHIRPS, and IMERG), and ERA5 reanalysis data. The results show that the WRF configurations achieve correlation coefficients (r) ranging from 0.27 to 0.62 against ENACTS and from 0.15 to 0.41 against rain gauges. The sensitivity analysis reveals that PBL schemes primarily influence temperature and humidity, while CPSs significantly affect precipitation. For the heavy rainfall event, several configurations accurately captured the observed patterns, particularly those using Tiedtke or Grell–Devenyi CPSs coupled with the Mellor–Yamada–Janjic (MYJ) PBL. However, the model showed limited skill in simulating localized convection during the moderate rainfall event. These findings highlight the importance of selecting appropriate parameterizations to enhance WRF-based precipitation forecasting, especially for extreme weather events in West Africa. Full article

Exploring the effects and mechanisms of spatial agglomeration of construction land resources on economic resilience across Chinese provinces will provide theoretical support for governments to optimize the allocation of productive forces and enhance economic resilience through rational distribution of construction land quotas. Based on the “Structure-Conduct-Performance (SCP)” analytical framework, this paper identifies spatial agglomeration through the share of the largest city and draws on the microeconomic concept of “elasticity” that reflects the relationships between variables to construct economic resilience with spatial relationship attributes. On this basis, it utilizes China’s provincial panel data gathered since 2000 and employs fixed-effects models, mediation models, moderation models, quantile regression, and subsample regression to examine the impact mechanisms of the spatial agglomeration of construction land on economic resilience. The research finds the following: the spatial agglomeration of construction land has a positive empowering effect on economic resilience; innovation and technical efficiency are important transmission paths for the spatial agglomeration of construction land to empower economic resilience; and further research shows that the empowering effect has an inverted U-shaped process, with the promoting effect being predominant. The empowering effect increases with rising quantiles and exhibits regional heterogeneity, showing an ascending gradient from eastern to western regions. The basic law in the western region is consistent with that of the whole country, and the scale of provincial construction land will strengthen the empowering effect. The research findings can provide decision-making references for the implementation and deepening of the main functional area strategy, as well as for strengthening the concentrated allocation of construction land quotas to advantageous regions. Full article

Amid growing demands for urban regeneration, metro station areas (MSAs) have emerged as critical spatial units for assessing renewal potential. However, their highly heterogeneous functional and spatial attributes pose challenges to precise classification and targeted strategy development. This study introduces the RFM (recency, frequency, and monetary) model—originally used in marketing—to the urban renewal domain. By mapping POI (point of interest) data, population density, and land price to the RFM dimensions, a three-dimensional evaluation framework is constructed. Using QGIS to process multi-source data for 118 MSAs in Beijing, we apply an improved five-quantile stratification method to classify station areas into eight renewal potential types. The results reveal a concentric spatial gradient: 24% of core-area MSAs are identified as Key-Value MSAs, while 23% of peripheral MSAs are categorized as General-Retention MSAs. Based on the classification, differentiated renewal strategies are proposed: high-potential MSAs should prioritize public space enhancement and walkability improvements, whereas low-potential MSAs should focus on upgrading basic transit infrastructure. The study provides a replicable method for classifying MSAs based on spatial and economic indicators, offering new theoretical insights and practical tools to guide evidence-based urban regeneration and station–city integration in high-density metropolitan areas such as Beijing. Full article

To integrate rapidly growing environmental, social, and governance (ESG) investments into agribusiness, it is essential to understand the decision-making mechanisms behind sustainable practices in small- and medium-sized farms. This study examines the role of corporate governance in promoting sustainable practices using structural equation modeling on survey data from 1111 Japanese farms. The results reveal that internal social sustainability practices, such as improving the work environment and employee well-being, are positively associated with corporate governance and, in turn, significantly enhance sales growth, cash flow, and succession prospects. In contrast, external social sustainability practices show a negative correlation with governance, reflecting the influence of socioemotional wealth and reputation-driven decision-making. Environmental sustainability practices correlate only with sustainable corporate governance, suggesting a lack of strategic integration. These findings underscore the importance of corporate governance as a factor in linking sustainable initiatives to economic outcome. Strengthening internal social sustainability through robust corporate governance is therefore critical for farmers aiming to improve performance through sustainability. Moreover, given that family management preferences shape sustainability choices, policymakers must consider both governance and socioemotional factors to effectively support agricultural sustainability. Full article

This study examines the link between deep approaches to learning (DAL) and undergraduate senior students’ employability skills and perceived satisfaction in STEM fields in the United States. DAL, comprising higher-order (HO) and reflective/integrated (RI) learning constructs, enhances the understanding of real-world applications and promotes reflective thinking about individual ideas in broader contexts. HO activities focus on analyzing, synthesizing, and applying new information in practical scenarios such as internships, classroom discussions, and presentations. RI activities involve integrating existing knowledge with new ideas. The efficacy of DAL in improving student outcomes including employability and satisfaction skills was investigated using Structural Equation Modeling (SEM), which included a Confirmatory Factor Analysis (CFA) to measure observed variables associated with the four latent factors (HO, RI, student satisfaction, and employability skills), followed by structural analysis to explore the relationship between these latent factors. Data from 14,292 senior students surveyed by the National Study of Student Engagement (NSSE) in 2018 were analyzed. The results indicated a significant positive effect of DAL on students’ satisfaction and perceived employability skills, underscoring its importance in higher education for STEM students. These findings can guide higher education institutions (HEIs) in focusing on DAL activities for meaningful learning outcomes and enhanced critical thinking. Full article

Accurate prediction of slab temperature during the continuous casting and rolling process is essential for optimizing reheating furnace scheduling and achieving energy savings and emission reductions in steel production. However, because of the dynamic boundary conditions caused by the complex transport processes, obtaining precise temperature data for slabs remains challenging. These difficulties lead to issues such as low hot charging rates, mixing of hot and cold slabs in reheating furnaces, and excessive heat loss from slabs after cutting. To address these challenges, this study develops a mathematical model to calculate slab temperatures during the continuous casting and rolling process, providing a foundation for production scheduling optimization. The model accounts for the coupled heat transfer effects induced by dynamic slab stacking and the stacking heat transfer effects resulting from slabs with varying cross-sectional dimensions. Validation against experimental data demonstrated the model’s accuracy and reliability. Key findings highlighted that neglecting dynamic stacking effects or simplifying slab dimensions introduces errors. These results enhance slab temperature tracking in complex processes and advance related theoretical understanding. Full article

Subject to magnetic saturation, temperature rise, and other factors, the electrical parameters of permanent magnet synchronous motors (PMSMs) in marine electric propulsion systems exhibit time-varying characteristics. Existing parameter identification algorithms fail to fully satisfy the requirements of high-performance PMSM control systems in terms of accuracy, response speed, and robustness. To address these limitations, this paper introduces multi-innovation theory and proposes a novel multi-innovation extended Kalman filter (MIEKF) for the identification of key electrical parameters of PMSMs, including stator resistance, d-axis inductance, q-axis inductance, and permanent magnet flux linkage. Firstly, the extended Kalman filter (EKF) algorithm is applied to linearize the nonlinear system, enhancing the EKF’s applicability for parameter identification in highly nonlinear PMSM systems. Subsequently, multi-innovation theory is incorporated into the EKF framework to construct the MIEKF algorithm, which utilizes historical state data through iterative updates to improve the identification accuracy and dynamic response speed. An MIEKF-based PMSM parameter identification model is then established to achieve online multi-parameter identification. Finally, a StarSim RCP MT1050-based experimental platform for online PMSM parameter identification is implemented to validate the effectiveness and superiority of the proposed MIEKF algorithm under three operational conditions: no-load, speed variation, and load variation. Experimental results demonstrate that (1) across three distinct operating conditions, compared to forget factor recursive least squares (FFRLS) and the EKF, the MIEKF exhibits smaller fluctuation amplitudes, shorter fluctuation durations, mean values closest to calibrated references, and minimal deviation rates and root mean square errors in identification results; (2) under the load increase condition, the EKF shows significantly increased deviation rates while the MIEKF maintains high identification accuracy and demonstrates enhanced anti-interference ability. This research has achieved a comprehensive improvement in parameter identification accuracy, dynamic response speed, convergence effect, and anti-interference performance, providing an electrical parameter identification method characterized by high accuracy, rapid dynamic response, and strong robustness for high-performance control of PMSMs in marine electric propulsion systems. Full article

(1) Background: Help-seeking behavior is a key way to maintain health and seek effective treatment, and it also helps to improve patients’ self-management ability. This study aimed to investigate the facilitating and hindering factors of help-seeking behaviors among patients with chronic diseases concerning their health issues. (2) Methods: Based on the Capability, Opportunity, and Motivation-Behavior (COM-B) model, 18 patients with chronic diseases in a tertiary hospital in Zhengzhou City, Henan Province, were selected for semi-structured in-depth interviews between July and November 2024 using a descriptive qualitative research approach. The collected data were analyzed using directed content analysis. (3) Results: A total of 18 interviews were conducted, and two themes and six sub-themes were extracted. The factors that promote health help-seeking behavior in patients with chronic diseases include ability (self-health monitoring ability, sufficient communication preparation ability), opportunity (health support in social bonds, effective support of medical staff), and motivation (good illness identity, past successful experience of health seeking help). Barriers include ability (symptom attribution bias, difficulty in identifying health information), opportunity (heavier financial burden, poor sense of gain in interactions), and motivation (fear and avoidance, stigma of illness). (4) Conclusions: There are some hindering factors and obvious contributing factors regarding health help-seeking behavior among patients with chronic diseases. Medical staff should prioritize guiding patients to seek help for health problems. The COM-B model can be applied to develop targeted intervention strategies for improving help-seeking behavior. This approach is beneficial for enhancing patients’ health management capabilities by promoting proactive health help-seeking practices. Full article

This study investigates how perceptual and emotional factors—perceived naturalness, aesthetic pleasure, environmental concern, and disgust—shape consumer acceptance of a human-hair-derived bio-fabricated textile product (a unisex cardholder). In a scenario-based online survey, participants viewed an AI-generated image accompanied by a short vignette. A purposive sample of young adults in Istanbul with prior experience purchasing sustainable textile products was recruited and screened. All constructs were measured with standard Likert-type scales and translated into Turkish using a two-way back-translation procedure. Data were analyzed with PLS-SEM. Model fit was acceptable, and the model accounted for a substantial share of the variance in adoption intention. Aesthetic pleasure showed a clear positive influence on adoption intention, whereas perceived naturalness did not display a direct effect. Environmental concern modestly strengthened the link between naturalness and adoption. Disgust emerged as the dominant moderator, fully conditioning the naturalness pathway and reducing—but not eliminating—the effect of aesthetic pleasure. Together, these findings indicate that perceived naturalness, aesthetic pleasure, environmental concern, and disgust jointly shape adoption intention and that practical emphasis should be placed on reducing feelings of disgust while enhancing aesthetic appeal. Full article

To increase the reliability of three-dimensional (3D) geological models in areas characterized by sparse well data and poor seismic quality, a sedimentary dynamics simulation was conducted on the J7 tidal delta sedimentary reservoir in the Y gas field, which is located in the West Siberian Basin. A 3D sedimentary model of the study area was developed by defining parameters such as bottom topography, water level, tidal range, river discharge, and wave amplitude. By integrating the reservoir characteristics, the sedimentary dynamics simulation results were transformed into a three-dimensional training template for multipoint geostatistical modeling. Simultaneously, the channel and bar parameters derived from the sedimentary dynamics simulation served as variable inputs for attribute modeling. Combined with well data, a 3D geological model of the reservoir was constructed and subsequently validated using verification wells. The results demonstrate that the reliability of reservoir lithology modeling—when constrained by three-dimensional training templates generated through sedimentary dynamics simulation—is significantly higher than that achieved using sequential Indicator simulation. Three-dimensional modeling of tidal delta reservoirs, employing coupled sedimentary dynamics simulations and multipoint geostatistical methods, can effectively enhance the reliability of reservoir geological models in areas with sparse well data, thereby providing a robust foundation for subsequent well deployment and development. Full article