Search Results (16,227)

Introduction: With the emergence of the expert patient and the expansion of health literacy, the importance of planning and building health technologies aimed at teaching and training health professionals, especially nurses, due to their activities with patients in Primary Health Care, with the aim of meeting the real and constant demands of the expert patient, is evident. Methods: Methodological study with a quantitative approach. The course was constructed based on a scope review, scientific reference, and observational visits during the months of September 2021 and August 2022. For validation, an organized electronic form was used with general information about the research and items of the course constructed for later evaluation by the judges with the three-point Likert scale and with the application of the Delphi Technique between the months of September and October 2022; for the agreement of the judges, the Content Validation Coefficient > 0.8 was considered. Results: Based on the content selected in the scope review, the reference contribution, and the observational visits, the course was constructed. Nine judges participated in the validation stage in Delphi I with a total Content Validation Coefficient above 0.90 and with some suggestions for modifications and improvements pointed out by them. In Delphi II, six judges evaluated the course, resulting in a total Content Validation Coefficient of 0.99. Conclusions: The course developed was considered valid to support the training of Primary Health Care nurses in the formation of the expert patient, with a view to promoting patient autonomy in self-care management, optimizing Primary Health Care, and reducing unnecessary hospital admissions. Full article

(1) Background: The COVID-19 pandemic presented unprecedented challenges for people with multiple sclerosis (PwMS) in Oman, necessitating targeted healthcare planning and patient support. This study aimed to investigate the impact of COVID-19 on MS management and disease course, incidence, and outcomes of COVID-19, psychosocial and mental health effects of the pandemic, and demographic and clinical predictors of the effects related to COVID-19 among Omani PwMS. (2) Methods: This cross-sectional study was conducted from January to April 2021. Adult (18–60 years) Omani PwMS completed a structured interview along with the Expanded Disability Status Scale (EDSS) and World Health Organization Well-being Index (WHO-5). Clinical data on relapses and disease-modifying therapies and adherence were analyzed. The data was statistically analyzed. (3) Results: Of 104 PwMS (73.1% female), 22.1% contracted COVID-19, with fatigue being the most reported symptom (87%). Female sex (p = 0.042), younger age (18–34 vs. 35–45 years; p = 0.014), diagnosis of COVID-19 (p = 0.037), and low current mental well-being scores (p = 0.021) predicted greater COVID-19-related effects. (4) Conclusion: These findings highlight the need to study the mental resilience of this subgroup of PwMS and provide them with targeted support during crises. Full article

Dam monitoring tracks environmental variables (water level, temperature) and structural responses (deformation, seepage, and stress) to assess safety and performance. Structural health monitoring (SHM) refers to the systematic observation and analysis of the structural condition over time, and it is essential in maintaining the safety, functionality, and long-term performance of dams. This review examines monitoring data applications, covering structural health assessment methods, historical motivations, and key challenges. It discusses monitoring components, data acquisition processes, and sensor roles, stressing the need to integrate environmental, operational, and structural data for decision making. Key objectives include risk management, operational efficiency, safety evaluation, environmental impact assessment, and maintenance planning. Methodologies such as numerical modeling, statistical analysis, and machine learning are critically analyzed, highlighting their strengths and limitations and the demand for advanced predictive techniques. This paper also explores future trends in dam monitoring, offering insights for engineers and researchers to enhance infrastructure resilience. By synthesizing current practices and emerging innovations, this review aims to guide improvements in dam safety protocols, ensuring reliable and sustainable dam operations. The findings provide a foundation for the advancement of monitoring technologies and optimization of dam management strategies worldwide. Full article

In highly urbanised and industrialised settings, managing environmental pressures and enhancing urban resilience demand integrated, spatially explicit approaches. This study presents a methodological framework that integrates topographic data, land cover information, and open geodata to produce a high-resolution vulnerability map. A multi-criteria analysis was performed using indicators such as land use, population density, proximity to emission sources, vegetation cover, and sensitive services (e.g., schools and hospitals). The result is a high-resolution vulnerability map that classifies the urban, peri-urban, and coastal zones into five levels of environmental risk. These evaluation levels are derived from geospatial analyses combining pollutant dispersion modelling with land-use classification, enabling the identification of the most vulnerable urban zones. These findings support evidence-based planning and can guide local governments and environmental agencies in prioritising Nature-based Solutions (NBSs), enhancing ecological connectivity, and reducing exposure for vulnerable populations. Full article

Soil moisture is a key factor in the exchange of energy and matter between the soil and atmosphere, playing a vital role in the hydrological cycle and agricultural management. Traditional monitoring methods are limited in achieving large-scale, real-time observations, while deep learning offers new avenues to model the complex nonlinear relationships between spectral features and soil moisture content. This study focuses on the Wei-Ku Oasis in Xinjiang, using multi-source remote sensing data (Landsat series and Sentinel-1) and in situ multi-layer soil moisture measurements. The BOSS feature selection algorithm was applied to construct 46 feature parameters, including vegetation indices, soil indices, and microwave indices, and to identify optimal variable sets for each depth. Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and their hybrid model (CNN-LSTM) were used to build soil moisture inversion models at various depths. Their performances were systematically compared on both training and testing sets, and the optimal model was used for spatiotemporal mapping. The results show that the CNN-LSTM-based multi-depth soil moisture inversion model achieved superior performance, with the 0–10 cm model showing the highest accuracy and a testing R² of 0.64, outperforming individual models. The testing R² values for the soil moisture inversion models at depths of 10–20 cm, 20–40 cm, and 40–60 cm were 0.59, 0.54, and 0.59, respectively. According to the mapping results, soil moisture in the 0–60 cm profile of the Wei-Ku Oasis exhibited a vertical gradient, increasing with depth. Spatially, soil moisture was higher in the central oasis and lower toward the periphery, forming a “center-high, edge-low” pattern. This study provides a high-accuracy method for multi-layer soil moisture remote sensing in arid regions, offering valuable data support for oasis water resource management and precision irrigation planning. Full article

Background: Pelvic fractures are complex injuries often associated with significant morbidity and mortality, requiring multidisciplinary management. This case series highlights the presentation, management strategies, and outcomes of patients with pelvic fractures treated at our institution. Methods: The medical records of 13 patients diagnosed with pelvic fractures from 1 January 2020 through 31 December 2023 were retrospectively reviewed. Demographic data, mechanism of injury, fracture pattern, associated injuries, treatment modalities, and outcomes were analyzed. Results: A total of 13 patients were included in the study, with ages ranging from 18–95 years. Six of the patients were male and seven were female. The most common mechanisms of injury were falls and pedestrians struck by vehicles. Associated injuries included traumatic brain injury (TBI), fractures including extremities, ribs, and vertebrae, visceral injury, and spinal cord injury. Treatment strategies ranged from conservative, non-surgical management to operative intervention, including interventional radiology embolization, external traction, open reduction and internal fixation (ORIF), and percutaneous screw stabilization. Additional interventions included chest tube placement, exploratory laparotomy, and craniectomy. Two patients died while in the hospital, one was discharged to a shelter, and the remaining 10 were discharged to various inpatient rehab facilities. Conclusions: Pelvic fractures pose significant clinical challenges due to their complexity and associated injuries. This case series underscores the importance of multidisciplinary intervention and treatment strategies in optimizing outcomes. Further studies should focus on the effectiveness of interventions, utilization of new technology, and multidisciplinary team planning. Full article

With the rapid development of the social economy, human activities have increasingly disrupted water environments, and the continuous input of pollutants poses significant challenges for water environment management. Taking the Xiaoxingkai Lake basin as the study area, this paper develops a social–economic–water environment model based on the system dynamics methodology, incorporating subsystems for population, agriculture, and water pollution. The model focuses on four key indicators of pollution severity, namely, total nitrogen (TN), total phosphorus (TP), chemical oxygen demand (COD), and ammonia nitrogen (NH₃-N), and simulates the changes in pollutant loads entering the river under five different scenarios from 2020 to 2030. The results show that agricultural non-point sources are the primary contributors to TN (79.5%) and TP (73.7%), while COD primarily originates from domestic sources (64.2%). NH₃-N is mainly influenced by urban domestic activities (44.7%) and agricultural cultivation (41.2%). Under the status quo development scenario, pollutant loads continue to rise, with more pronounced increases under the economic development scenario, thus posing significant sustainability risks. The pollution control enhancement scenario is most effective in controlling pollutants, but it does not promote socio-economic development and has high implementation costs, failing to achieve coordinated socio-economic and environmental development in the region. The dual-reinforcement scenario and moderate-reinforcement scenario achieve a balance between pollution control and economic development, with the moderate-reinforcement scenario being more suitable for long-term regional development. The findings can provide a scientific basis for water resource management and planning in the Xiaoxingkai Lake basin. Full article

This study presents an approach based on machine learning (ML) techniques to analyze the relationship between emergency room (ER) admissions for cardiorespiratory diseases (CRDs) and environmental factors. The aim of this study is the development and verification of an interpretable machine learning framework applied to environmental and health data to assess the relationship between environmental factors and daily emergency room admissions for cardiorespiratory diseases. The model’s predictive accuracy was evaluated by comparing simulated values with observed historical data, thereby identifying the most influential environmental variables and critical exposure thresholds. This approach supports public health surveillance and healthcare resource management optimization. The health and environmental data, collected through meteorological sensors and air quality monitoring stations, cover eleven years (2013–2023), including meteorological conditions and atmospheric pollutants. Four ML models were compared, with XGBoost showing the best predictive performance (R² = 0.901; MAE = 0.047). A 10-fold cross-validation was applied to improve reliability. Global model interpretability was assessed using SHAP, which highlighted that high levels of carbon monoxide and relative humidity, low atmospheric pressure, and mild temperatures are associated with an increase in CRD cases. The local analysis was further refined using LIME, whose application—followed by experimental verification—allowed for the identification of the critical thresholds beyond which a significant increase in the risk of hospital admission (above the 95th percentile) was observed: CO > 0.84 mg/m³, P_atm ≤ 1006.81 hPa, Tavg ≤ 17.19 °C, and RH > 70.33%. The findings emphasize the potential of interpretable ML models as tools for both epidemiological analysis and prevention support, offering a valuable framework for integrating environmental surveillance with healthcare planning. Full article

Over the past 30 years, the City of Portland, Oregon, USA, has emerged as a national leader in green stormwater infrastructure (GSI). The initial impetus for implementing sustainable stormwater infrastructure in Portland stemmed from concerns about flooding and water quality in the city’s two major rivers, the Columbia and the Willamette. Heavy rainfall often led to combined sewer overflows, significantly polluting these waterways. A partial solution was the construction of “The Big Pipe” project, a large-scale stormwater containment system designed to filter and regulate overflow. However, Portland has taken a more comprehensive and long-term approach by integrating sustainable stormwater management into urban planning. Over the past three decades, the city has successfully implemented GSI to mitigate these challenges. Low-impact development strategies, such as bioswales, green streets, and permeable surfaces, have been widely adopted in streetscapes, pathways, and parking areas, enhancing both environmental resilience and urban livability. This perspective highlights the history of the implementation of Portland’s GSI programs, current design and performance standards, and challenges and lessons learned throughout Portland’s recent history. Innovative approaches to managing runoff have not only improved stormwater control but also enhanced green spaces and contributed to the city’s overall climate resilience while addressing economic well-being and social equity. Portland’s success is a result of strong policy support, effective integration of green and gray infrastructure, and active community involvement. As climate change intensifies, cities need holistic, adaptive, and community-centered approaches to urban stormwater management. Portland’s experience offers valuable insights for cities seeking to expand their GSI amid growing concerns about climate resilience, equity, and aging infrastructure. Full article

With the growth of the global population and the increasing scarcity of arable land, traditional agricultural production is confronted with multiple challenges, such as efficiency improvement, precision operation, and sustainable development. The progressive advancement of artificial intelligence (AI) technology has created a transformative opportunity for the intelligent upgrade of agricultural equipment. This article systematically presents recent progress in computer vision, machine learning (ML), and intelligent sensing. The key innovations are highlighted in areas such as object detection and recognition (e.g., a K-nearest neighbor (KNN) achieved 98% accuracy in distinguishing vibration signals across operation stages); autonomous navigation and path planning (e.g., a deep reinforcement learning (DRL)-optimized task planner for multi-arm harvesting robots reduced execution time by 10.7%); state perception (e.g., a multilayer perceptron (MLP) yielded 96.9% accuracy in plug seedling health classification); and precision control (e.g., an intelligent multi-module coordinated control system achieved a transplanting efficiency of 5000 plants/h). The findings reveal a deep integration of AI models with multimodal perception technologies, significantly improving the operational efficiency, resource utilization, and environmental adaptability of agricultural equipment. This integration is catalyzing the transition toward intelligent, automated, and sustainable agricultural systems. Nevertheless, intelligent agricultural equipment still faces technical challenges regarding data sample acquisition, adaptation to complex field environments, and the coordination between algorithms and hardware. Looking ahead, the convergence of digital twin (DT) technology, edge computing, and big data-driven collaborative optimization is expected to become the core of next-generation intelligent agricultural systems. These technologies have the potential to overcome current limitations in perception and decision-making, ultimately enabling intelligent management and autonomous decision-making across the entire agricultural production chain. This article aims to provide a comprehensive foundation for advancing agricultural modernization and supporting green, sustainable development. Full article

Addressing climate change and advancing clean energy transitions demand holistic approaches that capture complex, interconnected system behaviors. This review focuses on the application of causal loop diagrams (CLDs) as a core systems-thinking methodology to understand and manage dynamic feedback within environmental, social, and technological domains. CLDs visually map the reinforcing and balancing loops that drive climate risks, clean energy adoption, and sustainable development, offering intuitive insights into system structure and behavior. Through a synthesis of empirical studies and case examples, this paper demonstrates how CLDs help identify leverage points in renewable energy policy, carbon management, and ecosystem resilience. Despite their strengths in simplifying complexity and enhancing stakeholder communication, challenges remain—including data gaps, model validation, and the integration of diverse knowledge systems. The review also examines recent innovations that improve CLD effectiveness, such as hybrid modeling approaches and digital tools that enhance transparency and decision support. By emphasizing CLDs’ unique capacity to reveal feedback mechanisms critical for climate action and energy planning, this study provides actionable recommendations for researchers, policymakers, and practitioners seeking to leverage systems thinking for transformative, sustainable solutions. Full article

Continuous progress in the sector of additive manufacturing has drastically aided the design and fabrication of urban furniture, offering high levels of customization and adaptability. This work looks into the potential of 3D printing to transform urban public spaces by allowing for the creation of functional, aesthetically pleasing, and user-centered furniture solutions. Through additive manufacturing processes, urban furniture can be tailored to meet the unique needs of diverse communities, allowing for the extended usage of sustainable materials, modular designs, and smart technologies. The flexibility of 3D printing also promotes the fabrication of complex, intricate designs that would be difficult or cost-prohibitive using traditional methods. Additionally, 3D-printed furniture can be optimized for specific environmental conditions, providing solutions that enhance accessibility, improve comfort, and promote inclusivity. The various advantages of 3D-printed urban furniture are examined, including reduced material waste and the ability to rapidly prototype and iterate designs alongside the potential for on-demand, local production. By embedding sensors and IoT devices, 3D-printed furniture can also contribute to the development of smart cities, providing real-time data for urban management and improving the overall user experience. As cities continue to encourage and adopt sustainable and innovative solutions, 3D printing is believed to play a crucial role in future urban infrastructure planning. Full article

Ventilation in subway and railway tunnels is a critical safety component, especially during fire emergencies, where effective smoke and heat management is essential for successful evacuation and firefighting efforts. The Subway Environmental Simulation (SES, Version 4.1) model is widely used for predicting airflow and thermal conditions during fire events, but its accuracy in real-world applications requires validation. This study compares SES predictions with experimental data from the Memorial Tunnel fire ventilation tests to evaluate its performance in simulating the effects of jet fans on longitudinal ventilation. The analysis focuses on SES’s ability to predict flow rate and temperature distributions. Results showed reasonable agreement between SES-predicted airflows and temperatures. However, SES tended to underpredict temperatures upstream and near the fire source, indicating a limitation in simulating thermal behavior close to the fire. These findings suggest that SES can be a reliable tool for tunnel ventilation design if certain safety margins, based on the error values identified in this study, are considered. Nonetheless, further improvements are necessary to enhance its accuracy, particularly in modeling heat transfer dynamics and the impact of fire-induced temperature changes. Future work should focus on conducting additional full-scale test validations and model refinements to improve SES’s predictive capabilities for fire safety planning. Full article

This study highlights the spatial, seasonal, and climatic variations in bird species richness across Türkiye, a country with rich avian richness situated at the intersection of major migratory routes. Bird species richness was calculated for each province. Differences between regions, Köppen–Geiger climate classes, and seasons were analyzed using the Kruskal–Wallis method. Non-parametric analysis of longitudinal data in factorial experiments was also employed to determine seasonal differences within regions and climate classes. The results revealed significant spatial variations in species richness, particularly between temperate and cold climate regions. While seasonal differences were generally less pronounced, they were critical for both migratory and resident bird species. Wetlands, coastal areas, and transitional habitats were identified as biodiversity hotspots for both resident and migratory birds. This study underscores the need to integrate regional, climatic, and seasonal variations into ecosystem-based management plans. Protecting critical habitats, enhancing connectivity through ecological corridors, and adopting adaptive conservation strategies are essential for sustaining Türkiye’s rich avian diversity. These results provide valuable insights for conservation planning and emphasize the importance of addressing spatial and seasonal dynamics to ensure long-term biodiversity preservation. Full article

Background: Arteriovenous fistulas (AVFs) may contribute to cardiac remodeling and consequently to an increased risk of heart failure and cardiovascular mortality in patients with end-stage kidney disease (ESKD). We aimed to assess cardiac changes following AVF creation and identify potential parameters associated with cardiac remodeling. Methods: In our prospective, single-center study, ESKD patients without significant pre-existing cardiac disease underwent 2D and 3D echocardiographic evaluation before and after AVF creation, along with AVF flow measurement. Cardiac remodeling was assessed using 3D indexed left and right ventricular end-diastolic volumes (LVEDVi, RVEDVi), while systolic function was assessed using longitudinal strain and 3D ejection fraction. Results: We included 20 patients (18 men; median age 73.5 years [IQR: 67–77]) with a mean AVF flow of 1140 ± 345 mL/min. At a median of 8.2 months (IQR: 7.3–9.3) following AVF creation, significant biventricular dilatation was observed: LVEDVi increased from 89 ± 14 to 97 ± 21 mL/m² (p < 0.05) and RVEDVi from 80 ± 15 to 91 ± 18 mL/m² (p < 0.05), while the systolic function of both ventricles did not change significantly. The right ventricle showed the most pronounced remodeling and it was independently associated with volume overload (p = 0.003) and elevated left ventricular filling pressure (p = 0.030), but not with AVF flow. Conclusions: Moderate AVF flow was associated with cardiac remodeling, primarily affecting the right ventricle. Fluid overload and left ventricular filling pressure were key factors associated with right ventricular remodeling, underscoring the need for careful fluid management and vascular access planning in ESKD patients. Full article