Search Results (18,050)

This comprehensive review examines hierarchical control principles and frameworks for grid-connected microgrids operating in environments prone to load shedding and under demand response. The particular emphasis is on South Africa’s current electricity grid issues, experiencing regular planned and unplanned outages, due to numerous factors including ageing and underspecified infrastructure, and the decommissioning of traditional power plants. The study employs a systematic literature review methodology following PRISMA guidelines, analysing 127 peer-reviewed publications from 2018–2025. The investigation reveals that conventional microgrid controls require significant adaptation to address the unique challenges brought about by scheduled power outages, including the need for predictive–proactive strategies that leverage known load-shedding schedules. The paper identifies three critical control layers of primary, secondary, and tertiary and their modifications for resilient operation in environments with frequent, planned grid disconnections alongside renewables integration, regular supply–demand balancing and dispatch requirements. Hybrid optimisation approaches combining model predictive control with artificial intelligence show good promise for managing the complex coordination of solar–storage–diesel systems in these contexts. The review highlights significant research gaps in standardised evaluation metrics for microgrid resilience in load-shedding contexts and proposes a novel framework integrating predictive grid availability data with hierarchical control structures. South African case studies demonstrate techno-economic advantages of adapted control strategies, with potential for 23–37% reduction in diesel consumption and 15–28% improvement in battery lifespan through optimal scheduling. The findings provide valuable insights for researchers, utilities, and policymakers working on energy resilience solutions in regions with unreliable grid infrastructure. Full article

Background: Intimate partner violence (IPV) represents a major public health problem in Europe, with significant physical, psychological, and social consequences. Nurses are often the first professionals capable of detecting early signs of IPV, yet they lack validated instruments to assess their clinical competency in detection, evaluation, documentation, and intervention. This study aimed to develop and validate the Intimate Partner Violence Nursing Competency Scale (IPVNCS), aligned with the Nursing Intervention Classification (NIC 6403). Methods: A cross-sectional psychometric study was conducted among registered nurses in the Community of Madrid. A 30-item Likert-type self-administered instrument (1–5 scale) was developed based on NANDA, NIC 6403, and NOC frameworks. A total of 202 nurses participated. Reliability was assessed through Cronbach’s alpha. Construct validity was examined using exploratory factor analysis (EFA) with Promax rotation and confirmatory factor analysis (CFA) using AMOS 26. Ethical approval was obtained (CEU San Pablo, code 843/24/104). Results: After item refinement, 26 items remained across four dimensions: (1) Intervention and Referral, (2) Detection and Assessment, (3) Documentation and Recording-keeping, (4) Psychosocial Support. The instrument showed excellent reliability ($\alpha$ = 0.97). KMO was 0.947 and Bartlett’s test was significant (p < 0.001). CFA demonstrated satisfactory fit: ${\chi }^2$/df = 2.066, RMSEA = 0.073, CFI = 0.92, TLI = 0.91, NFI = 0.86. The final model adequately represented the latent structure. After debugging, its psychometric properties were significantly improved. Four redundant items were eliminated, achieving internal consistency ($\alpha$ = 0.97), a KMO value of 0.947 and a significant Bartlett’s test of sphericity. It showed a better fit, according to ${\chi }^2$/df = (2.066); Parsimony = (720.736); RMR (0.0529; RMSEA (0.073); NFI (0.860); TLI (0.910) and CFI (0.920). The final model provides an adequate representation of the latent structure of the data. This study provides initial evidence of construct validity and internal consistency reliability of the IPVNCS. Conclusions: The IPVNCS is a valid and reliable tool to assess nursing competencies for clinical management of IPV. It supports structured evaluation across four core nursing domains, enabling improved educational planning, clinical decision-making, and quality of care for victims. The scale fills a gap in clinical nursing assessment tools and can support protocol development in emergency, primary care, and hospital settings. Full article

The complex topography and harsh natural environment of the Loess Plateau in Longzhong have been suffering from an undefined living circle structure, which has hindered rural planning and development. A rural community living circle is a spatial unit centered on meeting the needs of villagers, within which various service facilities are rationally allocated within a specific spatial scope. To refine its spatial patterns, the concept of living circles was introduced to address travel challenges. The extent of these living circles is affected by the accessibility of public service facilities and barriers to travel. Using land use data, DEM, population density, and road networks, this study employed the MCR model, gravity model, and ArcGIS spatial analysis to examine the patterns of rural community living circles. The focus was on analyzing the living circle structure of rural communities on the Loess Plateau in Longzhong, considering both natural and artificial environmental constraints. The results show: (1) Rural community living circles present multi-scale spatial features. The basic living circle covers a 15 min slow-travel area. The central living circle corresponds to village-level needs, accessible within 35 min by both slow and motorized travel. The town living circle covers a 10 km radius, reachable within 60 min by a mix of transport modes. The county living circle, dominated by motorized travel, represents the top tier of public service configuration. (2) Quantitatively, the delineation identified 2753 basic, 444 central, 19 township, and 1 county-level living circles in the Anding District of Dingxi City. The Northern, Eastern, and Southwest Zones suffer from fragmented mountainous landscapes, limiting mobility and accessibility. The Central Zone, however, benefits from a combination of mountainous terrain and river valley plains, offering superior service accessibility. (3) The analysis results based on the MCR model and gravity model aligned more closely with reality, reflecting the scale patterns of rural community living circles. The results of this study can provide theoretical guidance for rural planning, construction, and management in the hilly and gully areas of the Loess Plateau. Full article

Impulsive overtopping generated by dam-break surges is a critical hazard for dikes and flood-protection embankments, especially in reservoirs and mountainous catchments. Unlike classical coastal wave overtopping, which is governed by long, irregular wave trains and usually characterized by mean overtopping discharge over many waves, these dam-break-type events are dominated by one or a few strongly nonlinear bores with highly transient overtopping heights. Accurately predicting the resulting overtopping levels under such impulsive flows is therefore important for flood-risk assessment and emergency planning. Conventional cluster-then-predict approaches, which have been proposed in recent years, often first partition data into subgroups and then train separate models for each cluster. However, these methods often suffer from rigid boundaries and ignore the uncertainty information contained in clustering results. To overcome these limitations, we propose a GMM+MoE framework that integrates Gaussian Mixture Model (GMM) soft clustering with a Mixture-of-Experts (MoE) predictor. GMM provides posterior probabilities of regime membership, which are used by the MoE gating mechanism to adaptively assign expert models. Using SPH-simulated overtopping data with physically interpretable dimensionless parameters, the framework is benchmarked against XGBoost, GMM+XGBoost, MoE, and Random Forest. Results show that GMM+MoE achieves the highest accuracy ($R^2=0.9638$ on the testing dataset) and the most centralized residual distribution, confirming its robustness. Furthermore, SHAP-based feature attribution reveals that relative propagation distance and wave height are the dominant drivers of overtopping, providing physically consistent explanations. This demonstrates that combining soft clustering with adaptive expert allocation not only improves accuracy but also enhances interpretability, offering a practical tool for dike safety assessment and flood-risk management in reservoirs and mountain river valleys. Full article

Chronic pain is inherently multifactorial, with biological, psychological, and social factors contributing to neuropathic pain (NP) and central sensitization (CS) syndromes. Comorbidity between functional disorders and the lack of clinical biomarkers adds to the challenge of diagnosis and treatment, leading to frustration for healthcare professionals and patients. Available treatments are limited, increasing patient suffering with personal and financial costs. This systematic review examined multisensory processing alterations in chronic pain and reviewed current pharmacological and non-pharmacological interventions. A structured search was conducted on the PubMed database using the keywords Central Sensitization, Fibromyalgia, Complex Regional Pain Syndrome, and Neuropathic Pain, combined with the keywords Vision, Audition, Olfaction, Touch, Taste, and Proprioception. Papers were then filtered to discuss current treatment approaches. Articles within the last five years, from 2018 to 2023, have been included. Papers were excluded if they were animal studies; investigated tissue damage, disease processes, or addiction; or were conference proceedings or non-English. Results were summarized in table form to allow synthesis of evidence. As this study is a systematic review of previously published research rather than a clinical trial or experimental investigation, the risk of bias was assessed independently by at least two reviewers. 138 studies were identified and analyzed. Of these, 96 focused primarily on treatment options for chronic pain and were analyzed for this systematic review. There were a few emerging themes. No one therapy is effective, so a multidisciplinary approach to diagnosis, including pharmacological, somatic, and psychological treatment, is generally predicted to achieve the best outcomes. Cranial neurovascular compromise, especially of the trigeminal, glossopharyngeal, and potentially the vestibulocochlear nerve, is being increasingly revealed with the advancement of neuroimaging. Cortical and deep brain stimulation to evoke neuroplasticity is an emerging and promising therapy and warrants further investigation. Finally, including patients in their treatment plan allows them control and offers the ability to self-manage their pain. Risk of bias limits the ability to judge the quality of evidence. Full article

Background/Objectives: Self-care is a cornerstone of healthy aging and chronic disease management; however, evidence on the most effective intervention models for improving quality of life in older adults with chronic non-communicable diseases (NCDs) remains fragmented. This review aimed to evaluate the effectiveness of self-care interventions in promoting quality of life and health outcomes in older adults with NCDs. Methods: A systematic review was conducted in accordance with PRISMA 2020 guidelines and registered in PROSPERO (CRD420251040613). Randomized and non-randomized clinical trials published between 2019 and 2024 were retrieved from Scopus, Web of Science, and EBSCOhost. Eligible studies included adults aged ≥60 years with NCDs receiving self-care interventions. Data extraction and risk of bias assessment were independently performed using Joanna Briggs Institute tools. Results: Twenty-nine studies involving 7241 older adults were included. Self-care interventions comprised nurse-led educational programs, digital health strategies, community- and peer-based approaches, and person-centered care models. Multicomponent and continuous interventions demonstrated consistent improvements in physical and psychological domains of quality of life, self-efficacy, autonomy, symptom management, and treatment adherence. Digital interventions enhanced monitoring and engagement, although their effectiveness varied according to sensory and health literacy limitations. Conclusions: Structured, person-centered, and nurse-led self-care interventions are effective in improving quality of life and autonomy among older adults with NCDs. These findings support their integration into primary and community-based care, reinforcing their relevance for clinical practice, care planning, and the development of assistive and educational strategies in aging care. Full article

Urban rail transit networks in developing countries are rapidly expanding, entering a networked operational phase where accurate passenger flow forecasting is crucial for optimizing vehicle scheduling, resource allocation, and transportation efficiency. In the short term, accurate real-time forecasting enables the dynamic adjustment of train headways and crew deployment, reducing average passenger waiting times during peak hours and alleviating platform overcrowding; in the long term, reliable trend predictions support strategic planning, including capacity expansion, station retrofitting, and energy management. This paper proposes a novel hybrid forecasting model, EEMD-TiDE, that combines improved Ensemble Empirical Mode Decomposition (EEMD) with a Time Series Dense Encoder (TiDE) to enhance prediction accuracy. The EEMD algorithm effectively overcomes mode mixing issues in traditional EMD by incorporating white noise perturbations, decomposing raw passenger flow data into physically meaningful Intrinsic Mode Functions (IMFs). At the same time, the TiDE model, a linear encoder–decoder architecture, efficiently handles multi-scale features and covariates without the computational overhead of self-attention mechanisms. Experimental results using Xi’an Metro passenger flow data (2017–2019) demonstrate that EEMD-TiDE significantly outperforms baseline models. This study provides a robust solution for urban rail transit passenger flow forecasting, supporting sustainable urban development. Full article

This paper analyzes the implementation of the Geoportal SIG FESBAL–UPM, a Participatory Geographic Information System (PGIS) developed within the Master’s and Doctorate programs in Rural Development Project Planning and Sustainable Management at UPM. The study introduces a model integrated with Project-Based Learning (PBL), the Working With People (WWP) framework, and the CFS-RAI principles to address challenges in responsible food systems. The geoportal designed to be applied at the Food Bank–UPM Chair–FESBAL, acts as an innovative instrument for participation among the different stakeholders enabling the spatialization and analysis of data across social, environmental, and governance dimensions. Functionally, it offers a robust foundation for evidence-based decision-making, systematizes geographic information, and visualizes data via the web, supporting research, training, and community engagement actions. Furthermore, this study details the specific projects and activities developed under the three involved action lines: research, training, and community engagement, identifying strengths and weaknesses in each. The findings affirm that this participatory approach ensures that the proposed solutions are aligned with local needs and priorities, increasing the sustainability and long-term success of the projects implemented through the geoportal. Full article

Accurate mapping of land use and land cover (LULC) is crucial for various applications such as urban planning, environmental management, and sustainable development, particularly in rapidly growing urban areas. African cities such as Yaoundé, Cameroon, are particularly affected by this rapid and often uncontrolled urban growth with complex spatio-temporal dynamics. Effective modeling of LULC indicators in such areas requires robust algorithms for high-resolution images segmentation and classification, as well as reliable data with great spatio-temporal distributions. Among the most suitable data sources for these types of studies, Sentinel-2 image time series, thanks to their high spatial (10 m) and temporal (5 days) resolution, are a valuable source of data for this task. However, for an effective LULC modeling purpose in such dynamic areas, many challenges remain, including spectral confusion between certain classes, seasonal variability, and spatial heterogeneity. This study proposes a hybrid deep learning architecture combining U-Net and Convolutional Long Short-Term Memory (ConvLSTM) layers, allowing the spatial structures and temporal dynamics of the Sentinel-2 series to be exploited jointly. Applied to the Yaoundé region (Cameroon) over the period 2018–2025, the hybrid model significantly outperforms the U-Net and ConvLSTM models alone. It achieves a macro-average F1 score of 0.893, an accuracy of 0.912, and an average IoU of 0.811 on the test set. These segmentation performances reached up to 0.948, 0.953, and 0.910 for precision, F1-score, and IoU, respectively, on the built-up areas class. Moreover, despite its better performance, in terms of complexity, the figures confirm that the hybrid does not significantly penalize evaluation speed. These results demonstrate the relevance of jointly integrating space and time for robust LULC classification from multi-temporal satellite images. Full article

Sustainability assessment in UNESCO World Heritage city centres often treats spatial configuration, functional accessibility, and heritage governance as separate analytical domains. This study addresses this fragmentation by developing a composite assessment framework to evaluate morphological sustainability in historic urban cores. The Morphological Sustainability Model (MSM) and its numerical expression, the Morphological Sustainability Index (MSI), are applied to the Bursa Khans District for the 2020–2025 period. The model integrates Space Syntax variables (integration, connectivity, choice, and intelligibility), 15-Minute City indicators related to proximity, pedestrian accessibility, active mobility, and inclusivity, and Historic Urban Landscape-based governance evaluations derived from UNESCO-compliant management plans. These components are synthesised into six weighted composite indicators (BKH1–BKH6). Results show that the MSI increases from 0.38 in 2020 to 0.51 in 2025 (+34.2%), indicating a strengthened alignment between spatial configuration, pedestrian-oriented functional performance, and heritage governance capacity. The findings reveal a shift from car-oriented axial dominance toward a more pedestrian-centred spatial structure along the historic bazaar spine. Overall, the study demonstrates that the MSI provides a transferable, decision-support-oriented framework for assessing morphological sustainability in historic urban environments. Full article

Children with rare tumours and malformations may benefit from innovative imaging, including patient-specific 3D models that can enhance communication and surgical planning. The primary aim was to evaluate the impact of patient-specific 3D models on communication with families. The secondary aims were to assess their influence on medical management and to establish an efficient post-processing workflow. From 2021 to 2024, we prospectively included patients aged 3 months to 18 years with rare tumours or malformations. Families completed questionnaires before and after the presentation of a 3D model generated from MRI sequences, including peripheral nerve tractography. Treating physicians completed a separate questionnaire before surgical planning. Analyses were performed in R. Among 21 patients, diagnoses included 11 tumours, 8 malformations, 1 trauma, and 1 pancreatic pseudo-cyst. Likert scale responses showed improved family understanding after viewing the 3D model (mean score 3.94 to 4.67) and a high overall evaluation (mean 4.61). Physicians also rated the models positively. An efficient image post-processing workflow was defined. Although manual 3D reconstruction remains time-consuming, these preliminary results show that colourful, patient-specific 3D models substantially improve family communication and support clinical decision-making. They also highlight the need for supporting the development of MRI-based automated segmentation softwares using deep neural networks, which are clinically approved and usable in routine practice. Full article

Mapping forest growth potential across varying environments is challenging, especially when field measurements are limited. In this study, we integrated Airborne Laser Scanning (ALS) terrain derivatives and Sentinel-2 spectral indices to model Site Index (SI), using forest plantations, at 10-m spatial resolution across three ecologically distinct regions in British Columbia (Aleza Lake, Deception, and Eagle Hills). Random Forest regression models were calibrated using field-measured SI and a multistep variable-selection procedure that included Variance Inflation Factor (VIF) screening followed by model-based variable importance assessment. Model performance was evaluated using repeated 10-fold cross-validation. The combined ALS–Sentinel-2 models substantially outperformed single-source models, yielding cross-validated R² values of 0.63, 0.44, and 0.56 for Aleza Lake, Deception, and Eagle Hills, respectively, compared with R² values of 0.40, 0.40, and 0.46 for ALS-only models. Key predictors consistently included terrain metrics, such as the Topographic Position Index (TPI) and the Topographic Wetness Index (TWI), along with satellite-derived chlorophyll-sensitive indices including S2REP (Sentinel-2 red-edge position), MTCI (MERIS terrestrial chlorophyll), and GNDVI (Greenness Normalized Difference Vegetation Index). A general model using predictors common to all regions performed comparably (R² = 0.63, 0.41, 0.52), demonstrating the transferability and operational potential of the approach. These findings demonstrate that integrating ALS-derived terrain metrics with Sentinel-2 spectral indices provides a robust, age-independent framework for capturing spatial variability in forest productivity across landscapes. This multi-sensor fusion approach enhances traditional SI methods and single-sensor models, providing a scalable and operational tool for forest management and long-term planning in changing environmental conditions. Full article

Habitat utilization is a critical determinant of animal survival and reproductive success. Clarifying species-specific habitat preferences provides essential insights into ecological requirements and forms the basis for sound conservation planning. The Temminck’s Tragopan (Tragopan temminckii), a medium-sized, sexually dimorphic pheasant endemic to montane forests of central and southern China, is classified as a nationally protected Class II species. Nevertheless, its fine-scale habitat selection during the breeding season remains inadequately documented. In 2024, we conducted a field investigation in the Laojunshan National Nature Reserve, Sichuan Province, to examine microhabitat use during this critical period. Our analysis revealed a significant preference for sites characterized by greater tree and bamboo height, higher canopy and bamboo cover, increased litter coverage, and taller shrub layers. In contrast, the species consistently avoided locations dominated by dense, tall herbaceous vegetation. Principal Component Analysis identified six principal components, collectively explaining 71.78% of the total environmental variance. The first component was primarily associated with bamboo structural attributes, the second with tree-layer structure, and the third with proximity to forest edges and streams. These findings indicate that effective conservation of this pheasant requires targeted forest management practices that preserve this specific suite of habitat characteristics, which are essential for ensuring reproductive success and long-term population viability. Full article

Balancing protection and development is essential for mitigating anthropogenic threats and achieving sustainable development in coastal regions. However, integrated spatial planning that links marine protected areas (MPAs) with developed spaces and incorporates land–sea coordination remains insufficiently explored—despite global frameworks such as the “Post-2020 Global Biodiversity Framework” advocating for such integration. In this study, we used Xiamen, a typical bay city in China, as an example, assessed its habitat suitability through the MaxEnt model, and determined its key development areas through hotspot analysis, aiming to coordinate protection and development, as well as land and marine utilization in coastal areas. The results indicate the following: (1) existing protected areas require adjustments; (2) multiple development hotspots overlap, while several cold spots with limited potential for functional development were identified; (3) prioritizing MPAs in decision-making led to an approximate 42.8% increase in MPA coverage in Xiamen. Overall, this study produced a comprehensive plan that integrates both ecological and social objectives. Full article

Accurate, high-resolution information on economic forest resources, here referring to fruit-tree plantations and economic tree crops, is essential for land-use planning and resource management in arid regions. Xinjiang, China—one of the country’s most important fruit-producing areas—exhibits highly fragmented and heterogeneous distributions of economic tree plantations, posing challenges for large-scale and long-term monitoring. In this study, we integrated multi-source remote sensing data by combining multi-temporal Sentinel-2 optical imagery with Sentinel-1 SAR backscatter and texture features to characterize the spatial and temporal distribution patterns of major economic tree plantations from 2019 to 2024. An optimized Random Forest classifier was applied across five key production regions (Aksu, Bazhou, Hotan, Kashgar, and Turpan–Hami). The mapping results achieved overall accuracies ranging from 0.85 to 0.97, with Kappa coefficients between 0.80 and 0.95. The results indicate that economic tree plantations are predominantly distributed along oasis corridors of the Tarim Basin and the alluvial plains on both sides of the Tianshan Mountains, forming belt- and patch-like spatial patterns. While the overall spatial configuration remained relatively stable during the study period, localized expansion was observed, mainly associated with walnut, jujube, and grape plantations. These findings provide insights into the spatial dynamics of economic tree plantations and support land-use optimization and agricultural planning in arid and semi-arid regions. Full article