Search Results (11,115)

Microgrids (MGs) have emerged as pivotal players in the energy transition by enabling the efficient integration of distributed energy resources and the provision of ancillary services to the power system. Despite their technical capabilities, MGs still face economic and regulatory barriers that hinder their widespread deployment in electricity markets. This paper presents a structured methodological framework to assess the economic viability of MGs delivering services such as peak shaving, loss compensation, and voltage support, among others. The proposed approach considers three distinct scenarios: (1) MGs supplying energy to local loads, (2) hybrid MGs combining local supply with ancillary services, and (3) MGs exclusively dedicated to ancillary services. The framework incorporates adjusted levelized cost of electricity (LCOE), levelized avoided cost of electricity (LACE), and net value metrics, while accounting for tax incentives and market price signals. A case study based in Colombia (Cali and Camarones) validates the framework through simulations conducted in HOMER Pro V3.18.4 and MATLAB Online. The results indicate that remuneration schemes based on availability and service utilization significantly enhance the viability of MGs. The proposed methodology is applicable to emerging regulatory environments and offers guidance for designing public policies that promote the active participation of MGs in supporting grid operations. Full article

Practicing medical research based on the best evidence is gaining increased value and popularity among most medical societies in the current era. Good clinical practice (GCP) is internationally recognized as the scientific and ethical standard for the design, conduct, performance, auditing, recording, analysis, and reporting of clinical trials involving human subjects. GCP ensures the accuracy and credibility of trial while safeguarding the rights, integrity, and confidentiality of participants. Adherence to GCP facilitates the generation of high-quality studies that can be incorporated in Evidence-Based Medicine (EBM). The clinical practice of EBM seeks to integrate robust medical literature into daily medical practice. This process involves systematically searching for high-quality evidence, critically appraising the retrieved literature, applying sound clinical principles and finally evaluating the efficacy of the chosen approach. Although EBM has been evaluated in many resource settings, it has not been addressed sufficiently in the field of Pediatrics and more specifically in indigenous populations. In this review, we briefly explain the EBM approach and its applications in Pediatrics, in order to help physicians care for young subjects more efficiently by integrating the best available information into their routine clinical practice. Also, the basic good practice principles for conducting clinical trials in children and adolescents are highlighted, emphasizing the importance of applying high ethical principles in this vulnerable population. Full article

People Who Use Substances (PWUS) are among the most stigmatised groups in society. Stigma associated with substance use is known to be detrimental to the individual’s wellbeing, and substance use is often used as a mechanism by policy makers and services to legitimise exclusion. PWUS often do not benefit from the drug and alcohol services that are available to them. Community Asset Mapping (CAM) is a strengths-based approach involving the re-engagement of communities through active involvement in research. There are criticisms and concerns about equity and the burden on participants involved in CAM projects; however, the broad aim of CAM is to identify and document the strengths and pre-existing resources that exist within a community. In the following study, we utilised CAM processes and principles in a large city in the Northeast of England to enable people with lived experience of substance use and practitioners working in drug treatment services to come together and identify resources in the form of services and groups that support recovery. In the process, we were concerned with identifying, engaging with, and involving groups that were known to the recovery community but also were not part of an existing recovery network. Qualitative data were obtained from semi-structured interviews (n = 13) and focus groups (n = 2). A reflexive thematic analysis approach was used to analyse the transcriptions, and from this we generated four themes: (1) community visibility, (2) ownership of the recovery agenda, (3) the impact of stigma and shame, and (4) the benefits of involvement. Our findings revealed a partly fragmented but also well-established, connectedand resourced local recovery community in the city. We were also able to identify a number of recovery groups and services that had previously not been known to the existing recovery community. Additionally, we identified that public and societal substance-related stigma continued to be a barrier that inhibited individuals and recovery groups from making themselves more visible and available to others. Full article

With the widespread adoption of large language models (LLMs) in code generation tasks, geospatial code generation has emerged as a critical frontier in the integration of artificial intelligence and geoscientific analysis. This growing trend underscores the urgent need for systematic evaluation methodologies to assess the generation capabilities of LLMs in geospatial contexts. In particular, geospatial computation and visualization tasks in the JavaScript environment rely heavily on the orchestration of diverse frontend libraries and ecosystems, posing elevated demands on a model’s semantic comprehension and code synthesis capabilities. To address this challenge, we propose GeoJSEval—the first multimodal, function-level automatic evaluation framework for LLMs in JavaScript-based geospatial code generation tasks. The framework comprises three core components: a standardized test suite (GeoJSEval-Bench), a code submission engine, and an evaluation module. It includes 432 function-level tasks and 2071 structured test cases, spanning five widely used JavaScript geospatial libraries that support spatial analysis and visualization functions, as well as 25 mainstream geospatial data types. GeoJSEval enables multidimensional quantitative evaluation across metrics such as accuracy, output stability, resource consumption, execution efficiency, and error type distribution. Moreover, it integrates boundary testing mechanisms to enhance robustness and evaluation coverage. We conduct a comprehensive assessment of 20 state-of-the-art LLMs using GeoJSEval, uncovering significant performance disparities and bottlenecks in spatial semantic understanding, code reliability, and function invocation accuracy. GeoJSEval offers a foundational methodology, evaluation resource, and practical toolkit for the standardized assessment and optimization of geospatial code generation models, with strong extensibility and promising applicability in real-world scenarios. This manuscript represents the peer-reviewed version of our earlier preprint previously made available on arXiv. Full article

Comprehension of spatio-temporal groundwater recharge (GWR) under climate change is imperative to enhance water resources availability and management. The main aim of this study is to examine climate change’s effects on spatio-temporal GWR. This study was done by ensembling five climate models and the physically-based WetSpass-M model to estimate GWR during baseline (1986 to 2015), mid-term (2031 to 2060), and long-term (2071 to 2100) periods for the Representative Concentration Pathways (RCP) 4.5 and 8.5 scenarios. In comparison to the Identification of unit Hydrographs and Component flows from Rainfall, Evaporation, and Streamflow (IHACRES)’s baseflow and direct runoff with corresponding WetSpass-M model outputs, the statistical indices showed good performance in simulating water balance components. Projected future temperature and rainfall will likely increase dramatically compared to the baseline period for RCP4.5 and RCP8.5. In comparison to the baseline period, the annual GWR had been projected to increase by 4.28 mm for RCP4.5 for the mid-term (MidT4.5), 15.27 mm for the long-term (LongT4.5), 2.38 mm for the mid-term (MidT8.5), and 13.11 mm for the long-term for RCP8.5 (LongT8.5), respectively. The seasonal GWR findings showed an increasing pattern during winter and spring, whereas it declined in autumn and summer. The mean monthly GWR for MidT4.5, LongT4.5, MidT8.5, and LongT8.5 will increase by 0.34, 1.26, 0.18, and 1.07 mm, respectively. The watershed’s downstream areas were receiving the lowest amount of GWR, and prone to drought. Therefore, this study advocates and recommends that stakeholders participate intensively in developing and implementing climate change resilience initiatives and water resources management strategies to offset the detrimental effects in the downstream areas. Full article

In recent years, a growing global effort has been underway to reduce the Earth’s carbon footprint. One of the main strategies to achieve this goal is the utilization of available renewable energy resources. Among the largest and most inexhaustible is hydro-power. This paper presents an experimental study of three hydrokinetic turbines tested under real river conditions, aiming to evaluate their effectiveness in harnessing the kinetic energy of flowing water. The experiment is described in detail, including velocity field measurements conducted within the river section used for testing. Based on the experimental data, the main performance characteristics of the three turbines are presented, specifically their power output and efficiency. The importance of selecting an optimal riverbed site and customizing turbine runners to local flow conditions is highlighted, as even slight velocity fluctuations can significantly impact performance. Among the tested designs, the K1–6 turbine runner showed the highest power and efficiency, while the K2–4 runner provided superior rotational stability, making it promising for consistent energy output in variable flow environments Full article

Coastal communities and marine ecosystems face increasing risks due to changing ocean conditions, yet effective wave monitoring remains limited in many low-resource regions. This study investigates the use of seismic data to predict significant wave height (SWH), offering a low-cost and scalable solution to support coastal conservation and safety. We developed a baseline machine learning (ML) model and improved it using a longest-stretch algorithm for seismic data selection and station-specific hyperparameter tuning. Models were trained and tested on consumer-grade hardware to ensure accessibility and availability. Applied to the Sicily–Malta region, the enhanced models achieved up to a 0.133 increase in R² and a 0.026 m reduction in mean absolute error compared to existing baselines. These results demonstrate that seismic signals, typically collected for geophysical purposes, can be repurposed to support ocean monitoring using accessible artificial intelligence (AI) tools. The approach may be integrated into conservation planning efforts such as early warning systems and ecosystem monitoring frameworks. Future work may focus on improving robustness in data-sparse areas through augmentation techniques and exploring broader applications of this method in marine and coastal sustainability contexts. Full article

Climate change has prompted the adoption of sustainable measures to reduce greenhouse gas (GHG) emissions, particularly in urban transportation. The integration of renewable energy sources, such as solar energy, offers a promising strategy to enhance sustainability in urban transit systems. This study assessed solar irradiation along the tram route in Cuenca—an Andean city characterized by distinctive topographic and climatic conditions—with the aim of evaluating the technical feasibility of integrating solar energy into the tram infrastructure. A descriptive, applicative, and longitudinal approach was adopted. Solar irradiation was monitored using a system composed of a fixed station and a mobile station, the latter installed on a tram vehicle. Readings carried out over fourteen months facilitated the analysis of seasonal and spatial variability of the available solar resource. The fixed station recorded average irradiation values ranging from 3.80 to 4.61 kWh/m²·day, while the mobile station reported values between 2.60 and 3.41 kWh/m²·day, revealing losses due to urban shading, with reductions ranging from 14.7% to 18.8% compared to fixed-site values. It was estimated that a fixed photovoltaic system of up to 1.068 MWp could be installed at the tram maintenance depot using 580 Wp panels, with the capacity to supply approximately 81% of the annual electricity demand of the tram system. Complementary solar installations at tram stops, stations, and other related infrastructure are also proposed. The results demonstrate the technical feasibility of integrating solar energy—through fixed and mobile systems—into the tram infrastructure of Cuenca. This approach provides a scalable model for energy planning in urban transport systems in Andean contexts or other regions with similar characteristics. Full article

Background/Objectives: Severe acute pancreatitis (SAP) carries substantial morbidity and resource burden, and early risk stratification remains challenging with conventional scores that require serial observations. The aim of this study was to develop and compare supervised machine-learning (ML) pipelines—integrating feature selection and SHAP-based explainability—for early prediction of SAP at emergency department (ED) presentation. Methods: This retrospective, single-center cohort was conducted in a tertiary-care ED between 1 January 2022 and 1 January 2025. Adult patients with acute pancreatitis were identified from electronic records; SAP was classified per the Revised Atlanta criteria (persistent organ failure ≥ 48 h). Six feature-selection methods (univariate AUROC filter, RFE, mRMR, LASSO, elastic net, Boruta) were paired with six classifiers (kNN, elastic-net logistic regression, MARS, random forest, SVM-RBF, XGBoost) to yield 36 pipelines. Discrimination, calibration, and error metrics were estimated with bootstrapping; SHAP was used for model interpretability. Results: Of 743 patients (non-SAP 676; SAP 67), SAP prevalence was 9.0%. Compared with non-SAP, SAP patients more often had hypertension (38.8% vs. 27.1%) and malignancy (19.4% vs. 7.2%); they presented with lower GCS, higher heart and respiratory rates, lower systolic blood pressure, and more frequent peripancreatic fluid (31.3% vs. 16.9%) and pleural effusion (43.3% vs. 17.5%). Albumin was lower by 4.18 g/L, with broader renal–electrolyte and inflammatory derangements. Across the best-performing models, AUROC spanned 0.750–0.826; the top pipeline (RFE–RF features + kNN) reached 0.826, while random-forest-based pipelines showed favorable calibration. SHAP confirmed clinically plausible contributions from routinely available variables. Conclusions: In this study, integrating feature selection with ML produced accurate and interpretable early prediction of SAP using data available at ED arrival. The approach highlights actionable predictors and may support earlier triage and resource allocation; external validation is warranted. Full article

Campylobacter spp. are leading causes of bacterial gastroenteritis globally. Swine are recognized as an important reservoir for this pathogen. The emergence of antimicrobial resistance (AMR) and multidrug resistance (MDR) in Campylobacter is a global health concern. Traditional methods for detecting AMR and MDR, such as phenotypic testing or whole-genome sequencing, are resource-intensive and time-consuming. In the present study, we developed and validated a supervised machine learning model to predict MDR status in Campylobacter isolates from swine, using publicly available phenotypic AMR data collected by NARMS from 2013 to 2023. Resistance profiles for seven antimicrobials were used as predictors, and MDR was defined as resistance to at least one agent in three or more antimicrobial classes. The model was trained on 2013–2019 isolates and externally validated using isolates from 2020, 2021, and 2023. Random Forest showed the highest performance (accuracy = 99.87%, Kappa = 0.9962) among five evaluated algorithms, which achieved high balanced accuracy, sensitivity, and specificity in both training and external validation. Our feature importance analysis identified erythromycin, azithromycin, and clindamycin as the most influential predictors of MDR among Campylobacter isolates from swine. Our temporally validated, interpretable model provides a robust, cost-effective tool for predicting MDR in Campylobacter spp. and supports surveillance and early detection in food animal production systems. Full article

The growing dependence on fossil fuels has raised concerns over energy security, resource depletion, and environmental impacts, driving the need for renewable alternatives. Coffee husk, a widely available agro-industrial residue, represents an underutilized feedstock for biodiesel production. In this study, biodiesel was synthesized from coffee husk oil using a two-step transesterification process to address its high free fatty acid content (21%). Physicochemical analysis showed that Coffee Husk Oil Methyl Ester (CHOME) possessed a density of 863 kg m⁻³, viscosity of 4.85 cSt, and calorific value of 33.51 MJ kg⁻¹, compared to diesel with 812 kg m⁻³, 2.3 cSt, and 42.4 MJ kg⁻¹. FTIR analysis confirmed the presence of ester carbonyl and C–O functional groups characteristic of CHOME, influencing its combustion behavior. Engine tests were then conducted using B0, B10, B30, B50, and B100 blends under different loads, both with and without fuel preheating. Results showed that neat CHOME (B100) exhibited 11.8% lower brake thermal efficiency (BTE) than diesel, but preheating at 95 °C improved BTE by 5%, with preheated B10 slightly surpassing diesel by 0.5%. Preheating also reduced brake-specific fuel consumption by up to 7.75%. Emission analysis revealed that B100 achieved reductions of 6.4% CO, 8.3% HC, and 7.0% smoke opacity, while NOx increased only marginally (2.86%). Overall, fuel preheating effectively mitigated viscosity-related drawbacks, enabling coffee husk biodiesel to deliver competitive performance with lower emissions, highlighting its potential as a sustainable waste-to-energy fuel. Full article

This study investigates the primary frequency regulation dynamic characteristics of industrial steam extraction turbine units under deep peak regulation conditions. A high-fidelity integrated dynamic model was established, incorporating the governor system, steam turbine with extraction modules, and interconnected pipeline dynamics. Through comparative simulations and experimental validation, the model demonstrates high accuracy in replicating real-unit responses to frequency disturbances. For the power grid system in this study, the frequency disturbance mainly comes from three aspects: first, the power imbalance formed by the random mutation of the load side and the intermittence of new energy power generation; second, transformation of the energy structure directly reduces the available frequency modulation resources; third, the system-equivalent inertia collapse effect caused by the integration of high permeability new energy; the rotational inertia provided by the traditional synchronous unit is significantly reduced. In the cogeneration unit and its control system in Guangxi involved in this article, key findings reveal that increased peak regulation depth (30~50% rated power) exacerbates nonlinear fluctuations. This is due to boiler combustion stability thresholds and steam pressure variations. Key parameters—dead band, power limit, and droop coefficient—have coupled effects on performance. Specifically, too much dead band (>0.10 Hz) reduces sensitivity; likewise, too high a power limit (>4.44%) leads to overshoot and slow recovery. The robustness of parameter configurations is further validated under source-load random-intermittent coupling disturbances, highlighting enhanced anti-interference capability. By constructing a coordinated control model of primary frequency modulation, the regulation strategy of boiler and steam turbine linkage is studied, and the optimization interval of frequency modulation dead zone, adjustment coefficient, and frequency modulation limit parameters are quantified. Based on the sensitivity theory, the dynamic influence mechanism of the key control parameters in the main module is analyzed, and the degree of influence of each parameter on the frequency modulation performance is clarified. This research provides theoretical guidance for optimizing frequency regulation strategies in coal-fired units integrated with renewable energy systems. Full article

Population ageing and the increase in life expectancy have heightened the demand for long-term care in residential facilities. In Spain, it is projected that by 2054 the functional dependency rate among those aged 65 and over will exceed 53.0%, posing significant challenges for person-centred care. This study aims to examine institutionalised older adults’ perceptions of care in relation to their functional dependency, and how these perceptions shape their dignity and participation in residential life. A qualitative study with a phenomenological approach was conducted in a residential facility located in the north-east of Andalusia, Spain, involving eight residents with varying degrees of autonomy and dependency. Data were collected through individual semi-structured interviews and analysed using Colaizzi’s phenomenological method. The findings revealed that, as dependency increases, care is perceived as more impersonal, generating feelings of diminished dignity and greater exclusion. In addition, limitations were observed in communication and participation in residential life. Respect for privacy and personal preferences was particularly relevant for women. The study concludes that the care received is conditioned by the degree of dependency and the resources available. It is recommended to enhance staff training, increase staffing levels, and promote the active participation of residents. Full article

Urban sprawl is a growing issue in developing countries such as Jordan, where urban populations continue to expand rapidly and are projected to reach 70% of the global population by 2050. This urbanization creates significant challenges, particularly the depletion of natural resources and the reduction in green areas. This study proposes an approach to improve the selection of open green space locations by integrating Geographic Information Systems (GISs) with Nature-based Solutions (NbSs) for urban sustainability and resilience. Using Al-Sarih, Jordan, as a case study, GIS was applied to analyze environmental factors, including soil, meteorological, and geological data, through a weighted overlay analysis to assess potential park sites. The results indicated that most parks are situated in areas with suitable geological and soil conditions. However, their distribution is uneven, with dense coverage in the northern region and limited availability in southern and western parts. This imbalance highlights the need for equitable green space planning to ensure accessibility for all residents. This study underscores the value of integrating GIS and NbS in optimizing green infrastructure, providing a scientific framework for sustainable urban planning. It further emphasizes the importance of spatial and natural data interactions to support resilient city development. Full article

Investing in large-scale hydropower is on the rise in Ethiopia in accordance with the country’s climate-resilient green economy strategy. Rural electrification is a top priority on the development agenda of the country, with very limited off-grid interventions. Although small-scale hydropower can bring various social and economic benefits compared to other off-grid solutions, it is hardly localized in the country. The motivation for this research is to break this technological bottleneck by synergizing and strengthening the local capacity. Accordingly, this paper presents the full-scale crossflow turbine design and development process of a power plant constructed to give electricity access to about 450 households in a rural village called Amentila. Based on a site survey and the resource potential, the power plant was designed for a 125 kW peak at 0.3 m³/s of discharge with a 53 m head. The crossflow was selected based on the head, discharge, and simplicity of development with the available local capacities. The detailed design of the turbine and its auxiliary components was developed and simulated using SolidWorks and CFD ANSYS CFX. The power plant has a run-of-river design, targeting provision of power during peak hours. This study demonstrates an off-grid engineering solution with applied research on the water–energy–food–environment nexus. Full article