This study conducted a comprehensive geospatial analysis of wind energy siting suitability within the Southern African Power Pool region, employing Geographic Information Systems and multi-criteria decision analysis. By integrating key biophysical and infrastructural factors, including wind speed, proximity to power lines and roads, elevation, slope, and land cover this study evaluated potential locations for utility-scale wind farm development across 12 SAPP member countries. Comprehensive suitability maps were produced, categorizing areas from very low to very high potential based on a hybrid GIS-AHP framework. The analysis revealed significant spatial variations in suitability, identifying priority zones characterized by high mean wind speeds (exceeding 7–8 m/s), gentle slopes (0°–0.75°), and proximity to existing infrastructure. This study quantified suitable land areas, defining highly suitable parcels as contiguous clusters of high-potential pixels with a minimum mapping unit of 1 km 2 to ensure technical and economic feasibility. Mathematical modeling using Weibull parameters yielded shape factors ranging from 3.10 to 3.80 and scale factors of 11.06 to 12.27 m/s at 50 m heights in high-potential zones. These findings provide a data-driven tool for policymakers and investors to identify priority zones for sustainable wind energy development, fostering regional energy security and cross-border cooperation within the SAPP.

Hydropower is the leading source of renewable energy and plays a critical role in stabilizing power systems by balancing fluctuations in intermittent and inflexible energy sources. This study evaluated hydropower site suitability in the Central African Power Pool (CAPP) using datasets from CHIRPS (annual rainfall averages from 1981 to 2020), ESRI, SRTM DEM (30 arc-seconds (∼1 km) spatial resolution), FAO, and UNESCO/ISRIC to analyze slope, drainage density, flow accumulation, and related physical parameters. Data preprocessing in ArcGIS Pro ensured spatial consistency by standardizing and resampling all datasets to a uniform spatial resolution of 30 m × 30 m, normalizing, and reclassifying parameters into five suitability categories (1–5) based on predefined thresholds, such as drainage density ranges \(0.0–0.035 km/km 2 for low density), slope intervals (0°–2.14° for gentle slopes), and soil types relevant to hydropower development. The Analytical Hierarchy Process (AHP) structured hydropower suitability evaluation as a multi-criteria decision-making framework, wherein key parameters, including drainage density, slope, elevation, soil type, rainfall, and geology, were compared pairwise to determine their relative importance. Consistency checks validated the logical coherence of these comparisons. Subsequently, a weighted overlay combined these criteria spatially, producing a composite suitability map. Eligibility criteria for hydropower development were defined by reclassifying each parameter into the five suitability classes based on thresholds indicative of hydropower potential. The integrated results indicate that most of the CAPP region exhibits physical and environmental characteristics favorable for hydropower development. Low drainage density and gentle slopes coincide with loamy soils and tree cover, supporting hydropower viability. Elevation profiles and dendritic flow patterns further reinforce suitability, while stable geological formations, mainly undivided Precambrian rocks, underpin foundational stability. Rainfall variability and land use patterns complement these factors, culminating in approximately 74.07% of the CAPP area being classified as moderately to highly suitable for hydropower development. The dominant determining factors were Drainage Density (weight: 0.20), Flow Accumulation (0.18), and Elevation (0.15). These high-weight criteria, combined with favorable loamy soil textures and stable geological formations, represent the primary drivers of suitability in the region.

The geospatial assessment of solar energy siting suitability in the Southern African Power Pool (SAPP) is critical for supporting regional energy planning, alleviating energy poverty, and advancing climate-change mitigation efforts. This study employed a Geographic Information System (GIS)-based multi-criteria decision analysis framework integrating the Analytic Hierarchy Process (AHP) implemented in the IDRISI (TerrSet) environment and the Weighted Linear Combination (WLC) method to evaluate solar photovoltaic (PV) site suitability across 12 SAPP member countries. The assessment considered key criteria, including global horizontal irradiation, land use and land cover, terrain slope, and proximity to the road infrastructure. The results indicate that approximately 71.06

Hydropower is a vital renewable energy source for West Africa that addresses environmental concerns and energy security challenges. This study employed a geospatial multi-criteria decision analysis (MCDA) framework to assess hydroelectric site suitability across the West African Power Pool (WAPP). Nine biophysical parameters (drainage density, elevation, flow accumulation, geology, land cover, rainfall, slope, soil texture, and stream power index) were sourced from authoritative datasets (SRTM DEM, CHIRPS, FAO, and UNESCO/ISRIC), processed, normalized, and integrated into a Composite Appropriateness Index (CAI) within a GIS environment. The results revealed spatial variability in hydroelectric potential, with the most suitable sites characterized by moderate drainage density, low to moderate elevation (below 536 m), and dendritic flow accumulation patterns. The geology is predominantly Precambrian, with land cover dominated by bare ground and rangeland, and rainfall intensity is higher in coastal regions. Slope gradients are generally low, indicating limited potential energy head for large-scale hydropower but suitability for low-head hydropower technologies. The soil textures are mainly loamy with clay and sand components, influencing infrastructure feasibility. Validation against existing hydropower infrastructure demonstrated strong spatial correspondence, confirming the model's effectiveness for site prioritization. While this study focuses on geospatial suitability mapping, it highlights the implications for hydropower development planning in WAPP and underscores the need for future integration of dynamic hydrological and energy modeling, as well as socioeconomic and infrastructural constraints to provide a comprehensive energy potential assessment. This study offers a valuable spatial decision-support tool to guide sustainable hydropower development and regional energy planning in West Africa.

Construction noise constitutes the primary source of environmental pollution in urban development and revitalization. In recent years, the forecasting and regulation of construction noise has garnered significant attention. Consequently, in light of the attributes of unstable and nonlinear construction noise, a signal decomposition method utilizing the whale optimization algorithm (WOA) and variational mode decomposition algorithm (VMD) is developed to address intricate construction noise signals. WOA-VMD also maintains a significant advantage when compared with the latest Feature Mode Decomposition (FMD) method. The Intrinsic Mode Function (IMF) components derived from VMD hyperparameter tuning via WOA were forecasted using a Two-layer Stacked Long Short-Term Memory (TSLSTM) network. The suggested model demonstrated enhanced predictive capability compared to five distinct models (LSTM, GRU, EMD-LSTM, WOA-VMD-GRU, WOA-VMD-LSTM), achieving RMSE (0.040), MAE (0.029), MAPE (0.417%), R2 (0.963). Compared to the WOA-VMD-GRU and WOA-VMD-LSTM models, the MAPE has been improved by 58.47% and 113.35%, respectively, while the MAE has been enhanced by 31.73% and 66.16%, respectively. Furthermore, in this paper, a latest comprehensive Evaluation index, the Weighted Quality Evaluation (WQE) index, is adopted to conduct a comprehensive evaluation of the proposed model. Its average value is 0.291, thereby verifying that the proposed model has excellent prediction accuracy and integrity.

In recent years, although the incidence of accidents and the number of fatalities in the power industry have declined, the situation remains concerning with significant losses. Maintenance operations and electrical testing in power companies are particularly hazardous, ranking among the highest-risk activities. Despite extensive safety management in the power industry, research specifically on integrated substation maintenance and testing operations is limited. To fill this gap and prevent safety lapses and increased risks during on-site operations, this study proposes a safety management method for integrated substation maintenance and testing operations based on system dynamics (SD) and the integrated weighting method. First, risk factors were identified through literature review and refined using expert surveys. Subsequently, the integrated weighting method was employed to determine factor weights, and system dynamics modeling was used to analyze the intrinsic interactions among risk factors. A simulation experiment, using a specific substation as a case study, validated the model's applicability. Additionally, risk control measures were proposed from two directions: key factor control and phasebased control during the operational cycle. In terms of phase-based control, three risk control models were designed and validated, ultimately determining the optimal model to minimize risks. In summary, this study highlights the risks associated with integrated substation maintenance and testing new operations, and proposes a method to better analyze the intrinsic interactions of risk factors and control measures, providing theoretical and practical support for sustainable safety management by relevant departments.

Geo-dimensional analysis plays a vital role in modern energy planning by identifying suitable locations for renewable energy deployment, particularly for solar power. This study applies a multi-attribute decision-making framework using Geographic Information Systems, Analytic Hierarchy Process, and Weighted Linear Combination to evaluate solar site suitability across the Economic Community of West African States (ECOWAS). The spatial criteria were weighed and integrated to produce suitability maps. The results revealed that solar radiation in the region ranges from 3.31 to 6.73 kWh/m2/day, with approximately 84

Work‐related musculoskeletal disorders pose significant health risks to construction workers, making it essential to monitor their postures and identify physical exposure to mitigate these risks. This study presents a novel framework for real‐time ergonomic risk assessment of workers in construction environments. Specifically, this study develops a lightweight human pose estimation (HPE) model with a residual log‐likelihood estimation head and adopts pose‐tracking technology to enable real‐time recognition of workers’ three‐dimensional (3D) postures. In particular, this study proposes a novel co‐learning method that enables the HPE model to learn two‐dimensional (2D) and 3D features from multi‐dimension datasets simultaneously, substantially enhancing the model's ability to capture 3D postures from 2D images. The proposed framework facilitates real‐time ergonomic risk assessment, reducing potential risks to construction workers and offering promising practical applications.

Since the design and construction of the building envelope affects approximately 20-60% of the energy used to heat and cool the building, it is important to improve the energy efficiency of the building envelope. This study intends to obtain an optimal thermal environment model of vertical greening through software simulation in the later stage. For some countries and cities that are still relatively backward in green development, we can provide technical support and guidance. In this study, infrared imaging equipment was used to obtain the thermal environment parameters of vertical greening, and the corresponding thermal environment characteristics were analyzed to provide support for the construction of the thermal environment model of the vertical greening system. Secondly, a new microclimate numerical simulation software ENVI-met was used to establish a vertical greening system model, quantify the changes of thermal environment parameters under different conditions, and output the numerical simulation analysis results, including temperature and thermal comfort evaluation indicators, to evaluate the role relationship and influence mechanism under different conditions. It provides theoretical support for the development, application and prediction of vertical greening. This will improve the energy efficiency of buildings.

The prevalence of pterygium was 12%. Pterygium have been a common ophthalmic disease in the whole world, but its mechanism is still unclear. To comprehensively understand the reasons for the formation and progress of pterygium, here we analyzed the difference in transcriptomes between pterygium and healthy conjunctiva. Using the database of GSE51995 and GSE183153.We queried all data sets involving pterygium studies in GEO. GO annotation, KEGG pathway, and PPI enrichment analysis were used in the exploration of the mechanism. Then we validated the transcription level of the key network node genes with qRT-PCR. We identified 143 down-regulated genes and 221 up-regulated genes, and the bioinformatic analysis and qPCR validation confirmed 10 up-regulated and 7 down-regulated genes, what related AGER, ECM, estrogen and cAMP. We comprehensively analyzed two pterygium transcriptome data sets, GSE51995 and GSE183153. The PPI analysis result and the further qPCR result suggested that the AGER-RAGE pathway, ECM-receptor interaction, estrogen receptor pathway, and cAMP signal pathway might be involved in pterygium progression and development.

Underwater concrete structure crack detection and structural health condition assessment based on image processing is challenging. The complex underwater environment and severe image degradation seriously affect the accuracy of crack detection. To solve these problems, a monocular vision and image-enhanced fractal-based fractal science based on computer vision and image processing techniques are proposed to conduct a non-contact detection study of underwater concrete cracks. This study established a four-level structural health condition to assist in underwater crack measurement and safety assessment. The box-counting method was used as a practical tool to calculate the fractal dimension. Three distances of 0.5, 0.8, and 1.2 m were set to verify the effective distance of the algorithm. The results show that the method proposed in this study can effectively detect cracks in submerged concrete members within 0.6 m and help managers correctly determine the structure's health using the fractal dimension.

Deep foundation construction is characterized by significant unpredictability and growing uncertainty, which creates dynamic risks. Given that deep foundation construction is vulnerable to extreme weather conditions and intense human activity, the assessment of its hazards is of utmost relevance for construction safety management. To do so, this research used a powerful interpretive structural model (ISM) as a foundational framework to analyze the safety risk factors in deep foundation pits; the study also revealed the dynamic properties of the system by utilizing the special benefits of system dynamics (SD), which enabled a deeper comprehension of the system's complex behavior over time. To aid in successful risk assessment, a novel risk causal feedback diagram model that is adept at navigating the system's complexity and temporal fluctuations was established. The suggested model combines the advantages of SD and ISM to produce a solid and trustworthy framework for thoroughly evaluating hazards in deep foundation projects. Its all-encompassing methodology provides a more thorough and precise investigation, giving building experts useful information to strengthen safety measures. This study conducts a case simulation experiment on a resettlement housing project in Tong'an District, Xiamen City, to show the model's applicability. Additionally, this research suggests matching risk control strategies and develops plans to maximize risk management and raise the deep foundation pit's level of safety.

The geographical distribution and scientific evaluation of wind energy potential are crucial for regional energy planning. Wind energy is a renewable energy that can mitigate climate change. Several open-access World Bank databases and the ESRI (Environmental Systems Research Institute) Global were used to gather and process data through wind energy siting optimization in Fujian Province. This paper uses the fuzzy quantifiers of the multi-criteria decision-making (MCDM) approach in arc geographic information system (ArcGIS Pro) and the analytical hierarchy process (AHP) to handle the associated wind data uncertainties to obtain wind energy technology siting optimization for nine cities in Fujian Province. The converted database options and characteristics used the weighted overlay tool (WOT) to reflect the importance of wind farm project objectives. The sensitivity analysis tested the robustness and resilience of the integrated MCDM design for feasibility or viability. The results revealed that 21.743% of the area of Longyan City is suitable for siting wind energy. Other cities’ suitable areas comprise 14.117%, 12.800%, 5.250%, 4.621%, 4.020%, 4.020%, 3.430%, and 2.300%, respectively (Sanming, Ningde, Quanzhou, Putian, Zhangzhou, Nanping, Xiamen, and Fuzhou cities). Furthermore, a considerable amount of wind power is needed to supply the current primary energy deficit (60.0–84.0%) and satisfy the carbon emission reduction target. Wind farm installation in Fujian province is an opportunity to provide inexhaustible energy, generally affected by generation volume and operational span. Wind power is highly acceptable to local Chinese. Reasonably high understanding and excitement for wind farm investments exist among local authorities. Future research should consider wind data of the identified onshore optimization sites and design wind farms for the respective output power for pessimistic, average, and optimistic scenarios for possible wind farm development. Similarly, the long shoreline of about 1680.0 miles (or 2700.0 km) is a considerable source of offshore wind power prospecting, future research, and energy exploitation and harvesting opportunities.

Urbanization has led to increased construction of underground buildings to overcome limited surface space. However, safety concerns in urban underground commercial buildings have become a major issue due to frequent accidents. Ensuring safe evacuation in densely populated underground commercial buildings is crucial. This study focuses on the safe evacuation of an urban underground commercial street A in Fuzhou City. A simulation framework based on Building Information Modelling (BIM) is proposed, integrating the Fire Dynamic Simulator (FDS) and BIM. The framework overcomes limitations in fire dynamic simulators. Using a BIM model, the study analyzes the risk of each fire protection area and selects the most unfavorable area for simulation. PyroSim technology is employed to study the entire fire development process in the underground commercial street under three different fire scenarios. The analysis includes evacuation stair travel time and personnel available safe evacuation time (TASET). By comparing the results, the study optimizes building layout design and identifies factors affecting personnel evacuation efficiency. The simulation outcomes aim to minimize casualties and property losses, contributing to fire safety management in urban underground commercial streets.

This research investigates building energy consumption in the Fujian region of China, characterized by warm winters and hot summers. The study focuses on window configurations and their impact on heat exchange and solar gain management. Initially examining three aluminum alloy window frames, the study utilizes the Multi-Quality Metric Calculator (MQMC) software V1 to assess the benefits of filled insulating glass. The reference values for the heat transfer coefficient, visible transmittance, and sun shading coefficient are established. Subsequently, Ecotect software V5.6 is employed to conduct a comprehensive year-round energy consumption simulation analysis, identifying an optimal window layout tailored to Fujian’s climate. In the Fuzhou simulation, aluminum–plastic co-extruded windows exhibit the lowest cooling energy consumption, while aluminum alloy windows have the highest. Summer cooling energy consumption, comprising about 75% of the total annual energy usage in hot summer and warm winter regions, significantly influences overall energy consumption. Windows made of aluminum–plastic co-extruded material with superior thermal insulation qualities can greatly reduce building energy consumption. The results contribute valuable insights to sustainable building practices and energy-conscious designs in regions characterized by warm winters and hot summers.

Coastal cities are vulnerable to typhoon disasters due to their unique geographical location. Although significant progress has been made in the safety early warning system for construction sites, there are few studies on site safety management under typhoon disasters. Moreover, traditional manual methods of on-site emergency management are lagging behind and rigid, making it difficult to respond quickly to safety inspections manually. This study aims to automatically identify safety hazards and provide effective response measures during typhoon warnings, ultimately enhancing the safety of construction sites. Therefore, based on the MATLAB platform, this study developed a rapid safety inspection system (RSS-Typh) for the construction site during the typhoon warning period. In addition, the standard operating procedures (SOP) related to the system are formulated to assist the comprehensive safety inspection of the site before the typhoon and improve the inspection efficiency. The specific operation can be divided into three steps: 1.Using a combination of drones and handheld laser scanners to complete the complete construction site data acquisition in less than 2 h, and complete the modeling and refinement of the point cloud model in 5 h; 2.Using the developed hidden danger detection algorithm based on automatic point cloud to effectively identify the deformation-related hidden dangers at the construction site; 3.Use the designed automatic query security countermeasure GUI system to make rapid rectification. This study assists site managers in swiftly implementing effective safety management on construction sites, elevating the safety level of the site before typhoon. Furthermore, it serves to advance the automation of safety management in construction sites, promoting further developments in this field.

The geometric quality of precast concrete (PC) elements is critical in the construction and management of prefabricated construction. However, traditional manual inspection is time-consuming, inaccurate and costly. Therefore, an effective and low-cost non-contact inspection method is needed to support the geometric quality inspection of PC elements as a way to improve inspection efficiency and accuracy. In this study, an intuitive, effective and inexpensive geometric quality inspection method is proposed to evaluate the geometric quality of PC elements using 3D structural light scanning technology. The proposed method fits the point cloud data model of PC elements to the design model, visualizes the range of dimensional deviation of the whole element by the generated deviation chromatogram, and then evaluates the overall geometric quality of PC elements based on the local absolute deviation distance and the average deviation distance. The evaluation results classify PC elements into qualified products, partially unqualified products and severely defective products, which can be directly used to guide the treatment of unqualified products. The method is highly integrated with realistic construction and has a strong practical value. This study enriches the means of assessing the geometric quality of PC elements and shows potential for implementation in real practice.

Polyvinyl alcohol (PVA) concrete is a new green building material. In order to make it more widely used, this study used butylbenzene emulsion (SBL) to modify PVA fiber concrete. The enhancement mechanism of SBL on the PVA/cement interface was systematically investigated at multiple scales, including macroscopic mechanical properties, microstructural characteristics, nano-interface interactions. On a macro scale, the addition of SBL and PVA fibers can significantly improve the shear strength and flexural strength of composite concrete at 7 and 28 days, and SBL can make up for the decrease in compressive strength caused by PVA. On a micro scale, the corresponding polymer cement concrete was tested by scanning electron microscopy (SEM), X-ray diffraction (XRD) and Fourier transform infrared spectroscopy (FT-IR). It was observed that some gels and polymers filled the interfacial gap and effectively repaired the interfacial defects. The SBL brought the two interfaces closer together and described its bonding effect at the micro-interface. On the nano scale, SBL/PVA/C-S-H is modeled by molecular dynamics method. Binding energy, Relative concentrations, Radial distribution function, Mean-square displacement and Time correlation function were analyzed and calculated. The results show that SBL reduces the interfacial effect, enhances the interfacial hydrogen bond, van der Waals interaction, Ca-H coordination bond and stability, improves the interfacial adhesion, and further enhances the weak interfacial bond between organic polymer (PVA) and inorganic silicate (C-S-H).

ObjectivesTo enhance the daily training quality of athletes without inducing significant physiological fatigue, aiming to achieve a balance between training efficiency and load.Design methodsFirstly, we developed an activity classification training model using the random forest algorithm and introduced the “effective training rate” (the ratio of effective activity time to total time) as a metric for assessing athlete training efficiency. Secondly, a method for rating athlete training load was established, involving qualitative and quantitative analyses of physiological fatigue through subjective fatigue scores and heart rate data. Lastly, an optimization system for training efficiency and load balance, utilizing multiple inertial sensors, was created. Athlete states were categorized into nine types based on the training load and efficiency ratings, with corresponding management recommendations provided.ResultsOverall, this study, combining a sports activity recognition model with a physiological fatigue assessment model, has developed a training efficiency and load balance optimization system with excellent performance. The results indicate that the prediction accuracy of the sports activity recognition model is as high as 94.70%. Additionally, the physiological fatigue assessment model, utilizing average relative heart rate and average RPE score as evaluation metrics, demonstrates a good overall fit, validating the feasibility of this model.ConclusionsThis study, based on relative heart rate and wearable devices to monitor athlete physiological fatigue, has developed a balanced optimization system for training efficiency and load. It provides a reference for athletes’ physical health and fatigue levels, offering corresponding management recommendations for coaches and relevant professionals.