Search Results (834)

Girth welds are susceptible to defects under high internal pressure and stress. While phased array ultrasonic testing (PAUT) is widely used for non-destructive evaluation, manual inspection remains inefficient and highly dependent on expertise. Furthermore, existing deep learning models often struggle with low accuracy and high complexity. This paper proposes a PAUT defect classification method based on YOLOv8. First, median filtering is employed for denoising, and the results show that noise is effectively reduced while preserving key features, achieving PSNR values of 35.132, 35.938, and 36.138 for slag inclusion, pores, and lack of fusion (LOF), respectively. Subsequently, the spatial enrichment algorithm (SEA) is applied to enhance image details without amplifying noise, yielding a PSNR of 33.71 and an SSIM of 0.96. Finally, the YOLOv8 model is implemented for defect recognition. Experimental results demonstrate that the proposed approach achieves a superior balance between precision and recall with high reliability. This method offers a robust and efficient solution for automated PAUT evaluation in practical engineering applications. Full article

Urban trees are increasingly deployed as nature-based infrastructure to mitigate heat and manage stormwater, yet quantitative guidance on how species traits and site context shape transpiration remains fragmented. We conducted a systematic metadata analysis of seven field studies that measured daily transpiration rate in urban settings using heat-pulse methods. The units and spatial scales reported were harmonized with the sap flow density across active sapwood (J_s, g H₂O/${\mathrm{cm}}^2$/day) by converting reported stand transpiration and the outer 2 cm of sapwood sap flux using established Gaussian radial distribution functions for angiosperms and gymnosperms, which account for the non-linear decline in sap flux from the vascular cambium to the heartwood boundary. We then summarized distributions and tested group differences with Kruskal–Wallis and Dunn post hoc comparisons across wood anatomy, climate, soil texture, and planting configuration. Conifers exhibited significantly lower median $J_s$ (39.76 g/${\mathrm{cm}}^2$/day) than angiosperms, while the ring-porous group (median $J_s$ = 92.25 g/${\mathrm{cm}}^2$/day) and diffuse-porous groups (median $J_s$ = 96.70 g/${\mathrm{cm}}^2$/day) had similar distributions overall. Climate-modulated responses within wood anatomy groups differed, with diffuse-porous species exhibiting the highest median $J_s$ (152.59 g/${\mathrm{cm}}^2$/day) in semi-arid regions, ring-porous species maintaining comparatively stable median $J_s$ across climates (varying slightly between 80.72 and 99.32 g/${\mathrm{cm}}^2$/day), and conifers reaching their highest median $J_s$ (69.90 g/${\mathrm{cm}}^2$/day) in humid continental sites. Soil texture effects were consistent with moisture availability: sandy loam generally reduced $J_s$ relative to loam or silt loam for conifers and diffuse-porous species. Across anatomies, single trees transpired more than clustered trees or closed canopies. For example, planting as single trees increased median $J_s$ by 86% in conifers (from 33.01 to 61.37 g/${\mathrm{cm}}^2$/day) and by 45% in diffuse-porous species (from 81.31 to 118.25 g/${\mathrm{cm}}^2$/day). These results provide actionable ranges and contrasts to inform species selection and planting design for urban greening and runoff reduction, while highlighting data gaps for future research. Ultimately, by matching specific wood anatomies and planting configurations to local soil and climatic conditions, urban planners and ecohydrologists can strategically optimize urban forests to maximize targeted ecosystem services. Full article

The rapid growth of large-scale Internet of Things (IoT) deployments in urban environments requires accurate and energy-efficient localization methods for low-power wireless devices. In long-range wide-area networks (LoRaWAN), traditional GPS-based positioning is often impractical due to energy consumption constraints and signal propagation challenges in urban areas. This study proposes a hybrid localization system that integrates weighted centroid localization (WCL) with a machine learning (ML) regression model to improve outdoor positioning accuracy. The proposed approach first estimates approximate transmitter coordinates using a physically grounded WCL method based on received signal strength indicator (RSSI) measurements. These initial estimates are subsequently refined by ML models trained to learn nonlinear residual corrections. In addition to random partitioning, a spatial data splitting strategy is proposed and evaluated using a publicly available LoRaWAN dataset. The experimental results demonstrate that the hybrid WCL framework combined with a multilayer perceptron (MLP) significantly outperforms other ML models. The proposed method achieves a mean localization error of 160.47 m and a median error of 73.78 m. Compared to the baseline model, the integration of WCL reduces the mean localization error by approximately 29%, highlighting the effectiveness of incorporating physically interpretable priors into localization models. Full article

Net primary productivity (NPP) and its response to global climate change are one of the hot topics in global change research. Based on Net primary productivity remote sensing data and meteorological data, this study analyzed the spatiotemporal variation in vegetation NPP in Maowusu sandy land by using Sen trend analysis, Mann–Kendall significance test, coefficient of variation stability analysis, partial correlation and complex correlation analysis, and quantitatively analyzed the response of vegetation NPP to climate factors. The results showed that from 2001 to 2023, the overall vegetation NPP showed a significant upward trend, and the annual average increased from 124.28 g·(m⁻²·a)⁻¹ to 221.41 g·(m⁻²·a)⁻¹. The Theil–Sen median slope of NPP was +3.87 g·(m⁻²·a)⁻¹ with a coefficient of variation (CV) of 0.19, suggesting a robust but spatially variable greening trend. In total, 98.53% of the area showed an upward trend, with a very significant and significant increase area. The overall stability of vegetation NPP was strong, with an average coefficient of variation (CV) of 0.19 and a CV< of 0.30 in 97.96% of the regions, but the local area from southwest to east was highly volatile and there was a risk of secondary desertification. The influence of climate factors on vegetation NPP had significant spatial heterogeneity: precipitation was the key driving factor, and most areas were positively correlated. Potential evapotranspiration was positively correlated in the central and northern regions, and negatively correlated in some southern areas. The overall temperature has a negative effect, and only the local area has a weak promoting effect. Multi-correlation analysis shows that vegetation NPP is the result of the synergy of multiple climatic factors, and the hydrothermal coupling mechanism plays a decisive role in its spatial pattern. This study can provide a scientific basis for the restoration of vegetation ecosystems, environmental protection policy formulation, ecological protection and high-quality development of the Yellow River Basin in Maowusu Sandy Land. Full article

Degraded groundwater quality, characterized by elevated salinity and nitrate concentrations, poses significant public health concerns, particularly for vulnerable populations such as children. High content of nitrate in drinking water may lead to non-carcinogenic health risks, highlighting the urgent need for sustainable groundwater management strategies to protect both human health and environmental integrity. This study assesses the suitability of groundwater resources in the Regueb Basin for irrigation and drinking purposes, with particular attention paid to nitrate contamination. The Irrigation Water Quality Index (IWQI) indicates considerable spatial variability in groundwater quality, with values varying between 15.86 and 89.55 and a median of 41.69, reflecting differing levels of suitability for irrigation across the basin. Similarly, the Drinking Water Quality Index (DWQI) ranges from 149.16 to 982.42, with a median value of 445.71, suggesting significant concerns regarding groundwater suitability for drinking purposes. The health risk assessment (HHRA) based on the Nitrate Pollution Index (NPI) and the nitrate hazard quotient (HQ__nitrate) reveal substantial risks to human health. NPI values vary between 0.45 and 5.5, with a median of 1.65 indicating varying levels of nitrate pollution. The HQ__nitrate results show that all groundwater samples (100%) pose health risks for children (HQ > 1). For women, 75.61% of HQ values exceed the safe threshold, affecting approximately 80% of the study area, whereas for men, 48.48% of HQ values exceed 1, impacting about 36.67% of the area. Overall, these findings highlight the urgent need for effective groundwater management strategies to mitigate nitrate contamination and ensure the safe and sustainable use of the groundwater resources in the Regueb Basin. Full article

Cats are among the most beloved and affectionate companion animals to humans. Historically, they have been utilised to manage pests or offer comfort and companionship, a practice that continues today. Due to human malpractice, unowned free-roaming cats (as stray pets or feral cats) are now considered amongst the 100 worst invasive species, and are responsible for the decline and even the disappearance of many wild species worldwide. Free-roaming cats maintain their hunting instincts, causing problems for native species, which is recognised as a major issue in island biodiversity. Despite their impact, limited studies have been conducted to understand the spatial activity of free-roaming cats in the Mediterranean when they are away from their caregivers (owners who feed and care for their cats while allowing unrestricted outdoor roaming). To investigate this, we used GPS tracking collars to monitor 15 free-roaming cats on the island of Cyprus, during spring–autumn 2022. The monitored cats were active in a spectrum of different habitats, from forests and farmland to shrublands and the suburbs. We monitored cats for 5.6 days, on average, to investigate their home range sizes (KDE 95%; median: males = 55,678 m²; females = 11,377 m²), daily distance travelled (median: males = 1233 m; females = 538 m), and daily/nocturnal activity, and the factors that influence these patterns. The animals’ sex, shelter availability, and the type of coverage in an area show statistically significant differences in relation to their home range, while activity peaked during the afternoon hours, a finding that is also statistically confirmed. Although the sample size of the study is relatively small, the influence of environmental and anthropogenic factors on the home range of free-roaming cats in Cyprus is revealed. These findings offer quantitative evidence and can contribute to wildlife conservation and free-roaming cat management. Full article

Understanding the spatiotemporal streamflow patterns under future climate scenarios is critical for sustainable water resource management in large river basins. This study applied the Soil and Water Assessment Tool (SWAT), forced by five downscaled and bias-corrected CMIP6 global climate models, to evaluate historical (2008–2024) and future (2025–2049) streamflow patterns in the Missouri River Basin in the continental United States. Model calibration and validation were satisfactory, with NSE > 0.5, KGE ≥ 0.5, R² > 0.5, and PBIAS within ±25% at most USGS gauge stations. Future projections indicate spatially and temporally variable hydrological responses: The upper basin (Bismarck, North Dakota) is projected to experience lower flows across most percentiles and reduced extreme events, whereas the lower basin (Hermann, Missouri) shows decreased median flows but higher extremes. Recurrence interval analysis of 2-, 5-, 10-, 50-, 100-, and 500-year flows suggests that 100-year flows may decline by 11% at Bismarck and increase by 37.4% at Hermann. These results highlight the importance of integrating percentile-based and extreme event streamflow analyses with hydrologic modeling for assessing the spatiotemporal streamflow patterns under future climate scenarios in large-scale basins. Quantitative insights into future streamflow variability and its implications for flood risk mitigation, water resources management, and adaptive strategies were gained for one of North America’s largest river systems. Full article

Vegetation net primary productivity (NPP) is a key indicator of ecosystem functioning in tropical rainforests and has important implications for carbon cycling and ecosystem stability. Examining the spatial and temporal variation in vegetation NPP and the factors associated with it can help inform ecosystem management and responses to climate change. In this study, Hainan Tropical Rainforest National Park (HTR), China, was selected as a representative tropical rainforest ecosystem. MODIS NPP data, Landsat imagery, meteorological variables, topographic factors, soil data, and socioeconomic indicators were integrated to analyze the spatiotemporal evolution of vegetation NPP from 2000 to 2023. The Theil–Sen Median trend analysis and Mann–Kendall test were applied to detect temporal trends, while the Optimal Parameter Geographical Detector (OPGD) model was used to identify dominant driving factors and their nonlinear interactions. The results showed that vegetation NPP in HTR exhibited an overall increasing trend during the study period, although short-term fluctuations occurred. Spatially, NPP was higher in the west and south and lower in the east and north. Elevation, soil type, and land use type were the main variables associated with this pattern. Moreover, interactions between natural and human-related factors accounted for more of the spatial variation in NPP than individual factors considered separately. These findings improve the understanding of vegetation productivity dynamics in tropical rainforest ecosystems and provide scientific insights for carbon sequestration enhancement, ecological conservation, and sustainable ecosystem management in tropical rainforests under global climate change. Full article

Pedestrians are amongst the most vulnerable road user groups. Efforts to enhance pedestrian safety have mainly focused on intersections and midblock crossings. This study investigated the effect of bus stop environments on pedestrian safety in Kumasi, an area with a high incidence of pedestrian fatalities in Ghana. Crashes within a 50 m radius of bus stops were extracted using a spatial join. The Negative Binomial regression model was applied to model pedestrian crashes around bus stops as a function of three distinct non-collinear independent variable groups: road design features, bus stop characteristics, and pedestrian exposure measures. Formal bus stops were associated with higher crash rates than informal ones. The presence of medians and crosswalks was associated with lower crash rates, whereas wider carriageways were associated with higher crash rates. Higher crashes were linked to passing pedestrians and waiting pedestrians, while crossing pedestrians were associated with reduced crashes. These findings suggest that the combined effects of infrastructure and behavioural factors influence pedestrian safety at bus stops. Prioritising low-cost safety treatments, such as guard-railed waiting areas, marked crosswalks, medians, and raised crossings, around bus stops will yield substantial safety benefits for resource-constrained contexts and advance sustainable urban mobility. Full article

This paper presents the setup and evaluation of a high-resolution, regional, coupled ocean–atmosphere model to simulate tropical cyclones (TCs) in the North Atlantic Basin. This approach combines the Weather Research and Forecasting (WRF) atmospheric model and the Coastal and Regional Ocean Community (CROCO), featuring spatial resolutions of 9 km and 18 km, respectively, which are coupled through OASIS-MCT. A hindcast ensemble of 15 historical TCs was simulated using both the coupled and uncoupled model configurations. TC tracks and intensities were extracted using an automated detection algorithm and compared with observational data from the International Best Track Archive for Climate Stewardship (IBTrACS). The coupled model showed good overall performance in representing TC trajectories and intensity changes. The mean distance error between the simulated and observed TCs centers was 176 km. The median intensity difference was 6.4% with a tendency to slightly overestimate TC intensity. Performance varied across storms, with cases such as Dennis (2005) and Fiona (2022) simulated with relatively high accuracy, while others, including Eta (2020), exhibited larger errors. This coupled modeling system provides a promising tool for studying ocean–atmosphere interactions during TCs and for generating high-resolution 3D data for both the ocean and atmosphere. However, the limitations include computational expense and sensitivity to the model configuration choices. Full article

This study focused on the application of supervised learning in the field of renewable energy, specifically for predicting daily solar irradiance in Neiva, department of Huila, Colombia. To this end, decision tree and artificial neural network (DT and ANN, respectively) models were trained and tested using the online tool Google Colab. The main objective was based on the need to optimize energy planning processes at local and regional levels, motivated by the increase in demand for the integration of non-conventional energy sources and the spatial–temporal variability in solar resources in the country. A dataset consisting of 366 daily records for the year 2024 was obtained from the NASA POWER database at the geographic coordinates (2.930079, −75.255650) and used for training and evaluating the proposed models. Statistical and cleaning techniques were used, including the treatment of outliers using the moving-window median for the latter. Metrics, such as mean absolute error ($MAE$), root mean square error ($RMSE$), and coefficient of determination ($R^2$), were used to evaluate the models. Data inclusion and exclusion criteria were applied to ensure the quality and validity of the observations. Model performance was evaluated using a randomized Hold-Out validation strategy (90% training and 10% testing), which was repeated across multiple iterations. The performance metrics reported corresponded to the 10th iteration of the validation process after outlier treatment. Under this configuration, the DT model achieved a higher predictive performance ($R^2$ = 0.8882) compared with the ANN model ($R^2$ = 0.7679), demonstrating its effectiveness as a reliable approach for estimating daily solar irradiance under the studied conditions. This result was also confirmed by the decreased $MAE$ and $RMSE$ for the DT model, which indicated that this model performed better in predicting the real values than the ANN model. Finally, the added value of the study is to consolidate national evidence and open access tools to facilitate the development of sustainable energy policies in intermediate cities such as Neiva. Full article

This study assesses regional science and technology (S&T) innovation efficiency across 30 Chinese provinces from 2011 to 2022, incorporating a sustainable development perspective. Employing a non-oriented global frontier super-slack-based measure (SBM) model that accounts for undesirable outputs, along with kernel density estimation, cluster analysis, and Moran’s I, the research investigates the spatiotemporal evolution of innovation dynamics. The findings demonstrate a marked upward trend, with the national average efficiency score rising from 0.260 to 0.703. Temporally, efficiency advanced through three stages: an initial period of universally low efficiency, a phase of widening disparities, and a final stage of overall improvement and stabilization. Spatial analysis reveals a persistent “strong in the east, weak in the west” disequilibrium; however, absolute β-convergence tests indicate a significant reduction in regional disparities (p < 0.05). Kernel density estimation reveals a shift from a polarized “pyramid” shape to a more balanced “spindle-shaped” distribution. This is evidenced by a decrease in kurtosis and a rightward shift in the median. Spatial autocorrelation, as measured by the Global Moran’s I, evolved from a statistically insignificant distribution in 2011 to a strong positive correlation (0.223, p < 0.05) by 2022. This progression reflects a transition from isolated “unipolar” hubs to integrated “multi-center block linkages.” The results suggest that, although polarization is diminishing and the national innovation baseline is improving, policy efforts should prioritize the development of emerging innovation corridors to address the remaining east–west divide. Full article

Background/Objectives: LINAC-based single-isocenter (SIT) stereotactic radiosurgery (SRS) enables efficient treatment of multiple brain metastases but may compromise target conformity and increase low-dose brain exposure, particularly for spatially distributed lesions. Dual-isocenter techniques (DITs) may mitigate these limitations, while Gamma Knife (GK) remains the reference standard for high-selectivity radiosurgery. This study compares SIT- and DIT LINAC-based SRS with GK, focusing on target conformity and low-dose brain exposure under equivalent, zero-margin targeting assumptions. Methods: Twenty-eight patients with multiple brain metastases (197 lesions) were included in this retrospective planning study. For each patient, three plans were generated: a GK plan and LINAC-based SIT and DIT plans using automated dynamic conformal arc optimization (Elements Multiple Brain Metastases). All plans were generated using a zero-millimeter GTV-to-PTV margin strategy. For DIT, lesions were automatically clustered and assigned to two isocenters. Target coverage required ≥99% of each GTV to receive the prescription dose. Plan quality was evaluated using the Paddick Conformity Index (PCI) on a per-lesion basis and low-dose brain volumes (V12, V10, V5, V4, and V3 Gy) on a per-patient basis. Paired non-parametric tests and multivariable models were used to assess technique-related differences and associations with total target volume and lesion count. Results: GK achieved the highest median PCI (0.83), followed closely by DIT (0.77), while SIT plans demonstrated significantly lower conformity (0.73). Compared with GK, the median PCI difference was −0.05 for DIT and −0.08 for SIT. Conformity for DIT remained stable across lesion volumes and lesion counts, whereas GK conformity increased modestly with lesion size. Low-dose brain exposure differed significantly between techniques at all dose levels (p < 0.001). GK consistently yielded the lowest Vx volumes, SIT the highest, and DIT intermediate values. Relative to GK, SIT plans showed progressively larger increases in low-dose exposure at lower dose levels (mean ΔV3 ≈ +149 cc), while DIT reduced this low-dose spread (mean ΔV3 ≈ +117 cc). Total target volume was the dominant predictor of low-dose brain exposure across all techniques, with a smaller additional contribution from lesion count. Conclusions: DIT LINAC-based SRS significantly improves target conformity and reduces low-dose brain exposure compared with SIT delivery, achieving dosimetric performance that closely approximates Gamma Knife under equivalent zero-margin targeting assumptions. While Gamma Knife remains the reference standard for low-dose sparing, dual-isocenter planning represents a clinically robust and scalable alternative that effectively balances plan quality and treatment efficiency in patients with multiple brain metastases. Full article

In low-altitude complex environments, accurately reconstructing the three-dimensional (3D) flight trajectories of small unmanned aerial vehicles (UAV) without onboard positioning modules remains challenging. To address this issue, this paper proposes a multi-view ground camera-based UAV 3D trajectory detection method founded on an optical imaging geometric model. Multiple ground cameras are used to synchronously observe UAV flight, enabling stable 3D trajectory reconstruction without relying on onboard Global Navigation Satellite System (GNSS). At the two-dimensional (2D) observation level, a lightweight object detection model is employed for rapid UAV detection. Foreground segmentation is further introduced to extract accurate UAV contours, and geometric centroids are computed to obtain precise image plane coordinates. At the 3D reconstruction stage, camera extrinsic parameters are estimated using a back intersection method with ground control points, and the UAV spatial position in the world coordinate system is recovered via multi-view forward intersection. Field experiments demonstrate that the proposed method achieves stable 3D trajectory reconstruction in real urban environments, with a median error of 4.93 m and a mean error of 5.83 m. The mean errors along the X, Y, and Z axes are 2.28 m, 4.58 m, and 1.09 m, respectively, confirming its effectiveness for low-cost UAV trajectory monitoring. Full article

The Navel Orangeworm (Amyelois transitella Walker, 1863), a primary pest of nut crops native to North America, poses a significant potential threat to China’s agricultural biosecurity, yet its potential distribution dynamics under climate change remain unquantified. This study utilized the Biomod2 ensemble model platform to predict habitat suitability under current and future climate scenarios (SSP1-2.6 and SSP5-8.5). We evaluated the prediction accuracy of the ensemble model using calibration data, with TSS = 0.898 and AUC = 0.978, while spatially stratified cross-validation confirmed moderate spatial transferability to novel environments (median validation AUC = 0.60–0.75). The model identified thermal factors—Temperature Seasonality (Bio4) and the Mean Temperature of the Wettest Quarter (Bio8)—as the dominant drivers of distribution. While currently climatically suitable habitats are primarily confined to the tropical and subtropical regions of southern China, projections indicate a complex spatial shift driven by future warming: optimal southern habitats will undergo a net contraction due to heat stress, whereas low and moderately suitable areas will expand northward into key temperate agricultural areas. These results highlight that climate change will substantially alter the spatial topology of the pest’s climatic envelope, providing a critical scientific baseline of climatic suitability. These projections do not equate to realized invasion risk, which is further constrained by host availability, land use, irrigation, and human transport, offering a conservative framework for prioritizing early surveillance and optimizing quarantine measures. Full article