Search Results (6,307)

Spatial, temporal, and space–time scan statistics can be used for geographical surveillance, identifying temporal and spatial patterns, and detecting outliers. While statistical cluster analysis is a valuable tool for identifying patterns, optimizing resource allocation, and supporting decision-making, accurately predicting future spatial clusters remains a significant challenge. Given the known relative risks of spatial clusters over the past k time intervals, the main objective of the present study is to predict the relative risks for the subsequent interval, $k+1$. Building on our prior research, we propose a predictive Markov chain model with an embedded corrector component. This corrector utilizes either multiple linear regression or an exponential smoothing method, selecting the one that minimizes the relative distance between the observed and predicted values in the k-th interval. To test the proposed method, we first calculated the relative risks of statistically significant spatial clusters of COVID-19 mortality in the U.S. over seven time intervals from May 2020 to March 2023. Then, for each time interval, we selected the top 25 clusters with the highest relative risks and iteratively predicted the relative risks of clusters from intervals three to seven. The predictive accuracies ranged from moderate to high, indicating the potential applicability of this method for predictive disease analytic and future pandemic preparedness. Full article

Utilizing short-period dense seismic arrays, ambient noise tomography has proven effective in delineating continuous geological structures, a task critical for characterizing shale gas reservoir configurations. This study deployed 153 short-period seismic stations across the Xiahuayuan District in Zhangjiakou, a region with prospective shale gas deposits, to perform an ambient noise tomography survey. Through a meticulous process involving cross-correlation analysis, dispersion curve extraction, and subsequent inversion, a three-dimensional velocity structure model of the area was constructed. The model discerns subtle velocity changes within the 0–3 km depth interval, achieving a horizontal resolution of approximately 1.5 km in the 0–3 km stratum, thereby effectively delineating the shale reservoir structure. Integration of the velocity model with regional geological data facilitated a comprehensive interpretation and structural analysis of the prospective shale gas zone. Low-velocity anomalies observed within the velocity structure correspond to the spatial distribution of the Xiahuayuan Formation, likely attributable to the prevalent stratum of mudstone shale deposits within this formation. Employing a binary stratigraphic model, the study predicted shale content based on the velocity structure, with predictions exhibiting a moderate correlation (correlation coefficient of 0.58) with empirical data. This suggests the presented method as a viable rapid estimation technique for assessing the shale content of target strata. Full article

The accurate prediction of the spatial distribution of soil organic carbon (SOC) and the identification of the mechanisms underlying its spatial differentiation are of paramount significance for the conservation and utilization of land and regional sustainable development. A total of 512 soil samples were collected from Wuchang and Shuangcheng County in Harbin City, Heilongjiang Province, China, which served as the study area. Six machine learning models, including Random Forest (RF), AdaBoost, Support Vector Regression (SVR), weighted average, Stacking, and Blending, were utilized to predict the spatial distribution of SOC and analyze its spatial differentiation. The result reveals that 12 environmental variables, including soil type, bulk density, pH, average annual precipitation, average annual temperature, net primary productivity (NPP), land use type, normalized difference vegetation index (NDVI), slope, elevation, soil parent material, and distance to rivers, are effective influencing factors on SOC in the study area. It turns out that the Stacking model, with an R² of 0.4327, performed the best in this study, followed by the weighted average, Blending, RF, AdaBoost, and SVR models; a heterogeneous integrated learning model may be more robust than an individual learner. The predicted SOC content is generally lower in the northwestern arable land and higher in the southeastern forest land. In addition, SOC differentiation shows that forest land and grass land with dark brown soil or swamp soil, soil covering igneous and metamorphic rocks with various minerals, higher elevation and slope, and suitable water-thermal and soil intrinsic conditions for aerobic microbial activity benefit the enrichment of SOC in the study area. The enrichment and depletion of SOC are jointly influenced by pedogenesis, microbial activity, and biodiversity. Full article

The North China Plain is a crucial agricultural region in China, but irregular precipitation patterns have led to significant water shortages. To address this, analyzing the high-resolution dynamics of root-zone soil moisture transport is essential for optimizing irrigation strategies and improving water resource efficiency. The Richards equation is a robust model for describing soil moisture transport dynamics across multiple soil layers, yet its application at large spatial scales is hindered by its sensitivity to boundary conditions and model parameters. This study introduces a novel approach that, for the first time, employs a continuous time series of near-surface soil moisture as the upper boundary condition in the Richards equation to estimate high-resolution root-zone soil moisture in the North China Plain, thus enabling its large-scale application. Singular spectrum analysis (SSA) was first applied to reconstruct site-specific time series, filling in missing and singular values. Leveraging observational data from 617 monitoring sites across the North China Plain and multiple spatial covariates, we developed a machine learning model to estimate near-surface soil moisture at a 1 km resolution. This high-resolution, continuous near-surface soil moisture series then served as the upper boundary condition for the Richards equation, facilitating the estimation of root-zone soil moisture across the region. The results indicated that the machine learning model achieved a correlation coefficient (R) of 0.92 for estimating spatial near-surface soil moisture. Analysis of spatial covariates showed that atmospheric forcing factors, particularly temperature and evaporation, had the most substantial impact on model performance, followed by static factors such as latitude, longitude, and soil texture. With a continuous time series of near-surface soil moisture, the Richards equation method accurately predicted multi-layer soil moisture and demonstrated its applicability for large-scale spatial use. The model yielded R values of 0.97, 0.78, 0.618, and 0.43, with RMSEs of 0.024, 0.06, 0.08, and 0.11, respectively, for soil layers at depths of 10 cm, 20 cm, 40 cm, and 100 cm across the North China Plain. Full article

Land use and land cover (LULC) change, driven by environmental and human activities, significantly impacts ecosystems, climate, biodiversity, and socio-economic systems. This study focuses on Khyber Pakhtunkhwa (KPK), Pakistan, a region with sensitive ecosystems and diverse landscapes, to analyze LULC dynamics and their environmental consequences. Based on Landsat imagery from 2000, 2010, and 2020, we used the Random Forest algorithm on Google Earth Engine (GEE) to classify LULC, and the CA-ANN model to project future scenarios for 2030, 2050, and 2100. Additional simulations were conducted using the MOLUSCE Plugin in QGIS. The results revealed a 138.02% (4071.98 km²) increase in urban areas from 2000 to 2020, marking urbanization as a major driver of LULC change. Urban expansion strongly correlated with land surface temperature (LST) (R² = 0.89), amplifying the urban heat island effect. Rising LST showed negative correlations with the key environmental indices NDVI (−0.88), MNDWI (−0.49), and NDMI (−0.62), signaling declining vegetation cover, water resources, and soil moisture, respectively. Projections for 2100 predict LST rising to 55.3 °C, with NDVI, MNDWI, and NDMI dropping to 0.36, 0.17, and 0.21, respectively. Vegetation health, as indicated by the Leaf Area Index (LAI), also declined, with maximum and minimum values falling from 4.66 and −5.75 in 2000 to 2.16 and −2.55 in 2020, reflecting increased barren land and reduced greenness. The spatial analysis highlights significant transitions from vegetated to barren or urban land, leading to declining moisture levels, water stress, soil erosion, and biodiversity. Projections show continued reductions in forests, vegetation, and agricultural lands, replaced by barren and built-up areas. Declines in key indices such as NDVI, MNDWI, and NDMI indicate deteriorating vegetation, water resources, and soil moisture levels. These findings emphasize the need for sustainable urban planning and environmental management. Expanding urban green spaces, using reflective materials, and preserving vegetation and water resources are vital to mitigating heat island effects and maintaining ecological balance. Anticipated declines in LST, NDVI, MNDWI, NDMI, and LAI stress the urgency for climate adaptation strategies to protect human health, ecosystem services, and economic stability in KPK. Full article

Traffic flow prediction is a pivotal element in Intelligent Transportation Systems (ITSs) that provides significant opportunities for real-world applications. Capturing complex and dynamic spatio-temporal patterns within traffic data remains a significant challenge for traffic flow prediction. Different approaches to effectively modeling complex spatio-temporal correlations within traffic data have been proposed. These approaches often rely on a single model to capture temporal dependencies, which neglects the varying influences of different time periods on traffic flow. Additionally, these models frequently utilize either static or dynamic graphs to represent spatial dependencies, which limits their ability to address complex and overlapping spatial relationships. Moreover, some approaches struggle to fully capture spatio-temporal variations, leading to the exclusion of critical information and ultimately resulting in suboptimal prediction performance. Thus, this paper introduces the Adaptive Spatio-Temporal Attention-Based Multi-Model (ASTAM), an architecture designed to capture spatio-temporal dependencies within traffic data. The ASTAM employs multi-temporal gated convolution with multi-scale temporal input segments to model complex non-linear temporal correlations. It utilizes static and dynamic parallel multi-graphs to facilitate the modeling of complex spatial dependencies. Furthermore, this model incorporates a spatio-temporal self-attention mechanism to adaptively capture the dynamic and long-term spatio-temporal variations in traffic flow. Experiments conducted on four real-world datasets reveal that the proposed architecture outperformed 13 baseline approaches, achieving average reductions of 5.0% in MAE, 13.28% in RMSE, and 6.46% in MAPE across four datasets. Full article

Partial discharge (PD) is a commonly encountered discharge-related fault in transformers. Due to the unique characteristics of the environment where PD occurs, challenges such as difficulty in data acquisition and scarcity of samples arise. Convolutional neural networks (CNNs) are widely used in pattern recognition because of their strong feature extraction capabilities. To improve the recognition accuracy of PD models, this paper integrates CNN, bidirectional long short-term memory (BiLSTM), and an attention mechanism. In the proposed model, CNN is employed to extract local spatial and temporal features, BiLSTM is utilized to extract global bidirectional spatial and temporal features, and the attention mechanism assigns adaptive weights to the features. Additionally, to address the issues of sample scarcity and data imbalance, an improved GAN is introduced to augment the data. The experimental results demonstrate that the CNN-BiLSTM-attention method proposed in this paper significantly improves the prediction accuracy. With the help of GAN, the proposed method achieves a recognition accuracy of 97.36%, which is 1.8% higher than that of the CNN+CGAN(Conditional Generative Adversarial Network) method and 5.8% higher than that of thetraditional recognition model, SVM, making it the best-performing method among several comparable methods. Full article

Diffuse noxious inhibitory control (DNIC), also known as conditioned pain modulation (CPM) in humans, is a paradigm wherein the heterotopic application of a noxious stimulus results in the attenuation of another spatially distant noxious input. The pre-clinical and clinical studies show the involvement of several neurochemical systems in DNIC/CPM and point to a major contribution of the noradrenergic, serotonergic, and opioidergic systems. Here, we thoroughly review the latest data on the monoaminergic and opioidergic studies, focusing particularly on pre-clinical models of chronic pain. We also conduct an in-depth analysis of these systems by integrating the available data with the descending pain modulatory circuits and the neurochemical systems therein to bring light to the mechanisms involved in the regulation of DNIC. The most recent data suggest that DNIC may have a dual outcome encompassing not only analgesic effects but also hyperalgesic effects. This duality might be explained by the underlying circuitry and the receptor subtypes involved therein. Acknowledging this duality might contribute to validating the prognostic nature of the paradigm. Additionally, DNIC/CPM may serve as a robust paradigm with predictive value for guiding pain treatment through more effective targeting of descending pain modulation. Full article

To evaluate and compare the effectiveness of prediction models for Argentine squid Illex argentinus trawling grounds in the Southwest Atlantic high seas based on vessel position and fishing log data, this study used AIS datasets and fishing log datasets from fishing seasons spanning 2019–2024 (December to June each year). Using a spatial resolution of 0.1° × 0.1° and a monthly temporal resolution, we constructed two datasets—one based on vessel positions and the other on fishing logs. Fishing ground levels were defined according to the density of fishing locations, and combined with oceanographic data (sea surface temperature, 50 m water temperature, sea surface salinity, sea surface height, and mixed layer depth). A CNN-Attention deep learning model was applied to each dataset to develop Illex argentinus trawling ground prediction models. Model accuracy was then compared and potential causes for differences were analyzed. Results showed that the vessel position-based model had a higher accuracy (Accuracy = 0.813) and lower loss rate (Loss = 0.407) than the fishing log-based model (Accuracy = 0.727, Loss = 0.513). The vessel-based model achieved a prediction accuracy of 0.763 on the 2024 test set, while the fishing log-based model reached an accuracy of 0.712, slightly lower than the former, indicating the high accuracy and unique advantages of the vessel position-based model in predicting fishing grounds. Using CPUE from fishing logs as a reference, we found that the vessel position-based model performed well from January to April, whereas the CPUE-based model consistently maintained good accuracy across all months. The 2024 fishing season predictions indicated the formation of primary fishing grounds as early as January 2023, initially near the 46° S line of the Argentine Exclusive Economic Zone, with grounds shifting southeastward from March onward and reaching around 42° S by May and June. This study confirms the reliability of vessel position data in identifying fishing ground information and levels, with higher accuracy in some months compared to the fishing log-based model, thereby reducing the data lag associated with fishing logs, which are typically available a year later. Additionally, national-level fishing log data are often confidential, limiting the ability to fully consider fishing activities across the entire fishing ground region, a limitation effectively addressed by AIS vessel position data. While vessel data reflects daily catch volumes across vessels without distinguishing CPUE by species, log data provide a detailed daily CPUE breakdown by species (e.g., Illex argentinus). This distinction resulted in lower accuracy for vessel-based predictions in December 2023 and May–June 2024, suggesting the need to incorporate fishing log data for more precise assessments of fishing ground levels or resource abundance during those months. Given the near-real-time nature of vessel position data, fishing ground dynamics can be monitored in near real time. The successful development of vessel position-based prediction models aids enterprises in reducing fuel and time costs associated with indiscriminate squid searches, enhancing trawling efficiency. Additionally, such models support quota management in global fisheries by optimizing resource use, reducing fishing time, and consequently lowering carbon emissions and environmental impact, while promoting marine environmental protection in the Southwest Atlantic high seas. Full article

Analyzing habitat conditions and mapping habitat structures are crucial for monitoring ecosystems and implementing effective conservation measures, especially in the context of declining open grassland ecosystems in Europe. The marsh fritillary (Euphydryas aurinia), an endangered butterfly species, depends heavily on specific habitat conditions found in these grasslands, making it vulnerable to environmental changes. To address this, we conducted a comprehensive habitat suitability analysis within the Hainich National Park in Thuringia, Germany, leveraging very high-resolution (VHR) airborne, red-green-blue (RGB), and color-infrared (CIR) remote sensing data and deep learning techniques. We generated habitat suitability models (HSM) to gain insights into the spatial factors influencing the occurrence of E. aurinia and to predict potential habitat suitability for the whole study site. Through a deep learning classification technique, we conducted biotope mapping and generated fine-scale spatial variables to model habitat suitability. By employing various modeling techniques, including Generalized Additive Models (GAM), Generalized Linear Models (GLM), and Random Forest (RF), we assessed the influence of different modeling parameters and pseudo-absence (PA) data generation on model performance. The biotope mapping achieved an overall accuracy of 81.8%, while the subsequent HSMs yielded accuracies ranging from 0.69 to 0.75, with RF showing slightly better performance. The models agree that homogeneous grasslands, paths, hedges, and areas with dense bush encroachment are unsuitable habitats, but they differ in their identification of high-suitability areas. Shrub proximity and density were identified as important factors influencing the occurrence of E. aurinia. Our findings underscore the critical role of human intervention in preserving habitat suitability, particularly in mitigating the adverse effects of natural succession dominated by shrubs and trees. Furthermore, our approach demonstrates the potential of VHR remote sensing data in mapping small-scale butterfly habitats, offering applicability to habitat mapping for various other species. Full article

The integrity of habitat quality is a pivotal cornerstone for the sustainable advancement of local ecological systems. Rapid urbanization has led to habitat degradation and loss of biodiversity, posing severe threats to regional sustainability, particularly in extremely vulnerable arid zones. However, systematic research on the assessment indicators, limiting factors, and driving mechanisms of habitat quality in arid regions is notably lacking. This study takes Urumqi, an oasis city in China’s arid region, as a case study and employs the InVEST and PLUS models to conduct a dynamic evaluation of habitat quality in Urumqi from 2000 to 2022 against the backdrop of land use changes. It also simulates habitat quality under different scenarios for the year 2035, exploring the temporal and spatial dynamics of habitat quality and its driving mechanisms. The results indicate a decline in habitat quality. The habitat quality in the southern mountainous areas is significantly superior to that surrounding the northern Gurbantunggut Desert, and it exhibits greater stability. The simulation and prediction results suggest that from 2020 to 2035, habitat degradation will be mitigated under Ecological Protection scenarios, while the decline in habitat quality will be most pronounced under Business-As-Usual scenarios. The spatial distribution of habitat quality changes in Urumqi exhibits significant autocorrelation and clustering, with these patterns intensifying over time. The observed decline in habitat quality in Urumqi is primarily driven by anthropogenic activities, urban expansion, and climate change. These factors have collectively contributed to significant alterations in the landscape, leading to the degradation of ecological conditions. To mitigate further habitat quality loss and support sustainable development, it is essential to implement rigorous ecological protection policies, adopt effective ecological risk management strategies, and promote the expansion of ecological land use. These actions are crucial for stabilizing and improving regional habitat quality in the long term. Full article

This study utilizes NASA’s Normalized Difference Vegetation Index (NDVI) data from the Google Earth Engine (GEE) platform and employs methods such as mean analysis, trend analysis, and the Hurst index to assess NDVI dynamics in Xinjiang, with a particular focus on desert, meadow, and grassland vegetation. Furthermore, multiple linear regression, random forest, support vector machines, and XGBoost models are applied to construct and evaluate the NDVI prediction models. The key driving forces are identified and ranked based on the results of the optimal model. Changes in the vegetation cover in response to these driving forces are analyzed using the Mann–Kendall test and partial correlation analysis. The results indicate the following: (1) From 2000 to 2023, the annual variation in NDVI in Xinjian fluctuates at a rate of 0.0012 per year. The intra-annual trend follows an inverted U shape, with meadow vegetation exhibiting the highest monthly NDVI fluctuations. (2) During this period, the annual average NDVI in Xinjiang ranges from 0 to 0.3, covering 74.74% of the region. Spatially, higher NDVI values are observed in the north and northwest, while lower values are concentrated in the south and southeast. (3) The overall slope of the variation in NDVI in Xinjiang between 2000 and 2023 ranges between −0.034 and 0.047, indicating no significant upward trend. According to the Hurst index, future projections suggest a shift from vegetation improvement to potential degradation. (4) Machine learning models are developed to predict NDVI, with random forest and XGBoost showing the highest precision. Soil moisture, runoff, and potential evaporation are identified as key drivers. In the last 24 years, the temperatures in Xinjiang have generally increased, while precipitation, soil moisture, and runoff have declined. There is a significant negative correlation between NDVI and both temperature and potential evaporation, while the correlation between NDVI and precipitation, soil moisture, and runoff is positive and significant, with distinct spatial variations throughout the region. The overall trend of vegetation cover in Xinjiang has been increasing, but the future outlook is less promising. Enhanced environmental monitoring and protective measures are essential moving forward. Full article

Tidal sites can present uneven seabed bathymetry features that induce favourable or adverse pressure gradients and are sources of turbulence, and so are likely to affect the operation, performance, and wake recovery dynamics of deployed tidal-stream turbines. Large-eddy simulations are conducted to analyse the unsteady loading of a tidal turbine subjected to the wake of an upstream turbine that interacts with a two-dimensional ridge located between the two turbines. Relative to an isolated turbine, blade fatigue loading is increased by up to 43% when subject to the wake of a turbine located 8 turbine diameters upstream interacting with a ridge located 2 turbine diameters upstream, whereas for the same spacing, the turbine wake led to a limited 6% reduction in loading and the ridge wake only caused a 79% increase. For larger spacings, the trends were similar, but the magnitude of difference reduced. Predictions of fatigue loads with a blade element momentum model (BEMT) provided a good agreement for flat bed conditions. However, the ridge-induced pressure gradient drives rapid spatial change of coherent flow structures, which limits the applicability of Taylor’s frozen turbulence hypothesis adopted in the BEMT. Reasonable prediction of rotor loading with BEMT was found to be obtained using the turbulent onset flow field at a plane one-diameter upstream of the turbine. This is more accurate than use of the planes at the rotor plane or two-diameters upstream, as coherent structures represent those modified by wake recovery and rotor induction in the approach flow to the turbine. Full article

Mechanical belt sanding is critical in the manufacturing of bamboo and bamboo products, where surface roughness is commonly used to quantitatively evaluate the surface quality. In this study, flattened bamboo workpieces were sanded using P80 and P120 abrasive belts to create different surfaces. The linear roughness parameters, namely Rz, Ra, Rq, Rsk, Rku, and Rmr(c), were measured using both a stylus profilometer and a 3D profilometer. Statistical t-tests were conducted to determine the significance of differences between the two methods. Additionally, roughness profiles were analyzed in the frequency domain using Fast Fourier Transform (FFT) and Power Spectral Density (PSD) methods. A Random Forest (RF) regression model was also developed to predict the roughness values and figure out the dominant factors between granularity and measurement methods. The results revealed that both the stylus and 3D profilometers provided reliable comparisons of Rz, Ra, Rq, and Rmr (50%) for different grit sizes. However, resolution differences between the two methods were found to be critical for accurately interpreting roughness values. Variations in Rsk and Rku highlighted differences in sensitivity and detection range, particularly at finer scales, between the two methods. The stylus profilometer, with its higher spatial resolution and finer sampling density, demonstrated greater sensitivity to finer surface details. This was consistent with the FFT and PSD analyses, which showed that the stylus profilometer captured higher-frequency surface components more effectively. Furthermore, the RF model indicated that the choice of measurement method had negligible impact on the evaluation of the selected roughness parameters, suggesting that standardizing measurement techniques may not be essential for consistent roughness assessments of sanded bamboo surfaces. Full article

Nitrate leaching from soil presents a significant threat to soil health, as it can result in nutrient loss, soil acidification, and structural damage. It is crucial to quantify the spatial heterogeneity of nitrate leaching and its drivers. A total of 509 observational data points regarding nitrate leaching in northern China were collected, capturing the spatial and temporal variations across crops such as winter wheat, maize, and greenhouse vegetables. A machine learning (ML) model for predicting nitrate leaching was then developed, with the random forest (RF) model outperforming the support vector machine (SVM), extreme gradient boosting (XGBoost), and convolutional neural network (CNN) models, achieving an R² of 0.75. However, the performance improved significantly after integrating the four models with Bayesian optimization (all models had R² > 0.56), which realized quantitative prediction capabilities for nitrate leaching loss concentrations. Moreover, the XGBoost model exhibited the highest fitting accuracy and the smallest error in estimating nitrate leaching losses, with an R² value of 0.79 and an average absolute error (MAE) of 3.87 kg/ha. Analyses of the feature importance and SHAP values in the optimal XGBoost model identified soil organic matter, chemical nitrogen fertilizer input, and water input (including rainfall and irrigation) as the main indicators of nitrate leaching loss. The ML-based modeling method developed overcomes the difficulty of the determination of the functional relationship between nitrate loss intensity and its influencing factors, providing a data-driven solution for estimating nitrate–nitrogen loss in farmlands in North China and strengthening sustainable agricultural practices. Full article