Search Results (17,098)

Under the global impetus toward carbon peak and carbon neutrality, large-scale renewable energy integration has become a key driver in transforming traditional power grids into new power systems. Meanwhile, the growing adoption of advanced artificial intelligence (AI) approaches, especially large-scale models, heavily relies on high-performance computing (HPC) resources, which pose significant sustainability challenges due to their energy consumption and carbon emissions. This study introduces a newly developed carbon emission model (CEM) that accounts for both embodied and operational emissions in HPC systems. The CEM integrates parameters such as energy intensity coefficients, workload distribution patterns, and renewable deficiency rates, providing a lifecycle perspective of emissions in HPC-based AI applications for power systems. Results reveal that operational emissions dominate, constituting 87% of the total lifecycle footprint. Different regions exhibit varying carbon emissions, and on average, increasing the renewable energy share from 20% to 50% reduces total emissions by 43%, while a full transition to renewable energy achieves a 92% reduction. Circular economy practices, including hardware recycling and sustainable design, are also highlighted to mitigate embodied emissions. This study offers quantitative evidence and actionable insights for power industry stakeholders, enabling the balance between high-performance AI computations and ambitious carbon neutrality goals in renewable-integrated systems. Full article

Exploring the spatiotemporal evolution pattern of agricultural functions, analyzing their tradeoff and synergies, carrying out multifunctional zoning, and determining the combination and expansion direction of agricultural functions in combination with regional development strategies are conducive to guiding the adjustment of agricultural structure and promoting the sustainable development of regional agriculture. In this context, based on the county scale and statistical data, this paper uses the agricultural function evaluation index system to measure and analyze the agricultural function index of Sichuan Province and its mutual relations. Spatial overlay analysis is used to analyze the agricultural function index for agricultural leading function zoning. Cluster analysis is used to evaluate the agricultural function results to explore the agricultural multifunctional zoning scheme of Sichuan Province. The results show that the spatial and temporal distribution of agricultural product supply, agricultural leisure, ecological services, employment and social security services are heterogeneous, and the agricultural multifunction index of Sichuan Province shows a spatial distribution pattern of high in the east and low in the west. The synergistic effect between the supply function of agricultural products and the function of employment and life security is the strongest. From 2010 to 2020, the relationships between the ecological service function and the supply function of agricultural products, the agricultural leisure function, employment, and the social security function change from irrelevant or there being a tradeoff effect to there being a significant synergistic effect. The leading areas of the ecological service function are mainly distributed in western and northern Sichuan. The leading areas of the agricultural product supply function are mainly distributed in eastern and southern Sichuan. Agricultural multifunctional zoning in Sichuan Province is divided into the agricultural leisure function, the agricultural product supply cooperative functional area, the weak cooperative functional area, the strong cooperative functional area, and the agricultural leisure priority functional area. The spatiotemporal heterogeneity of agricultural functions and the changes in tradeoffs and synergies in Sichuan Province have a significant impact on the development of agricultural functions. The research results can provide s theoretical reference for agricultural multifunctional zoning in the study area and provide guidance and suggestions for the sustainable development of agricultural economy and society in Sichuan Province. Full article

Accurate information on crop planting and spatial distribution is critical for understanding and tracking long-term land use changes. The method of using deep learning (DL) to extract crop information has been applied in large-scale datasets and plain areas. However, current crop classification methods face some challenges, such as poor image time continuity, difficult data acquisition, rugged terrain, fragmented plots, and diverse planting conditions in complex scenes. In this study, we propose the Complex Scene Crop Classification U-Net (CSCCU), which aims to improve the mapping accuracy of staple crops in complex scenes by combining multi-spectral bands with spectral features. CSCCU features a dual-branch structure: the main branch concentrates on image feature extraction, while the auxiliary branch focuses on spectral features. In our method, we use the hierarchical feature-level fusion mechanism. Through the hierarchical feature fusion of the shallow feature fusion module (SFF) and the deep feature fusion module (DFF), feature learning is optimized and model performance is improved. We conducted experiments using GaoFen-2 (GF-2) images in Xiuwen County, Guizhou Province, China, and established a dataset consisting of 1000 image patches of size 256, covering seven categories. In our method, the corn and rice accuracies are 89.72% and 88.61%, and the mean intersection over union (mIoU) is 85.61%, which is higher than the compared models (U-Net, SegNet, and DeepLabv3+). Our method provides a novel solution for the classification of staple crops in complex scenes using high-resolution images, which can help to obtain accurate information on staple crops in larger regions in the future. Full article

The energy transition towards achieving carbon neutrality is marked by the decarbonization of the power system and a high degree of electrification in end-use sectors. The decarbonization of the power system primarily relies on large-scale renewable energy, nuclear power, and fossil fuel-based power with carbon capture technologies. This structure of power supply introduces significant uncertainty in electricity supply. Due to the technological progress in end-use sectors and spatial reallocation of industries in China, the load curve and power supply curve is very different today. However, most studies’ analyses of future electricity systems are based on today’s load curve, which could be misleading when seeking to understand future electricity systems. Therefore, it is essential to thoroughly analyze changes in end-use load curves to better align electricity demand with supply. This paper analyzes the characteristics of electricity demand load under China’s future energy transition and economic transformation pathways using the Integrated Energy and Environment Policy Assessment model of China (IPAC). It examines the electricity and energy usage characteristics of various sectors in six typical regions, provides 24-h load curves for two representative days, and evaluates the effectiveness of demand-side response in selected provinces in 2050. The study reveals that, with the transition of the energy system and the industrial relocation during economic transformation, the load curves in China’s major regions by 2050 will differ notably from those of today, with distinct characteristics emerging across different regions. With the costs of solar photovoltaic (PV) and wind power declining in the future, the resulting electricity price will also differ significantly from today. Daytime electricity prices will be notably lower than those during the evening peak, as the decrease in solar PV and wind power output leads to a significant increase in electricity costs. This pricing structure is expected to drive a strong demand-side response. Demand-side response can significantly improve the alignment between load curves and power supply. Full article

Biodiversity maintenance mechanisms have been central to the study of community ecology, and the negative density dependence effect plays an important role in maintaining species diversity in forest communities. However, the strength and direction of the negative density dependence effect may change at different latitudinal gradients, and theory predicts that the negative density dependence effect increases with decreasing latitude. Using three provinces in northeastern China as the study target, we selected forest ecosystems in 15 locations according to the latitude gradient and analyzed the mixing of large- and small-diameter trees and adjacent tree species at different latitudinal gradients by the second-order characteristic function of mark mingling (The species mingling was used as “constructed marks” and we developed a second-order characteristic function of mark mingling useful for comparing spatial species mingling via random assignment of species patterns at specific ecological scales). It was found that the tree species mixed level of the large trees was higher, that of the small trees was lower in the stands at the middle and low latitudes (40, 41, and 43), and the tree species mixed level of the large or small trees was lower in the stands at high latitudes (45 and 46). Also, the level of mixing of large trees with surrounding tree species was significantly different among latitudes within the small scale (0–5 m). More importantly, the peak value of the difference in the second-order characteristic function of mark mingling (Δv(r)) of the stand increased gradually with decreasing latitude. The results indicated that the difference in tree species mixing degree between large and small trees was increasing, and this phenomenon was more obvious at the small scale (0–10 m). In general, we found that the negative density dependence effect in the late successional forest system showed a variation trend with latitude gradient, which showed that with the decrease in latitude, the negative density dependence effect in the stands was increasing. The results showed that in temperate forests, in low-latitude stands (40–43° N), there is significant peak in species mingling differences at small scales (0–10 m). Spatial heterogeneity thinning should be prioritized, and rare tree species should be replanted within a 10 m radius to alleviate intraspecific competition. In contrast, in high-latitude stands (45–46° N), human disturbance should be reduced to maintain the natural community structure. These measures can provide precise management strategies for regional biodiversity conservation. This study revealed the response of the intensity of the negative density dependence effect to changes in latitudinal gradients, and provides new ideas for maintaining and controlling regional species diversity. Full article

Medical image analysis is critical for diagnosing and planning treatments, particularly in addressing heart disease, a leading cause of mortality worldwide. Precise segmentation of the left atrium, a key structure in cardiac imaging, is essential for detecting conditions such as atrial fibrillation, heart failure, and stroke. However, its complex anatomy, subtle boundaries, and inter-patient variations make accurate segmentation challenging for traditional methods. Recent advancements in deep learning, especially semantic segmentation, have shown promise in addressing these limitations by enabling detailed, pixel-wise classification. This study proposes a novel segmentation framework Adaptive Multiscale U-Net (AMU-Net) combining Convolutional Neural Networks (CNNs) and transformer-based encoder–decoder architectures. The framework introduces a Contextual Dynamic Encoder (CDE) for extracting multi-scale features and capturing long-range dependencies. An Adaptive Feature Decoder Block (AFDB), leveraging an Adaptive Feature Attention Block (AFAB) improves boundary delineation. Additionally, a Spectral Synthesis Fusion Head (SFFH) synthesizes spectral and spatial features, enhancing segmentation performance in low-contrast regions. To ensure robustness, data augmentation techniques such as rotation, scaling, and flipping are applied. Laplacian approximation is employed for uncertainty estimation, enabling interpretability and identifying regions of low confidence. Our proposed model achieves a Dice score of 93.35, a Precision of 94.12, and a Recall of 92.78, outperforming existing methods. Full article

The high-quality development of China’s fishery economy serves as its core objective, with robust financial support playing a pivotal role. This study employs provincial panel data spanning 2005 to 2020 and utilizes the entropy method to evaluate the level of high-quality development in China’s fishery economy across three dimensions: fundamental security, sustainability, and comprehensive efficiency. From the perspectives of formal and informal finance, it compares their support effects on different aspects of high-quality development in China’s fishery economy, while also exploring the mechanisms underlying these effects by considering factors such as industrial uncertainty and economic scale. The findings indicate that, overall, the support provided by both formal and informal finance for high-quality development in the fishery economy is insufficient. Further analysis reveals a significant threshold effect of fishery economic scale, with turning points at 108.44 billion CNY and 232.98 billion CNY for formal and informal finance, respectively. For higher-level indicators, such as sustainability and comprehensive efficiency, formal and informal financial systems demonstrate complementary roles, depending on the scale of the regional fishery economy. Furthermore, industrial uncertainty serves as a significant mediating factor only for formal financial support, with the levels of sustainability and comprehensive efficiency most affected. Full article

Soil erosion is a major environmental concern, especially in sensitive ecosystems like the Loess Plateau of China, where certain geological and climatic circumstances exacerbate the erosion process. Terracing and mulching are popular soil erosion management strategies in this region. However, their combined effects under varied rainfall intensities are poorly understood. The purpose of this study is to assess the performance of various terracing–mulch combinations in reducing water erosion under different rainfall intensities. The experimental layout included a control plot (C), non-terraced mulch applications (NTr-M), fish-scale pits with mulch (FSPs-M), zig terraces with mulch (ZTr-M), level bench terraces with mulch (LBTr-M), and trench terraces with mulch (TTr-M). Controlled artificial rainfall experiments were carried out under different intensities, and runoff and soil loss data were collected to evaluate the effects of the combinations. The event-based WEPP simulations, calibrated for the Loess Plateau, demonstrated strong predictive accuracy, as evidenced by the high correlation coefficients (R² = 0.97 for runoff; R² = 0.86 for soil loss) and Nash–Sutcliffe efficiency (NSE = 0.93 for runoff; NSE = 0.89 for soil loss), confirming their reliability in simulating erosion processes when compared to measured values. Our results revealed significant differences (p < 0.05) in mean runoff and soil loss among the treatments, ranked in the order LBTr-M < TTr-M < ZTr-M < FSPs-M < NTr-M < C. Incremental response analysis also revealed that the control plot (C) was the most sensitive to changes in rainfall intensity, followed by FSPs-M and NTr-M. In contrast, LBTr-M was found to be the most stable strategy. These findings highlight the importance of optimizing micro-relief construction and mulch application to enhance erosion control and support the recommendation of LBTr-M, TTr-M, and ZTr-M as effective strategies. Conversely, FSPs-M and NTr-M proved less effective under higher rainfall intensities. These findings emphasize the need to optimize micro-relief construction and mulch application for erosion management, as well as suggest that such strategies could be applied to the Loess Plateau and other erosion-prone regions worldwide with similar climatic and topographic conditions. Full article

Automatic glacier extraction from remote sensing images is the most important approach for large scale glacier monitoring. Commonly used band calculation indices to enhance glacier information are not effective in identifying shadowed glaciers and debris-covered glaciers. In this study, we used the Kolmogorov–Smirnov test as the theoretical basis and determined the most suitable band calculation indices to distinguish different land cover classes by comparing inter-sample separability and reasonable threshold range ratios of different indices. We then constructed a glacier classification decision tree. This approach resulted in the development of a method to automatically extract glacier areas at given spatial and temporal scales. In comparison with the commonly used indices, this method demonstrates an improvement in Cohen’s kappa coefficient by more than 3.8%. Notably, the accuracy for shadowed glaciers and debris-covered glaciers, which are prone to misclassification, is substantially enhanced by 108.0% and 6.3%, respectively. By testing the method in the Qilian Mountains, the positive prediction value of glacier extraction was calculated to be 91.8%, the true positive rate was 94.0%, and Cohen’s kappa coefficient was 0.924, making it well suited for glacier extraction. This method can be used for monitoring glacier changes in global mountainous regions, and provide support for climate change research, water resource management, and disaster early warning systems. Full article

Territorial Space (TS) is characterized by its multifunctionality. The identification and management of Territorial Spatial Functions (TSFs) across multi-scale is crucial for achieving the SDGs. However, previous studies have primarily concentrated on the variations in TSFs within the administrative or grid units at a single scale, with multi-scale investigations remaining a challenge. This study focuses on the typical karst region of Guangxi province in China and develops a Multi-Scale Fusion model (MSF) for assessing TSFs and employs a coupling coordination degree (CCD) model to examine the TSFs relationships. Furthermore, principal component analysis (PCA) is used to classify various types of influencing factors, and the Revealed Comparative Advantage (RCA) index is employed to identify the primary types of influencing factors at the county level. The study integrates coupling coordination types and advantage factors into the zoning process. The results demonstrate: (1) Ecological function is the dominant function. At the administrative unit scale, production and living functions exhibit a spatial pattern of “high in the southeast and low in the northwest”, while ecological function shows the opposite pattern. Under grid units scale and multi-scale fusion, the high and low texture characteristics of production and ecological functions are more pronounced. (2) TSFs are primarily characterized by slight and moderate disorder. Slight disorder is widely distributed, while moderate disorder is predominantly found in the northwest karst mountainous regions. In contrast, coordinated relationships are more frequently observed in urban areas. (3) The driver types of TSFs can be categorized into four categories: Terrain-Population, Agriculture Development, Location-Economy, and Non-Agriculture Development. By integrating the TSFs relationships, six zones are delineated. Based on this, precise and differentiated optimization suggestions are proposed to promote orderly utilization and sustainable development of TS. Full article

Water scarcity significantly challenges agricultural systems worldwide, especially in tropical areas such as the Dominican Republic. This study explores integrating satellite-based remote sensing technologies and field-based soil moisture sensors to assess water stress and optimize irrigation management in avocado orchards in Puerto Escondido, Dominican Republic. Using multispectral imagery from the Landsat 8 and 9 satellites, key vegetation indices (NDVI and SAVI) and NDWI, a water-related index that specifically indicates changes in crop water contents, rather than vegetation vigor, were derived to monitor vegetation health, growth stages, and soil water contents. Crop coefficient (Kc) values were calculated from these vegetation indices and combined with reference evapotranspiration (ETo) estimates derived from three meteorological models (Hargreaves–Samani, Priestley–Taylor, and Blaney–Criddle) to assess crop water requirements. The results revealed that soil moisture data from sensors at 30 cm depth strongly correlated with satellite-derived estimates, reflecting avocado trees’ critical root zone dynamics. Additionally, seasonal patterns in the vegetation indices showed that NDVI and SAVI effectively tracked vegetative growth stages, while NDWI indicated changes in the canopy water content, particularly during periods of water stress. Integrating these satellite-derived indices with field measurements allowed a comprehensive assessment of crop water requirements and stress, providing valuable insights for improving irrigation practices. Finally, this study demonstrates the potential of remote sensing technologies for large-scale water stress assessment, offering a scalable and cost-effective solution for optimizing irrigation practices in water-limited regions. These findings advance precision agriculture, especially in tropical environments, and provide a foundation for future research aimed at enhancing data accuracy and optimizing water management practices. Full article

Panoramic radiography is vital in dentistry, where accurate detection and segmentation of diseased regions aid clinicians in fast, precise diagnosis. However, the current methods struggle with accuracy, speed, feature extraction, and suitability for low-resource devices. To overcome these challenges, this research introduces a unique YOLO-DentSeg model, a lightweight architecture designed for real-time detection and segmentation of oral dental diseases, which is based on an enhanced version of the YOLOv8n-seg framework. First, the C2f(Channel to Feature Map)-Faster structure is introduced in the backbone network, achieving a lightweight design while improving the model accuracy. Next, the BiFPN(Bidirectional Feature Pyramid Network) structure is employed to enhance its multi-scale feature extraction capabilities. Then, the EMCA(Enhanced Efficient Multi-Channel Attention) attention mechanism is introduced to improve the model’s focus on key disease features. Finally, the Powerful-IOU(Intersection over Union) loss function is used to optimize the detection box localization accuracy. Experiments show that YOLO-DentSeg achieves a detection precision (mAP50(Box)) of 87%, segmentation precision (mAP50(Seg)) of 85.5%, and a speed of 90.3 FPS. Compared to YOLOv8n-seg, it achieves superior precise and faster inference times while decreasing the model size, computational load, and parameter count by 44.9%, 17.5%, and 44.5%, respectively. YOLO-DentSeg enables fast, accurate disease detection and segmentation, making it practical for devices with limited computing power and ideal for real-world dental applications. Full article

Icebergs pose significant risks to shipping, offshore oil exploration, and underwater pipelines. Detecting and monitoring icebergs in the North Atlantic Ocean, where darkness and cloud cover are frequent, is particularly challenging. Synthetic aperture radar (SAR) serves as a powerful tool to overcome these difficulties. In this paper, we propose a method for automatically detecting and classifying icebergs in various sea conditions using C-band dual-polarimetric images from the RADARSAT Constellation Mission (RCM) collected throughout 2022 and 2023 across different seasons from the east coast of Canada. This method classifies SAR imagery into four distinct classes: open water (OW), which represents areas of water free of icebergs; open water with target (OWT), where icebergs are present within open water; sea ice (SI), consisting of ice-covered regions without any icebergs; and sea ice with target (SIT), where icebergs are embedded within sea ice. Our approach integrates statistical features capturing subtle patterns in RCM imagery with high-dimensional features extracted using a pre-trained Vision Transformer (ViT), further augmented by climate parameters. These features are classified using XGBoost to achieve precise differentiation between these classes. The proposed method achieves a low false positive rate of 1% for each class and a missed detection rate ranging from 0.02% for OWT to 0.04% for SI and SIT, along with an overall accuracy of 96.5% and an area under curve (AUC) value close to 1. Additionally, when the classes were merged for target detection (combining SI with OW and SIT with OWT), the model demonstrated an even higher accuracy of 98.9%. These results highlight the robustness and reliability of our method for large-scale iceberg detection along the east coast of Canada. Full article

This study explores the transformation of the ethno-linguistic identity of Kazakhstani student youth within the multilingual context of Kazakhstan, considering the impact of the country’s language policies. Our research analyzes language choices, focusing on the knowledge and factors influencing parents and Kazakhstani youth when making decisions about children’s language education, as well as the strategies they adopt for language use. The empirical basis of this study is a sociolinguistic survey conducted among 823 Kazakhstani university students aged 18 to 30 from various regions of the country in 2023. Data were collected through questionnaires and interviews, which were subjected to both qualitative and quantitative analysis. The findings were supplemented by the results of the most recent national census. This comparative analysis provides a comprehensive understanding of the current state and emerging trends in the ethno-linguistic identity of Kazakhstani youth and the broader linguistic landscape of the country. The results indicate that large-scale state initiatives aimed at reinforcing the status of the Kazakh language have had a positive impact on its recognition. However, the data also reveal persistent fluctuations in ethno-linguistic identity, which can be attributed to various extralinguistic factors. This study highlights the role of both educational and family language policies as key drivers in shaping ethno-linguistic identity. Full article

Background: Intra-articular calcaneal fracture (CF) treatment is associated with a high risk of complications, but closed reduction and internal fixation (CRIF) is a minimally invasive alternative for treatment. Methods: Forty-eight patients treated with CRIF and CALCAnail^® due to intra-articular CF between 2016 and 2021 were analyzed to check union time, complication rate, and functionality after the intervention. Functional and pain outcomes were assessed, including the Maryland Foot Score (MFS), American Orthopedic Foot & Ankle Society (AOFAS) scale questionnaires, and the numerical pain scale (NRS) at mid-term follow-ups 2–5 years after the intervention. Results: Intervention increased median Böhler’s angle from 21.5° to 32° (p < 0.01). The median bone union time was 12 weeks. The risk of malunion was higher in patients with Sanders type 4 (RR = 2.28; 95% CI 1.11–4.72) and those operated on later than the 2nd day after injury (RR = 2.1; 95% CI 1.08–4.09). Patients with at least one of the comorbidities (nicotinism, diabetes, obesity) had a higher risk of intensive pain (NRS > 3) 2–5 years after surgery (RR = 1.69; 95% CI 1.06–2.68), and 84% were satisfied with their treatment. Other complications included complex regional pain syndrome in two patients (4%), malunion in three (6%), and surgical site infection in two (4%). The MFS had a median score of 85 points, while that of the AOFAS was 82 points. Conclusions: CRIF, with the use of the CALCAnail^® implant, allows doctors to restore anatomical relationships around the subtalar joint, resulting in good clinical and functional results. Full article