Search Results (6,187)

Remote sensing spatiotemporal fusion technology can provide abundant data source information for assimilating crop growth model data, enhancing crop growth monitoring, and providing theoretical support for crop irrigation management. This study focused on the winter wheat planting area in the southeastern part of the Loess Plateau, a typical semi-arid region, specifically the Linfen Basin. The SEBAL and ESTARFM were used to obtain 8 d, 30 m evapotranspiration (ET) for the growth period of winter wheat. Then, based on the ‘localization’ of the CERES-Wheat model, the fused results were incorporated into the data assimilation process to further determine the optimal assimilation method. The results indicate that (1) ESTARFM ET can accurately capture the spatial details of SEBAL ET (R > 0.9, p < 0.01). (2) ESTARFM ET can accurately capture the spatial details of SEBAL ET (R > 0.9, p < 0.01). The calibrated CERES-Wheat ET characteristic curve effectively reflects the ET variation throughout the winter wheat growth period while being consistent with the trend and magnitude of ESTARFM ET variation. (3) The correlation between Ensemble Kalman filter (EnKF) ET and ESTARFM ET (R² = 0.7119, p < 0.01) was significantly higher than that of Four-Dimensional Variational data assimilation (4DVar) ET (R² = 0.5142, p < 0.01) and particle filter (PF) ET (R² = 0.5596, p < 0.01). The results of the study provide theoretical guidance to improve the yield and water use efficiency of winter wheat in the region, which will help promote sustainable agricultural development. Full article

Water quality in urban streams often reflects the broader environmental challenges posed by dense population centers, where pollution from untreated sewage and runoff can significantly degrade ecosystems. This study examines the spatial and temporal variations of carbon, nitrogen, and phosphorus concentrations in the Tamanduateí River, which runs through the Metropolitan Region of São Paulo, Brazil. Data were sourced from the annual reports of the Environmental Company of the State of São Paulo (CETESB) covering the period from 2011 to 2022. Between 2011 and 2017, carbon and phosphorus concentrations declined, likely due to sanitation improvements. However, since 2017, these concentrations have been rising again, indicating renewed pollution inputs, primarily from untreated sewage. Nitrogen levels remained consistently high, with elevated concentrations observed upstream, linked especially to domestic effluent discharges. The recent increase in phosphorus levels is also of concern. The absence of spatial variation in phosphorus suggests diffuse pollution from urban areas, while nitrogen decreases downstream, possibly due to biological assimilation. The study underscores the pressing need for enhanced sewage management. Drawing from the successful revitalization of the Cheonggyecheon stream in Seoul, implementing nature-based solutions and regular maintenance could effectively reduce nutrient pollution and improve water quality, facilitating the restoration of the Tamanduateí River. Full article

An Urban Heat Island (UHI) is an important variable in climate and environmental studies. Nowadays, population growth and urbanization development are the most important factors affecting the temperature increase in urban areas, which cause the creation of heat islands in urban areas. (1) Background: This study explores the influence of major land uses on the creation of Urban Heat Islands in Urmia city, Iran. (2) Methods: To achieve the aim of this study, Landsat satellite data including Landsat 5 and 8 imageries from the time periods of 1990 and 2023 were used. With the series of data processing and analyses on vegetation cover and land surface temperature, the impact of main land uses on the creation of Urban Heat Islands and the intensification of their effects have been investigated. (3) Results: The analysis reveals that barren lands consistently exhibit the highest temperature, while garden lands show the lowest temperature across both periods. In addition, the spatial distribution of Urban Heat Islands demonstrates a clustered pattern throughout the study period, with hot spots mainly located in the northwestern and southwestern areas. (4) Conclusions: This study’s findings can be helpful for urban policymakers and planners to develop practical strategies to mitigate UHIs and improve climate resilience in cities. Full article

Background: Although evidence indicates that load carriage may have an influence on walking patterns, the specific impacts of progressively increased loads on spatial and temporal gait asymmetries remain underexplored. Therefore, the primary aim of this study was to examine whether an increased load carriage had an effect on spatiotemporal gait asymmetries among intervention police officers. Methods: For the purpose of this study, 96 male intervention police officers were recruited and assessed under four load conditions: (i) “No load”, (ii) “a 5 kg load”, (iii) “a 25 kg load”, and (iv) “a 45 kg load”. Spatial and temporal gait parameters were measured using a pedobarographic platform (Zebris FDM). The spatial and temporal gait parameters, along with the ground reaction forces beneath different foot regions, were examined. The gait asymmetry for each parameter was calculated using the formula (x_{right −} x_left)/0.5 × (x_right + x_left)*100%, where “x” represents the numerical value of each parameter for the left and right sides of the body. Results: The findings indicated no statistically significant differences in the spatiotemporal parameters, nor ground reaction force gait asymmetries between the left and right foot, during walking under a progressively increased load carriage. Additionally, the parameter values for both the left and right sides of the body remained consistent, with a high intercorrelation observed across all of the loading conditions. The gait speed and ground reaction forces, which served as covariates, did not significantly change the spatiotemporal gait asymmetries. Conclusions: In summary, this study demonstrates that an increased load carriage did not lead to a progressive rise in spatiotemporal gait asymmetries in professional intervention police officers. However, further examination using an advanced 3-D gait analysis and an assessment of physiological patterns and adaptations is recommended to identify and confirm the key factors influencing gait asymmetry. Full article

Bladder cancer (BLAC) is a malignant tumor with high morbidity and mortality. The establishment of a prognostic model for BLAC is of great significance for clinical prognosis prediction and treatment guidance. Lactylation modification is a newly discovered post-transcriptional modification of proteins, which is closely related to the occurrence and development of tumors. Multiple omics data of BLAC were obtained from the GEO database and TCGA database. The Lasso algorithm was used to establish a prognostic model related to lactylation modification, and its predictive ability was tested with a validation cohort. Functional enrichment analysis, tumor microenvironment analysis, and treatment response evaluation were performed on the high- and low-risk groups. Single-cell and spatial transcriptome data were used to analyze the distribution characteristics of model genes and their changes during epithelial carcinogenesis. A prognostic model consisting of 12 genes was constructed. The survival rate of the high-risk group was significantly lower than that of the low-risk group. The multiple ROC curve showed that the prediction efficiency of the model was higher than that of the traditional clinical tumor grading. Functional enrichment analysis showed that glycolysis and hypoxia pathways were significantly upregulated in the high-risk group. The high-risk group was more sensitive to most first-line chemotherapy drugs, while the low-risk group had a better response to immunotherapy. Single-cell sequencing analysis revealed the dynamic changes of model genes during the transition of epithelial cells to squamous-differentiated cells. Spatial transcriptome analysis showed the spatial distribution characteristics of the model genes. The lactylation-related models have a satisfactory predictive ability and the potential to guide the clinical treatment of BLAC. This model has significant biological implications at the single-cell level as well as at the spatial level. Full article

Background/Objectives: Anoikis-related genes (ANRGs) are crucial in the invasion and metastasis of breast cancer (BC). The underlying role of ANRGs in the prognosis of breast cancer patients warrants further study. Methods: The anoikis-related prognostic signature (ANRS) was generated using a variety of machine learning methods, and the correlation between the ANRS and the tumor microenvironment (TME), drug sensitivity, and immunotherapy was investigated. Moreover, single-cell analysis and spatial transcriptome studies were conducted to investigate the expression of prognostic ANRGs across various cell types. Finally, the expression of ANRGs was verified by RT-PCR and Western blot analysis (WB), and the expression level of PLK1 in the blood was measured by the enzyme-linked immunosorbent assay (ELISA). Results: The ANRS, consisting of five ANRGs, was established. BC patients within the high-ANRS group exhibited poorer prognoses, characterized by elevated levels of immune suppression and stromal scores. The low-ANRS group had a better response to chemotherapy and immunotherapy. Single-cell analysis and spatial transcriptomics revealed variations in ANRGs across cells. The results of RT-PCR and WB were consistent with the differential expression analyses from databases. NU.1025 and imatinib were identified as potential inhibitors for SPIB and PLK1, respectively. Additionally, findings from ELISA demonstrated increased expression levels of PLK1 in the blood of BC patients. Conclusions: The ANRS can act as an independent prognostic indicator for BC patients, providing significant guidance for the implementation of chemotherapy and immunotherapy in these patients. Additionally, PLK1 has emerged as a potential blood-based diagnostic marker for breast cancer patients. Full article

Cloud image prediction is a spatio-temporal sequence prediction task, similar to video prediction. Spatio-temporal sequence prediction involves learning from historical data and using the learned features to generate future images. In this process, the changes in time and space are crucial for spatio-temporal sequence prediction models. However, most models now rely on stacking convolutional layers to obtain local spatial features. In response to the complex changes in cloud position and shape in cloud images, the prediction module of the model needs to be able to extract both global and local spatial features from the cloud images. In addition, for irregular cloud motion, more attention should be paid to the spatio-temporal sequence features between input cloud image frames in the temporal sequence prediction module, considering the extraction of temporal features with long temporal dependencies, so that the spatio-temporal sequence prediction network can learn cloud motion trends more accurately. To address these issues, we have introduced an innovative model called SAM-Net. The self-attention module of this model aims to extract an inner image frame’s spatial features of global and local dependencies. In addition, a memory mechanism has been added to the self-attention module to extract interframe features with long temporal and spatial dependencies. Our method shows better performance than the PredRNN-v2 model on publicly available datasets such as MovingMNIST and KTH. We achieved the best performance in both the 4-time-step and 10-time-step typhoon cloud image predictions. On a cloud dataset consisting of 10 time steps, we observed a decrease in MSE of 180.58, a decrease in LPIPS of 0.064, an increase in SSIM of 0.351, and a significant improvement in PSNR of 5.56 compared to PredRNN-v2. Full article

Hyperspectral image (HSI) classification is a crucial technique that assigns each pixel in an image to a specific land cover category by leveraging both spectral and spatial information. In recent years, HSI classification methods based on convolutional neural networks (CNNs) and Transformers have significantly improved performance due to their strong feature extraction capabilities. However, these improvements often come with increased model complexity, leading to higher computational costs. To address this, we propose a compact and efficient spectral-spatial feature extraction and attention-based neural network (SSFAN) for HSI classification. The SSFAN model consists of three core modules: the Parallel Spectral-Spatial Feature Extraction Block (PSSB), the Scan Block, and the Squeeze-and-Excitation MLP Block (SEMB). After preprocessing the HSI data, it is fed into the PSSB module, which contains two parallel streams, each comprising a 3D convolutional layer and a 2D convolutional layer. The 3D convolutional layer extracts spectral and spatial features from the input hyperspectral data, while the 2D convolutional layer further enhances the spatial feature representation. Next, the Scan Block module employs a layered scanning strategy to extract spatial information at different scales from the central pixel outward, enabling the model to capture both local and global spatial relationships. The SEMB module combines the Spectral-Spatial Recurrent Block (SSRB) and the MLP Block. The SSRB, with its adaptive weight assignment mechanism in the SToken Module, flexibly handles time steps and feature dimensions, performing deep spectral and spatial feature extraction through multiple state updates. Finally, the MLP Block processes the input features through a series of linear transformations, GELU activation functions, and Dropout layers, capturing complex patterns and relationships within the data, and concludes with an argmax layer for classification. Experimental results show that the proposed SSFAN model delivers superior classification performance, outperforming the second-best method by $1.72\%$, $5.19\%$, and $1.94\%$ in OA, AA, and Kappa coefficient, respectively, on the Indian Pines dataset. Additionally, it requires less training and testing time compared to other state-of-the-art deep learning methods. Full article

Large language models (LLMs) have demonstrated remarkable capabilities in document processing, data analysis, and code generation. However, the generation of spatial information in a structured and unified format remains a challenge, limiting their integration into production environments. In this paper, we introduce a benchmark for generating structured and formatted spatial outputs from LLMs with a focus on enhancing spatial information generation. We present a multi-step workflow designed to improve the accuracy and efficiency of spatial data generation. The steps include generating spatial data (e.g., GeoJSON) and implementing a novel method for indexing R-tree structures. In addition, we explore and compare a series of methods commonly used by developers and researchers to enable LLMs to produce structured outputs, including fine-tuning, prompt engineering, and retrieval-augmented generation (RAG). We propose new metrics and datasets along with a new method for evaluating the quality and consistency of these outputs. Our findings offer valuable insights into the strengths and limitations of each approach, guiding practitioners in selecting the most suitable method for their specific use cases. This work advances the field of LLM-based structured spatial data output generation and supports the seamless integration of LLMs into real-world applications. Full article

The InVEST model, with its ability to perform spatial visualization and quantification, is an important tool for mapping ecosystem services. However, the spatial accuracy and simulating performance of the model are deeply influenced by the land use parameter, which often relies on the accuracy of land use/cover data. To address this issue, we propose a novel method for optimizing the land use parameter of the InVEST model based on the vegetation–impervious surface–soil (V–I–S) model and a machine learning algorithm. The optimized model is called Sub-InVEST, and it improves the performance of assessing ecosystem services on a sub-pixel scale. The conceptual steps are (i) extracting the V–I–S fraction of remote sensing images based on the spectral unmixing method; (ii) determining the mapping relationship of the V–I–S fraction between land use/cover type using a machine learning algorithm and field observation data; (iii) inputting the V–I–S fraction into the original model instead of the land use/cover parameter of the InVEST model. To evaluate the performance and spatial accuracy of the Sub-InVEST model, we employed the habitat quality module of InVEST and multi-source remote sensing data, which were applied to acquire Sub-InVEST and estimate the habitat quality of central Guangzhou city from 2000 to 2020 with the help of the LSMA and ISODATA methods. The experimental results showed that the Sub-InVEST model is robust in assessing ecosystem services in sets of complex ground scenes. The spatial distribution of the habitat quality of both models revealed a consistent increasing trend from the southwest to the northeast. Meanwhile, linear regression analyses observed a robust correlation and consistent linear trends, with R² values of 0.41, 0.35, 0.42, 0.39, and 0.47 for the years 2000, 2005, 2010, 2015, and 2020, respectively. Compared with the original model, Sub-InVEST had a more favorable performance in estimating habitat quality in central Guangzhou. The spatial depictions and numerical distribution of the results of the Sub-InVSET model manifest greater detail and better concordance with remote sensing imagery and show a more seamless density curve and a substantially enhanced probability distribution across interval ranges. Full article

The accurate prediction of global forest soil respiration (Rs) is critical for climate change research. Rs consists of autotrophic (Ra) and heterotrophic (Rh) respiration, which respond differently to environmental factors. Predicting Rs as a single flux can be biased; therefore, Ra and Rh should be predicted separately to improve prediction accuracy. In this study, we used the SRDB_V5 database and the random forest model to analyze the uncertainty in predicting Rs using a single global model (SGM) and Ra/Rh using a specific categorical model (SCM) and predicted the spatial dynamics of the distribution pattern of forest Ra, Rh, and Rs in the future under the two different climate patterns. The results show that Rs is higher under tropical and inland climatic conditions, while Rh fluctuates less than Ra and Rs. In addition, the SCM predictions better capture key environmental factors and are more consistent with actual data. In the SSP585 (high emissions) scenario, Rs is projected to increase by 19.59 percent, while in the SSP126 (low emissions) scenario, Rs increases by only 3.76 percent over 80 years, which underlines the need for SCM in future projections. Full article

This study focuses on the impacts of climate change on hydrological processes in watersheds and proposes an integrated approach combining a weather generator with a multi-site conditional generative adversarial network (McGAN) model. The weather generator incorporates ensemble GCM predictions to generate regional average synthetic weather series, while McGAN transforms these regional averages into spatially consistent multi-site data. By addressing the spatial consistency problem in generating multi-site synthetic weather series, this approach tackles a key challenge in site-scale climate change impact assessment. Applied to the Jinghe River Basin in west-central China, the approach generated synthetic daily temperature and precipitation data for four stations under different shared socioeconomic pathways (SSP1-26, SSP2-45, SSP3-70, SSP5-85) up to 2100. These data were then used with a long short-term memory (LSTM) network, trained on historical data, to simulate daily river flow from 2021 to 2100. The results show that (1) the approach effectively addresses the spatial correlation problem in multi-site weather data generation; (2) future climate change is likely to increase river flow, particularly under high-emission scenarios; and (3) while the frequency of extreme events may increase, proactive climate policies can mitigate flood and drought risks. This approach offers a new tool for hydrologic–climatic impact assessment in climate change studies. Full article

The accurate prediction of the niche and the potential distribution of a species is a fundamental and key content for biodiversity related research in ecology and biogeography, especially for protected species. Biotic interactions have a significant impact on species distribution but are often overlooked by SDMs. Therefore, it is crucial to incorporate biotic interaction factors into SDMs to improve their predictive performance. The Teinopalpus aureus Mell, 1923 is endemic to high altitudes in southern East Asia, renowned for its exceptional beauty and rarity. Despite the significant conservation value, its spatial distribution remains unclear. This study integrated climate data, host plants, and empirical expert maps to predict its potential distribution. The results indicated that utilizing the species richness of host plants as a surrogate for biotic interactions was a simple and effective way to significantly improve the predictive performance of the SDMs. The current suitable distribution of T. aureus and its host plants is highly fragmented, primarily concentrated in the Nanling and Wuyi Mountains, and consisting of numerous isolated small populations. Given climate change, their distribution is significantly shrinking, increasing the threatened level in the future. Especially for the population of T. aureus hainani Lee, the likelihood of extinction is extremely high. Abiotic factors not only directly affect the distribution of T. aureus but also indirectly impact it through the host plants. This was evident in the delayed response of T. aureus to climate change compared to its host plants, which is called the “hysteresis effect” caused by biotic interactions. Overall, we tentatively suggest regarding T. aureus as a vulnerable species. In the future, multiple measures could be taken to indirectly protect the feeding and habitat resources of T. aureus by conserving host plants, thereby enhancing its survival prospects. Full article

To address the performance and computational complexity issues in speech enhancement for hearing aids, a speech enhancement algorithm based on a microphone array and a lightweight two-stage convolutional recurrent network (CRN) is proposed. The algorithm consists of two main modules: a beamforming module and a post-filtering module. The beamforming module utilizes directional features and a complex time-frequency long short-term memory (CFT-LSTM) network to extract local representations and perform spatial filtering. The post-filtering module uses analogous encoding and two symmetric decoding structures, with stacked CFT-LSTM blocks in between. It further reduces residual noise and improves filtering performance by passing spatial information through an inter-channel masking module. Experimental results show that this algorithm outperforms existing methods on the generated hearing aid dataset and the CHIME-3 dataset, with fewer parameters and lower model complexity, making it suitable for hearing aid scenarios with limited computational resources. Full article

Clusters of knowledge-intensive industries and manufacturing industries form industrial agglomeration in Step I and activate innovation in Step II. Industry clusters are formed by building segments. “Construction sequencing” in the construction industry refers to the process of determining the sequence of segments to optimize a project’s resources, budget, and scheduled timeline. The process usually begins by dividing a project into segments. Urban segments consist of public spaces, airports, factories, health, housing, etc. A “segment” is a component of a cluster; the organization of a cluster consists of constructing segments. These segments can be divided into four main categories: human resources, physical infrastructure, institutions, and the living environment. Each segment has a specific function in the process of building a cluster. This study focused on innovation in Step II and extended the Fujita–Thisse model of spatial economics to hypothesize that research expenditure per researcher leads to value being added. The Granger causality was tested for the knowledge and manufacturing industries in nine major countries including China and the U.S. The results showed that the hypothesis was significant in identifying the starting segment of innovation in Step II. Accordingly, it can be concluded that research funding is the start switch that triggers innovation. The policy implication is that activating innovation in cluster policies begins with the establishment of a research fund for researchers in its assigned clusters. Full article