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Spatially continuous fine particulate matter (PM2.5) mapping with hourly updated is essential for monitoring environmental pollution and promoting public health. The intensive observation of geostationary satellite enables accurate estimation of PM2.5 at a fine-scale. However, current estimation models are still limited by their weak transferability and hard to provide a robust hourly PM2.5 estimation. In this research, we aim to estimate the daytime PM2.5 concentrations at fine spatial and temporal resolution (1 km and hourly) in mainland China using an improved deep learning algorithm and the AOD products from geostationary satellite Himwari-8. An Adaptive Spatio-Temporal Multiscale Neural Network (ASTMNN) which contains three sub-networks and an adaptive weight was proposed to capture the spatiotemporal heterogeneity of hourly PM2.5. The three subnetworks of ASTMNN are spatial adjacency module (SaM), temporal adjacency module (TaM) and global module (GM), which used to incorporate the information from spatial neighborhood, temporal neighborhood, and global spatiotemporal range, respectively. And the weight function combines the outputs from the three subnetworks, where the weights were adaptively trained from the model optimization. The proposed model outperformed most current hourly PM2.5 estimation models with the sample-based, time-based, and site-based cross-validation (CV) R2 of 0.94, 0.89 and 0.83, respectively. Besides, we used our PM2.5 product to track extreme dust events. Our findings provide valuable implications for tracking continuous variation in particulate pollution using geostationary satellites.

Agriculture faces the dual challenge of sustainably increasing productivity to meet the food demand of a rapidly growing population and adapting to climate change. Despite significant efforts to develop more adaptive and productive crop cultivars and to improve water and nutrient management practices, the potential of crops to tackle this challenge by optimizing soil resource utilization remains underexplored. Here, we propose that optimizing root systems to promote the efficient acquisition of soil resources can increase yield, improve resilience to climate variability, and reduce environmental impacts. This optimization can be achieved through genetic manipulation at the crop species level and effective management of cropping systems at the field level (e.g., intercropping, rotation, and agroforestry). Advances in three-dimensional soil data collection, linking root traits to plant performance, and modelling of climate-soil–plant-management interactions are paving the way for soil-smart cropping. Effective communication and knowledge exchange with stakeholders beyond the scientific community are vital for accelerating the development and adoption of soil-smart practices for climate-smart and sustainable agricultural production.

Accurate and detailed spatial–temporal soil information is crucial for soil quality assessment worldwide, particularly in the countries with large populations and extensive agricultural areas. Using remote sensing technology to generate bare soil reflectance composites has been shown as a prerequisite for effectively modeling soil properties. However, most bare soil extraction methods rely on the single-period satellite imagery, making it difficult to produce a complete bare soil map. Although some developed methods have explored the advantages of multitemporal images, single indicators (e.g., Normalized Difference Vegetation Index and Normalized Burn Ratio 2) are prone to misidentifying bare soil as other land cover types such as impervious surface. Additionally, these methodologies were designed for specific areas and coarse spatial resolution images, leaving their generalizability to other areas or larger scales underexplored. Therefore, we proposed a Two-Dimensional Bare Soil Separation (TDBSS) framework to generate the bare soil composites of Chinese cropland at 10-m spatial resolution using multi-temporal Sentinel-2 images. This method employs the Normalized Difference Red/Green Redness Index and Soil Adjusted Vegetation Index as bidimensional indicators. We identified optimal thresholds for these indicators by analyzing ecoregion-specific samples and then implemented them across nine major agricultural zones in China. Additionally, we evaluated the framework against three prevalent bare soil extraction methods (i.e., Barest Pixel Composite, Soil Composite Mapping Processor, and Geospatial Soil Sensing System) based on spatial accuracy. The results showed that TDBSS outperformed the others with the highest overall accuracy of 78.28% and the lowest omission error of 0.198. The findings indicated that the TDBSS algorithm is competent in mapping bare soil at a national scale. The produced composite map of bare soil reflectance is particularly valuable for retrieving soil attributes in Chinese cropland. The TDBSS method can be easily implemented across broad areas with computational efficiency, contributing to land management, food security, and the development of policies for precision agriculture.

Accurate delineation of agricultural parcels is crucial for ensuring food security and implementing precision agriculture practices. However, one of the primary challenges in parcel delineation revolves around the accurate extraction of parcel edges due to the diverse morphologies of these parcels. Current deep learning models for agricultural parcel delineation often produce unclosed or incomplete edges, highlighting the need to enhance the edge-awareness of the model. In this study, we proposed a refined edge-aware U-Net (REAUNet) to extract accurate agricultural parcel boundaries from Sentinel-2 images and GF-2 images. REAUNet integrates four innovative components for edge enhancement, i.e., the deep supervision, the edge detection block, the dual attention block and the refine module. The edge detection block and the dual attention block strengthen the ability of capturing spatial contextual information, and the deep supervision constrains parcel delineation across multiple scales, and the refine module further refines the parcel edges. Together, these components enhance the edge-awareness of the model, resulting in more accurate agricultural parcel delineation. From the ablation experiment, REAUNet with all four components reached the higher accuracy than the scenarios that only three out of four components were retained, which suggested that the inclusion of all four components was essential. The comparative evaluation showed that our proposed model surpassed the representative deep learning models for both thematic and geomantic accuracy. REAUNet demonstrated an increase of 2.0 % in F1-score and a decrease of 3.4 % in GTC (Global Total-Classification) compared to the SEANet, and exhibited a 0.8 % increase in IoU and a 2 % decrease in GTC relative to the MPSPNet. We also assessed the capability of the REAUNet model transferred for a new geographic region, and reported that a pre-train REAUNet model with only a small number of local samples could achieve a satisfactory accuracy. To conclude, the newly proposed REAUNet model reaches a high mapping accuracy for delineating agricultural parcels from the high-resolution images. With the powerful edge-aware capability, REAUNet can effectively utilize limited training samples to transfer a deep learning model for a new region, facilitating the generation of agricultural parcel products and a large-scale agricultural parcel mapping in practical applications.

Objective To construct a teaching case resource library for pharmacy teaching, which meets the needs from the new health care-oriented reformation in tertiary A-level public hospital. Methods A case compiling group was organized to collect, sort and retrieve cases. The case compilation was completed according to the specification for the preparation of professional cases for Master candidates of Pharmacy degree. After case modification, review and screening, the cases were chosen to be included in the group and finally to create a case resource database. Results Totally 34 typical cases in literature and 109 high-frequency drug and administration examples from pharmacy department, a certain hospital, Capital medical university from January 1st to August 31, 2021 were selected to build the database. Conclusions The case resource library for pharmacy training in tertiary A-level public hospital was constructed which may effectively support the training of qualified clinical pharmacists.

Using survey data of Missouri small producers in 2022, this paper explores their perceptions of climate change and factors affecting their adoption of climate-smart practices. A synthesized climate change perception (CCP) index is developed to assess the effects of cognition and social demographics of producers on climate-smart practices (CSPs) adoption. The results show that 86.0% of participants have relatively high perceptions of climate change, and 82.0% have adopted at least one climate-smart practice; the higher the climate change perception index, the more likely the producers will adopt climate-smart practices. The findings encourage education on knowledge related to climate change, the relationship between climate change and agriculture, and how CSPs can reduce climate change. Specifically, we find specialty crop producers are more likely to adopt CSPs than other producers; and a one-unit increase in the CCP index can increase the CSP adoption rate by 4.61% for livestock producers and 2.61% for organic producers, suggesting relevant education programs can be tailored to the needs of livestock and organic farmers to improve their adoption rates of CPSs. Further, more resources are needed to support the farmers who reside in severely climate change – impacted areas for their sustainable production and climate sustainability. [ABSTRACT FROM AUTHOR]

As data science advances, automated machine learning (AutoML) gains attention for lowering barriers, saving time, and enhancing efficiency. However, with increasing data dimensionality, AutoML struggles with large-scale feature sets. Effective feature selection is crucial for efficient AutoML in multi-task applications. This study proposes an efficient modeling framework combining a multi-stage feature selection (MSFS) algorithm and AutoSklearn, a robust and efficient AutoML framework, to address high-dimensional data challenges. The MSFS algorithm includes three stages: mutual information gain (MIG), recursive feature elimination with cross-validation (RFECV), and a voting aggregation mechanism, ensuring comprehensive consideration of feature correlation, importance, and stability. Based on multi-source and time series remote sensing data, this study pioneers the application of AutoSklearn for forest fire risk prediction. Using this case study, we compare MSFS with five other feature selection (FS) algorithms, including three single FS algorithms and two hybrid FS algorithms. Results show that MSFS selects half of the original features (12/24), effectively handling collinearity (eliminating 11 out of 13 collinear feature groups) and increasing AutoSklearn's success rate by 15%, outperforming two FS algorithms with the same number of features by 7% and 5%. Among the six FS algorithms and non-FS, MSFS demonstrates the highest prediction performance and stability with minimal variance (0.09%) across five evaluation metrics. MSFS efficiently filters redundant features, enhancing AutoSklearn's operational efficiency and generalization ability in high-dimensional tasks. The MSFS–AutoSklearn framework significantly improves AutoML's production efficiency and prediction accuracy, facilitating the efficient implementation of various real-world tasks and the wider application of AutoML. [ABSTRACT FROM AUTHOR]

Massive medical data are indispensable for training diagnostic models to provide high-quality health monitoring services. The methods for sharing data in existing works involve securely and essentially copying data but often overlook the integration and efficiency of data storage, exchange and application. In this paper, we propose a Secure Decentralized Trading Alliance (SDTA) to encompass the entire process holistically. With monetary incentives, we formulate a chain-net structure for recording data digests and authentic transactions, thereby transforming data sharing into data trading without duplicating data storage. Data privacy is promised by encryption. To manage and employ encrypted medical data, users can update and search their encrypted data using an index and keywords, subsequently retrieving data within the SDTA framework. It is realized by a novel dynamic searchable symmetric encryption (SSE) with an |$l$| -level access strategy, which confines users to data pertinent solely to them, thus circumventing unnecessary data leakage. We scrutinize the storage efficiency and prove the fairness and security of SDTA. Finally, we generate datasets of varying sizes, where the time required to search for a single keyword is approximately 0.04 s with 1 000 000 (keyword, identifier) pairs, showing it quite acceptable. [ABSTRACT FROM AUTHOR]

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The National surveillance data showed that homosexual transmission played a considerable role in new HIV infections in China. The emphasis on antiretroviral therapy and prevention of mother-to-child transmission provided chances for reproduction among people living with HIV/AIDS. Issues of fertility desire have a paucity of data among HIV-positive men who have sex with men (MSM). This cross-sectional study has assessed fertility attitudes and associated factors, as well as the reproductive knowledge among HIV-positive MSM. Analysis was mainly based on the multinomial regression model. The study included 129 participants, and almost all of the participants (96.1%) were between 18 and 30 years old and 82.2% of them were single. About 35.6% expressed a fertility desire. MSM without siblings tended to have fertility desire (OR = 0.236, 95%CI: 0.078∼0.712, p = 0.010). Surrogacy (36.4%) was the most desired method among the 86 respondents who had the desire or did not make a decision. While the accuracy of the reproductive knowledge was only 69.6%. In summary, we recommend that providers offer much more professional information and develop assisted reproductive technology to meet the reproductive aspirations of HIV-positive MSM. [ABSTRACT FROM AUTHOR]