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With the advancement of Internet of Things (IoT) technology, underwater target detection and tracking have become increasingly important for ocean monitoring and resource management. Existing methods often fall short in handling high-noise and low-contrast images in complex underwater environments, lacking precision and robustness. This paper introduces a novel SVGS-DSGAT model that incorporates GraphSage for capturing and processing complex structural data, SVAM for guiding attention toward critical features, and DSGAT for refining feature relationships by emphasizing differences and similarities. These components work together to enhance the model’s robustness and precision in underwater target recognition and tracking. The model integrates IoT technology to facilitate real-time data collection and processing, optimizing resource allocation and model responsiveness. Experimental results demonstrate that the SVGS-DSGAT model achieves an mAP of 40.8% on the URPC 2020 dataset and 41.5% on the SeaDronesSee dataset, significantly outperforming existing mainstream models. This IoT-enhanced approach not only excels in high-noise and complex backgrounds but also improves the overall efficiency and scalability of the system. This research provides an effective IoT solution for underwater target detection technology, offering significant practical application value and broad development prospects.

Abstract This article examines the fire safety of rubber flooring used in an ice sports center in China. The fire characteristics and parameters of the rubber floor were determined through material fire tests. Subsequently, a fire safety analysis was conducted using the fire intrinsic safety method. Additionally, numerical simulation was employed to investigate fire and evacuation scenarios, particularly when the safety grade of the floor decoration materials falls below standard requirements. The results indicate that, under specific safety conditions, the ice sports center can utilize B1 grade rubber flooring in the required area while ensuring compliance with fire safety standards.

Weather radar holds the capability to monitor the extensive migration of bird and insect species. In particular, polarimetric weather radar can enhance aerial ecological monitoring by quantifying target shape through the measurement of polarization moments. This article introduces an intelligent algorithm to classify bird and insect migration using polarimetric weather radar data. A radar image dataset was formed by intentionally curating typical migratory data of birds and insects captured by the polarimetric weather radar. Next, point features and spatial texture features were extracted from the radar images in the dataset for training a classifier using a supervised learning approach, resulting in a classification accuracy of 93.56%. Furthermore, the importance of the features was analyzed, uncovering that the most influential attribute was the reflectivity factor at 33.83%, surpassing the cumulative influence of other dual-polarization moments. In addition, spatial textures also played an essential role for the classifier, collectively weighing 35.65%. Lastly, the proposed method was validated with bird radar data, attaining an accuracy level of 95.36%.

Ground clutter poses a significant challenge to the accuracy of weather radar products, especially for aeroecology monitoring. Operational weather radar commonly employs a fixed coherent pulse integration (CPI) for clutter suppression. However, the phenomenon of strong clutter spanning multiple CPIs can result in severe spectral leakage. The independent implementation of clutter suppression within each CPI fails to fully utilize clutter data, significantly compromising clutter suppression effectiveness. Therefore, we propose an adaptive coherent pulse integration (ACPI) method based on the amplitude-phase characteristics of echoes in azimuth. Clutter and nonclutter regions are determined firstly based on the stability of the echo phase in azimuth. Then, the clutter region is further subdivided using phase discontinuities and amplitude notches between different clutter areas. Finally, an appropriate CPI can be assigned to each strong clutter instance. The effectiveness and the practicality of the ACPI are verified through the simulations and measured data from Chinese operational weather radar.

Hog insurance and rural environmental protection are complementary to each other. Studying the environmental effects of hog insurance is imperative for safeguarding food safety and promoting the long-term development of the agricultural insurance industry. Informed by the risk management theory and sustainable development theory, this paper constructs a theoretical framework for the impact of policy-supported hog insurance on the green total factor productivity (GTFP) of hog farming. Utilizing panel data from China’s hog-dominant production areas spanning from 2005 to 2021, the slacks-based measures of directional distance functions (SBM-DDF) model and multiple-time-point difference-in-differences (DID) approach were used to measure GTFP and explore the effects of hog insurance on GTFP and the underlying mechanisms. The findings indicate a substantial enhancement in GTFP due to hog insurance. The conclusion drawn was robust to various tests. The mechanism is that hog insurance fosters GTFP by expanding the breeding scale, adjusting the planting–breeding structure, and promoting technological progress. Furthermore, the environmental effects of hog insurance policy are more pronounced in economically developed regions, with significant effects observed on the GTFP of free-range, small-scale, and medium-scale hog-farming households. This study contributes new evidence to the field of assessing the environmental impact of agricultural insurance policies and provides valuable insights for furthering green transformation and development in the hog insurance-supported breeding industry.

Abstract Fasting is a popular dietary strategy because it grants numerous advantages, and redox regulation is one mechanism involved. However, the precise redox changes with respect to the redox species, organelles and tissues remain unclear, which hinders the understanding of the metabolic mechanism, and exploring the precision redox map under various dietary statuses is of great significance. Twelve redox-sensitive C. elegans strains stably expressing genetically encoded redox fluorescent probes (Hyperion sensing H2O2 and Grx1-roGFP2 sensing GSH/GSSG) in three organelles (cytoplasm, mitochondria and endoplasmic reticulum (ER)) were constructed in two tissues (body wall muscle and neurons) and were confirmed to respond to redox challenge. The H2O2 and GSSG/GSH redox changes in two tissues and three organelles were obtained by confocal microscopy during fasting, refeeding, and satiation. We found that under fasting condition, H2O2 decreased in most compartments, except for an increase in mitochondria, while GSSG/GSH increased in the cytoplasm of body muscle and the ER of neurons. After refeeding, the redox changes in H2O2 and GSSG/GSH caused by fasting were reversed in most organelles of the body wall muscle and neurons. In the satiated state, H2O2 increased markedly in the cytoplasm, mitochondria and ER of muscle and the ER of neurons, while GSSG/GSH exhibited no change in most organelles of the two tissues except for an increase in the ER of muscle. Our study systematically and precisely presents the redox characteristics under different dietary states in living animals and provides a basis for further investigating the redox mechanism in metabolism and optimizing dietary guidance.

Abstract Each year, billions of birds migrate across the globe, and interpretation of weather radar signals is increasingly being used to document the spatial and temporal migration patterns in Europe and America. Such approaches are yet to be applied in the East Asian‐Australasian Flyway (EAAF), one of the most species‐rich and threatened flyways in the world. Logistical challenges limit direct on‐ground monitoring of migratory birds in many parts of the EAAF, resulting in knowledge gaps on population status and site use that limit evidence‐based conservation planning. Weather radar data have great potential for achieving comprehensive migratory bird monitoring along the EAAF. In this study, we discuss the feasibility and challenges of using weather radar to complement on‐ground bird migration surveys in the flyway. We summarize the location, capacity and data availability of weather radars across EAAF countries, as well as the spatial coverage of the radars with respect to migrants' geographic distribution and migration hotspots along the flyway, with an exemplar analysis of biological movement patterns extracted from Chinese weather radars. There are more than 430 weather radars in EAAF countries, covering on average half of bird species' passage and non‐breeding distributions, as well as 70% of internationally important sites for migratory shorebirds. We conclude that the weather radar network could be a powerful resource for monitoring bird movements over the full annual cycle throughout much of the EAAF, providing estimates of migration traffic rates, site use, and long‐term population trends, especially in remote and less‐surveyed regions. Analyses of weather radar data would complement existing ornithological surveys and help understand the past and present status of the avian community in a highly threatened flyway.

The problem of high grain production costs, which is not conducive to sustainable agricultural development and food security, is highlighted in the context of China’s “large country and small household farmers”. Reducing the grain production costs through factor allocation and organizational drive has become particularly important. Based on 768-grain peasant households in China, this paper uses OLS regression and robust regression to examine the effects of agricultural factor inputs and cooperatives on grain production costs. It analyzes the synergistic and substitution effects between farmers’ factor inputs and cooperatives in grain production. It was found that: (1) in farmers’ grain production, reductions in the grain production costs can be realized by expanding the area under cultivation, improving the use of agricultural machinery, and increasing technological inputs; (2) a reduction in the grain production costs can also be realized through cooperatives driving farmers into grain production; (3) cooperatives can provide farmers with various types of agricultural production services in grain production and cooperative-driven substitution effects between the agricultural factor inputs of farm households. The findings of this paper contribute to the enrichment of research in the field of agricultural production and are important for enhancing agricultural sustainability and reducing grain production costs.

Weather radar plays an important role in monitoring aerial animal migration, providing a stable data source for biological studies with large-scale coverage and consecutive-time samples. The accurate estimation of bird density from weather radar echoes is fundamental for quantitative biological studies. We analyzed the bird observation model in weather radar, and proposed a method to build the bird quantification model by jointly utilizing dual-polarization Doppler weather radar and scanning bird radar. We designed a detailed process to remove tracks or echoes from non-bird targets, ensuring the effectiveness of bird observations. The field experiments validated the quantification method, showing that the average radar cross section of birds in Jinan was 19.09 dBscm (i.e., 81.19 cm2; 95% confidence interval, CI: 18.92–19.27 dBscm) for the S-band weather radar, with an R2 of 0.79. In addition, through the correlation analysis, we found that the ground terrain may affect the distribution pattern of aerial bird density.

Promoting farmers’ participation in rural industrial integration and driving farmers’ agricultural production with cooperatives and agribusinesses are conducive to realizing cost saving, efficiency, and green production and guaranteeing food security and sustainable agricultural development. Based on the microsurvey data of 1039 grain farmers in Henan Province, China in 2022, this paper examined the impact of contractual choices of farmers’ participation in rural industrial integration on agricultural green productivity while analyzing the mechanism of action by using OLS regression, a causal mediation analysis of instrumental variables, propensity score matching, and two-stage least squares (2SLS). The study found that: (1) farmers’ participation in a contract, driven by cooperatives or agribusinesses to carry out agricultural production, is conducive to improving their agricultural green productivity, but the effect of each main body to drive farmers varies; (2) farmers’ participation in a contract, through cooperatives or agribusinesses to obtain all kinds of agricultural production services—such as agricultural machinery services, agricultural supply services, and technical guidance services—improves the use of agricultural machinery, the standardization of chemical fertilizers, pesticides, and other agricultural materials’ use, increases technical guidance, and improves agricultural green productivity. The findings of this paper suggest policy and practical implications for safeguarding food security and promoting sustainable agriculture, as well as enriching research on agricultural productivity.

Agriculture has become a main source of greenhouse gas emissions globally, so limiting agricultural carbon emissions is an important part of achieving carbon neutrality in China. The reduction of agricultural carbon emissions needs the support of green credit. This paper explores the spatial effect and mechanism of green credit on agricultural carbon emissions. Specifically, based on panel data of 30 Chinese provinces from 2009 to 2019, the relationship between green credit and agricultural carbon emissions is empirically analyzed using spatial econometric and mediating effect models. The results show that green credit has a significant inhibitory effect on agricultural carbon emissions. Meanwhile, green credit can also limit agricultural carbon emissions in surrounding areas through spatial spillover effects. Moreover, the study reveals that green credit can indirectly suppress agricultural carbon emissions by promoting agricultural green technology innovation. Based on the above conclusions, this paper puts forward policy recommendations for the development of green credit and the reduction of agricultural carbon emissions.

This study addresses the problem of restricted ability for large-scale monitoring due to the limited cruising time of unmanned aerial vehicles (UAV) by identifying an optimal leaf ChlorophyII content (LCC) inversion machine learning model at different scales and under different parameterization schemes based on simultaneous observations of ground sampling, UAV flight, and satellite imagery. The following results emerged: (1) The correlation coefficient between most remote sensing features (RSFs) and LCC increased as the remote scale expanded; thus, the scale error caused by the random position difference between GPS and measuring equipment should be considered in field sampling observations. (2) The LCC simulation accuracy of the UAV multi-spectral camera using four machine learning algorithms was ExtraTree > GradientBoost > AdaBoost > RandomForest, and the 20- and 30-pixel scales had better accuracy than the 10-pixel scale, while the accuracy for three feature combination schemes ranked combination of extremely significantly correlated RSFs > combination of significantly correlated and above RSFs > combination of all features. ExtraTree was confirmed as the optimal model with the feature combination of scheme 2 at the 20-pixel scale. (3) Of the Sentinel-2 RSFs, 27 of 28 were extremely significantly correlated with LCC, while original band reflectance was negatively correlated, and VIs were positively correlated. (4) The LCC simulation accuracy of the four machine learning algorithms ranked as ExtraTree > GradientBoost > RandomForest > AdaBoost. In a comparison of two parameterization schemes, scheme 1 had better accuracy, while ExtraTree was the best algorithm, with 11 band reflectance as input RSFs; the RMSE values for the training and testing data sets of 0.7213 and 1.7198, respectively.

Triangulation is an important task in the 3D reconstruction of computer vision. It seems simple to find the position of a point in 3D space when its 2D perspective projections in multi-view images are given and the corresponding camera projection matrices are known. However, in practice, multiple lines in 3D space do not intersect at one point because of noise. Then how to calculate the optimal 3D point of intersection becomes difficult. While there have been multiple methods trying to solve this problem, there is no systematic comparison between them. In this paper, we reviewed various currently existing variants of triangulation method and compared them through extensive experiments. The speed and accuracy of these methods have been compared using both synthetic and real datasets. We presented the results of experiments and summarized the advantages, limitations, and applicability of these methods so that to provide a guide for users when they need to choose an appropriate triangulation method for their given applications. Moreover, based on above analysis we proposed an improved method which shows better performance.

Reuse of reclaimed water in constructed wetlands is a promising way to conserve water resources and improve water quality, and it is playing a very important role in wetland restoration and reconstruction. This study utilized reflectance spectra of wetland vegetation to estimate nitrogen content in water in the Beijing Bai River constructed wetland, a typically constructed wetland that uses reclaimed water. Canopy reflectance spectra of two dominant plants in the wetland, including reed and cattail, were acquired using a spectrometer (350–2500 nm). Simultaneously, water samples were collected to measure water quality. To establish the appreciate relationship between total nitrogen content (TN) and reflectance spectra, both simple and multiple regression models, including simple ration spectral index (SR), normalized difference spectral index (ND), stepwise multiple linear regression (SMLR) model, and partial least squares regression (PLSR), were adopted in this study. The results showed that (1) compared with simple regression models (SR and ND), multiple regressions models (SMLR and PLSR) could provide a more accurate estimation of TN concentration in the wetland environment. Among these models, the PLSR model had the highest accuracy and was proven to be the most useful tool to reveal the relationship between the spectral reflectance of wetland plants and the total nitrogen consistency of wetland at the canopy scale. (2) The inversion effect of TN concentration in water is slightly better than that of wetland vegetation, and the reflection spectrum of the reed can predict TN concentration more accurately than that of cattail. The finding not only provides solid evidence for the potential application of remote sensing to detect water eutrophication but also enhances our understanding of the monitoring and management of water quality in urban wetlands using recycled water.

The trajectory planning of UAV with nonholonomic constraints is usually taken as differential algebraic equation to solve the optimal control problem of functional extremum under the condition of inequality constraints. However, it can be challenging to meet the requirements of real-time for the high complexity. A differential flat theory based on B-spline trajectory planning can replace the optimal control problem with nonlinear programming and be a good means to achieve the efficient trajectory planning of an UAV under multiple dynamic constraints. This research verifies the feasibility of this theory with actual flight experiments.

Industry convergence is the future trend of industrial development in rural areas and is conducive to high-quality agriculture development. To explore the development dynamics of industry convergence. This paper selects data from 31 provincial administrative regions in China from 2009 to 2019. It uses the entropy power method to measure the development quality of rural industrial integration in China and empirically studies the impact of transportation infrastructure on rural industrial integration using a spatial panel autoregressive model. The study found that: (1) from 2009–2019, the development quality of rural industrial integration is on the rise, but the development is uneven between regions; (2) transport infrastructure strongly promotes the development of rural industrial integration; (3) with the help of transport infrastructure, rural industrial integration in this region will improve the quality of rural industrial integration in the surrounding areas; and (4) the impact of transportation facilities varies in different regions and at different stages of development of rural industrial integration. The results of this paper are beneficial to improving transportation infrastructure planning and exploring the driving force of high-quality agriculture development, enriching the research of spatial land use, and providing valuable insights for developing industry convergence in other countries and regions.

Three Microwave Radiation Imagers (MWRI) were carried onboard the FengYun-3B/C/D satellites and have collected more than 10 years of data since 2010. To create a robust climate quality of data, MWRI level one data were reprocessed with new calibration. This study evaluates the performance of retrieving global soil moisture from recalibrated MWRI data (RCD) and quantifies the difference of retrieved soil moisture between operational calibration data (OCD) and RCD. Soil Moisture Operational Products System (SMOPS) products from NOAA on four days of different seasons were collocated with MWRI brightness temperatures, and then the collocated data were used for training an algorithm through machine learning. The retrieved soil moisture products using OCD and RCD were evaluated against the independent SMOPS products, in situ networks and SMAP soil moisture product. It is shown that the algorithm from the random forest is suitable for FY-3D recalibrated MWRI data, with a coefficient of determination (R2) of 0.7223, a mean bias of −0.0062 and an unbiased root mean square difference (ubRMSD) of 0.0476 m3 m−3 compared with SMOPS products over the period from 12 July 2018 to 31 December 2019. The difference of retrieved soil moisture using OCD and RCD is spatially heterogeneous. Both temporal and spatial coverage and accuracy of the existing FY-3D operational soil moisture products are significantly improved.

Weather radar data can capture large-scale bird migration information, helping solve a series of migratory ecological problems. However, extracting and identifying bird information from weather radar data remains one of the challenges of radar aeroecology. In recent years, deep learning was applied to the field of radar data processing and proved to be an effective strategy. This paper describes a deep learning method for extracting biological target echoes from weather radar images. This model uses a two-stream CNN (Atrous-Gated CNN) architecture to generate fine-scale predictions by combining the key modules such as squeeze-and-excitation (SE), and atrous spatial pyramid pooling (ASPP). The SE block can enhance the attention on the feature map, while ASPP block can expand the receptive field, helping the network understand the global shape information. The experiments show that in the typical historical data of China next generation weather radar (CINRAD), the precision of the network in identifying biological targets reaches up to 99.6%. Our network can cope with complex weather conditions, realizing long-term and automated monitoring of weather radar data to extract biological target information and provide feasible technical support for bird migration research.

Large-scale insect migration may cause severe plant diseases and pests which can exert serious impact on agriculture. Predicting the trajectory and destination of insect migration accurately is an effective way to prevent pests. Here, an insect migration trajectory simulation method was proposed with two stages of insect migration, taking off and cruising, considered. First, four criteria based on actual weather condition were proposed to determine the take-off area of the insect migration. Then, a method based on Lagrange diffusion model was proposed to simulate the cruising trajectory. This step needs to combine with the insect orientation strategy and insect trajectory speed which can be, respectively, provided by entomological radar and weather radar. Finally, by comparing the determined take-off area and the simulated insect cruising trajectory with the monitoring results of the weather radar, the effectiveness of the method was verified.

The weather radar can operate in all weathers and all time, and has a large coverage area. Besides monitoring the weather, the weather radar can receive other echoes including biological echoes. In order to utilise weather radar biological monitoring capability, recognising and classifying local insect and bird echoes is one of the biggest obstacles for analysing their migration, foraging, and reproduction activities. Here, a pixel-wise classification method based on the fully convolutional network (FCN) is proposed which is trained by the radar reflectivity and the spectral width images. Moreover, to increase the biometric detection accuracy, the region growing method is combined for achieving the region edge alignment. Finally, the proposed method is validated based on the real weather radar datasets in Yantai. The FCN training results have a high pixel accuracy of 92.96%, and the region growing method performs well in the edge alignment.

Weather radars provide continuous recording over extensive spatial coverage, which is valuable for biologists who observe and study biological activities over a wide range of temporal and spatial scales. Through the interpretation of weather radar observations, powerful biological inferences can be obtained. However, when it comes to certain biological problems, such as the determination of biological parameters related to airborne biological densities, weather radar data needs to be processed based on certain assumptions before. This article analyses and calculates the impact of the phenomenon of migratory insects gathering into layers on the interpretation of weather radar data and develops a biomass estimation method with known bio-spatial distribution characteristics. Quantitative research was conducted on the situation of migrating insects across the Bohai Bay in the autumn of 2012.

Soil moisture (SM) products derived from passive satellite missions are playing an increasingly important role in agricultural applications, especially crop monitoring and disaster warning. Evaluating the dependability of satellite-derived soil moisture products on a large scale is crucial. In this study, we assessed the level 2 (L2) SM product from the Chinese Fengyun-3C (FY-3C) radiometer against in-situ measurements collected from the Chinese Automatic Soil Moisture Observation Stations (CASMOS) during a one-year period from 1 January 2016 to 31 December 2016 across Henan in China. In contrast, we also investigated the skill of the Advanced Microwave Scanning Radiometer 2 (AMSR2) and Soil Moisture Active/Passive (SMAP) SM products simultaneously. Four statistical parameters were used to evaluate these products’ reliability: mean difference, root-mean-square error (RMSE), unbiased RMSE (ubRMSE), and the correlation coefficient. Our assessment results revealed that the FY-3C L2 SM product generally showed a poor correlation with the in-situ SM data from CASMOS on both temporal and spatial scales. The AMSR2 L3 SM product of JAXA (Japan Aerospace Exploration Agency) algorithm had a similar level of skill as FY-3C in the study area. The SMAP L3 SM product outperformed the FY-3C temporally but showed lower performance in capturing the SM spatial variation. A time-series analysis indicated that the correlations and estimated error varied systematically through the growing periods of the key crops in our study area. FY-3C L2 SM data tended to overestimate soil moisture during May, August, and September when the crops reached maximum vegetation density and tended to underestimate the soil moisture content during the rest of the year. The comparison between the statistical parameters and the ground vegetation water content (VWC) further showed that the FY-3C SM product performed much better under a low VWC condition (2) than a high VWC condition (>0.3 kg/m2), and the performance generally decreased with increased VWC. To improve the accuracy of the FY-3C SM product, an improved algorithm that can better characterize the variations of the ground VWC should be applied in the future.

Automatic soil moisture data with a temporal resolution of 1 hr, a spatial resolution of 30 km, from the period June 1-September 1, 2017, and from Henan province in China, and simulation results from the Community Land Model (CLM4.0) were first compared, and a calibration study was further conducted and investigated. The operational status of the instruments was confirmed, and the data passed quality control. The corresponding simulation was performed using the CLM4.0. Small spatial scale variations in observed soil moisture contents were found, but relatively large spatial variations were observed in the simulated soil moisture contents. Since a relatively large bias and weak correlation were found between the simulation and observations, it is necessary to bias-correct the model outputs in order to improve the prediction capability of the numerical models. After bias correction was conducted, the outliers accounting for around 10% of the actual samples were identified and removed for each layer. The observed and simulated data were found to be linearly distributed, and the probability density distribution of deviations between observed and simulated values showed a normal distribution. The mean bias (BIAS) and root mean square error (RMSE) decreased by 100% and 50%, respectively, and the correlation co-efficient (CORR) increased by about 10 times. These improvements showed that the bias-corrected data met the requirements for the evaluation and application of soil moisture data, which can be of great significance in improving future land simulation and assimilation. [ABSTRACT FROM AUTHOR]