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At present, the redundancy of real-time measurement in distribution network is low and the mixed measurement is incompatible, Hybrid measurement combined with multi-source asynchronous data such as SCADASCADA/ smart distribution transformer information acquisition system (SDTIAS), anew state estimation based on SCADASCADA and SDTIAS is proposed in this paper. Firstly, SDTIAS and SCADA measurements are matched and synchronized; Then, the quadratic multi-source asynchronous measurement model is constructed, the quadratic equality constraint is established, the unified modeling of multi-source asynchronous measurement is realized; Finally, the proposed model and algorithm are simulated and verified in the standard test system, The results show the error seven times lower than traditional methods, and the calculation efficiency (100 Statistics) is increased by 35%, indicating better accuracy and higher efficiency.

Glutamate dehydrogenase (GDH), a key metabolic enzyme that is ubiquitous across almost all living species, is essential for cell survival. To elucidate the characteristics and functions of the GDH gene in the sea urchin, we cloned and characterized the full-length cDNA of a novel GDH homolog from Strongylocentrotus intermedius, herein designated SiGDH. The full-length SiGDH gene was 2499 bp, with an open reading frame (ORF) of 1635 bp, encoding 544 amino acids. Bioinformatic analyses revealed that the predicted SiGDH protein contained the conserved ELFV_dehydrog_N and ELFV_dehydrog domains and that this protein had the highest sequence identity with the GDH protein from Strongylocentrotus purpuratus. Tissue-specific differences in SiGDH relative expression patterns and enzyme activity levels were observed, and the highest relative expression and total enzyme activity of SiGDH were determined to be in the gonad. Changes in SiGDH relative expression and enzyme activity in the gonad were observed after both gradual and acute heat stress. Together, our observations help to clarify the characteristics of this GDH homolog, as well as its associations with heat resistance in echinoderms.

Underwater images tend to suffer from critical quality degradation, such as poor visibility, contrast reduction, and color deviation by virtue of the light absorption and scattering in water media. It is a challenging problem for these images to enhance visibility, improve contrast, and eliminate color cast. This paper proposes an effective and high-speed enhancement and restoration method based on the dark channel prior (DCP) for underwater images and video. Firstly, an improved background light (BL) estimation method is proposed to estimate BL accurately. Secondly, the R channel’s transmission map (TM) based on the DCP is estimated sketchily, and a TM optimizer integrating the scene depth map and the adaptive saturation map (ASM) is designed to refine the afore-mentioned coarse TM. Later, the TMs of G–B channels are computed by their ratio to the attenuation coefficient of the red channel. Finally, an improved color correction algorithm is adopted to improve visibility and brightness. Several typical image-quality assessment indexes are employed to testify that the proposed method can restore underwater low-quality images more effectively than other advanced methods. An underwater video real-time measurement is also conducted on the flipper-propelled underwater vehicle-manipulator system to verify the effectiveness of the proposed method in the real scene.

MicroRNA-7 (miR-7) is a highly conserved short non-coding RNA involved in various bioprocesses via the regulation of multiple target genes. To enrich our knowledge of the functions of miR-7 in innate immune regulation in echinoderms, we first investigated the targeting relationship between miR-7 and PAK1 in the sea cucumber Apostichopus japonicus and then explored the functions of miR-7, the PAK1 gene, and the miR-7/PAK1 axis in the pathogen-induced immune response of A. japonicus. Our results showed that miR-7 can bind to the 3ʹUTR of PAK1 and negatively regulate the expression of PAK1 in A. japonicus. Overexpression and inhibition of miR-7 and inhibition of the expression of PAK1 can alter phagocytosis, cellular agglutination, and lysozyme contents in A. japonicus. Both miR-7 and the PAK1 gene are involved in immune defense against Vibrio splendidus infection; the miR-7/AjPAK1 axis showed immune regulatory function at 48 to 72 h post-infection (hpi) after V. splendidus infection in A. japonicus. In summary, the results of this study established that miR-7 regulates the pathogen-induced immune response by targeting PAK1 in A. japonicus.

In this work, a new type of Co2VO4 hollow nanocube (CoVO-HNC) was synthesized through an ion exchange process using ZIF-67 nanocubes as a template. The hollow nanocubic structure of the CoVO-HNC provides an abundance of redox sites and shortens the ion/electron diffusion path. As the electrode material of supercapacitors, the specific capacitance of CoVO-HNC is 427.64 F g−1 at 1.0 A g−1. Furthermore, an asymmetric supercapacitor (ASC) was assembled using CoVO-HNC and activated carbon (AC) as electrodes. The ASC device attains an energy density of 25.28 Wh kg−1 at a high-power density of 801.24 W kg−1, with 78% capacitance retention after 10,000 cycles at 10 A g−1.

Three-dimensional point clouds, as an advanced imaging technique, enable researchers to capture plant traits more precisely and comprehensively. The task of plant segmentation is crucial in plant phenotyping, yet current methods face limitations in computational cost, accuracy, and high-throughput capabilities. Consequently, many researchers have adopted 3D point cloud technology for organ-level segmentation, extending beyond manual and 2D visual measurement methods. However, analyzing plant phenotypic traits using 3D point cloud technology is influenced by various factors such as data acquisition environment, sensors, research subjects, and model selection. Although the existing literature has summarized the application of this technology in plant phenotyping, there has been a lack of in-depth comparison and analysis at the algorithm model level. This paper evaluates the segmentation performance of various deep learning models on point clouds collected or generated under different scenarios. These methods include outdoor real planting scenarios and indoor controlled environments, employing both active and passive acquisition methods. Nine classical point cloud segmentation models were comprehensively evaluated: PointNet, PointNet++, PointMLP, DGCNN, PointCNN, PAConv, CurveNet, Point Transformer (PT), and Stratified Transformer (ST). The results indicate that ST achieved optimal performance across almost all environments and sensors, albeit at a significant computational cost. The transformer architecture for points has demonstrated considerable advantages over traditional feature extractors by accommodating features over longer ranges. Additionally, PAConv constructs weight matrices in a data-driven manner, enabling better adaptation to various scales of plant organs. Finally, a thorough analysis and discussion of the models were conducted from multiple perspectives, including model construction, data collection environments, and platforms.

Exploring the key technologies of agricultural robots is an inevitable trend in the development of smart agriculture. It is significant to continuously transplant and develop novel algorithms and models to update agricultural robots that use light detection and ranging (LiDAR) as a remote sensing method. This paper implements a method for extracting and estimating rapeseed leaves through agricultural robots based on LiDAR point cloud, taking leaf area (LA) measurement as an example. Firstly, the three-dimensional (3D) point cloud obtained with a terrestrial laser scanner (TLS) were used to extract crop phenotypic information. We then imported the point cloud within the study area into a custom hybrid filter, from which the rapeseed point cloud was segmented. Finally, a new LA estimation model, based on the Delaunay triangulation (DT) algorithm was proposed, namely, LA-DT. In this study, a crop canopy analyzer, LAI-2200C, was used to measure rapeseed LA in farmland. The measured values were employed as standard values to compare with the calculated results obtained using LA-DT, and the differences between the two methods were within 3%. In addition, 100 individual rapeseed crops were extracted, and the output of the LA-DT model was subjected to linear regression analysis. The R² of the regression equation was 0.93. The differences between the outputs of the LAI-2200C and LA-DT in these experiments passed the paired samples t-test with significant correlation (p < 0.01). All the results of the comparison and verification showed that the LA-DT has excellent performance in extracting LA parameters under complex environments. These results help in coping with the complex working environment and special working objects of agricultural robots. This is of great significance for expanding the interpretation methods of agricultural 3D information.

Three-dimensional (3D) laser point cloud technology is an important research method in the field of agricultural remote sensing research. The collection and processing technology of terrestrial light detection and ranging (LiDAR) point cloud of crops has greatly promoted the integration of agricultural informatization and intelligence. In a smart farmland based on 3D modern agriculture, the manager can efficiently and conveniently achieve the growth status of crops through the point cloud collection system and processing model integrated in the smart agricultural system. To this end, we took field maize as the research object in this study and processed four sets of field maize point clouds, named Maize-01, Maize-02, Maize-03, and Maize-04, respectively. In this research, we established a field individual maize segmentation model with the density-based clustering algorithm (DBSCAN) as the core, and four groups of field maize were used as research objects. Among them, the value of the overall accuracy (OA) index, which was used to evaluate the comprehensive performance of the model, were 0.98, 0.97, 0.95, and 0.94. Secondly, the multi-condition identification method was used to separate different maize organ point clouds from the individual maize point cloud. In addition, the organ stratification model of field maize was established. In this organ stratification study, we take Maize-04 as the research object and obtained the recognition accuracy rates of four maize organs: tassel, stalk, ear, and leaf at 96.55%, 100%, 100%, and 99.12%, respectively. We also finely segmented the leaf organ obtained from the above-mentioned maize organ stratification model into each leaf individual again. We verified the accuracy of the leaf segmentation method with the leaf length as the representative. In the linear analysis of predicted values of leaf length, R2 was 0.73, RMSE was 0.12 m, and MAE was 0.07 m. In this study, we examined the segmentation of individual crop fields and established 3D information interpretations for crops in the field as well as for crop organs. Results visualized the real scene of the field, which is conducive to analyzing the response mechanism of crop growth and development to various complex environmental factors.

Work-related injury insurance (WII) has undergone a long period of rapid growth in China. Therefore, its sustainability, funding, and system of payments deserve serious attention. By analyzing the changes of the numbers of insured, the number of compensated, and the cumulative balance of WII Fund, this article systematically reviews and summarizes the various stages of the development of WII in China. The relationship between the WII Fund’s revenue and expenditure between 1997 and 2018 is analyzed using statistical methods, including the augmented Dickey–Fuller (ADF) unit root test, the cointegration test, the Granger causality test and an error correction model. Since 1996, China’s WII has been repeatedly adjusted and constantly growing. Our empirical evidence shows that there is a long-term equilibrium and cointegration relationship between the WII Fund’s revenue and expenditure. Revenue is the Granger cause of expenditure, but it does not conform to the fund-raising principle. WII Fund has a large accumulated balance over the years, which violates the principles of fund management and has caused considerable controversy. China’s WII has undergone several regulatory upgrades and management mode adjustments to better ensure its sustainable development. The fund has maintained a basic balance of revenue and expenditure. Nevertheless, at present, China’s WII Fund is facing the pressure of declining fund balance and, as such, medium- and long-term development risks deserve careful attention. As there is no sufficient literature on WII Fund management, this study is a valuable contribution to the existing body of knowledge.