Design and Simulation Analysis of Bolt Group Connection of BS-Type Flange Cast Steel Right-Angle Sea Valve

BS type flanged cast steel right-angle sea valve is an important valve used to stop the backflow of medium in the ship pipeline system. The valve and the pipeline are connected by a bolt connection. To ensure the reliability of the bolt connection, the theoretical calculation and finite element method are used to verify the reliability of the design of bolt connection. The theoretical result and the result of finite element analysis (using ANSYS) show that the largest stress on the bolt is located in the middle of the bolt. This paper provides solutions for the verifying the design of bolt connection in valves based on comparing the results of theoretical calculation and finite element analysis

Design and Simulation Analysis of Bolt Group Connection of BS-Type Flange Cast Steel Right-Angle Sea Valve

BS type flanged cast steel right-angle sea valve is an important valve used to stop the backflow of medium in the ship pipeline system. The valve and the pipeline are connected by a bolt connection. To ensure the reliability of the bolt connection, the theoretical calculation and finite element method are used to verify the reliability of the design of bolt connection. The theoretical result and the result of finite element analysis (using ANSYS) show that the largest stress on the bolt is located in the middle of the bolt. This paper provides solutions for the verifying the design of bolt connection in valves based on comparing the results of theoretical calculation and finite element analysis

Design of Intelligent Detection Platform for Wine Grape Pests and Diseases in Ningxia

In order to reduce the impact of pests and diseases on the yield and quality of Ningxia wine grapes and to improve the efficiency and intelligence of detection, this paper designs an intelligent detection platform for pests and diseases. The optimal underlying network is selected by comparing the recognition accuracy of both MobileNet V2 and YOLOX_s networks trained on the Public Dataset. Based on this network, the effect of adding attention mechanism and replacing loss function on recognition effect is investigated by permutation in the Custom Dataset, resulting in the improved network YOLOX_s + CBAM. The improved network was trained on the Overall Dataset, and finally a recognition model capable of identifying nine types of pests was obtained, with a recognition accuracy of 93.35% in the validation set, an improvement of 1.35% over the original network. The recognition model is deployed on the Web side and Raspberry Pi to achieve independent detection functions; the channel between the two platforms is built through Ngrok, and remote interconnection is achieved through VNC desktop. Users can choose to upload local images on the Web side for detection, handheld Raspberry Pi for field detection, or Raspberry Pi and Web interconnection for remote detection

Research on Participatory Design Method of Mixed Bean Planting Machinery

To promote the realization of collaborative design path of mixed bean planting machinery by studying the application of participatory design method in mixed bean planting machinery. To clarify the importance of industrial design for the innovation and development of mixed bean planting machinery, use participatory design methods to coordinate the development trend of industrial design and mixed bean planting machinery, and guide design practice to achieve joint research and joint design, thus realizing the innovative development of mixed bean planting machinery

Object Detection Algorithm for Lingwu Long Jujubes Based on the Improved SSD

The detection of Lingwu long jujubes in a natural environment is of great significance for robotic picking. Therefore, a lightweight network of target detection based on the SSD (single shot multi-box detector) is presented to meet the requirements of a low computational complexity and enhanced precision. Traditional object detection methods need to load pre-trained weights, cannot change the network structure, and are limited by equipment resource conditions. This study proposes a lightweight SSD object detection method that can achieve a high detection accuracy without loading pre-trained weights and replace the Peleenet network with VGG16 as the trunk, which can acquire additional inputs from all of the previous layers and provide itself characteristic maps to all of the following layers. The coordinate attention module and global attention mechanism are added in the dense block, which boost models to more accurately locate and identify objects of interest. The Inceptionv2 module has been replaced in the first three additional layers of the SSD structure, so the multi-scale structure can enhance the capacity of the model to retrieve the characteristic messages. The output of each additional level is appended to the export of the sub-level through convolution and pooling operations in order to realize the integration of the image feature messages between the various levels. A dataset containing images of the Lingwu long jujubes was generated and augmented using pre-processing techniques such as noise reinforcement, light variation, and image spinning. To compare the performance of the modified SSD model to the original model, a number of experiments were conducted. The results indicate that the mAP (mean average precision) of the modified SSD algorithm for object inspection is 97.32%, the speed of detection is 41.15 fps, and the parameters are compressed to 30.37% of the original networks for the same Lingwu long jujubes datasets without loading pre-trained weights. The improved SSD target detection algorithm realizes a reduction in complexity, which is available for the lightweight adoption to a mobile platform and it provides references for the visual detection of robotic picking

Physiological, Metabolic and Transcriptional Responses of Basil (Ocimum basilicum Linn. var. pilosum (Willd.) Benth.) to Heat Stress

As a medicinal and edible plant, basil (Ocimum basilicum Linn. var. pilosum (Willd.) Benth.) has rich nutrition and significant economic value. The increase in heat stress caused by global warming adversely affects the growth and yield of plants. However, the response mechanism of basil to heat stress is poorly understood. This work investigated the changes in phenotype, metabolome, and transcriptome in basil under heat stress. The results showed that heat stress triggered severe oxidative damage and photosynthesis inhibition in basil. Metabonomic analysis showed that, compared to the control group, 29 significantly differentially accumulated metabolites (DAMs) were identified after 1 d of heat treatment, and 37 DAMs after the treatment of 3 d. The DAMs were significantly enriched by several pathways such as glycolysis or gluconeogenesis; aminoacyl-tRNA biosynthesis; and alanine, aspartate, and glutamate metabolism. In addition, transcriptomic analysis revealed that 15,066 and 15,445 genes were differentially expressed after 1 d and 3 d of heat treatment, respectively. Among them, 11,183 differentially expressed genes (DEGs) were common response genes under 1 d and 3 d heat treatment, including 5437 down-regulated DEGs and 6746 up-regulated DEGs. All DEGs were significantly enriched in various KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways, most dominated by glyoxylate and dicarboxylate metabolism, followed by starch and sucrose metabolism, and by the biosynthesis and metabolism of other secondary metabolites. Overall, all the above results provided some valuable insights into the molecular mechanism of basil in response to heat stress

Physiological, Metabolic and Transcriptional Responses of Basil (<i>Ocimum basilicum</i> Linn. var. <i>pilosum</i> (Willd.) Benth.) to Heat Stress

As a medicinal and edible plant, basil (Ocimum basilicum Linn. var. pilosum (Willd.) Benth.) has rich nutrition and significant economic value. The increase in heat stress caused by global warming adversely affects the growth and yield of plants. However, the response mechanism of basil to heat stress is poorly understood. This work investigated the changes in phenotype, metabolome, and transcriptome in basil under heat stress. The results showed that heat stress triggered severe oxidative damage and photosynthesis inhibition in basil. Metabonomic analysis showed that, compared to the control group, 29 significantly differentially accumulated metabolites (DAMs) were identified after 1 d of heat treatment, and 37 DAMs after the treatment of 3 d. The DAMs were significantly enriched by several pathways such as glycolysis or gluconeogenesis; aminoacyl-tRNA biosynthesis; and alanine, aspartate, and glutamate metabolism. In addition, transcriptomic analysis revealed that 15,066 and 15,445 genes were differentially expressed after 1 d and 3 d of heat treatment, respectively. Among them, 11,183 differentially expressed genes (DEGs) were common response genes under 1 d and 3 d heat treatment, including 5437 down-regulated DEGs and 6746 up-regulated DEGs. All DEGs were significantly enriched in various KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways, most dominated by glyoxylate and dicarboxylate metabolism, followed by starch and sucrose metabolism, and by the biosynthesis and metabolism of other secondary metabolites. Overall, all the above results provided some valuable insights into the molecular mechanism of basil in response to heat stress

Research on Grape-Planting Structure Perception Method Based on Unmanned Aerial Vehicle Multispectral Images in the Field

In order to accurately obtain the distribution of large-field grape-planting sites and their planting information in complex environments, the unmanned aerial vehicle (UAV) multispectral image semantic segmentation model based on improved DeepLabV3+ is used to solve the problem that large-field grapes in complex environments are affected by factors such as scattered planting sites and complex background environment of planting sites, which makes the identification of planting areas less accurate and more difficult to manage. In this paper, firstly, the standard deviation (SD) and interband correlation of UAV multispectral images were calculated to obtain the best band combinations for large-field grape images, and five preferred texture features and two preferred vegetation indices were screened using color space transformation and grayscale coevolution matrix. Then, supervised classification methods, such as maximum likelihood (ML), random forest (RF), and support vector machine (SVM), unsupervised classification methods, such as the Iterative Self-organizing Data Analysis Techniques Algorithm (ISO DATA) model and an improved DeepLabV3+ model, are used to evaluate the accuracy of each model in combination with the field visual translation results to obtain the best classification model. Finally, the effectiveness of the classification features on the best model is verified. The results showed that among the four machine learning methods, SVM obtained the best overall classification accuracy of the model; the DeepLabV3+ deep learning scheme based on spectral information + texture + vegetation index + digital surface model (DSM) obtained the best accuracy of overall accuracy (OA) and frequency weight intersection over union (FW-IOU) of 87.48% and 83.23%, respectively, and the grape plantation area relative error of extraction was 1.9%. This collection scheme provides a research basis for accurate interpretation of the planting structure of large-field grapes

Regulation of pollen lipid body biogenesis by MAP kinases and downstream WRKY transcription factors in Arabidopsis.

Signaling pathways that control the activities in non-photosynthetic plastids, important sites of plant metabolism, are largely unknown. Previously, we demonstrated that WRKY2 and WRKY34 transcription factors play an essential role in pollen development downstream of mitogen-activated protein kinase 3 (MPK3) and MPK6 in Arabidopsis. Here, we report that GLUCOSE-6-PHOSPHATE/PHOSPHATE TRANSLOCATOR 1 (GPT1) is a key target gene of WRKY2/WRKY34. GPT1 transports glucose-6-phosphate (Glc6P) into plastids for starch and/or fatty acid biosynthesis depending on the plant species. Loss of function of WRKY2/WRKY34 results in reduced GPT1 expression, and concomitantly, reduced accumulation of lipid bodies in mature pollen, which leads to compromised pollen viability, germination, pollen tube growth, and male transmission in Arabidopsis. Pollen-specific overexpression of GPT1 rescues the pollen defects of wrky2 wrky34 double mutant. Furthermore, gain-of-function activation of MPK3/MPK6 enhances GPT1 expression; whereas GPT1 expression is reduced in mkk4 mkk5 double mutant. Together, this study revealed a cytoplasmic/nuclear signaling pathway capable of coordinating the metabolic activities in plastids. High-level expression of GPT1 at late stages of pollen development drives Glc6P from cytosol into plastids, where Glc6P is used for fatty acid biosynthesis, an important step of lipid body biogenesis. The accumulation of lipid bodies during pollen maturation is essential to pollen fitness and successful reproduction