Preparation and Micro Mechanical Properties of Nano-Al2O3 Particles Strengthened Ni-based Composite Coatings

AbstractNi-based composite solution containing nano-Al2O3 particles was prepared by high-energy mechanical and chemical processes. The microstructure and nano-particle content of nano-Al2O3/Ni composite coatings were determined by SEM, EDS and TEM. The micro mechanical properties were tested by nano-indentation technique, and the strengthening mechanism was analyzed. The results show that 85 percent of particles in the solution are dispersed in size of nano meter, nano-particles co-deposited in the coating increases by a factor of 53 percent and the structure of the composite coating is more compact and uniform than that of Ni coating. Nano-Al2O3/Ni coatings exhibit excellent micro mechanical properties, the nanohardness and Young's modulus are 7.04GPa and 225GPa respectively, which are attributed to finer crystals strengthening, dispersion strengthening and high- density dislocations strengthening

Improved XLNet modeling for Chinese named entity recognition of edible fungus

IntroductionThe diversity of edible fungus species and the extent of mycological knowledge pose significant challenges to the research, cultivation, and popularization of edible fungus. To tackle this challenge, there is an urgent need for a rapid and accurate method of acquiring relevant information. The emergence of question and answer (Q&A) systems has the potential to solve this problem. Named entity recognition (NER) provides the basis for building an intelligent Q&A system for edible fungus. In the field of edible fungus, there is a lack of a publicly available Chinese corpus suitable for use in NER, and conventional methods struggle to capture long-distance dependencies in the NER process.MethodsThis paper describes the establishment of a Chinese corpus in the field of edible fungus and introduces an NER method for edible fungus information based on XLNet and conditional random fields (CRFs). Our approach combines an iterated dilated convolutional neural network (IDCNN) with a CRF. First, leveraging the XLNet model as the foundation, an IDCNN layer is introduced. This layer addresses the limited capacity to capture features across utterances by extending the receptive field of the convolutional kernel. The output of the IDCNN layer is input to the CRF layer, which mitigates any labeling logic errors, resulting in the globally optimal labels for the NER task relating to edible fungus.ResultsExperimental results show that the precision achieved by the proposed model reaches 0.971, with a recall of 0.986 and an F1-score of 0.979.DiscussionThe proposed model outperforms existing approaches in terms of these evaluation metrics, effectively recognizing entities related to edible fungus information and offering methodological support for the construction of knowledge graphs

Winter wheat ear counting based on improved YOLOv7x and Kalman filter tracking algorithm with video streaming

Accurate and real-time field wheat ear counting is of great significance for wheat yield prediction, genetic breeding and optimized planting management. In order to realize wheat ear detection and counting under the large-resolution Unmanned Aerial Vehicle (UAV) video, Space to depth (SPD) module was added to the deep learning model YOLOv7x. The Normalized Gaussian Wasserstein Distance (NWD) Loss function is designed to create a new detection model YOLOv7xSPD. The precision, recall, F1 score and AP of the model on the test set are 95.85%, 94.71%, 95.28%, and 94.99%, respectively. The AP value is 1.67% higher than that of YOLOv7x, and 10.41%, 39.32%, 2.96%, and 0.22% higher than that of Faster RCNN, SSD, YOLOv5s, and YOLOv7. YOLOv7xSPD is combined with the Kalman filter tracking and the Hungarian matching algorithm to establish a wheat ear counting model with the video flow, called YOLOv7xSPD Counter, which can realize real-time counting of wheat ears in the field. In the video with a resolution of 3840×2160, the detection frame rate of YOLOv7xSPD Counter is about 5.5FPS. The counting results are highly correlated with the ground truth number (R2 = 0.99), and can provide model basis for wheat yield prediction, genetic breeding and optimized planting management

Malignant glomus tumor of prostate: A case report

We reported an 85-year-old patient with malignant glomus tumor (GT) of the prostate. He presented with urinary frequency for more than 2 years and gross hematuria for 7 days. Computed tomography scan showed that the prostate was markedly irregularly enlarged, and the boundary between the prostate and the posterior wall of the bladder was unclear. Bilateral kidneys and ureters were dilated. Biochemical examinations showed that the serum potassium was 7.24 mmol/L and the serum creatinine was 974.6 μmol/L. Transurethral diagnostic resection was performed after restoring homeostasis through several times of bedside blood filtration. The pathological diagnosis was malignant GT. The patient’s renal function recovered after bilateral nephrostomy, and he refused further treatment and was out of contact after 9 months. We summarize the clinical and histopathological features of malignant GT of the prostate in order to improve the early recognition of the disease by clinicians

Development of Weed Detection Method in Soybean Fields Utilizing Improved DeepLabv3+ Platform

Accurately identifying weeds in crop fields is key to achieving selective herbicide spraying. Weed identification is made difficult by the dense distribution of weeds and crops, which makes boundary segmentation at the overlap inaccurate, and thus pixels cannot be correctly classified. To solve this problem, this study proposes a soybean field weed recognition model based on an improved DeepLabv3+ model, which uses a Swin transformer as the feature extraction backbone to enhance the model’s utilization of global information relationships, fuses feature maps of different sizes in the decoding section to enhance the utilization of features of different dimensions, and adds a convolution block attention module (CBAM) after each feature fusion to enhance the model’s utilization of focused information in the feature maps, resulting in a new weed recognition model, Swin-DeepLab. Using this model to identify a dataset containing a large number of densely distributed weedy soybean seedlings, the average intersection ratio reached 91.53%, the accuracy improved by 2.94% compared with that before the improvement with only a 48 ms increase in recognition time, and the accuracy was superior to those of other classical semantic segmentation models. The results showed that the Swin-DeepLab network proposed in this paper can successfully solve the problems of incorrect boundary contour recognition when weeds are densely distributed with crops and incorrect classification when recognition targets overlap, providing a direction for the further application of transformers in weed recognition

Network Public Opinion Monitoring System for Agriculture Products Based on Big Data

The influence of online public opinion on agricultural product safety on the society is increasing. In order to correctly guide the direction of online public opinion on agricultural products, help the agricultural sector turn from passive to active public opinion, timely prevent the spread of negative public opinion, and reduce the negative impact on public opinion hot events, it is especially important to improve the ability of monitoring agricultural products’ network public opinion. This research is based on big data technology to develop an agricultural products’ network public opinion monitoring system that can collect, process, and analyze data in real time, discover and track hot topics, and calculate and visualize the polarity of public sentiment. The use of big data technology to increase the processing speed aims to strengthen the public’s supervision of the public opinion on the network security of agricultural products and provide an effective basis of the decision-making of relevant departments