In the research of green vegetation coverage in the field of remote sensing image segmentation, crop planting area is often obtained by semantic segmentation of images taken from high altitude. This method can be used to obtain the rate of cultivated land in a region (such as a country), but it does not reflect the real situation of a particular farmland. Therefore, this paper takes low-altitude images of farmland to build a dataset. After comparing several mainstream semantic segmentation algorithms, a new method that is more suitable for farmland vacancy segmentation is proposed. Additionally, the Strip Pooling module (SPM) and the Mixed Pooling module (MPM), with strip pooling as their core, are designed and fused into the semantic segmentation network structure to better extract the vacancy features. Considering the high cost of manual data annotation, this paper uses an improved ResNet network as the backbone of signal transmission, and meanwhile uses data augmentation to improve the performance and robustness of the model. As a result, the accuracy of the proposed method in the test set is 95.6%, mIoU is 77.6%, and the error rate is 7%. Compared to the existing model, the mIoU value is improved by nearly 4%, reaching the level of practical application.

Using a study design that enabled direct comparison of the rumen microbiota between sheep dams and their lambs, between littermates, and between sheep dams and lambs from other mothers, we identified the heritable subsets of rumen bacteriota in Hu sheep, some of which may play important roles in affecting the growth traits of young lambs. Some maternal rumen bacteria could help predict the growth traits of the young offspring, and they may assist in breeding of and selection for high-performance sheep.

Background The most common materials of artificial blood vessels are polyethylene terephthalate and polytetrafluoroethylene. But polycarbonate polyurethane (PCU) is an ideal material for vascular prostheses because of their excellent characteristics. As far as we know, our artificial blood vessel is the first type of hybrid PCU/polyester three-layered large-diameter artificial blood vessel in the world. Objective The purpose of this preclinical animal experiment is to evaluate the hemocompatibility, histocompatibility, effectiveness, and safety of the three-layered large-diameter artificial blood vessel in sheep. Methods The artificial blood vessels took place of the initial segments of the sheep’s thoracic aorta by end-to-end anastomosis. Results All of the 14 sheep are male, their average body weight (BW) was 30.57 ± 3.95 kg. All 14 artificial blood vessels successfully replaced the thoracic aortas. 5 sheep did not survive to the end of the experiment, while the remaining 9 sheep did. After the surgery, the blood biochemical and blood routine indicators fluctuate slightly within the normal range. The angiography showed that the implanted artificial blood vessels were unobstructed without obvious stenosis or expansion. 24 weeks after surgery, the lumen surfaces of the artificial blood vessels were covered by endothelia in different degrees, and the average endothelialization rate was 69.44% (range: 20% to 100%). Conclusions This artificial blood vessel is the first to use PCU in large-diameter artificial vascular grafts. It has excellent blood compatibility, wonderful biocompatibility, high endothelialization rate, and 100% patency.

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The traceability system studied by predecessors had the problems of single traceability product, incomplete information collection, low automation level and so on. This study used the Internet of things and automatic information collection technology, combined with the growth and development characteristics of agricultural products in the planting industry, analyzed and traced the key nodes, and developed a general-type traceability system for agricultural products in the planting industry. The system consists of a Web terminal and an Android terminal. The Android terminal is mainly responsible for collecting field farming data, The Web terminal is connected to the Internet of things platform, the regulatory platform and the Android terminal, and is mainly responsible for the collection of Internet of things data and field farming data, as well as receiving the recall information of problematic products from the regulatory platform. The system has been effectively applied in 5 categories and 18 subcategories of planting agricultural products in Henan Province, and its universality has been preliminarily proved.