Traditional methods of cow estrus monitoring technology are not suitable for the current needs of large-scale, intensive and welfare-based farming. There is a need to improve the detection rate of cow estrus and to reduce the emergency response caused by wearing contact devices. Furthermore, it is necessary to verify the practical effectiveness of the LOGISITC and SV (support vector machine) models for modeling cow estrus. In this paper, we have proposed a non-contact cow estrus monitoring method based on the thermal infrared images of cows and have proposed a lab-color-space-based feature extraction method for the thermal infrared images of cow eyes and vulvas. The test subjects were 10 Holstein cows, monitored on a fixed basis, to determine the best segmentation contour. The LOGISTIC and SVM (support vector machine) models were used to establish the cow estrus model using the thermal infrared temperature variation in cows in estrus and cows not in estrus. The experimental results showed that the heat detection rate of the LOGISTIC-based model was 82.37% and the heat detection rate of the SVM-based model was 81.42% under the optimal segmentation profile. The highest temperature in the eye and vulva of cows was the input, and the recall rate was above 86%. The heat monitoring method based on thermal infrared images does not cause stress to cows and meets the needs of modern dairy farming for welfare breeding.

Background: The objective of this study was to compare the incidence of severe systemic inflammatory response syndrome (sSIRS) after total aortic arch replacement between patients who underwent moderate hypothermic circulatory arrest (MHCA) and those who underwent deep hypothermic circulatory arrest (DHCA).Methods: At Fuwai Hospital, 600 patients who underwent total aortic arch replacement with MHCA or DHCA from January 2013 to December 2016 were consecutively enrolled and divided into DHCA (14.1–20.0°C) and MHCA (20.1–28.0°C) groups. Preliminary statistical analysis revealed that some baseline indicators differed between the two groups; therefore, propensity score matching (PSM) was used to balance the covariates. Post-operative sSIRS as the primary outcome was compared between the groups both before and after PSM.Results: A total of 275 (45.8%) patients underwent MHCA, and 325 (54.2%) patients underwent DHCA. After PSM analysis, a total of 191 matched pairs were obtained. The overall incidence of sSIRS was 27.3%. There was no significant difference in post-operative sSIRS between the MHCA group and the DHCA group in either the overall cohort or the PSM cohort (no-PSM: P = 0.188; PSM: P = 0.416); however, post-operative sSIRS was increased by ~4% in the DHCA group compared with the MHCA group in both the no-PSM and PSM cohorts (no-PSM: 29.5 vs. 24.7%; PSM: 29.3 vs. 25.1%). Both before and after PSM, the rates of gastrointestinal hemorrhage and pulmonary infection and post-operative length of stay were significantly increased in the DHCA group compared with the MHCA group (P < 0.05), and the remaining secondary outcomes were not significantly different between the groups.Conclusions: MHCA and DHCA are associated with comparable incidences of sSIRS in patients following total aortic arch replacement for type A aortic dissection. However, the MHCA group had a shorter cardiopulmonary bypass time, a shorter post-operative length of stay and lower pulmonary infection and gastrointestinal hemorrhage rates than the DHCA group. We cautiously recommend the use of MHCA for most total arch replacements in patients with type A aortic dissection.

Sea buckthorn is an extremely drought-tolerant, resilient and sustainable crop that can be grown in areas with harsh climates and scarce resources to provide a source of nutrition and income for the local population. The use of image-based yield estimation methods allows for better management of sea buckthorn cultivation to improve its productivity and sustainability, while the error in fruit yield information due to occlusion can be well reduced by combining and analysing the image features extracted using binocular cameras. In this paper, mature wild sea buckthorn in the mountainous areas north of Hohhot City, Inner Mongolia Autonomous Region, were used as the study target. Firstly, complete images of sea buckthorn branches were collected by binocular cameras and features were extracted. The extracted features include the colour index of sea buckthorn fruits, the number of fruits and a total of four texture parameters, ASM, CON, COR and HOM. The features with significant correlation to sea buckthorn fruit weight were selected by correlation calculation of the feature parameters, the obtained correlation features were introduced into the BP neural network model for training and then the sea buckthorn estimation model was obtained. The results showed that the best yield estimation model was achieved by combining the COR index with the colour index and the number of sea buckthorn fruits, with a coefficient of determination R² = 0.99267 and a root mean square error RMSE = 0.5214.