In order to investigate the cutting mechanical characteristics of Caragana Korshinskii (C.K.) branches and explore the optimal combination of cutting parameters to support the subsequent equipment development, this paper explores the relationship between branch diameter D, average cutting speed v, wedge angle β, slip cutting angle α, cutting height h, cutting gap t, moisture content M and peak cutting force by using a homemade swing-cut branch cutting test bench with peak cutting force of branches as the target value under unsupported and supported cutting methods, respectively, through single-factor tests. Based on the single-factor test, v, β, α and t were selected as the test factors, and a multi-factor test was conducted with the peak cutting force as the target. Test result: The best combination of unsupported cutting in the range of multi-factor test is v for 3.315 m·s−1, β for 20°, α for 20°, when the peak cutting force is 95.690 N. Supported cutting multi-factor test range to get the best combination of v for 3.36 m·s−1, β for 20°, α for 20°, t for 1.38 mm, when the peak cutting force is 53.082 N. The errors of the predicted peak cutting force and the measured peak cutting force of the obtained model were 1.3% and 3.9%, respectively, which prove that the cutting parameters were optimized reliably. This research can provide a theoretical basis for subsequent development the C.K. harvesting equipment.

Aims Animal-assisted therapy (AAT) relieves pain by creating a relaxed and comfortable environment to reduce anxiety in children. Yet little is known about its effects on pain in children. This study aims to systematically evaluate the effects of AAT on pain in children. Methods Eight databases including PubMed, Cochrane Library, Web of Science, CINAHL Complete, Chinese Biomedical Database (CBM), Weipu Database (VIP), China Knowledge Resource Integrated Database (CNKI) and Wanfang Database were retrieved, and all randomized controlled trials or controlled clinical trial using AAT on children’s pain were recruited from inception to October 2019. Two reviewers independently screened literature, extracted data and assessed the risk of bias of the included studies. RevMan 5.3 software was employed for meta-analysis. Results Seven published studies containing 4 RCTs and 3 CCTs were included for the systematic review. The results of meta-analysis showed that AAT could reduce children’s pain when compared with the control group [ MD = −0.53, 95% CI (−0.77, −0.30), P < 0.00001]. Conclusion Current evidence shows that AAT can relieve pain in children to some extent. Considering the limited quality and quantity of the available studies, more high quality studies should be performed to verify the above conclusion.

No abstract

Background: Hypertension (HTN) has been considered as a health concern in developing countries. And Hui is a minority group with a large population in China. Its genetic background, inadequate access to health services, eating habits, religious belief, ethnic customs, and other factors differ from that of other ethnic groups, which may influence the prevalence of HTN. However, there is no current meta-analysis on the prevalence and risk factors of HTN among Hui population. Thus we conducted a systematic review aiming to estimate the pooled prevalence and risk factors of HTN among Hui population. Methods: PubMed, The Cochrane library, Web of science, CINAHL Complete, Weipu Database (VIP), China Knowledge Resource Integrated Database (CNKI), Wanfang Database, and SinoMed were systematically searched from inception to February 28, 2020 with publication language restricted to English and Chinese. We included cross-sectional, case–control, or cohort studies that focused on prevalence and risk factors of HTN among Hui population. Two investigators independently assessed the risk of bias of the studies included in the review using tools developed by JBI. Meta-analysis was conducted using Stata 12.0 software package. Results: Twenty-three studies were identified with a total of 30,565 study participants. The overall pooled prevalence of HTN was 28% (95% confidence interval [CI]: 24%–32%, I 2 = 98.8%, P < .001). Stratified by gender, the pooled prevalence of HTN in Hui was 26% (95%CI: 20%–33%, I 2 = 97.6%, P < .001) for males and 30% (95%CI: 23%–37%, I 2 = 98.3%, P < .001) for females. Pooled prevalence of HTN in Hui was 2% (95%CI: 2%–6%, I 2 = 70.6%, P = .065), 10% (95%CI: 3%–17%, I 2 = 83.7%, P < .001), 22% (95%CI: 12%–32%, I 2 = 87.9%, P < .001), 37% (95%CI: 20%–53%, I 2 = 94.0%, P < .001), 39% (95%CI: 24%–54%, I 2 = 97.7%, P < .001) and 42% (95%CI: 29%–56%, I 2 = 95.6%, P < .001) for those aged 18 to 29, 30 to 39, 40 to 49, 50 to 59, 60 to 69, and ≥70 years, respectively. Pooled prevalence of HTN in Hui was 22% (95%CI: 14%–29%, I 2 = 97.9%, P < .001) in urban areas and 23% (95%CI: 16%–30%, I 2 = 95.8%, P < .001) in rural areas. Daily salt intake (odd ratio [OR] = 3.94, 95%CI...

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.

No abstract

This paper researches on methods of the color image segmentation method of Lingwu long jujubes based on the maximum entropy to achieve the accuracy of image segmentation and improve accuracy of machine recognition. According to law between the color of Lingwu long jujubes and characteristic of environment, starting from the hue information, this paper is first to explore the difference between the hue of Lingwu long jujubes and the environment which it lives and then use maximum entropy to segment image. It finds optimal threshold by mathematical criterion judging the accuracy of image segmentation. The method of pre-processing of image is mean filter firstly. Then, it extracts hue information of true color image and uses maximum entropy for image segmentation, judging accuracy of image segmentation by segmentation area whether it is in accordance with the 3σ principle. Mathematical morphology is used for smoothing image and eliminating small holes. Finally, segmented image will be obtained through labeling the image by using methods of labeled image and using characteristic parameters for extracting feature. By comparing the segmentation effect with artificial method of the 30 Lingwu long jujubes images, it proves that the color image segmentation method of Lingwu long jujubes based on the maximum entropy has good effect to extract the object region. The accuracy of segmentation rate is up to 89.60%. The time that the algorithm run is 1.3132 s.

Locating Lingwu Long Jujubes is a key step for the automatic picking of the jujubes which can lower labour costs. In this paper, a method for detecting Lingwu Long Jujubes in a natural environment with a three-dimensional (3D) point cloud is proposed. First, the jujubes are preliminarily extracted in two-dimensional (2D) image. Then, the points are fitted to ellipsoid by least square (LS) in the sample consensus framework. The model scores are different when sample size is changed. The variable sample consensus (VARSAC) is proposed to get higher score than random sample consensus (RANSAC). The normal vector and the distance need to be calculated in score calculation of the RANSAC. However, this score calculation method is complex and time-consuming. Thus, a new method called two-ellipsoid-boundingcounting (TEBC) is proposed. The TEBC produces two auxiliary ellipsoids that are obtained by scaling semiaxis of the model. The points, which is bounded by two auxiliary ellipsoids, are regarded as inliers. The functional value of every candidate point is calculated to select the inliers. The valid ellipsoids are determined by the prior information and the invariants. Finally, the centre, size and the attitude angle of the jujubes are solved using eigenvalues and eigenvectors. Experiments are carried out on synthetic and real datasets. The experimental results show that the proposed method can faster and more accurately detect jujube. The speed of the VARSAC+TEBC is approximately 4 times faster than that of the RANSAC in the real dataset. INDEX TERMS Ellipsoid fitting, object locating, sample size, two-ellipsoid-bounding-counting.