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In the mechanized harvesting of Lycium barbarum L. (L. barbarum), there are prominent problems such as low harvesting efficiency, high damage rate, incomplete separation of leaves and delayed transportation. Therefore, an integrated L. barbarum harvester was designed and developed in this study, which has the functions of picking, undertaking, transportation, winnowing and collection. The design requirements and constraints were identified by cultivation agronomy. Through simulation and physical tests, the tarpaulin was determined as the undertaking material. This machine achieved efficient picking with a vibrating picker with a multi-degree-of-freedom picking arm. The two-stage conveyor belts and the intermediate receiving plate were designed for low loss transportation of fruit. The axial flow fan and secondary buffer device were used to realize winnowing and reduce the damage rate. Through the three-factor and three-level orthogonal test, an optimal working parameter combination was determined: the vibration frequency of the picker was 20 Hz, the conveyor speed was 4 m/min, the airflow speed of the fan was 7 m/s. A field test was conducted under these parameters, and the results showed that the harvesting efficiency was about five times that of manual harvesting. The integrated L. barbarum harvester basically met the harvesting requirements and provided a new scheme for mechanized harvesting.

Caragana korshinskii (C.K.) flat stubble residue is an abundant biomass energy source in China. Because branch cutting is closely related to the harvesting of forest biomass, it is practical for forestry production and ecological development to investigate the effects of cutting parameters on the peak cutting force and cutting energy consumption of C.K. branches. In this study, the effect of cutting parameters on the peak cutting force and cutting power consumption of branches was investigated by single-factor and multi-factor tests using an independently developed reciprocating cutting test bench, and an optimization model was established. The interaction term of average cutting speed and tool cutting edge inclination angle significantly affected the peak cutting force, while the interaction term of cutting clearance and wedge angle had a significant effect on the cutting energy consumption. The optimal combination of cutting parameters was an average cutting speed of 0.5 m/s, cutting clearance of 1.4 mm, wedge angle of 25°, and tool cutting edge inclination angle of 20°. With this combination of parameters, the corresponding peak cutting force was 644.38 N, and the cutting energy consumption was 5.90 J, which was less than 5% relative error between each performance index and the theoretical optimized value.

Ecological corridors (ECs) are important management tools to protect biodiversity by linking fragile habitats, especially for highly mobile organisms. ECs in terrestrial landscapes work as passages on land or in water. However, the significance of ECs to migratory species in estuaries has not been well elucidated. Based on annual fishery investigation in the Yangtze estuary and their dominance index rank, three of the top five species, including Larimochthys polyactis, Coilia mystus, and Gobiidae, exhibited absolute dominance in spring during the past 5 years. The temporal and spatial density variance of C. mystus supported its short-distance migration pattern. Redundancy analysis and the MaxEnt model predicted optimum habitats for C. mystus. C. mystus larvae survival was significantly related to salinity, total nitrogen, pH, reactive silicate, dissolved oxygen, surface water temperature, and chlorophyll-a in May and to salinity, surface water temperature, permanganate index, suspended particles, total nitrogen, and total phosphorus in August. The MaxEnt model predicted a broader longitudinal distribution range from offshore to the upstream freshwater area but narrower latitudinal distribution in the southern branch in May than in August. Finally, we delineated migratory corridors connecting optimum habitats for C. mystus using the least-cost route method. Optimum habitats close to the coastlines in the south branch might play a significant role in maintaining population or community connectivity in the Yangtze estuary. Our findings provide a perspective and method to quantify and facilitate the harmonious development of socioeconomy and fishery biodiversity conservation.

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

Background: There is a paucity of systematic reviews on the associated factors of mortality among ST-elevation myocardial infarction (STEMI) patients after percutaneous coronary intervention (PCI). This meta-analysis was designed to synthesize available evidence on the prevalence and associated factors of mortality after PCI for adult patients with STEMI. Materials and Methods: Databases including the Cochrane Library, PubMed, Web of Science, Embase, Ovid, Scopus, ProQuest, MEDLINE, and CINAHL Complete were searched systematically to identify relevant articles published from January 2008 to March 2020 on factors affecting mortality after PCI in STEMI patients. Meta-analysis was conducted using Stata 12.0 software package. Results: Our search yielded 91 cohort studies involving a total of 199, 339 participants. The pooled mortality rate for STEMI patients after PCI was 10%. After controlling for grouping criteria or follow-up time, the following 17 risk factors were significantly associated with mortality for STEMI patients after PCI: advanced age (odds ratio [OR] = 3.89), female (OR = 2.01), out-of-hospital cardiac arrest (OR = 5.55), cardiogenic shock (OR = 4.83), renal dysfunction (OR = 3.50), admission anemia (OR = 3.28), hyperuricemia (OR = 2.71), elevated blood glucose level (OR = 2.00), diabetes mellitus (OR = 1.8), chronic total occlusion (OR = 2.56), Q wave (OR = 2.18), without prodromal angina (OR = 2.12), delay in door-to-balloon time (OR = 1.72), delay in symptom onset-to-balloon time (OR = 1.43), anterior infarction (OR = 1.66), ST-segment resolution (OR = 1.40), and delay in symptom onset-to-door time (OR = 1.29). Conclusion: The pooled prevalence of mortality after PCI for STEMI patients was 10%, and 17 risk factors were significantly associated with mortality for STEMI patients after PCI.

Objective: To comprehensively compare the effectiveness of cold and heat therapies for delayed onset muscle soreness using network meta-analysis. Methods: Eight Chinese and English databases were searched from date of establishment of the database to 31 May 2021. Cochrane risk-of-bias tool was used to analyse the included randomized controlled trials. Potential papers were screened for eligibility, and data were extracted by 2 independent researchers. Results: A total of 59 studies involving 1,367 patients were eligible for this study. Ten interventions were examined: contrast water therapy, phase change material, the novel modality of cryotherapy, cold-water immersion, hot/warm-water immersion, cold pack, hot pack, ice massage, ultrasound, and passive recovery. Network meta-analysis results showed that: (i) within 24 h after exercise, hot pack was the most effective for pain relief, followed by contrast water therapy; (ii) within 48 h, the ranking was hot pack, followed by the novel modality of cryotherapy; and (iii) over 48 h post-exercise, the effect of the novel modality of cryotherapy ranked first. Conclusion: Due to the limited quality of the included studies, further well-designed research is needed to draw firm conclusions about the effectiveness of cold and heat therapies for delayed onset muscle soreness. Lay abstract The effects of different methods of cold and heat therapy on pain in patients with delayed onset muscle soreness are debated, and there is uncertainty regard­ing the most effective of these therapies. The aim of this study was to evaluate the effects of different cold and heat treatments on pain in patients with delayed onset muscle soreness. Using network meta-analysis and rank­ing, it was found that, within 48 h post-exercise, use of hot-pack was superior to other interventions, whereas, over 48 h post-exercise, cryotherapy was the optimal intervention for pain relief in patients with delayed onset muscle soreness.

ABSTRACT To improve the picking rate and reduce the wrong picking rate of the existing wolfberry harvesting machinery, in this study, the binding force and physical appearance of the immature fruit stalk, mature fruit stalk, flower, and leaf were studied to guide the designing of new-generation wolfberry harvesting machinery and lay theoretical foundation for further studies on biomechanical properties of wolfberry. By preprocessing the experimental data with the Pauta criterion, the distribution range of the binding force and physical appearance of stalk were obtained; the binding force was not influenced by the picking temperature, mass of the fruit, and location of the branch of the fruit. The length-diameter ratio of mature fruit was confirmed by image processing. The constitutive equations for immature fruit stalks and mature fruit stalks were established. The breaking strength of the mature fruit stalk was obviously higher than that of the immature fruit stalk; moreover, the breaking strength of the mature and immature fruit stalk of the four wolfberry varieties differed obviously. The experimental results obtained in this study can provide detailed data for the parameter design of a new-generation wolfberry harvesting machinery.

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.

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.

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.

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-bounding-counting (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.

To solve the problems of poor sawing surface quality, severe blade wear and high power consumption caused by unreasonable working parameters in the process of Caragana korshinskii (C.K.) stumping, this study explored the effects of branch diameter (D), sawing speed (vc), feeding speed (vf), cutting inclination (α), number of circular saw teeth (T) and moisture content (M) on sawing power consumption (P) and sawing surface quality (A) through a single-factor test using a homemade branch sawing bench. Based on the Box–Behnken design principle, a multi-factor test was carried out based on a single-factor test with vc, vf, α and T as influencing factors and with P and A as targets, establishing a regression model. The test results show that the sawing power consumption (P) increases with increasing D, decreases with increasing M, and decreases first and then increases with increasing vc, vf, α and T; the sawing surface quality (A) increases first and then decreases with increasing D, increases with increasing M, and first increases and then decreases with increasing vc, vf, α and T. The optimum combination of parameters for the regression model was obtained with vc of 45.24 m/s, vf of 0.34 m/s, α of 10° and T of 100, which resulted in the P of 177.46 J and A of 85.87%. The errors between the predicted and actual values of P and A are 3.1% and 6%, respectively. The study can provide information to support the development of subsequent C.K. stubble equipment.

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.

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.

Background: In 2009, China launched its new round of health care reform. One of its priorities was to strengthen primary care and establish a family practice system.Objective: This study examined the relationship between use of family practice physician for primary care and the quality of primary care.Methods: Eight health care organizations were sampled from three cities in Guangdong Province, China. One thousand six hundred and forty five patients of age 18 and older who visited one of the study sites as their usual source of primary care were systematically selected and surveyed using Johns Hopkins China-validated Primary Care Assessment Tool (PCAT). Analyses were performed to compare the quality of primary care experienced between patients who contracted for family practice services and those who did not.Results: Total PCAT score as well as scores in first contact-access, continuity, comprehensiveness and coordination domains were higher among patients who contracted with family practice services, compared to those who did not. In addition, patients who sought care through family practice services reported greater satisfaction with their care experience.Conclusion: This study provided evidence that family practice has the potential to provide higher-quality primary care, which may encourage patients to seek family practice physicians for their primary care needs, and help family practitioners better perform gatekeeping functions. [ABSTRACT FROM AUTHOR]