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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.

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.

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.

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.

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.