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The scale of white foreign fibers in bobbin yarn is small, resulting in multiple types of data imbalance in the dataset. These imbalances include a severe imbalance of foreign fiber pixels compared to background pixels and an imbalance in the size target scale. Consequently, conventional semantic segmentation networks struggle to segment these fibers effectively. First, in tackling the scarcity of white foreign fiber instances within bobbin yarn samples, this research utilizes original foreign fiber images to train the DCGAN and generate adequate training samples. Secondly, a multiscale residual U-Net is constructed to extract foreign fiber features from different scales. The network is encouraged to learn semantic features at each scale and each layer of the decoding stage. This overcomes the problem of scale imbalance in the foreign fiber dataset and enhances the model's capability to extract weak semantic information from small targets. Thirdly, a weighted binary cross-entropy loss function is integrated into the network's training phase to rectify the class imbalance and refine segmentation performance. This function adjusts the weighting of foreign fiber pixel data, thereby addressing the disproportionate distribution between foreign fibers and background pixels within the dataset. Finally, the proposed method is experimentally validated using a dataset of white foreign fibers. The experimental results show that the proposed method achieves better results in the critical evaluation metrics, as evidenced by the accuracy of 97.52 %, the MIoU of 95.26 %, the DICE coefficient of 81.29 %, and the F1 Score of 84.92 %. These statistics demonstrate the method's efficacy in achieving high-precision segmentation of white foreign fibers.

Due to the high cost of data acquisition in many specific fields, such as intelligent agriculture, the available data is insufficient for the typical deep learning paradigm to show its superior performance. As an important complement to deep learning, few-shot learning focuses on pattern recognition tasks under the constraint of limited data, which can be used to solve practical problems in many application fields with data scarcity. This survey summarizes the research status, main models and representative achievements of few-shot learning from four aspects: model fine-tuning, meta-learning, metric learning and data enhancement, and especially introduces the few-shot learning-driven typical applications in intelligent agriculture. Finally, the current challenges of few-shot learning and its development trends in intelligent agriculture are prospected.

As a crucial economic crop, the health status of cotton directly impacts farmers' income and the national economy. Therefore, timely and accurate detection and identification of cotton diseases and pests are of significant importance, aiding in reducing the adverse effects of diseases and pests on cotton yield and quality. The existing research struggles to address the balance between resource consumption and detection accuracy in cotton disease and pest detection. Moreover, diseases and pests often occur beneath the canopy, and the orthorectification of drone imagery may result in insufficient feature information and prolonged processing time, among other issues. To address the aforementioned issues, this article proposes a precise detection method for cotton Verticillium wilt based on unmanned aerial vehicle multiangle remote sensing guided by a satellite time-series monitoring model. Specifically, first, combining Sentinel-1 microwave and Sentinel-2 optical time-series images, we constructed a cotton Verticillium wilt monitoring model based on extreme gradient boosting algorithm to identify areas affected by the disease invasion. Subsequently, after identifying the blocks affected by the disease, we collected multispectral remote sensing data captured from multiple angles by unmanned aerial vehicles and compared different combinations of vegetation indices and bands. Finally, we constructed a precise classification model for cotton Verticillium wilt based on support vector machine radial basis function classification method. The experimental results indicate that the joint microwave and optical time-series monitoring model achieved overall accuracy (OA) of 81.73% and Kappa coefficient of 0.63, meeting the monitoring requirements of the first stage. Based on the SVM with RBF and the optimal band combination, the OA value of the comprehensive image captured at −58° angle reached 96.74%, with Kappa coefficient of 0.93, meeting the requirements of precise classification detection in the second stage.

In this article, a novel deep learning framework, fuzzy EfficientDet, is proposed to address the challenge of accurately detecting larch infested by Coleophora laricella pests in UAV imagery, where the key innovation is the incorporation of the fuzzy spatial attention mechanism (FSAM), which can effectively deal with the problem of model uncertainty due to the complexity of environmental transformations and image features. First, this study designs and implements a global–local squeeze-and-excitation module, which profoundly integrates global and local feature information, realizes the dynamic adaptation of the importance of feature channels in the EfficientNet, and thus improves the overall feature expression efficiency of the network. Second, this study constructed a dense Bi-FPN architecture, which adds a dense connection structure to the original Bi-FPN to enhance the modeling accuracy for small targets and long-range spatial dependencies. Finally, this study develops the FSAM, which can effectively mitigate the unstable performance of the EfficientDet in the face of image feature fluctuations triggered by changes in lighting conditions and seasonal effects. Experiments demonstrate that the proposed fuzzy EfficientDet model shows superior performance compared to the traditional SSD, Faster R-CNN, YOLO V5, and the unimproved EfficientDet target detection method on the Swedish Forest Agency (2021) dataset, with its mAP as high as 94.29%. This result demonstrates that fuzzy EfficientDet provides an efficient and reliable solution when dealing with the task of target detection in UAV images, especially in dealing with environmental uncertainty and complex feature extraction.

Abstract Wind has been admitted as one of the most promising renewable energy resources in multinational regionalization policies. However, the energy conversion and utilization are challenging due to the technique reliability and cost issues. Hydraulic wind turbine (HWT) may solve the above problems. HWT is taken as a research object, and the maximum power point tracking (MPPT) control strategy is proposed collaborating with active disturbance rejection control (ADRC) and linear quadratic regulator (LQR) control methods, to solve multiplicative nonlinearity problems in the plant models and the influence of external disturbance on control performance in the MPPT control process. A nonlinear simulation model is built to explain the main findings from the experiments and obtain a better understanding of the effect of time‐varying system parameters and random fluctuation in wind speed. The collaborative control algorithm is experimentally verified on a 24‐kW HWT semi‐physical test platform that results in a promising energy conversion rate, plus the hydraulic parameters can satisfy the demand, accordingly. Ultimately, the potential challenges of implementing this technique in a smart wind energy conversion system are discussed to give a further design guidance, either theoretically or practically.

Identifying drivable areas between orchard rows is crucial for intelligent agricultural equipment. However, challenges remain in this field’s accuracy, real-time performance, and generalization of deep learning models. This study proposed the SwinLabNet model in the context of jujube orchards, an innovative network model that utilized a lightweight CNN-transformer hybrid architecture. This approach optimized feature extraction and contextual information capture, effectively addressing long-range dependencies, global information acquisition, and detailed boundary processing. After training on the jujube orchard dataset, the SwinLabNet model demonstrated significant performance advantages: training accuracy reached 97.24%, the mean Intersection over Union (IoU) was 95.73%, and the recall rate was as high as 98.36%. Furthermore, the model performed exceptionally well on vegetable datasets, highlighting its generalization capability across different crop environments. This study successfully applied the SwinLabNet model in orchard environments, providing essential support for developing intelligent agricultural equipment, advancing the identification of drivable areas between rows, and laying a solid foundation for promoting and applying intelligent agrarian technologies.

With the development of agriculture, the complexity and dynamism of orchard environments pose challenges to the perception and positioning of inter-row environments for agricultural vehicles. This paper proposes a method for extracting navigation lines and measuring pedestrian obstacles. The improved YOLOv5 algorithm is used to detect tree trunks between left and right rows in orchards. The experimental results show that the average angle deviation of the extracted navigation lines was less than 5 degrees, verifying its accuracy. Due to the variable posture of pedestrians and ineffective camera depth, a distance measurement algorithm based on a four-zone depth comparison is proposed for pedestrian obstacle distance measurement. Experimental results showed that within a range of 6 m, the average relative error of distance measurement did not exceed 1%, and within a range of 9 m, the maximum relative error was 2.03%. The average distance measurement time was 30 ms, which could accurately and quickly achieve pedestrian distance measurement in orchard environments. On the publicly available TUM RGB-D dynamic dataset, YOLOD-SLAM2 significantly reduced the RMSE index of absolute trajectory error compared to the ORB-SLAM2 algorithm, which was less than 0.05 m/s. In actual orchard environments, YOLOD-SLAM2 had a higher degree of agreement between the estimated trajectory and the true trajectory when the vehicle was traveling in straight and circular directions. The RMSE index of the absolute trajectory error was less than 0.03 m/s, and the average tracking time was 47 ms, indicating that the YOLOD-SLAM2 algorithm proposed in this paper could meet the accuracy and real-time requirements of agricultural vehicle positioning in orchard environments.

Vapor bubbles in cryogenic fluids may collapse violently under subcooled and pressurized conditions. Despite important implications for engineering applications such as cavitation erosion in liquid propellant rocket engines, these intense phenomena are still largely unexplored. In this paper, we systematically investigate the ambient conditions leading to the occurrence of violent collapses in liquid nitrogen and analyze their thermodynamic characteristics. Using Brenner’s time ratio χ, the regime of violent collapse is identified in the ambient pressure–temperature parameter space. Complete numerical simulations further refine the prediction and illustrate two classes of collapses. At 1 10 does the collapse show more inertial features. A mechanism analysis pinpoints a critical time when the surrounding liquid enters supercritical state. The ultimate collapse intensity is shown to be closely associated with the dynamics at this moment. Our study provides a fresh perspective to the treatment of cavitation in cryogenic fluids. The findings can be instrumental in engineering design to mitigate adverse effects arising from intense cavitational activities.

Acoustic waves offer a non-destructive, safe, and cost-effective means of monitoring the environment, with a potential application in soil water content monitoring. However, extracting soil water information from acoustic signals is still challenging. To tackle this issue, we have developed a low-frequency swept acoustic signal detection device and system. We conducted soil penetration testing using low-frequency swept acoustic signals. The swept-frequency acoustic signals passing through the soil were transformed into time–frequency spectrogram. Using the Swin-Transformer model, we established a regression model between the time–frequency spectrogram of the swept frequencies and the soil water content. Predictions were made both on a laboratory test dataset and through field trials using the calibrated model. The results indicate that the RMSE, MAE, and R2 values between the observed and the model's outputs of water content (%) for the test laboratory dataset are 0.191, 0.081, and 0.999, respectively, using the Swin-Transformer model. In the case of the field trials, the RMSE, MAE, and R2 values between the predicted and observed values are 6.715 %, 1.829 %, and 0.711, respectively. These studies demonstrate that this method is highly effective in predicting soil water content, with the best results achieved at a resolution of 20 PPI (Pixels Per Inch) and within the frequency range of 260–360 Hz. It provides an efficient approach for acoustic soil water content detection, effectively resolves the difficulty in building models caused by the single-parameter limitation in traditional acoustic model.

Excessive synthesis of triglycerides and cholesterol accelerates the progression of hepatic steatosis in metabolic-associated fatty liver disease (MAFLD). However, the precise mechanism by which 6-gingerol mitigates hepatic steatosis in MAFLD model mice has yet to be fully understood. The present study observed that 6-gingerol administration exhibited significant protective effects against obesity, insulin resistance, and hepatic steatosis in mice subjected to a high-fat diet (HFD), and mitigated lipid accumulation in HepG2 cells treated with palmitate (PA). Following the hepatic lipidomic analysis, we confirmed that the AMPK-SREBPs signaling pathway as the underlying molecular mechanism by which 6-gingerol inhibited triglyceride and cholesterol biosynthesis, both in vivo and in vitro, through Western blot and immunofluorescence assay. Additionally, the application of an AMPK agonist/inhibitor further validated that 6-gingerol promoted AMPK activation by increasing the phosphorylation level of AMPK in vitro. Notably, the inhibitory effect of 6-gingerol on cholesterol biosynthesis, rather than triglyceride biosynthesis, was significantly diminished after silencing SREBP2 using a lentiviral plasmid shRNA in HepG2 cells. Our study demonstrates that 6-gingerol mitigates hepatic triglyceride and cholesterol biosynthesis to alleviate hepatic steatosis by activating the AMPK-SREBPs signaling pathway, indicating that 6-gingerol may be a potential candidate in the therapy of MAFLD.

The accurate recognition of tree trunks is a prerequisite for precision orchard yield estimation. Facing the practical problems of complex orchard environment and large data flow, the existing object detection schemes suffer from key issues such as poor data quality, low timeliness and accuracy, and weak generalization ability. In this paper, an improved YOLOv8 is designed on the basis of data flow screening and enhancement for lightweight jujube tree trunk accurate detection. Firstly, the key frame extraction algorithm was proposed and utilized to efficiently screen the effective data. Secondly, the CLAHE image data enhancement method was proposed and used to enhance the data quality. Finally, the backbone of the YOLOv8 model was replaced with a GhostNetv2 structure for lightweight transformation, also introducing the improved CA_H attention mechanism. Extensive comparison and ablation results show that the average precision of the quality-enhanced dataset over that of the original dataset increases from 81.2% to 90.1%, and the YOLOv8s-GhostNetv2-CA_H model proposed in this paper reduces the model size by 19.5% compared to that of the YOLOv8s base model, with precision increasing by 2.4% to 92.3%, recall increasing by 1.4%, mAP@0.5 increasing by 1.8%, and FPS being 17.1% faster.

Driven by edge computing, how to efficiently deploy the meta-learner LSTM in the resource constrained FPGA terminal equipment has become a big problem. This paper proposes a compression strategy based on LSTM meta-learning model, which combined the structured pruning of the weight matrix and the mixed precision quantization. The weight matrix was pruned into a sparse matrix, then the weight was quantified to reduce resource consumption. Finally, a LSTM meta-learning accelerator was designed based on the idea of hardware–software cooperation. Experiments show that compared with mainstream hardware platforms, the proposed accelerator achieves at least 50.14 times increase in energy efficiency.

High-quality agricultural multi-view stereo reconstruction technology is the key to precision and informatization in agriculture. Multi-view stereo reconstruction methods are an important part of 3D vision technology. In the multi-view stereo 3D reconstruction method based on deep learning, the effect of feature extraction directly affects the accuracy of reconstruction. Aiming at the actual problems in orchard fruit tree reconstruction, this paper designs an improved multi-view stereo structure based on the combination of remote sensing and artificial intelligence to realize the accurate reconstruction of jujube tree trunks. Firstly, an automatic key frame extraction method is proposed for the DSST target tracking algorithm to quickly recognize and extract high-quality data. Secondly, a composite U-Net feature extraction network is designed to enhance the reconstruction accuracy, while the DRE-Net feature extraction enhancement network improved by the parallel self-attention mechanism enhances the reconstruction completeness. Comparison tests show different levels of improvement on the Technical University of Denmark (DTU) dataset compared to other deep learning-based methods. Ablation test on the self-constructed dataset, the MVSNet + Co U-Net + DRE-Net_SA method proposed in this paper improves 20.4% in Accuracy, 12.8% in Completion, and 16.8% in Overall compared to the base model, which verifies the real effectiveness of the scheme.

Few flower buds in a high-yield year are the main factors restricting the yield of Camellia oleifera in the next year. However, there are no relevant reports on the regulation mechanism of flower bud formation. In this study, hormones, mRNAs, and miRNAs were tested during flower bud formation in MY3 (“Min Yu 3,” with stable yield in different years) and QY2 (“Qian Yu 2,” with less flower bud formation in a high-yield year) cultivars. The results showed that except for IAA, the hormone contents of GA3, ABA, tZ, JA, and SA in the buds were higher than those in the fruit, and the contents of all hormones in the buds were higher than those in the adjacent tissues. This excluded the effect of hormones produced from the fruit on flower bud formation. The difference in hormones showed that 21–30 April was the critical period for flower bud formation in C. oleifera; the JA content in MY3 was higher than that in QY2, but a lower concentration of GA3 contributed to the formation of the C. oleifera flower bud. JA and GA3 might have different effects on flower bud formation. Comprehensive analysis of the RNA-seq data showed that differentially expressed genes were notably enriched in hormone signal transduction and the circadian system. Flower bud formation in MY3 was induced through the plant hormone receptor TIR1 (transport inhibitor response 1) of the IAA signaling pathway, the miR535-GID1c module of the GA signaling pathway, and the miR395-JAZ module of the JA signaling pathway. In addition, the expression of core clock components GI (GIGANTEA) and CO (CONSTANS) in MY3 increased 2.3-fold and 1.8-fold over that in QY2, respectively, indicating that the circadian system also played a role in promoting flower bud formation in MY3. Finally, the hormone signaling pathway and circadian system transmitted flowering signals to the floral meristem characteristic genes LFY (LEAFY) and AP1 (APETALA 1) via FT (FLOWERING LOCUS T) and SOC1 (SUPPRESSOR OF OVEREXPRESSION OF CO 1) to regulate flower bud formation. These data will provide the basis for understanding the mechanism of flower bud alternate formation and formulating high yield regulation measures for C. oleifera.

Camellia oleifera Abel is a highly valued woody edible oil tree, which is endemic to China. It has great economic value because C. oleifera seed oil contains a high proportion of polyunsaturated fatty acids. C. oleifera anthracnose caused by Colletotrichum fructicola, poses a serious threat to C. oleifera growth and yield and causes the benefit of the C. oleifera industry to suffer directly. The WRKY transcription factor family members have been widely characterized as vital regulators in plant response to pathogen infection. Until now, the number, type and biological function of C. oleifera WRKY genes are remains unknown. Here, we identified 90 C. oleifera WRKY members, which were distributed across 15 chromosomes. C. oleifera WRKY gene expansion was mainly attributed to segmental duplication. We performed transcriptomic analyses to verify the expression patterns of CoWRKYs between anthracnose-resistant and -susceptible cultivars of C. oleifera. These results demonstrated that multiple candidate CoWRKYs can be induced by anthracnose and provide useful clues for their functional studies. CoWRKY78, an anthracnose-induced WRKY gene, was isolated from C. oleifera. It was significantly down-regulated in anthracnose-resistant cultivars. Overexpression of CoWRKY78 in tobacco markedly reduced resistance to anthracnose than WT plants, as evidenced by more cell death, higher malonaldehyde content and reactive oxygen species (ROS), but lower activities of superoxide dismutase (SOD), peroxidase (POD), as well as phenylalanine ammonia-lyase (PAL). Furthermore, the expression of multiple stress-related genes, which are associated with ROS-homeostasis (NtSOD and NtPOD), pathogen challenge (NtPAL), and pathogen defense (NtPR1, NtNPR1, and NtPDF1.2) were altered in the CoWRKY78-overexpressing plants. These findings increase our understanding of the CoWRKY genes and lay the foundation for the exploration of anthracnose resistance mechanisms and expedite the breeding of anthracnose-resistant C. oleifera cultivars.

Sheep aggression detection is crucial for maintaining the welfare of a large-scale sheep breeding environment. Currently, animal aggression is predominantly detected using image and video detection methods. However, there is a lack of lightweight network models available for detecting aggressive behavior among groups of sheep. Therefore, this paper proposes a model for image detection of aggression behavior in group sheep. The proposed model utilizes the GhostNet network as its feature extraction network, incorporating the PWConv and Channel Shuffle operations into the GhostConv module. These additional modules improve the exchange of information between different feature maps. An ablation experiment was conducted to compare the detection effectiveness of the two modules in different positions. For increasing the amount of information in feature maps of the GhostBottleneck module, we applied the Inverted-GhostBottleneck module, which introduces inverted residual structure based on GhostBottleneck. The improved GhostNet lightweight feature extraction network achieves 94.7% Precision and 90.7% Recall, and its model size is only 62.7% of YOLOv5. Our improved model surpasses the original model in performance. Furthermore, it addresses the limitation of the video detection model, which was unable to accurately locate aggressive sheep. In real-time, our improved model successfully detects aggressive behavior among group sheep.

In response to the issues of high honeysuckle-picking costs and low efficiency in honeysuckle picking, this study has devised a comb-brush-type picking device, considering the unique characteristics of honeysuckle plants. We elucidated the device’s structure and operational principles and designed critical components within the picking mechanism. Subsequently, through theoretical analysis, we identified the primary factors influencing the device’s operational performance. We then used the honeysuckle picking rates, honeysuckle breakage rates, and impurity rates as assessment metrics. Utilizing a one-factor test, we determined the permissible ranges for each factor. Employing the response surface methodology, we analyzed the interactions among these factors and conducted model parameter optimization. This optimization identified the optimal parameter combination: a forward speed of 3.99 km/h, a driving shaft speed of 316.53 rpm, and a picking teeth length of 70 mm. Finally, we performed verification tests using these optimized parameters. The results demonstrated that the maximum relative error between test verification values and model-optimized predictions was 4.86%. This outcome confirms that the comb-brush-type honeysuckle-picking device can meet the operational requirements of mechanized harvesting and offers valuable insights for developing harvesting devices for vine plants.

The improved delayed detached eddy simulation method with shear stress transport model was used to analyze the evolution of vortex structure, velocity and pressure fields of swirling jet. The influence of nozzle pressure drop on vortex structure development and turbulence pulsation was investigated. The development of vortex structure could be divided into three stages: Kelvin-Helmholtz (K-H) instability, transition stage and swirling flow instability. Swirling flow could significantly enhance radial turbulence pulsation and increase diffusion angle. At the downstream of the jet flow, turbulence pulsation dissipation was the main reason for jet velocity attenuation. With the increase of pressure drop, the jet velocity, pulsation amplitude and the symmetry of velocity distribution increased correspondingly. Meanwhile the pressure pulsation along with the axis and vortex transport intensity also increased significantly. When the jet distance exceeded about 9 times the dimensionless jet distance, the impact distance of swirling jet could not be improved effectively by increasing the pressure drop. However, it could effectively increase the swirl intensity and jet diffusion angle. The swirling jet is more suitable for radial horizontal drilling with large hole size, coalbed methane horizontal well cavity completion and roadway drilling and pressure relief, etc.

Acanthopanax senticosus extract (ASE), a dietary supplement with antifatigue, neuroprotective, and immunomodulatory properties, has been widely used due to its high polyphenol content. Our previous study showed that ASE could be used to treat Parkinson’s disease (PD) as it contains multiple monoamine oxidase B inhibitors prescribed in early PD. However, its mechanism remains ambiguous. In this study, we investigated the protective effects of ASE on MPTP-induced PD in mice and explored the underlying mechanisms of action. We found that the administration of ASE significantly improved motor coordination in mice with MPTP-induced PD. As shown by quantitative proteomic analysis, 128 proteins’ expression significantly changed in response to ASE administration, most of which were involved with Fcγ receptor-mediated phagocytosis in macrophages and monocytes signaling pathway, PI3K/AKT signaling pathway, and insulin receptor signaling pathway. Furthermore, the network analysis results showed that ASE modulates protein networks involved in regulating cellular assembly, lipid metabolism, and morphogenesis, all of which have implications for treating PD. Overall, ASE served as a potential therapeutic because it regulated multiple targets to improve motor deficits, which could lay the strong foundation for developing anti-PD dietary supplements.

Underground gas storage (UGS) is a crucial method for mitigating seasonal fluctuations in natural gas consumption. However, in China, UGS is primarily achieved through the conversion of abandoned gas reservoirs with limited storage capacity. Radial jet drilling (RJD) is an effective technology for the secondary development of depleted reservoirs. The multiorifice nozzle is a critical component that can efficiently break rock and create radial holes to increase gas production. In this study, we investigate the impact of nozzle structure on energy conversion efficiency through numerical simulations and experiments. Additionally, we design a swirling multiorifice nozzle and verify its effectiveness in field applications. Our findings indicate that the nozzle pressure drop and vorticity are primarily generated at the acute angle of the orifices. The number of forward orifices is directly proportional to energy loss, while the discharge coefficient and hydraulic performance initially increase and then decrease. Swirling multiorifice nozzle have fewer backward orifices, so they have less energy loss and a larger discharge coefficient. It has achieved better rock-breaking results in field applications. In conclusion, this study provides theoretical guidance and technical support for the secondary development of gas storage.

Abstract Background At present, the residual film pollution in cotton fields is crucial. The commonly used recycling method is the manual-driven recycling machine, which is heavy and time-consuming. The development of a visual navigation system for the recovery of residual film is conducive, in order to improve the work efficiency. The key technology in the visual navigation system is the cotton stubble detection. A successful cotton stubble detection can ensure the stability and reliability of the visual navigation system. Methods Firstly, it extracts the three types of texture features of GLCM, GLRLM and LBP, from the three types of images of stubbles, residual films and broken leaves between rows. It then builds three classifiers: Random Forest, Back Propagation Neural Network and Support Vector Machine in order to classify the sample images. Finally, the possibility of improving the classification accuracy using the texture features extracted from the wavelet decomposition coefficients, is discussed. Results The experiment proves that the GLCM texture feature of the original image has the best performance under the Back Propagation Neural Network classifier. As for the different wavelet bases, the vertical coefficient texture feature of coif3 wavelet decomposition, combined with the texture feature of the original image, is the feature having the best classification effect. Compared with the original image texture features, the classification accuracy is increased by 3.8%, the sensitivity is increased by 4.8%, and the specificity is increased by 1.2%. Conclusions The algorithm can complete the task of stubble detection in different locations, different periods and abnormal driving conditions, which shows that the wavelet coefficient texture feature combined with the original image texture feature is a useful fusion feature for detecting stubble and can provide a reference for different crop stubble detection.

Abstract Background Due to the high cost of data collection for magnetization detection of media, the sample size is limited, it is not suitable to use deep learning method to predict its change trend. The prediction of physical and chemical properties of magnetized water and fertilizer (PCPMWF) by meta-learning can help to explore the effects of magnetized water and fertilizer irrigation on crops. Method In this article, we propose a meta-learning optimization model based on the meta-learner LSTM in the field of regression prediction of PCPMWF. In meta-learning, LSTM is used to replace MAML’s gradient descent optimizer for regression tasks, enables the meta-learner to learn the update rules of the LSTM, and apply it to update the parameters of the model. The proposed method is compared with the experimental results of MAML and LSTM to verify the feasibility and correctness. Results The average absolute percentage error of the meta-learning optimization model of meta-learner LSTM is reduced by 0.37% compared with the MAML model, and by 4.16% compared with the LSTM model. The loss value of the meta-learning optimization model in the iterative process drops the fastest and steadily compared to the MAML model and the LSTM model. In cross-domain experiments, the average accuracy of the meta-learning optimized model can still reach 0.833. Conclusions In the case of few sample, the proposed model is superior to the traditional LSTM model and the basic MAML model. And in the training of cross-domain datasets, this model performs best.

In order to solve the problems of low efficiency and subjectivity of manual observation in the process of group-sheep-aggression detection, we propose a video streaming-based model for detecting aggressive behavior in group sheep. In the experiment, we collected videos of the sheep’s daily routine and videos of the aggressive behavior of sheep in the sheep pen. Using the open-source software LabelImg, we labeled the data with bounding boxes. Firstly, the YOLOv5 detects all sheep in each frame of the video and outputs the coordinates information. Secondly, we sort the sheep’s coordinates using a sheep tracking heuristic proposed in this paper. Finally, the sorted data are fed into an LSTM framework to predict the occurrence of aggression. To optimize the model’s parameters, we analyze the confidence, batch size and skipping frame. The best-performing model from our experiments has 93.38% Precision and 91.86% Recall. Additionally, we compare our video streaming-based model with image-based models for detecting aggression in group sheep. In sheep aggression, the video stream detection model can solve the false detection phenomenon caused by head impact feature occlusion of aggressive sheep in the image detection model.

The non-destructive detection of soluble solids content (SSC) in fruit by near-infrared (NIR) spectroscopy has a good application prospect. At present, the application of portable devices is more common. The construction of an accurate and stable prediction model is the key for the successful application of the device. In this study, the visible and near-infrared (Vis/NIR) spectra of Korla fragrant pears were collected by a commercial portable measurement device. Different pretreatment methods were used to preprocess the raw spectra, and the partial least squares (PLS) model was constructed to predict the SSC of pears for the determination of the appropriate pretreatment method. Subsequently, PLS and least squares support vector machine (LS-SVM) models were constructed based on the preprocessed full spectra. A new combination (BOSS-SPA) of bootstrapping soft shrinkage (BOSS) and successive projections algorithm (SPA) was used for variable selection. For comparison, single BOSS and SPA were also used for variable selection. Finally, three types of models, namely, PLS, LS-SVM, and multiple linear regression (MLR), were constructed based on different input variables. Comparing the prediction performance of all models, it showed that the BOSS-SPA-PLS model based on 17 variables obtained the best SSC assessment ability with rp of 0.94 and RMSEP of 0.27 °Brix. The overall result indicated that portable measurement with Vis/NIR spectroscopy can be used for the detection of SSC in Korla fragrant pears.

The magnetized water and fertilizer liquid can produce biological effect of magnetic field on crops, but its residual magnetic field strength is difficult to be expressed quantitatively in real time, and accurate prediction of it is helpful to define the scope of action of liquid magnetization. In this paper, a prediction model for liquid magnetization series data is presented. It consists of conditional generative adversarial network (CGAN) and projected gradient descent (PGD) algorithm. First, the real training dataset is used as the input of PGD attack algorithm to generate antagonistic samples. These samples are added to the training of CGAN as true samples for data enhancement. Second, the training dataset is used as both the generator and discriminator input of CGAN to constrain the model, capture distribution of the real data. Third, a network model with three layers of CNN is built and trained inside CGAN. The input model is constructed by using the structure of two-dimensional convolution model to predict data. Lastly, the performance of the model is evaluated by the error between the final generated predicted value and the real value, and the model is compared with other prediction models. The experimental results show that, with limited data samples, by combining PGD attack with CGAN, the distribution of the real data can be more accurately captured and the data can be generated to meet the actual needs.

The stubble after cotton harvesting was used as the detection object to achieve the visual navigation operation for residual film recovery after autumn. An improved (You Only Look Once v3) YOLOv3-based target detection algorithm was proposed to detect cotton stubble. First, field images of residual film recycling were collected. Considering the inconsistency between stubble size and shape, a segmented labeling data set of stubble is proposed. Secondly, the Darknet-53 backbone of the original YOLOv3 network is improved to accommodate tiny targets. Next, the prediction anchor box of the improved detection backbone is clustered using K-means++, and the size of the prediction anchor box suitable for improved YOLOv3 is determined. Finally, for the false detection points after detection, a mean value denoising method is used to remove the false detection points. Feature points are extracted from the denoised stubble, and the candidate points are fitted by the least square method to obtain the navigation line. The optimal model with a mean average precision (mAP) of 0.925 is selected for testing at the test stage. The test results show that the algorithm in this article can detect the stubble of residual film recovery images at different locations, different time periods, and different camera depression angles without misdetection. The detection speed of a single image is 98.6 ms. Based on an improvement over YOLOv3, the improved model has a significantly higher detection rate in different scenarios than YOLOv3. This can provide practical technical support for the visual navigation of residual film recovery.

In large-scale meat sheep farming, high CO2 concentrations in sheep sheds can lead to stress and harm the healthy growth of meat sheep, so a timely and accurate understanding of the trend of CO2 concentration and early regulation are essential to ensure the environmental safety of sheep sheds and the welfare of meat sheep. In order to accurately understand and regulate CO2 concentrations in sheep barns, we propose a prediction method based on the RF-PSO-LSTM model. The approach we propose has four main parts. First, to address the problems of data packet loss, distortion, singular values, and differences in the magnitude of the ambient air quality data collected from sheep sheds, we performed data preprocessing using mean smoothing, linear interpolation, and data normalization. Second, to address the problems of many types of ambient air quality parameters in sheep barns and possible redundancy or overlapping information, we used a random forests algorithm (RF) to screen and rank the features affecting CO2 mass concentration and selected the top four features (light intensity, air relative humidity, air temperature, and PM2.5 mass concentration) as the input of the model to eliminate redundant information among the variables. Then, to address the problem of manually debugging the hyperparameters of the long short-term memory model (LSTM), which is time consuming and labor intensive, as well as potentially subjective, we used a particle swarm optimization (PSO) algorithm to obtain the optimal combination of parameters, avoiding the disadvantages of selecting hyperparameters based on subjective experience. Finally, we trained the LSTM model using the optimized parameters obtained by the PSO algorithm to obtain the proposed model in this paper. The experimental results show that our proposed model has a root mean square error (RMSE) of 75.422 μg·m−3, a mean absolute error (MAE) of 51.839 μg·m−3, and a coefficient of determination (R2) of 0.992. The model prediction curve is close to the real curve and has a good prediction effect, which can be useful for the accurate prediction and regulation of CO2 concentration in sheep barns in large-scale meat sheep farming.

The complexity of a supply chain makes product safety or quality issues extremely difficult to track, especially for the basic agricultural food supply chains of people's daily diets. The existing agricultural food supply chains present several major problems, such as numerous participants, inconvenient communication caused by long supply chain cycles, data distrust between participants and the centralized system. The emergence of blockchain technology effectively solves the pain-point problem existing in the traceability system of agricultural food supply chains. This paper proposes a framework based on the consortium and smart contracts to track and trace the workflow of agricultural food supply chains, implement traceability and shareability of supply chains, and break down the information islands between enterprises as much as possible to eliminate the need for the central institutions and agencies and improve the integrity of the transaction records, reliability and security. At the same time, farmers record details of the environment and crop growth data in the InterPlanetary File System (IPFS) and store file IPFS hashes in smart contracts, which not only increases data security but also alleviates the blockchain storage explosion problem. This framework has been applied in Shanwei Lvfengyuan Modern Agricultural Development Co., Ltd. Although there are still many defects, the framework has successfully realized functions such as disintermediation and tracing of agricultural product information through QR codes. Thus, the framework proposed in this paper is of great significance and reference value for enterprises to ensure product quality and safety traceability.

It is commonly known that Global Positioning System (GPS) can alleviate travelling difficulties of automobile drivers, and generally we hold the view that it reduces the driver's stress when they are in unfamiliar road conditions. In this research, an in-laboratory experiment and an in-car experiment are conducted to find out whether GPS instructions can reduce or may induce additional mental stress of drivers. Electrocardiography (ECG) signals are collected in the experiments and the extracted heart rate variability (HRV) features are used for analysis. Three binary classifiers, specifically Support Vector Machine, k-Nearest Neighbor (k-NN) and Random Forest, are trained based on the data collected in the in-laboratory experiment, where the stress state is elicited by the Stroop color word Test. The k-NN classifier outperforms the other two classifiers, and thus is applied to the data collected in the in-car experiment, to identify drivers' stress state under different driving events, such as waiting for traffic lights, turning, under GPS instructions, and traffic conditions like overtaking, or changing lanes. During each event, whether the driver is in stress or relaxed state for each time instant is predicted based on the trained classifier. The percentages of time that the driver is in stress state for each type of events are computed. It shows that GPS instructions cause the second largest time-percentage of stress state, lower than that caused by the turning event, but higher than that caused by the events of waiting for traffic lights and other traffic conditions.

Yellowhorn (Xanthoceras sorbifolium) seeds can have as high as 67% oil content and are especially rich in oleic acid, linoleic acid, and nervonic acid. Exploration of the lipid biosynthesis regulatory network is essential for increasing the yellowhorn oil content. Long non-coding RNAs (lncRNAs) play important roles in various plant biological processes; however, there is no report on the identification of lncRNAs involved in yellowhorn seed development and lipid biosynthesis affecting oil production. We performed whole transcriptome sequencing of yellowhorn seeds at four developmental stages and identified 16,920 putative lncRNAs. Among them, 325 lncRNAs were revealed to trans-regulate 58 key genes in fatty acid (FA) and triacylglycerol (TAG) biosynthesis pathways. Of these, ECR-2–LNC_009778 was found to be involved in nervonic acid biosynthesis and DGAT-1–LNC_009778 was beneficial to TAG accumulation. sRNA-seq was performed, and 55 microRNAs (miRNAs) were found to target 26 genes involved in FA and TAG biosynthesis; miR396a-4 targets FAD2, affecting linoleic acid biosynthesis, and miR156f-5p targets PDAT-2, contributing to TAG accumulation. Interestingly, 30 lncRNA–miRNA–gene modules involved in FA and TAG biosynthesis were identified, in which the KCS11-1–miR156g-2–LNC_000849 module was found to participate in nervonic acid synthesis, and the DGAT-2–miR172j–LNC_005874 module was assumed to contribute to the accumulation of TAG. Our results constitute the first comprehensive identification of lncRNAs in developing seeds of yellowhorn and serve as a new theoretical reference for improving oil content in the future.

There are some problems with estrus detection in ewes in large-scale meat sheep farming: mainly, the manual detection method is labor-intensive and the contact sensor detection method causes stress reactions in ewes. To solve the abovementioned problems, we proposed a multi-objective detection layer neural network-based method for ewe estrus crawling behavior recognition. The approach we proposed has four main parts. Firstly, to address the problem of mismatch between our constructed ewe estrus dataset and the YOLO v3 anchor box size, we propose to obtain a new anchor box size by clustering the ewe estrus dataset using the K-means++ algorithm. Secondly, to address the problem of low model recognition precision caused by small imaging of distant ewes in the dataset, we added a 104 × 104 target detection layer, making the total target detection layer reach four layers, strengthening the model’s ability to learn shallow information and improving the model’s ability to detect small targets. Then, we added residual units to the residual structure of the model, so that the deep feature information of the model is not easily lost and further fused with the shallow feature information to speed up the training of the model. Finally, we maintain the aspect ratio of the images in the data-loading module of the model to reduce the distortion of the image information and increase the precision of the model. The experimental results show that our proposed model has 98.56% recognition precision, while recall was 98.04%, F1 value was 98%, mAP was 99.78%, FPS was 41 f/s, and model size was 276 M, which can meet the accurate and real-time recognition of ewe estrus behavior in large-scale meat sheep farming.

In view of the problem whereby the floor-standing jujube pickup device designed by the research group has a large resistance when the comb teeth (CT) enter the soil, the strip brush can easily bend, and the operation effect is poor. In this paper, the structural parameters of the CT and bar brush in the mechanical floor date-picking device are determined by theoretical analysis. EDEM software was used to establish the discrete element simulation model of the floor-standing jujube soil–jujube pickup device. According to the simulation results, it is determined that the angle of the CT entering the soil (ACT) can change freely within 30–33° and meet the operation requirements. Through the single-factor test on the pickup rate of jujube and the soil hilling quantity, it is determined that the value range of the CT working speed (CWS) is 0.2–0.4 m/s, and the value range of the CT entering the soil (DCS) is 5–45 mm. Simulation and field verification tests were carried out on the determined operation parameter range. It was found that when the CWS was 0.2 m/s and the DCS was 5 mm, the pickup rates in the simulation test and the field verification test were 84.17% and 91.23%, respectively, and the relative error was the largest but not more than 8%. The result shows that the operation parameters and range determined by the discrete element method were reliable. This study is expected to provide the working parameter basis for the subsequent design of a floor-standing jujube pickup device.

High precision navigation along specific paths is required for plant protection operations in dwarf and densely planted jujube orchards in southern Xinjiang. This study proposes a robotic path planning and navigation method for dense planting of red jujube orchards based on the improved A* and dynamic window approach (DWA) algorithms using Laser Radar to build maps. First, kinematic and physical robot simulation models are established; a map of the densely planted jujube orchard is constructed using Laser Radar. The robot’s position on the constructed map is described using an adaptive Monte Carlo positioning algorithm. Second, a combination of the improved A* and DWA algorithms is used to implement global and real-time local path planning; an evaluation function is used for path optimisation. The proposed path planning algorithm can accurately determine the robot’s navigation paths, with the average error U, average linear path displacement error, and L-shaped navigation being 2.69, 2.47, and 2.68 cm, respectively. A comparison experiment is set up in the specific path navigation section. The experimental results show that the improved fusion algorithm reduces the average navigation positioning deviation by 0.91cm and 0.54 cm when navigating L and U-shaped specific paths. The improved fusion algorithm is superior to the traditional fusion algorithm in navigation accuracy and navigation stability. It can improve the navigation accuracy of the dense planting jujube garden and provide a reference method for the navigation of the plant protection operation in the densely planted jujube orchards.

Abstract Backgrounds Inflammation is recognized as the key pathological mechanism of type 2 diabetes. The hypoglyceamic effects of berberine (BBR) are related to the inhibition of the inflammatory response, but the mechanism is not completely clear. Methods The inflammatory polarization of Raw264.7 cells and primary peritoneal macrophages were induced by LPS, and then effects and underlying mechanisms of BBR were explored. An inflammatory model was established by LPS treatment at different concentrations for different treatment time. An ELISA assay was used to detect the secretions of TNF-α. RT-PCR was applied to detect M1 inflammatory factors. The F4/80+ ratio and CD11c+ ratio of primary peritoneal macrophages were determined by flow cytometry. The expressions of p-AMPK and TLR4 were detected by Western blot. The cytoplasmic and nuclear distributions of NFκB p65 were observed by confocal microscopy. The binding of TLR4 to MyD88 was tested by CoIP, and the affinity of BBR for TLR4 was assessed by molecular docking. Results Upon exposure to LPS, the secretion of TNF-α and transcription of inflammatory factors in macrophages increased, cell morphology changed and protrusions appeared gradually, the proportion of F4/80+CD11c+ M1 macrophages increased, and the nuclear distribution of NFκB p65 increased. BBR pretreatment partially inhibited the changes mentioned above. However, the expression of TLR4 and p-AMPK did not change significantly after LPS intervention for 3 h. Meanwhile, CoIP showed that the interaction between TLR4 and MyD88 increased, and BBR inhibited the binding. Molecular docking suggested that BBR might interact with TLR4. Conclusions Inflammatory changes were induced in macrophages after LPS stimulation for 3 h, and BBR pretreatment inhibited inflammatory polarization. BBR might interact with TLR4 and disturb TLR4/MyD88/NFκB signalling pathway, and it might be the mechanism by which BBR attenuated inflammation in the early phase.

Abstract Background Non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease and is characterized by excessive hepatic lipid accumulation. Many studies have suggested that lipid overload is the key initial factor that contributes to hepatic steatosis. Our previous study indicated that diosgenin (DSG) has a beneficial effect on energy metabolism, but the underlying mechanism remains unclear. Methods Human normal hepatocytes (LO2 cells) were incubated with palmitic acid to establish the cell model of nonalcoholic fatty liver. The effects of DSG on lipid metabolism, glucose uptake and mitochondrial function were evaluated. Furthermore, the mechanism of DSG on oxidative stress, lipid consumption and lipid synthesis in LO2 cells was investigated. Results The results indicated that palmitic acid induced obvious lipid accumulation in LO2 cells and that DSG treatment significantly reduced the intracellular lipid content. DSG treatment upregulated expression of lipolysis proteins, including phospho-AMP activated protein kinase (p-AMPK), phospho-acetyl-coA carboxylase (p-ACC) and carnitine acyl transferase 1A (CPT-1A), and inhibited expression of lipid synthesis-related proteins, including sterol regulatory element-binding protein 1c (SREBP-1c) and fatty acid synthase (FAS). Additionally, DSG-treated cells displayed a marked improvement in mitochondrial function, with less production of reactive oxygen species and a higher mitochondrial membrane potential compared with the model group. Conclusion This study suggests that DSG can reduce intracellular lipid accumulation in LO2 cells and that the underlying mechanism may be related to the improving oxidative stress, increasing fatty acid β-oxidation and decreasing lipid synthesis. The above changes might be mediated by the activation of the AMPK/ACC/CPT-1A pathway and inhibition of the SREBP-1c/FAS pathway.

Abstract Background Wu-Mei-Wan (WMW) is a traditional Chinese herbal formulation that is clinically prescribed to treat diabetes mellitus in China. WMW has been shown to alleviate damage in pancreatic β cells, but the underlying mechanism remains unclear. This study aims to explore how WMW plays a protective role in pancreatic islets. Methods Drug testing and mechanism analyses were performed on mice treated with three concentrations of WMW (4800, 9600, and 19,200 mg/kg/bw) for four consecutive weeks. Blood was collected from both db/db and wild-type mice to determine fasting blood glucose (FBG) and serum insulin levels. The expression of proteins related to apoptosis, cysteinyl aspartate-specific proteinase 12 (caspase-12) and B-cell leukemia 2 (Bcl-2), was measured by western blot. Interleukin-1β (IL-1β), interleukin-18 (IL-18), monocyte chemoattractant protein-1α (MCP-1α), and tumor necrosis factor-α (TNF-α) in the pancreas were tested with enzyme-linked immunosorbent assay (ELISA). Immunohistochemistry staining of F4/80 was performed to measure the pancreatic infiltration of macrophages. Western blot and immunofluorescence staining of the NLRP3 inflammasome were used to measure the expression of proteins related to apoptosis and inflammation. Results WMW dose-dependently reduced FBG and promoted serum insulin secretion in db/db mice compared to the wild-type controls. WMW protected pancreatic β cells with a pattern of decreasing caspase-12 and increasing Bcl-2 expression. WMW also reversed the upregulated production of IL-1β, IL-18, MCP-1α, and macrophage-specific surface glycoprotein F4/80 in diabetic mice. In addition, the protein expression levels of NLRP3 inflammasome components NLRP3, ASC, and caspase-1 (P20) were higher in db/db mice than in wild-type controls. Conclusions WMW inhibits the activation of the NLRP3 inflammasome to protect pancreatic β cells and prevent type 2 diabetes mellitus development.

We propose a lightweight neural network-based method to detect the estrus behavior of ewes. Our suggested method is mainly proposed to solve the problem of not being able to detect ewe estrus behavior in a timely and accurate manner in large-scale meat sheep farms. The three main steps of our proposed methodology include constructing the dataset, improving the network structure, and detecting the ewe estrus behavior based on the lightweight network. First, the dataset was constructed by capturing images from videos with estrus crawling behavior, and the data enhancement was performed to improve the generalization ability of the model at first. Second, the original Darknet-53 was replaced with the EfficientNet-B0 for feature extraction in YOLO V3 neural network to make the model lightweight and the deployment easier, thus shortening the detection time. In order to further obtain a higher accuracy of detecting the ewe estrus behavior, we joined the feature layers to the SENet attention module. Finally, the comparative results demonstrated that the proposed method had higher detection accuracy and FPS, as well as a smaller model size than the YOLO V3. The precision of the proposed scheme was 99.44%, recall was 95.54%, F1 value was 97%, AP was 99.78%, FPS was 48.39 f/s, and Model Size was 40.6 MB. This study thus provides an accurate, efficient, and lightweight detection method for the ewe estrus behavior in large-scale mutton sheep breeding.

The poor adaptability of existing harvesting machinery and low work efficiency is observed due to the low-level mechanized harvesting of Xinjiang jujube orchards. A mechanical floor-standing jujube picker was designed on the basis of the characteristics of the high fruit drop of the Xinjiang jujube harvest period. The machine can pick up the jujubes, which have been manually collected into strips on the ground. The structure and working parameters of the pickup device are determined in accordance with the pickup principle. The conditions for satisfying the picking operation were analyzed, and the range of motion speed ratio parameters to meet the pickup operation was obtained. Furthermore, the contact process of jujubes on the conveying and separating device was analyzed and studied; the factors affecting the collision damage and conveying the balance of jujubes were analyzed; and the basic structural parameters of the conveying and separating devices were determined. A mechanical floor-standing jujube picker was also fabricated for trial. A three-factor and three-level Box– Behnken test was performed by taking the forward speed of the machine, the speed of the strip brush roller shaft, and the hole pitch height of the profiling rod as factors, and the pickup, breakage, and impurity rates of jujubes as indicators. Results show that when the forward speed of the machine was 0.3 m·s−1, the speed of the strip brush roller shaft was 53 rpm and the hole pitch height of the profiling rod was 60 mm. The jujube pickup rate of the machine was 92.11%, the breakage rate was 2.07%, and the impurity rate was 4.15%. The relative error with the prediction model was less than 8%. Thus, the model is reliable and meets the operational requirements. This study can provide a reference for the mechanical floor-standing jujube picker.

Environmental conditions, such as photoperiod and temperature, can affect male fertility in plants. While this feature is heavily exploited in rice to generate male-sterile lines for hybrid breeding, the underlying molecular mechanisms remain largely unknown. In this study, we use a transcriptomics approach to identify key genes and regulatory networks affecting pollen maturation in rice anthers in response to different day lengths. A total of 11,726 differentially expressed genes (DEGs) were revealed, of which 177 were differentially expressed at six time points over a 24-h period. GO enrichment analysis revealed that genes at all time points were enriched in transport, carbohydrate, and lipid metabolic processes, and signaling pathways, particularly phytohormone signaling. In addition, co-expression network analysis revealed four modules strongly correlated with photoperiod. Within these four modules, 496 hub genes were identified with a high degree of connectivity to other photoperiod-sensitive DEGs, including two previously reported photoperiod- and temperature-sensitive genes affecting male fertility, Carbon Starved Anther and UDP-glucose pyrophosphorylase, respectively. This work provides a new understanding on photoperiod-sensitive pollen development in rice, and our gene expression data will provide a new, comprehensive resource to identify new environmentally sensitive genes regulating male fertility for use in crop improvement.

The aim of this paper is to optimize the working parameters of the arc-shaped nail-tooth roller-type recovery machine for sowing layer residual film. Firstly, the tooth roller device of the residual film recovery machine is designed, and the main working parameters affecting the operation of the machine and the value range of each parameter are determined through the analysis of the operation process. Secondly, virtual simulation technology is used to establish a virtual simulation model of the interaction process between the tooth roller device and soil. At the same time, taking the soil-hilling quantity as the index, we build a quadratic regression mathematical model with three factors—the forward speed, rotation speed, and working depth—using the Box–Behnken method. Consequently, the analysis of the simulation results show that the order of the most significant factors is working depth, rotation speed, and forward speed. The optimal combination of working parameters are as follows: a forward speed of 4.5 km/h, a rotation speed of 43.2 r/min, and a working depth of 100.0 mm. Meanwhile, the predicted value of the soil-hilling quantity is 23.1 kg. Finally, we carried out field tests using the optimal combination parameters; the results show that the normal residual film collection rate is 66.8%, the soil-hilling quantity is 24.2 kg, and the relative error between the test value and the predicted value is 4.8%. This indicates that the devised DEM simulation model can be used to predict the operational performance of the tooth roller device in the working process. This study provides a reference that can be used in the planning and boundary enhancement of agricultural machinery and equipment.

Low working efficiency is an important reason for the limited application of the traditional aspirated-air type jujube fruit pickup machine. In this study, a provoke-suction type harvester for ground jujube fruit (PSH) was designed, based on the principle of negative pressure suction after shoveling the jujube fruit mechanically. The main working parameters were analyzed and the structures of the key devices were designed. Then, a three-factor and three-level Box–Behnken method was used to evaluate the performance of the PSH. The results showed that the pickup rate, impurity rate, and working efficiency were 99.36%, 5.63%, and 1672.3 kg∙h−1, respectively; as the forward speed, provoke teeth buried depth, and airflow velocity were 0.21 kg∙h−1, 74 mm, and 26.4 kg∙h−1, respectively. Furthermore, the verification results showed that the pickup rate, impurity rate, and working efficiency were 98.05%, 5.97%, and 1591.2 kg∙h−1, respectively, moreover, the relative errors were 1.32%, 6.04%, and 4.85%, respectively, indicating that the parameter optimization model can accurately predict the test results. The working efficiency of the PSH was significantly improved compared with the traditional aspirated-air type jujube fruit pickup machine. This research can provide a reference for the development of the jujube fruit pickup machine.

Sea buckthorn (Hippophae rhamnoides) is an ecologically and economically important species. Here, we assessed the diversity of 78 accessions cultivated in northern China using 8 agronomic characteristics, oil traits (including oil content and fatty acid composition) in seeds and fruit pulp, and SSR markers at 23 loci. The 78 accessions included 52 from ssp. mongolica, 6 from ssp. sinensis, and 20 hybrids. To assess the phenotypic diversity of these accessions, 8 agronomic fruit traits were recorded and analyzed using principal component analysis (PCA). The first two PCs accounted for approximately 78% of the variation among accessions. The oil contents were higher in pulp (3.46-38.56%) than in seeds (3.88-8.82%), especially in ssp. mongolica accessions. The polyunsaturated fatty acid (PUFA) ratio was slightly lower in the seed oil of hybrids (76.06%) than that of in ssp. mongolica (77.66%) and higher than that of in ssp. sinensis (72.22%). The monounsaturated fatty acid (MUFA) ratio in the pulp oil of ssp. sinensis (57.00%) was highest, and that in ssp. mongolica (51.00%) was equal to the ratio in the hybrids (51.20%). Using canonical correspondence analysis (CCA), we examined the correlation between agronomic traits and oil characteristics in pulp and seeds. Oil traits in pulp from different origins were correlated with morphological groupings (r = 0.8725, p = 0.0000). To assess the genotypic diversity, 23 SSR markers (including 17 loci previously reported) were used among the 78 accessions with 59 polymorphic amplified fragments obtained and an average PIC value of 0.2845. All accessions were classified into two groups based on the UPGMA method. The accessions of ssp. sinensis and ssp. mongolica were genetically distant. The hybrid accessions were close to ssp. mongolica accessions. The 8 agronomic traits, oil characteristics in seed and pulp oils, and 23 SSR markers successfully distinguished the 78 accessions. These results will be valuable for cultivar identification and genetic diversity analysis in cultivated sea buckthorn.

During granulation, a serious wear problem may be found in flat die as a key component of a flat die pellet mill. Specific to this problem, Glycyrrhiza uralensis was selected as the wear-causing material to investigate the wear mechanism of the flat die. Additionally, carburizing steel (20Cr and 20CrMnTi) and stainless steel (4Cr13) commonly used in flat die were adopted to conduct wear tests. To explore the influence of Glycyrrhiza uralensis powder and rods on friction and wear properties of the above three types of steel materials, a CFT-I general-purpose tester for surfaces was applied under dry friction conditions. Moreover, x-ray diffractometer (XRD), three-dimensional profilometry, scanning electron microscopy (SEM) and energy disperse spectroscopy (EDS) were used to analyze the phase compositions, surface morphologies, and elementary compositions of the samples. As demonstrated by relevant results, the influence of Glycyrrhiza uralensis on the flat die is primarily embodied in abrasive, adhesive, and fatigue wear, and a thermal oxidation reaction occurs on the surface of the flat die. By comparing the wear conditions of the three steel materials between the powder and rods of Glycyrrhiza uralensis , it is found that flat die damages caused by glycyrrhiza rods are more severe than those of its powder. Additionally, the lowest friction coefficients are generated by 20CrMnTi, which are 0.40 and 0.88, respectively. In terms of the mean wear depth, its values are 1.2 and 2 μ m, which are below those of 20Cr and 4Cr13. The results herein reveal that flat die made of 20CrMnTi have excellent wear and ductile fracture resistance characteristics. Hence, this study may provide a theoretical guide for selecting flat die materials.

The actin cytoskeleton is crucial for plant morphogenesis, and organization of actin filaments (AF) is dynamically regulated by actin-binding proteins. However, the roles of actin-binding proteins, particularly type II formins, in this process remain poorly understood in plants. Here, we report that a type II formin in rice, Oryza sativa formin homolog 3 (OsFH3), acts as a major player to modulate AF dynamics and contributes to rice morphogenesis. osfh3 mutants were semi-dwarf with reduced size of seeds and unchanged responses to light or gravity compared with mutants of osfh5, another type II formin in rice. osfh3 osfh5 mutants were dwarf with more severe developmental defectiveness. Recombinant OsFH3 could nucleate actin, promote AF bundling, and cap the barbed end of AF to prevent elongation and depolymerization, but in the absence of profilin, OsFH3 could inhibit AF elongation. Different from other reported type II formins, OsFH3 could bind, but not bundle, microtubules directly. Furthermore, its N-terminal phosphatase and tensin homolog domain played a key role in modulating OsFH3 localization at intersections of AF and punctate structures of microtubules, which differed from other reported plant formins. Our results, thus, provide insights into the biological function of type II formins in modulating plant morphology by acting on AF dynamics.

Tea oil camellia (Camellia oleifera), an important woody oil tree, is a source of seed oil of high nutritional and medicinal value that is widely planted in southern China. However, there is no report on the identification of the miRNAs involved in lipid metabolism and seed development in the high- and low-oil cultivars of tea oil camellia. Thus, we explored the roles of miRNAs in the key periods of oil formation and accumulation in the seeds of tea oil camellia and identified miRNA–mRNA regulatory modules involved in lipid metabolism and seed development. Sixteen small RNA libraries for four development stages of seed oil biosynthesis in high- and low-oil cultivars were constructed. A total of 196 miRNAs, including 156 known miRNAs from 35 families, and 40 novel miRNAs were identified, and 55 significantly differentially expressed miRNAs were found, which included 34 upregulated miRNAs, and 21 downregulated miRNAs. An integrated analysis of the miRNA and mRNA transcriptome sequence data revealed that 10 miRNA–mRNA regulatory modules were related to lipid metabolism; for example, the regulatory modules of ath-miR858b–MYB82/MYB3/MYB44 repressed seed oil biosynthesis, and a regulation module of csi-miR166e-5p–S-ACP-DES6 was involved in the formation and accumulation of oleic acid. A total of 23 miRNA–mRNA regulatory modules were involved in the regulation of the seed size, such as the regulatory module of hpe-miR162a_L-2–ARF19, involved in early seed development. A total of 12 miRNA–mRNA regulatory modules regulating growth and development were identified, such as the regulatory modules of han-miR156a_L+1–SPL4/SBP2, promoting early seed development. The expression changes of six miRNAs and their target genes were validated using quantitative real-time PCR, and the targeting relationship of the cpa-miR393_R-1–AFB2 regulatory module was verified by luciferase assays. These data provide important theoretical values and a scientific basis for the genetic improvement of new cultivars of tea oil camellia in the future.

Discrete element method (DEM) simulation is an important method to analyze the interaction relationship between materials and equipment, and to develop machinery and/or equipment. However, it is necessary to input specific simulation parameters when establishing a DEM simulation model. In this study, the interval values were measured through angle of repose tests of fallen jujube fruit (FJF), and the simulation angle of repose tests for FJF were established with EDEM software (DEM Solutions Ltd. Edinburgh, Scotland, UK). Then, the Plackett-Burman design, steepest ascent search experiment, and center composite design experimental methods were utilized to obtain the specific values of the simulation parameters from the interval values. The results showed that significant influencing factors in the simulation angle of repose include the Poisson’s ratio, the static friction coefficient between FJF, and the static friction coefficient between FJF and the steel plate, for which the optimal values were 0.248, 0.480, and 0.309, respectively. The angle of repose tests’ results showed that the error was 0.53% between the simulation angle of repose (29.69°) and the angle of repose (29.85°). In addition, the flow rate test results showed that the average error was 5.84% between the physical and simulation tests. This indicated that the calibrated parameters were accurate and reliable, and that the simulation model can accurately represent the physical tests. Consequently, this study provides an EDEM model of FJF that was essential in designing machinery and equipment through the EDEM simulation method.

In view of the serious film wrapping phenomenon and poor film removal effect of the film removal devices of residual film recovery machines, a combined “mechanical + air flow” film removal device is designed. It is mainly composed of vane-type film removal rollers and diversion shells and can complete film removal and film transportation in turn. The analysis and parameter design of the key working parts, named film stripping blades, are carried out. The condition of film removal is calculated by force analysis, and the internal flow field of the device is simulated based on the Fluent software. Taking rotating speed of the vane-type film removal roller, the inclination angle of the film stripping blade, and the diameter of the roller as test factors, and the area ratio of the vortex region to the effective region as the evaluation index, a three-factor three-level orthogonal simulation test is designed. The response surface model of each test factor is established, and the significance of each test factor on the evaluation index is analyzed. Through optimization, the optimal parameter combination suitable for the film removal flow field is obtained as follows: the rotating speed of the vane-type film removal roller is 283 r/min, the inclination angle of the film stripping blade is 25° and the diameter of the roller is 219 mm. Under the optimal combination of parameters, the device is manufactured, and the effect of the device is verified by a field test. The results show that the film removal rate of the device is 98.04%, and there is no film wrapping phenomenon in the operation process, which can meet the needs of residual film recovery before sowing.

Background: Memory-impairment was one of the common characteristics in patients with diabetes mellitus. The release of chronic inflammation mediators and insulin resistance in diabetic brain gave rise to the generation of toxic factor Aβ42 which was the marker of Alzheimer’s disease. In addition, the impairment of memory in diabetes mellitus was also correlated predominantly with uptake/metabolism of glucose in medial prefrontal cortex (mPFC). Previously, anti-inflammation and hypoglycemic effects of berberine (BBr) have been described in peripheral tissues. For better understanding the effects of BBr on cognitive action in diabetics, we investigated the functions of BBr involved in anti-inflammation and ameliorating insulin resistance in prefrontal cortex of diabetic rats.Methods: Intragastric administration of BBr (187.5 mg/Kg/d) was used in diabetic rats. Fear-condition assay was applied for cognitive assessment, and relative protein expressions were detected by western-blot. The glucose uptake in prefrontal cortex of diabetic rats was tested by Positron-Emission Tomography imaging. The levels of inflammation mediators were determined by commercial ELISA kits.Results: The inflammation mediator release and insulin resistance in the mPFC of diabetic rats was inhibited by BBr. The activation of PI3K/Akt/mTOR and MAPK signaling pathway, as well as two novel isoforms PKCη and PKC𝜀 and the translocation of NF-κB in neuron were also down-regulated by BBr; furthermore, the neuron specific glucose transporter GLUT3 was remarkably augmented by 2–3 times when compared with diabetic group; meanwhile, BBr also promoted glucose uptake in the brain. Additionally BBr decreased the expressions of amyloid precursor protein and BACE-1, and the production of oligomeric Aβ42. Finally, it accelerates the reinforcement of the information and ameliorates cognitive impairment.Conclusion: BBr inhibited the activation of inflammation pathway and insulin resistance in the mPFC of diabetic rats. Finally, it improved the lesion of cognition in diabetic rats.