Control Mechanism and Experimental Study on Electric Drive Seed Metering Device of Air Suction Seeder

Under the condition of high-speed operation, the traditional mechanical seeder is easy to cause problems such as the drop of the qualified rate of sowing, the increase in the rate of missing sowing and the low precision of adjusting the grain distance, which seriously affects the sowing precision and efficiency. In this paper, a brushless DC motor sliding film variable structure control system is designed for the air-suction corn seeder, so as to realize the precise control of the rotation speed and the seed metering amount of the seed metering disc. The experimental results show that the faster the operation speed of the electrically driven air-suction seed metering device, the greater the standard deviation of sowing distance. The qualified rate of seeding, average spacing, standard deviation distribution and coefficient of variation of the electric seeding device are better than those of the mechanical seeding device

Effects of Straw Mulching on Soil Temperature and Maize Growth in Northeast China

In China, corn is growing in large quantities, and a large amount of straw is produced each year. Improper straw treatment may cause environmental problems. Covering the fields after straw crushing can prevent soil erosion and increase soil fertility, which has become a recommended method of straw treatment. The effects of straw mulching on soil water content, soil temperature and maize growth traits were analyzed through comparative experiments. The results showed that straw mulching had heat insulation effect. In May and June, when the average temperature was low, straw mulching kept the ground temperature at a low value, resulting in late emergence of crops and poor growth in the nutritional stage. In July and August when the temperature is higher, the higher ground temperature is maintained, which makes the crop grow better in reproductive growth stage and yield higher. In addition, straw mulching makes the soil water content higher and has a positive effect on Maize Cultivation in northeastern China for Rain-fed agriculture

Diagnostic Value of Transbronchial Needle Aspiration and Endobronchial Ultrasoundguided Transbronchial Needle Aspiration for Hilar and Mediastinal Lymph Nodes in Lung Cancer Patients

Background and objective Transbronchial needle aspiration (TBNA) and endobronchial ultrasoundguided TBNA (EBUS-TBNA) have been applied to the diagnosis for mediastinal lymph nodes. The aim of this study is to evaluate the clinical value and safety of TBNA and EBUS-TBNA on hilar and mediastinal lymph nodes of lung cancer patients. Methods Two hundred fifty patients with suspected lung cancer were enrolled. All petients with hilar and/or mediastinal lymphoadenopathy found by CT scan received TBNA, biopsy and brushing. EBUS-TBNA was performed in 15 patients among them. Results Lung cancer were confirmed in 180 patients by TBNA, biopsy and brushing. The positive rates were 82.86%, 51.24% and 45.45%. Fifteen patients after EBUS-TBNA had a positive rate of 91.67%. Conclusion TBNA and EBUS-TBNA were proved to be safe procedure with a high yield for the diagnosis of hilar and mediastinal lymph nodes in lung cancer patients

Optimization of Millet Axial Flow Threshing and Separation Device Based on Discrete Element Method

The difficulties of threshing and separation of millet have not been solved yet which has restricted the development of the millet industry because of the special biological structure and lack of professional agricultural machinery. In order to improve the quality of millet harvest and meet the market demand for millet, in this paper, according to the branching structure of millet, the millet earhead model was established by Discrete Element Method. Using virtual models of millet and device, the simulation tests were carried out whose results have shown that the threshing effect of the rasp-bar threshing element is better than that of the teeth threshing element. Then the rotor structure was optimized into a combined type of the rasp-bar and the teeth. A three-factor five-level quadratic orthogonal rotation combination test was carried out whose results have shown that the combined rotor can meet the requirements of millet harvest

Sweat permeable and ultrahigh strength 3D PVDF piezoelectric nanoyarn fabric strain sensor

Commercial wearable piezoelectric sensors possess excellent anti-interference stability due to their electronic packaging. However, this packaging renders them barely breathable and compromises human comfort. To address this issue, we develop a PVDF piezoelectric nanoyarns with an ultrahigh strength of 313.3 MPa, weaving them with different yarns to form three-dimensional piezoelectric fabric (3DPF) sensor using the advanced 3D textile technology. The tensile strength (46.0 MPa) of 3DPF exhibits the highest among the reported flexible piezoelectric sensors. The 3DPF features anti-gravity unidirectional liquid transport that allows sweat to move from the inner layer near to the skin to the outer layer in 4 s, resulting in a comfortable and dry environment for the user. It should be noted that sweating does not weaken the piezoelectric properties of 3DPF, but rather enhances. Additionally, the durability and comfortability of 3DPF are similar to those of the commercial cotton T-shirts. This work provides a strategy for developing comfortable flexible wearable electronic devices

Modelling the geographical spread of HIV among MSM in Guangdong, China: a metapopulation model considering the impact of pre-exposure prophylaxis

Men who have sex with men (MSM) make up the majority of new human immunodeficiency virus (HIV) diagnoses among young people in China. Understanding HIV transmission dynamics among the MSM population is, therefore, crucial for the control and prevention of HIV infections, especially for some newly reported genotypes of HIV. This study presents a metapopulation model considering the impact of pre-exposure prophylaxis (PrEP) to investigate the geographical spread of a hypothetically new genotype of HIV among MSM in Guangdong, China. We use multiple data sources to construct this model to characterize the behavioural dynamics underlying the spread of HIV within and between 21 prefecture-level cities (i.e. Guangzhou, Shenzhen, Foshan, etc.) in Guangdong province: the online social network via a gay social networking app, the offline human mobility network via the Baidu mobility website, and self-reported sexual behaviours among MSM. Results show that PrEP initiation exponentially delays the occurrence of the virus for the rest of the cities transmitted from the initial outbreak city; hubs on the movement network, such as Guangzhou, Shenzhen, and Foshan are at a higher risk of 'earliest' exposure to the new HIV genotype; most cities acquire the virus directly from the initial outbreak city while others acquire the virus from cities that are not initial outbreak locations and have relatively high betweenness centralities, such as Guangzhou, Shenzhen and Shantou. This study provides insights in predicting the geographical spread of a new genotype of HIV among an MSM population from different regions and assessing the importance of prefecture-level cities in the control and prevention of HIV in Guangdong province. This article is part of the theme issue 'Data science approach to infectious disease surveillance'

Prediction of recurrence of ischemic stroke within 1 year of discharge based on machine learning MRI radiomics

PurposeThis study aimed to investigate the value of a machine learning-based magnetic resonance imaging (MRI) radiomics model in predicting the risk of recurrence within 1 year following an acute ischemic stroke (AIS).MethodsThe MRI and clinical data of 612 patients diagnosed with AIS at the Second Affiliated Hospital of Nanchang University from March 1, 2019, to March 5, 2021, were obtained. The patients were divided into recurrence and non-recurrence groups according to whether they had a recurrent stroke within 1 year after discharge. Randomized splitting was used to divide the data into training and validation sets using a ratio of 7:3. Two radiologists used the 3D-slicer software to label the lesions on brain diffusion-weighted (DWI) MRI sequences. Radiomics features were extracted from the annotated images using the pyradiomics software package, and the features were filtered using the Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis. Four machine learning algorithms, logistic regression (LR), Support Vector Classification (SVC), LightGBM, and Random forest (RF), were used to construct a recurrence prediction model. For each algorithm, three models were constructed based on the MRI radiomics features, clinical features, and combined MRI radiomics and clinical features. The sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve (AUC) were used to compare the predictive efficacy of the models.ResultsTwenty features were selected from 1,037 radiomics features extracted from DWI images. The LightGBM model based on data with three different features achieved the best prediction accuracy from all 4 models in the validation set. The LightGBM model based solely on radiomics features achieved a sensitivity, specificity, and AUC of 0.65, 0.671, and 0.647, respectively, and the model based on clinical data achieved a sensitivity, specificity, and AUC of 0.7, 0.799, 0.735, respectively. The sensitivity, specificity, and AUC of the LightGBM model base on both radiomics and clinical features achieved the best performance with a sensitivity, specificity, and AUC of 0.85, 0.805, 0.789, respectively.ConclusionThe ischemic stroke recurrence prediction model based on LightGBM achieved the best prediction of recurrence within 1 year following an AIS. The combination of MRI radiomics features and clinical data improved the prediction performance of the model

An Indoor Positioning Algorithm for Wearable Device Using Deep Learning Regression Prediction Model in IoT Applications

To solve the problem of low positioning accuracy and ease environmental impact of wearable devices in the Internet of things, a wearable device indoor positioning algorithm based on deep learning was proposed. Firstly, a basic model of deep learning composed of an input layer, hidden layer, and output layer is proposed to realize the continuous prediction and positioning with higher accuracy. Secondly, the automatic stacking encoder is trained with signal strength data, and features are extracted from a large number of signal strength samples with noise to build the location fingerprint database. Finally, the stacking automatic coding machine is used to obtain the signal strength characteristics of the points to be measured, which are matched with the signal strength characteristics in the fingerprint database, and the location of the points to be measured is estimated by the nearest neighbor algorithm. The experimental results show that the indoor positioning algorithm based on the stacking automatic coding machine has higher positioning accuracy, and the average error of points on the complete path can reach within 3 m in 93% cases

A ROTARY BLADE DESIGN FOR PADDY FIELDS WITH LONG RICE STRAW BASED ON EDEM

ABSTRACT The paddy field machine uses excessive power during paddy field preparation because of the high distribution density of rice straw. In this study, a rotary blade is created to address this problem. The structural parameters of the rotary blade were designed and the dynamic analysis of the rotary blade's soil-cutting process was completed to establish a model of the rotary blade's power consumption. Through the model, the primary factors influencing the rotary blade's power consumption were identified. A composite soil bin model of rice straw‒muddy layer‒bottom soil was established in EDEM software, with the bending angle of the front blade, the working width of a single blade, and the thickness of the blade as the test factors. The straw burying rate, power consumption, and surface flatness after rotary tillage were used as evaluation indicators to conduct multi-factor simulation tests on the composite soil bin model. The optimized analysis of the test data showed that the optimal geometric parameters for the rotary blade were 49 mm working width, 108° front blade bending angle, and 4 mm blade thickness. A field verification test was carried out on the optimized rotary blade, and the test results showed that the surface flatness after rotary tillage was 3.25 cm, the qualified rate of rotary tillage depth was 93.3%, and the degree of mud mixing was 3.41 kg/dm3, which was suitable for the land preparation requirements of paddy fields