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In the realm of high-speed precision broadcasting, the existing seeder opener proves inadequate for the speed of the seeding operation. We focus on the duckbill opener and employ the quadratic regression orthogonal rotation combination test design method to optimize the structural parameters of the opener. Throughout the experiment, the primary performance metrics encompassed the opener's working resistance and the side dumping distance. The selected experimental factors comprised the penetration angle, the angle of soil entry gap, the shovel body width, and the shovel length. The optimal arrangement of structural parameters has been determined: a penetration angle, a soil entry gap angle, a shovel body width of 21 mm, and a shovel length of 142 mm. These parameters contribute to increased velocity, reduced operational resistance, and minimal soil disturbance. Under this combination, the relative deviations between the recorded measurements and the theoretical outcomes for working resistance and the side dumping distance stand at 4.24% and 1.06%, respectively; these confirm the credibility of the optimization results. We performed adaptability testing and conducted a comparative analysis under various operational conditions to assess the innovative opener's ability to reduce force, minimize soil disruption, and maintain depth stability. The findings are as follows: At a depth of 5 cm and velocities ranging from 6 km/h to 8 km/h, an average working resistance reduction of 19.73%, a 5.64% decrease in the side dumping distance, and an average depth stability of 89.5% were observed. When operated at a speed of 7 km/h with a depth ranging from 3 cm to 5 cm, an average reduction of 19.66% in operational resistance, a 2.59% decrease in the side dumping distance, and an average depth stability of 91.1% were recorded. These results illustrate the innovative opener's capacity to significantly reduce working resistance and side dumping distance while satisfying the depth stability requisites. [ABSTRACT FROM AUTHOR]

The thermo-electro-mechanical coupling (TEMC) problem of the piezoelectric structure was analyzed. And the stabilized node-based smoothed radial point interpolation method (SNS-RPIM) was utilized during the computation. And the fundamental formulas were derived under thermo-mechanical load and the model of the problem was analyzed with triangular elements. The existence of thermal stress was taken into consideration. The dynamic behaviors for the TEMC problem were investigated by SNS-RPIM and finite element method (FEM) when different kinds of thermo-mechanical loadings were applied. The superiority of the proposed scheme for the TEMC problems was verified. The empirical constant was set to negative. Besides, the temperature rise can make the model softer and thermal load increase, which leads to a decrease of structural stiffness and natural frequency, thus, the displacement increases. [ABSTRACT FROM AUTHOR]

Diabetes mellitus complicated with heart failure has high mortality and morbidity, but no reliable diagnoses and treatments are available. This study aimed to develop and verify a new model nomogram based on clinical parameters to predict diastolic cardiac dysfunction in patients with Type 2 diabetes mellitus (T2DM). 3030 patients with T2DM underwent Doppler echocardiography at the First Affiliated Hospital of Shenzhen University between January 2014 and December 2021. The patients were divided into the training dataset (n = 1701) and the verification dataset (n = 1329). In this study, a predictive diastolic cardiac dysfunction nomogram is developed using multivariable logical regression analysis, which contains the candidates selected in a minor absolute shrinkage and selection operator regression model. Discrimination in the prediction model was assessed using the area under the receiver operating characteristic curve (AUC-ROC). The calibration curve was applied to evaluate the calibration of the alignment nomogram, and the clinical decision curve was used to determine the clinical practicability of the alignment map. The verification dataset was used to evaluate the prediction model's performance. A multivariable model that included age, body mass index (BMI), triglyceride (TG), creatine phosphokinase isoenzyme (CK-MB), serum sodium (Na), and urinary albumin/creatinine ratio (UACR) was presented as the nomogram. We obtained the model for estimating diastolic cardiac dysfunction in patients with T2DM. The AUC-ROC of the training dataset in our model was 0.8307, with 95% CI of 0.8109–0.8505. Similar to the results obtained with the training dataset, the AUC-ROC of the verification dataset in our model was 0.8083, with 95% CI of 0.7843–0.8324, thus demonstrating robust. The function of the predictive model was as follows: Diastolic Dysfunction = −4.41303 + 0.14100*Age(year)+0.10491*BMI (kg/m2) +0.12902*TG (mmol/L) +0.03970*CK-MB (ng/mL) −0.03988*Na(mmol/L) +0.65395 * (UACR > 30 mg/g) + 1.10837 * (UACR > 300 mg/g). The calibration plot diagram of predicted probabilities against observed DCM rates indicated excellent concordance. Decision curve analysis demonstrated that the novel nomogram was clinically useful. Diastolic cardiac dysfunction in patients with T2DM can be predicted by clinical parameters. Our prediction model may represent an effective tool for large-scale epidemiological study of diastolic cardiac dysfunction in T2DM patients and provide a reliable method for early screening of T2DM patients with cardiac complications. This study used clinical parameters to predict diastolic cardiac dysfunction in patients with T2DM. This study established a nomogram for predicting diastolic cardiac dysfunction by multivariate logical regression analysis. Our predictive model can be used as an effective tool for large-scale epidemiological study of diastolic cardiac dysfunction in patients with T2DM and provides a reliable method for early screening of cardiac complications in patients with T2DM. [ABSTRACT FROM AUTHOR]

External defects of kiwifruit seriously affect its added commercialization. To address the existing problems, kiwifruit external defects detection has a few methods for detecting multi-category defects and weak adaptability to complex images. In this study, we proposed ResNet combined with CBAM for the automatic detection of external defects in kiwifruit. The experiment first built an acquisition device to obtain high-quality images. The optimal fusion scheme of ResNet and CBAM was investigated, the network training parameters were optimized, and Adam was used to accelerate the convergence speed of the model. It was found that the average recognition accuracy of ResNet34 + CBAM for kiwifruit was 99.6%, and all evaluation metrics were greater than 99%. Meanwhile, the experiment selected AlexNet, VGG16, InceptionV3, ResNet34, and ResNet34 + CBAM for comparison. The results showed that the recognition accuracy of ResNet34 + CBAM was 7.9%, 12.7%, 11.8%, and 4.3% higher than that of AlexNet, VGG16, InceptionV3, and ResNet34, respectively. Therefore, it can be concluded that ResNet34 + CBAM has the advantages of high recognition accuracy and good stability for kiwifruit external defect sample detection. It provides a technical guarantee for online detection and sorting of kiwifruit and other fruit defects. [ABSTRACT FROM AUTHOR]

In this paper, an optimal inspection model for three-dimensional structures under random loading is presented from the view of reliability. Neuber method and finite element analysis as well as experiential stress intensity factor (SIF) were applied in the analysis of fatigue life. Based on the series system theory, the reliability function for the structural fatigue life is given. Treat the crack initiation life (CIL) as service life and the crack propagation life (CPL) as inspection period, the optimal inspection period was obtained under a given reliability. The proposed method is proved to be feasible by a numerical example, which involves a bracket under random loading. [ABSTRACT FROM AUTHOR]