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To address the low clearance rate issue of the anti-blocking mechanism for maize no-till planters in the Huang-Huai-Hai Plain of China, experiments and simulations were conducted to analyze the individual and collective movements of straw under the action of the round roller-claw anti-blocking mechanism. A tracer-based measurement method for straw displacement was applied firstly. Experimental results showed that the straw forward displacement could be characterized by the average horizontal displacements of longitudinal and lateral tracers, while the straw side displacement could be characterized by the lateral displacement of the longitudinal tracer. The straw forward displacement was 58.95% greater than the side displacement. Forward, side, and total displacements of straw increased as the mechanism's forward speed increased from 3 km/h to 7 km/h, with corresponding rates of increase at 233.98%, 43.20%, and 162.47%, respectively. Furthermore, a model of straw–soil–mechanism interaction was constructed in EDEM 2022 software. The relative error between experimental and simulated straw clearance rates was 11.20%, confirming the applicability of the simulation model for studying straw–soil–mechanism interaction. Based on the simulation model, three straw tracers of different lengths were selected to study the motion behavior of straw. It was inferred that despite differences in straw length, the movement behaviors of the three straw tracers under the influence of the anti-blocking mechanism were similar. Additionally, longer straws exhibited greater displacements in all directions. This paper serves as a reference for studying straw motion behavior influenced by anti-blocking mechanisms. [ABSTRACT FROM AUTHOR]

The poor shape of the cavity formed by the planar 5R parallel transplanting mechanism will cause Salvia miltiorrhiza seedlings to tilt while transplanting them. In order to improve the quality of the cavity in Salvia miltiorrhiza planting, this paper analyzed the structural composition and working principle of a planar 5R parallel transplanting mechanism for Salvia miltiorrhiza and established the bidirectional coupling model between the transplanting mechanism and the soil. Based on the model, a regression analysis model and the influence of three factors and five levels were obtained by using the experimental optimization design method, which reflected the relationship between the parameters of the mechanism on the parameters of the cavity. In terms of the optimization objective and regression model, the optimal parameter combination of the transplanting mechanism was obtained by multi-objective parameter optimization. A virtual test of cavity formation was conducted on the transplanting mechanism for Salvia miltiorrhiza with an optimal parameter combination. The results proved that the parameters of cavity output via the regression model and the measurement from the bidirectional coupling model were basically consistent, which verifies the accuracy of our parameter optimization for the transplanting mechanism. This paper provides a new approach to the sustainable improvement of a Salvia miltiorrhiza transplanting mechanism from the perspective of the interaction between the machine and the soil. [ABSTRACT FROM AUTHOR]

A geared five-bar transplanting mechanism can meet the agronomic requirements for the vertical planting of Salvia miltiorrhiza. In order to improve the planting quality, this paper analyzed the structural composition and working principle of a transplanting mechanism and established an interactive human–computer auxiliary interface through a kinematic model. With the aid of an auxiliary interface, by taking the parameters of the transplanting mechanism as the factors and the parameters of the absolute trajectory and posture for the planter as the index, an orthogonal experimental design with five factors and five levels was carried out, and the optimal combination of the parameters of the mechanism was obtained. According to the optimal combination of the parameters of the mechanism, the structure of the transplanting mechanism was designed, a geared five-bar transplanting mechanism for Salvia miltiorrhiza prototype was developed, and a test bench system was built. The actual trajectory of the endpoint for the transplanting mechanism's prototype was obtained using high-speed photographic technology. The bench test results showed that according to a comparison of the actual trajectory, the posture for the planter and the theoretical analysis results were basically consistent, which verified the correctness, rationality, and consistency of the optimal design for the mechanism. [ABSTRACT FROM AUTHOR]

Mechanical-chemical synergy has been proven efficient in weed control. However, characterizing the state of the weed–soil complex after mechanical weeding and revealing its effects on subsequent herbicide application is still challenging, which restricts the implementation of this technology. This paper first presents a method to characterize the state of the weed–soil complex from the perspectives of the fragmentation and composite characteristics. The regrowth of the weed–soil complex and the effects of complemented herbicide-reduced spraying on weed control efficacy and crop yield were then investigated. The results showed that the typical diameters of the weed–soil complexes were 10.67 cm and 2.82 cm after inter-row hoe shovel and intra-row finger weeding, respectively. There were mainly two and four weed–soil complex states after inter-row and intra-row weeding, respectively. The regrowth rate corresponding to the weed–soil complex state with the largest component proportion after inter-row and intra-row weeding was 76.91% and 18.37%, respectively. The additional chemical herbicide sprayed on the weed–soil complex significantly improved the fresh weight control efficacy of 95.12% for the preposed inter-row mechanical weeding and 138.07% for the preposed intra-row mechanical weeding in the maize silking stage. The maize yield of inter-row mechanical–75% chemical application treatment was 9.27% higher than that of chemical treatment. Mechanical weeding creates a suitable weed–soil complex state for subsequent chemical application and improves the synergistic weeding effect. [ABSTRACT FROM AUTHOR]

A variable velocity parallel four-bar cavitating mechanism for well-cellar can form the well-cellar cavitation which suits for well-cellar transplanting under a continuous operation. In order to improve the cavitating quality, this paper analyzed the structural composition and working principle of the cavitating mechanism and established the bidirectional coupling model of multi-body dynamics and the discrete element between the cavitating mechanism and soil through Recurdyn and EDEM software. Based on the model, a three-factor, five-level quadratic orthogonal rotational combination design test was conducted with the parameters of the cavitating mechanism as the experimental factors and the parameters of the cavitation as the response index to obtain the optimal parameter combination, and a virtual simulation test was conducted for the optimal parameter combination in order to study the cavitating law of the cavitating mechanism and soil. The test results showed that the depth of the cavitation was 188.6 mm, the vertical angle of the cavitation was 90.4°, the maximum diameter of the cavitation was 76.1 mm, the minimum diameter of the cavitation was 68.5 mm, and the variance in the diameters for the cavitation was 5.42 mm2. The cavitating mechanism with optimal parameters based on the Recurdyn–EDEM bidirectional coupling mode could further improve the cavitating quality. [ABSTRACT FROM AUTHOR]

The heat-transfer characteristics of 128 small-sized plate-fin heat sinks in a supercomputer chassis are investigated with CFD simulation. The V-shaped curves of the chip temperature versus fin pitch and fin thickness are derived and a thermal resistance model is built to explore the profile and obtain the convective heat-transfer coefficient of the heat sinks. It turns out that the V-shaped profile arises from the joint action of the thermal conduction and convection of heat sink, which can be attributed to the intricacy of the dependencies of thermal resistances on either fin pitch or thickness. It can be further concluded that Biot criterion is applicable to estimate the Biot number of large-scale plate-fin heat sink but not applicable for the small-sized one. The convective heat-transfer coefficient is a complicated function of fin pitch and fin thickness. The empirical formulas of heat transfer are obtained and the fin pitch and fin thickness are optimized. [DOI: 10.1.115/1.2804102] Keywords: plate-fin heat sink array, heat transfer, thermal resistance model, empirical design rule, revised Biot criterion

This paper developed a model for predicting the temporal failure probability of prestressed concrete (PC) highway bridges. The model updates predictions based on data from nondestructive testing and visual inspections. Chloride-induced corrosion is taken as the main cause for deterioration, and a gamma process describes the reduction in structural capacity. A nonhomogeneous Poisson process models vehicle arrival, in which the vehicle load variability follows a two-peak lognormal mixture distribution. After each inspection, Bayesian inference updates select model parameters and, consequently, the associated temporal structural resistance. Bridge managers can use the updated failure probability predictions to evaluate the remaining useful life of the bridge and determine the maintenance scheme and budget accordingly. A real bridge example illustrated the methodology and justified the probability distributions used for deterioration and vehicle load. Through comparisons with three existing methods, we argued that the proposed model provides more-conservative recommendations, yet existing methods tend to underestimate failure probabilities. [ABSTRACT FROM AUTHOR]

At present, the application of Electroencephalogram (EEG) signal classification to human intention-behavior prediction has become a hot topic in the brain computer interface (BCI) research field. In recent studies, the introduction of convolutional neural networks (CNN) has contributed to substantial improvements in the EEG signal classification performance. However, there is still a key challenge with the existing CNN-based EEG signal classification methods, the accuracy of them is not very satisfying. This is because most of the existing methods only utilize the feature maps in the last layer of CNN for EEG signal classification, which might miss some local and detailed information for accurate classification. To address this challenge, this paper proposes a multi-scale CNN model-based EEG signal classification method. In this method, first, the EEG signals are preprocessed and converted to time-frequency images using the short-time Fourier Transform (STFT) technique. Then, a multi-scale CNN model is designed for EEG signal classification, which takes the converted time-frequency image as the input. Especially, in the designed multi-scale CNN model, both the local and global information is taken into consideration. The performance of the proposed method is verified on the benchmark data set 2b used in the BCI contest IV. The experimental results show that the average accuracy of the proposed method is 73.9 percent, which improves the classification accuracy of 10.4, 5.5, 16.2 percent compared with the traditional methods including artificial neural network, support vector machine, and stacked auto-encoder. [ABSTRACT FROM AUTHOR]

Recently, coronary heart disease has attracted more and more attention, where segmentation and analysis for vascular lumen contour are helpful for treatment. And intravascular optical coherence tomography (IVOCT) images are used to display lumen shapes in clinic. Thus, an automatic segmentation method for IVOCT lumen contour is necessary to reduce the doctors’ workload while ensuring diagnostic accuracy. In this paper, we proposed a deep residual segmentation network of multi-scale feature fusion based on attention mechanism (RSM-Network, Residual Squeezed Multi-Scale Network) to segment the lumen contour in IVOCT images. Firstly, three different data augmentation methods including mirror level turnover, rotation and vertical flip are considered to expand the training set. Then in the proposed RSM-Network, U-Net is contained as the main body, considering its characteristic of accepting input images with any sizes. Meanwhile, the combination of residual network and attention mechanism is applied to improve the ability of global feature extraction and solve the vanishing gradient problem. Moreover, the pyramid feature extraction structure is introduced to enhance the learning ability for multi-scale features. Finally, in order to increase the matching degree between the actual output and expected output, the cross entropy loss function is also used. A series of metrics are presented to evaluate the performance of our proposed network and the experimental results demonstrate that the proposed RSM-Network can learn the contour details better, contributing to strong robustness and accuracy for IVOCT lumen contour segmentation. [ABSTRACT FROM AUTHOR]

Frequent epileptic seizures cause damage to the human brain, resulting in memory impairment, mental decline, and so on. Therefore, it is important to detect epileptic seizures and provide medical treatment in a timely manner. Currently, medical experts recognize epileptic seizure activity through the visual inspection of electroencephalographic (EEG) signal recordings of patients based on their experience, which takes much time and effort. In view of this, this paper proposes a one-dimensional convolutional neural network-long short-term memory (1D CNN-LSTM) model for automatic recognition of epileptic seizures through EEG signal analysis. Firstly, the raw EEG signal data are pre-processed and normalized. Then, a 1D convolutional neural network (CNN) is designed to effectively extract the features of the normalized EEG sequence data. In addition, the extracted features are then processed by the LSTM layers in order to further extract the temporal features. After that, the output features are fed into several fully connected layers for final epileptic seizure recognition. The performance of the proposed 1D CNN-LSTM model is verified on the public UCI epileptic seizure recognition data set. Experiments results show that the proposed method achieves high recognition accuracies of 99.39% and 82.00% on the binary and five-class epileptic seizure recognition tasks, respectively. Comparing results with traditional machine learning methods including k-nearest neighbors, support vector machines, and decision trees, other deep learning methods including standard deep neural network and CNN further verify the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, a copper/benzocyclotene thin film based microstrip bandpass filter featured by a thick dielectric layer is presented. For thickening dielectric layer, a 30 μm‐deep micromachined groove with embedded benzocyclotene polymer is fabricated on the surface of silicon substrate. Mechanical polishing is utilized to planarize the benzocyclotene polymer embedded in the groove. Atomic Force Microscopy is used to characterize the surface roughness of benzocyclobutene (BCB) polymer film before and after polishing. With two additional BCB layer, a total thickness of 75.4 μm dielectric layer is realized, and the insertion loss of the filter can be reduced by 1.34 dB theoretically. The measured insertion loss of fabricated filter is −2.2 dB, which can be further reduced via tighter control of device parameters. [ABSTRACT FROM AUTHOR]

Anomaly detection is an important issue in trajectory data mining. Various approaches have been proposed to address this issue. However, most previous studies focus only on outlier detection but rarely on pattern mining of anomalous trajectories. Mining patterns of anomalous trajectories can reveal the underlying mechanisms of these outliers. This paper studies four distinct patterns of anomalous trajectories, and proposes a method to detect and classify them. First, we present the difference and intersection set (DIS) distance metric to evaluate the similarity between any two trajectories. Based on this distance, we design an anomaly score function to quantify the differences between different types of anomalous trajectories and normal trajectories. We further propose an anomalous trajectory detection and classification (ATDC) method to find anomalies in different anomalous patterns. Finally, we evaluate the proposed ATDC method through extensive experiments on real cab trajectory data. The results show that the proposed approach outperforms existing methods by a significant margin. [ABSTRACT FROM AUTHOR]

Fault detection and diagnosis (FDD) is crucial for stable, reliable, and safe operation of industrial equipment. In recent years, deep learning models have been widely used in data-driven FDD methods because of their automatic feature learning capability. In general, these models are trained on historical sensor data, and therefore, it is very difficult to meet the real-time requirement of online FDD applications. Since transfer learning can solve different but similar problems in the target domain efficiently and effectively with the knowledge learned from the source domain, this paper proposes an online fault diagnosis method based on a deep transfer convolutional neural network (TCNN) framework. The TCNN framework is made up of an online CNN based on LeNet-5 and several offline CNNs with a shallow structure. First, time-domain signal data are converted into images that contain abundant fault information and are suitable as the input of CNN. Then, the online CNN is constructed to automatically extract representative features from the converted images and classify faults. Finally, in order to improve the real-time performance of the online CNN, several offline CNNs are also constructed and pretrained on related data sets. By directly transferring the shallow layers of the trained offline CNNs to the online CNN, the online CNN can significantly improve the real-time performance and successfully address the issue of achieving the desired diagnostic accuracy within limited training time. The proposed method is validated on two bearing data sets and one pump data set, respectively. The prediction accuracy of the proposed method using three data sets are 99.88%, 99.13%, and 99.98%, respectively. The experimental results also indicate that the improvement of accuracy is 19.21% for the motor bearing case, 29.82% for the rolling mill bearing case, and 33.26% for the pump case during the early stage of learning. [ABSTRACT FROM AUTHOR]

Despite the new ideas were inspired in medical treatment by the rapid advancement of three-dimensional (3D) printing technology, there is still rare research work reported on 3D printing of coronary arteries being documented in the literature. In this work, the application value of 3D printing technology in the treatment of cardiovascular diseases has been explored via comparison study between the 3D printed vascular solid model and the computer aided design (CAD) model. In this paper, a new framework is proposed to achieve a 3D printing vascular model with high simulation. The patient-specific 3D reconstruction of the coronary arteries is performed by the detailed morphological information abstracted from the contour of the vessel lumen. In the process of reconstruction which has 5 steps, the morphological details of the contour view of the vessel lumen are merged along with the curvature and length information provided by the coronary angiography. After comparing with the diameter of the narrow section and the diameter of the normal section in CAD models and 3D printing model, it can be concluded that there is a high correlation between the diameter of vascular stenosis measured in 3D printing models and computer aided design models. The 3D printing model has high-modeling ability and high precision, which can represent the original coronary artery appearance accurately. It can be adapted for prevascularization planning to support doctors in determining the surgical procedures. [ABSTRACT FROM AUTHOR]

Wafer warpage is becoming a serious problem when adopting wafer level package (WLP), because of the diversity of materials used in redistribution layer and the complicacy of constitutive model during deformation. Most studies focus on the organic material used in WLP, such as PI and epoxy resin, and trying to reduce the wafer warpage by numerical simulation or adjusting the manufacturing process. However, the effect of copper trace layer on wafer warpage, which is proved to be significant, is often neglected. In this paper, the microstructures of four sets of copper films electroplated using different parameters are studied, and the wafer warpage introduced by copper layer is in situ measured. Then, the mathematical relation among plastic strain rate, film stress, and temperature is established. In the end, the detailed relationship between the microstructure and warpage evolution is revealed, and methods to reduce the wafer warpage introduced by copper layer are provided. [ABSTRACT FROM AUTHOR]

The article presents an element increment method that is developed by current time increment method of train traction calculation. A railway route was divided, breaking it down into elements of different lengths. A whole train movement simulation curve ( v- t curve and v- S curve) was formed by splitting the joints of each of the elements' individual simulation curves. During this process, the train velocity variance was calculated by time increment method with assistance of polynomial fitting technology. Additionally, a step-by-step method with iteration was used to combine each element and makes the whole simulation curve continuous. Meanwhile, the energy-saving issue was also taken into account to optimize the simulation curve. This article gives more details about the modeling by providing an example of a railway route based on moving block control. The element increment method is a more effective way to calculate train traction of high-speed railway, and it is an alternative method to train movement simulation for aiding macroscopic railway transportation planning. Copyright © 2017 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Thick Cu films are widely used in wafer level packaging, and the stress evolution during subsequent thermal cycling as well as the induced wafer warpage may make a significant impact on product yielding and needs to be investigated. The stress evolution behavior of 5 μm thick as-electroplated Cu films during thermal cycling are in situ investigated by wafer warpage measurement. It is revealed by microstructure analyses that the grain growth during thermal cycling is ignorable in current work, but dramatic atomic diffusion has occurred, suggesting the deformation mechanism is dominated by diffusional creep. As the dominant diffusion mechanism differs at different temperatures, an equivalent diffusional energy that has a linearly correlation to temperature is proposed, and consequently a stress evolution model based on the equivalent diffusional energy is deduced. Compared with conventional work, the current model has fewer fitted parameters, and shows better agreement with the experiment results. [ABSTRACT FROM AUTHOR]

Polyimide(PI)/Cu composite thick films are widely used in wafer level packaging (WLP), and a common problem is the defects such as void, delamination, crack or wafer warpage induced by thermal stress. Compared with traditional rigid substrate/Cu/passivation system, PI imposes quite a different boundary constraint on Cu, resulting in a special stress evolution, and the corresponding mechanism is far from fully understood. Five sets of composites are constructed to investigate the influence of PI on thermal stress evolution in Cu film by means of in situ wafer warpage measurement under thermal cycling. Together with finite element analyses, it's counterintuitive to find that although PI indeed reduces the stress in Cu, it exacerbates overall wafer warpage at room temperature. Warpage evolution reveals that composites consisting of substrate/PI/Cu sustains a moderate compressive stress while bare PI film is totally stress relaxed at high temperature, indicating that Cu and PI restrains stress relaxation reciprocally. It suggests that mutual effect should be considered when evaluating the stress distribution in polymer-metal composite thick films. [ABSTRACT FROM AUTHOR]

In order to manage Eospalax (Myospalax) baileyi (Plateau zokors) pests with physical method, the efficacy of four different arrow traps was tested to control the plateau zokor in Sichuan Northwest Prairie, China. Population densities of 39.7- 42.1 ha-1 were found in the test plots. Such densities require control measures since pasture damages and economic losses are huge. Traps differed in trigger rates, from 83.8% (three-arrow type), 93.8% (infrared type), 93.9% (T-type), to 94.3% (onearrow type). Capture rates ranged from 84.1 (three-arrow type), 91.8% (infrared type), 93.7% (Ttype) to 93.8% (one-arrow type). The single-arrow trap has the highest efficacy and is also the cheapest, which is important knowledge for zokor control in western China. [ABSTRACT FROM AUTHOR]

In the present paper, we aimed to understand the community dynamics of rodents in post-earthquake. The rodents were seasonally trapped by night trapping method from Jul. 2009 (summer) to Apr. 2013 (spring) in farmhouses and farmland habitats in Yingxiu, a town stricken by earthquake in 2008, and then rodents community dynamics were analyzed. 276 individuals of II species were collected, among which 147 individuals were trapped by 4725 snap-traps in farmhouses while the rest 129 individuals were captured by 4746 snap-traps in farmland. In farmhouses, Rattus tanezumi was the main species before Jan. 2012, but none was trapped since Apr. 2012. The trap success of Anourosorex squamipes varied periodically from Jul. 2009 to Oct. 2010 and kept low for one and a half year before staying as 'rarely trapped individuals' since Apr. 2012. The trap success of Rattus norvegicus was mostly stable as a main species during the study period. The rodents community dynamic showed drastic fluctuations with no periodic regularity. However, to some extent. Shannon-wiener diversity index and Pielou evenness index showed a rising trend in farmland, and a down trend in farmhouses. And the trends were not significantly affected by the precipitation and temperature. Our investigations revealed that the rodent species quickly adapted to various microhabitats in Yingxiu town. The rodents community dynamic was closely related with human activities after the earthquake, such as houses reconstruction, agriculture reestablishment, demolition and the people turnover, which could change rodents' living and feeding conditions. [ABSTRACT FROM AUTHOR]