Effect of the Al/AlOx interfacial stacking sequence on the transport properties of alumina tunnel junctions.

Superconducting quantum bits based on Al/AlOx/Al Josephson junction devices are among the most developed quantum bits at present. The microstructure of the device interface critically affects the electrical properties of Josephson junctions, which in turn severely affects the superconducting quantum bits. Further progress towards scalable superconducting qubits urgently needs to be guided by novel analysis mechanisms or methods to improve the performance of junctions. A direct experimental study of the atomic structure of the device is very challenging. Therefore, we simulated three-dimensional Al/α-Al2O3/Al Josephson junction devices via first-principles electronic structure and ballistic transport calculations to investigate the relationship between transport properties and the Al/Al2O3 stacking sequence. This work elucidates in detail the effects of the aluminum and alumina stacking sequence on the electron transport properties of the Al/Al2O3/Al system at the microscopic level by combining first-principles density functional theory and a non-equilibrium Green's function formalism. It is first revealed that the oxygen termination mode exhibits the least sensitivity to conductance changes in the Al/Al2O3 stacking sequence, offering useful theoretical guidance for increasing the yield of fixed-frequency multi-qubit quantum chips which require tight control on qubit frequency.

Gearbox Fault Diagnosis Method Based on Multidomain Information Fusion.

Traditional methods of gearbox fault diagnosis rely heavily on manual experience. To address this problem, our study proposes a gearbox fault diagnosis method based on multidomain information fusion. An experimental platform consisting of a JZQ250 fixed-axis gearbox was built. An acceleration sensor was used to obtain the vibration signal of the gearbox. Singular value decomposition (SVD) was used to preprocess the signal in order to reduce noise, and the processed vibration signal was subjected to short-time Fourier transform to obtain a two-dimensional time-frequency map. A multidomain information fusion convolutional neural network (CNN) model was constructed. Channel 1 was a one-dimensional convolutional neural network (1DCNN) model that input a one-dimensional vibration signal, and channel 2 was a two-dimensional convolutional neural network (2DCNN) model that input short-time Fourier transform (STFT) time-frequency images. The feature vectors extracted using the two channels were then fused into feature vectors for input into the classification model. Finally, support vector machines (SVM) were used to identify and classify the fault types. The model training performance used multiple methods: training set, verification set, loss curve, accuracy curve and t-SNE visualization (t-SNE). Through experimental verification, the method proposed in this paper was compared with FFT-2DCNN, 1DCNN-SVM and 2DCNN-SVM in terms of gearbox fault recognition performance. The model proposed in this paper had the highest fault recognition accuracy (98.08%).

Research on Three-Phase Asynchronous Motor Fault Diagnosis Based on Multiscale Weibull Dispersion Entropy.

Three-phase asynchronous motors have a wide range of applications in the machinery industry and fault diagnosis aids in the healthy operation of a motor. In order to improve the accuracy and generalization of fault diagnosis in three-phase asynchronous motors, this paper proposes a three-phase asynchronous motor fault diagnosis method based on the combination of multiscale Weibull dispersive entropy (WB-MDE) and particle swarm optimization-support vector machine (PSO-SVM). Firstly, the Weibull distribution (WB) is used to linearize and smooth the vibration signals to obtain sharper information about the motor state. Secondly, the quantitative features of the regularity and orderliness of a given sequence are extracted using multiscale dispersion entropy (MDE). Then, a support vector machine (SVM) is used to construct a classifier, the parameters are optimized via the particle swarm optimization (PSO) algorithm, and the extracted feature vectors are fed into the optimized SVM model for classification and recognition. Finally, the accuracy and generalization of the model proposed in this paper are tested by adding raw data with Gaussian white noise with different signal-to-noise ratios and the CHIST-ERA SOON public dataset. This paper builds a three-phase asynchronous motor vibration signal experimental platform, through a piezoelectric acceleration sensor to discern the four states of the motor data, to verify the effectiveness of the proposed method. The accuracy of the collected data using the WB-MDE method proposed in this paper for feature extraction and the extracted features using the optimization of the PSO-SVM method for fault classification and identification is 100%. Additionally, the proposed model is tested for noise resistance and generalization. Finally, the superiority of the present method is verified through experiments as well as noise immunity and generalization tests.

A Parallelizable Task Offloading Model with Trajectory-Prediction for Mobile Edge Networks.

As an emerging computing model, edge computing greatly expands the collaboration capabilities of the servers. It makes full use of the available resources around the users to quickly complete the task request coming from the terminal devices. Task offloading is a common solution for improving the efficiency of task execution on edge networks. However, the peculiarities of the edge networks, especially the random access of mobile devices, brings unpredictable challenges to the task offloading in a mobile edge network. In this paper, we propose a trajectory prediction model for moving targets in edge networks without users' historical paths which represents their habitual movement trajectory. We also put forward a mobility-aware parallelizable task offloading strategy based on a trajectory prediction model and parallel mechanisms of tasks. In our experiments, we compared the hit ratio of the prediction model, network bandwidth and task execution efficiency of the edge networks by using the EUA data set. Experimental results showed that our model is much better than random, non-position prediction parallel, non-parallel strategy-based position prediction. Where the task offloading hit rate is closed to the user's moving speed, when the speed is less 12.96 m/s, the hit rate can reach more than 80%. Meanwhile, we we also find that the bandwidth occupancy is significantly related to the degree of task parallelism and the number of services running on servers in the network. The parallel strategy can boost network bandwidth utilization by more than eight times when compared to a non-parallel policy as the number of parallel activities grows.

GhGASA10-1 promotes the cell elongation in fiber development through the phytohormones IAA-induced.

BACKGROUND: Cotton is an important cash crop. The fiber length has always been a hot spot, but multi-factor control of fiber quality makes it complex to understand its genetic basis. Previous reports suggested that OsGASR9 promotes germination, width, and thickness by GAs in rice, while the overexpression of AtGASA10 leads to reduced silique length, which is likely to reduce cell wall expansion. Therefore, this study aimed to explore the function of GhGASA10 in cotton fibers development. RESULTS: To explore the molecular mechanisms underlying fiber elongation regulation concerning GhGASA10-1, we revealed an evolutionary basis, gene structure, and expression. Our results emphasized the conservative nature of GASA family with its origin in lower fern plants S. moellendorffii. GhGASA10-1 was localized in the cell membrane, which may synthesize and transport secreted proteins to the cell wall. Besides, GhGASA10-1 promoted seedling germination and root extension in transgenic Arabidopsis, indicating that GhGASA10-1 promotes cell elongation. Interestingly, GhGASA10-1 was upregulated by IAA at fiber elongation stages. CONCLUSION: We propose that GhGASA10-1 may promote fiber elongation by regulating the synthesis of cellulose induced by IAA, to lay the foundation for future research on the regulation networks of GASA10-1 in cotton fiber development.

O-terminated interface for thickness-insensitive transport properties of aluminum oxide Josephson junctions.

Alumina Josephson junction has demonstrated a tremendous potential to realize superconducting qubits. Further progress towards scalable superconducting qubits urgently needs to be guided by novel analysis mechanisms or methods to reduce the thickness sensitivity of the junction critical current to the tunnel barrier. Here, it is first revealed that the termination mode of AlOx interface plays a crucial role in the uniformity of critical current, and we demonstrate that the O-terminated interface has the lowest resistance sensitivity to thickness. More impressively, we developed atomically structured three-dimensional models and calculated their transport properties using a combination of quantum ballistic transport theory with first-principles DFT and NEGF to examine the effects of the Al2O3 termination mode and thickness variations. This work clarifies that O-terminated interface can effectively improve the resistance uniformity of Josephson junction, offering useful guidance for increasing the yield of fixed-frequency multi-qubit quantum chips which require tight control on qubit frequency.

Detecting and Classifying Nuclei Using Multi-Scale Fully Convolutional Network.

The detection and classification of nuclei play an important role in the histopathological analysis. It aims to find out the distribution of nuclei in the histopathology images for the next step of analysis and research. However, it is very challenging to detect and localize nuclei in histopathology images because the size of nuclei accounts for only a few pixels in images, making it difficult to be detected. Most automatic detection machine learning algorithms use patches, which are small pieces of images including a single cell, as training data, and then apply a sliding window strategy to detect nuclei on histopathology images. These methods require preprocessing of data set, which is a very tedious work, and it is also difficult to localize the detected results on original images. Fully convolutional network-based deep learning methods are able to take images as raw inputs, and output results of corresponding size, which makes it well suited for nuclei detection and classification task. In this study, we propose a novel multi-scale fully convolution network, named Cell Fully Convolutional Network (CFCN), with dilated convolution for fine-grained nuclei classification and localization in histology images. We trained CFCN in a typical histology image data set, and the experimental results show that CFCN outperforms the other state-of-the-art nuclei classification models, and the F1 score reaches 0.750.

[Clinical efficacy of recombinant human interleukin 11 on thrombocytopenia in pre-aplastic anemia].

To evaluate the clinical efficacy of recombinant human Interleukin 11 in the treatment of pre-aplastic anemia, six patients with pre-aplastic anemia were injected with rhIL-11 of 6 million units once a day during 7-14 days. Blood platelet counts were taken on day 8, 15, 30 and 60 after the treatment, and bone marrow examination was performed on day 15 as compared with those before treatment. The results showed that platelet counts in 3 out of 6 patients increased remarkably (50%), one of the six increased moderately (16.7%), another case of the six increased slightly (16.7%), platelet in one out of six did not significantly increase (16.7%), the total efficacy rate is 83.3%, the amount of megakaryocyte in bone marrow of all six patients increased, the side effect of the rhIL-11 treatment was light. In conclusion, the efficacy of recombinant human Interleukin-11 in the treatment of thrombocytopenia patients with pre-aplastic anemia is satisfactory. As the number of the cases is too small to conclude, further exploration needs accumulation of more applications.