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Globalization has greatly promoted student mobility around the world. Being a developing economy, China witnessed significant growth of students studying internationally, especially with the number of students study at undergraduate programs. However, empirical research on high school students' choice and the decision-making process of pursuing undergraduate programs abroad in the Chinese context is rarely found. Using a locally developed survey questionnaire, this study intends to explore this phenomenon. An analysis of the survey data reveals that cultural and social capital that parents possessed have significant impact on students' choice of pursuing undergraduate education abroad. This study also shows that these young Chinese high school students are quite rational education consumers. A majority of them have the self-efficacy to make informed decisions.

Land use changes are the main cause for the changes of carbon storage, which is of great importance for maintaining regional carbon balance to make multi-scenario projections of future land use change and explore its impacts on carbon storage. In recent years, under the combination of natural factors and policies, with the land use changing significantly, carbon storage of the Weihe River Basin has also changed. Based on the PLUS-InVEST model, we assessed and predicted the spatial and temporal variations of ecosystem carbon storage in the Weihe River Basin and explored the impacts of land-use change. The results showed that land use distribution pattern of the Weihe River Basin did not change much from 2000 to 2020, which was characterized by the decreases of cropland area and the increases of the area of the remaining land use types. The main ways of land use type conversion were cropland to built-up land and inter-conversion of cropland, forest, grassland. Carbon storage in the Weihe River Basin showed an upward trend from 2000 to 2020, with a total increment of 15.31xl06 t. The areas with high carbon storage presented the characteristics of "northeast patch-western scatter-central and southern belt", while low carbon storage distributed in the Guanzhong Plain urban agglomeration located in the lower basin. Compared to 2020, carbon storage in the Weihe River Basin in 2030 would increase under the four scenarios. Carbon storage would increase the least under the economic development scenario, and the most under the ecological protection scenario. The variation of carbon storage in spatial distribution would be embodied in the staggered zone of cropland, forest, and grassland in the upper basin. The results could provide data support for land use management decisions and carbon storage enhancement in the Weihe River Basin. [ABSTRACT FROM AUTHOR]

Cerebral stroke, a type of cerebrovascular disease, has become the second leading cause of death globally. It is closely related to many diseases, including hypertension, diabetes, senile dementia, and so forth. As traditional Chinese medicine formulas are increasingly used to treat stroke and its associated diseases, people have begun to use machine learning methods to analyze Chinese medicine prescriptions and summarize their laws. In this study, we collected the data from classic Chinese formulations. Using the Jaccard similarity coefficient method, we calculated the similarity between different prescriptions. We then employed average linkage clustering, categorizing medicine prescriptions for chronic diseases such as diabetes, hypertension, and coronary heart disease into 12 groups. Some of these included "Heart Failure and Warm Kidney Soup", "Sinus Chamber Junction Syndrome, Quadruple One Depression, Yi Qi, Activating Blood and Feeding Heart Soup", "Nerves One, Depression One, Tian Ma Hook and Vine Drink", and "Bawei Antihypertensive Decoction, Anemia Decoction, Hypotension Decoction, Myocardial Live Drink". We observed that similar prescriptions had more meaningful mutual references. Subsequently, a correlation algorithm was used to analyze the "indications" and "prescription composition", revealing 11 effective correlation rules. Among these, palpitations were strongly correlated with Astragalus membranaceus, Angelica sinensis, and cassia twig; weakness with Salvia miltiorrhiza, A. membranaceus, and A. sinensis; headaches with Ligusticum wallichii; and vertigo with A. membranaceus. These findings provided a theoretical reference for using traditional Chinese medicine in treating cerebral stroke and associated illnesses. [ABSTRACT FROM AUTHOR]

Three kinds of Cu-Ni-Al alloys, whose chemical compositions are located in the Cu-rich corner of the isothermal section of the Cu-Ni-Al ternary phase diagram, were prepared by melting and casting firstly, and then solution and aging treatments were carried out. The microstructure was characterized and the competitive formation process of Ni-Al intermetallics were discussed. The results show that there are little amounts of NiAl phase at the grain boundary and needle- or particle-like Ni3Al phase inside the Cu matrix in all the as-cast alloys, although they are in the single-phase area. Solution and aging treatments mainly result in the disappearance and precipitation of Ni3Al phase, but the precipitations during aging are much smaller than those in the as-cast alloys. Thermodynamics and kinetics calculation indicate that the NiAl intermetallic wins out in the solidification process because of its lower change in Gibbs free energy, while Ni3Al phase is first to precipitate during aging due to its lower formation enthalpy and required Al concentration. The most important contribution of this work is that it proves that intermetallics can precipitate from the so-called single-phase zone in the Cu-rich corner of the Cu-Ni-Al phase diagram, which is the necessary prerequisite for the realization of high strength and high electrical conductivity. [ABSTRACT FROM AUTHOR]

At present, the Synthetic Aperture Radar (SAR) image classification method based on Convolution Neural Network (CNN) has faced some problems such as poor noise resistance and generalization ability. Spiking Neural Network (SNN) is one of the core components of brain-like intelligence and has good application prospects. This article constructs a complete SAR image classifier based on unsupervised and supervised learning of SNN by using spike sequences with complex spatio-temporal information. We firstly expound on the spiking neuron model, the receptive field of SNN, and the construction of spike sequence. Then we put forward an unsupervised learning algorithm based on STDP and a supervised learning algorithm based on gradient descent in series. The average classification accuracy of single layer and bilayer unsupervised learning SNN in three categories images on MSTAR dataset is 81.1% and 82.9%, respectively. Furthermore, the convergent output spike sequences of unsupervised learning can be used as teaching signals. Based on the TensorFlow framework, a single layer supervised learning SNN is built from the bottom, and the classification accuracy reaches 90.2%. By comparing noise resistance and model parameters between SNNs and CNNs, the effectiveness and outstanding advantages of SNN are verified. Code to reproduce our experiments is available at https://github.com/Jiankun-chen/Supervised-SNN-with-GD. [ABSTRACT FROM AUTHOR]

Soybean is an important global crop for edible protein and oil, and plant height is a main breeding goal which is closely related to its plant shape and yield. In this research, a high-density genetic linkage map was constructed by 1996 SNP-bin markers on the basis of a recombinant inbred line population derived from Dongnong L13 × Henong 60. A total of 33 QTL related to plant height were identified, of which five were repeatedly detected in multiple environments. In addition, a 455-germplasm population with 63,306 SNP markers was used for multi-locus association analysis. A total of 62 plant height QTN were detected, of which 26 were detected repeatedly under multiple methods. Two candidate genes, Glyma.02G133000 and Glyma.05G240600 , involving in plant height were predicted by pathway analysis in the regions identified by multiple environments and backgrounds, and validated by qRT-PCR. These results enriched the soybean plant height regulatory network and contributed to molecular selection-assisted breeding. [ABSTRACT FROM AUTHOR]

Building segmentation of synthetic aperture radar (SAR) images is a challenging task that has not been solved well. High-resolution interferometric SAR (InSAR) images can provide delicate textures and interferometric phase images useful for building segmentation. However, current semantic segmentation networks in computer vision cannot be directly applied in InSAR building segmentation tasks to get good results because of the InSAR images’ particularity. In this article, we present a novel complex-valued convolutional and multifeature fusion network (CVCMFF Net) specifically for building semantic segmentation of InSAR images. This CVCMFF Net not only learns from the complex-valued SAR images but also considers multiscale and multichannel feature fusion. It can effectively segment the layover, shadow, and background on both the simulated InSAR building images and the real airborne InSAR images. The segmentation performance of CVCMFF Net is significantly improved compared with those of other state-of-the-art networks. By feature visualization, the feature extraction rule and feature fusion mechanism of the network are explored. We hope that the proposed network can be beneficial to InSAR phase filtering, phase unwrapping, and information extraction in urban areas. [ABSTRACT FROM AUTHOR]

Background: Hypertension has been reported as the most prevalent comorbidity in patients with coronavirus disease 2019 (COVID-19). This retrospective study aims to compare the clinical characteristics and outcomes in COVID-19 patients with or without hypertension. Methods: A total of 944 hospitalized patients with laboratory-confirmed COVID-19 were included from January to March 2020. Information from the medical record, including clinical features, radiographic and laboratory results, complications, treatments, and clinical outcomes, were extracted for the analysis. Results: A total of 311 (32.94%) patients had comorbidity with hypertension. In COVID-19 patients with hypertension, the coexistence of type 2 diabetes (56.06% vs 43.94%), coronary heart disease (65.71% vs 34.29%), poststroke syndrome (68.75% vs 31.25%) and chronic kidney diseases (77.78% vs 22.22%) was significantly higher, while the coexistence of hepatitis B infection (13.04% vs 86.96%) was significantly lower than in COVID-19 patients without hypertension. Computed tomography (CT) chest scans show that COVID-19 patients with hypertension have higher rates of pleural effusion than those without hypertension (56.60% vs 43.40%). In addition, the levels of blood glucose [5.80 (IQR, 5.05– 7.50) vs 5.39 (IQR, 4.81– 6.60)], erythrocyte sedimentation rate (ESR) [28 (IQR, 17.1– 55.6) vs 21.8 (IQR, 11.5– 44.1), P=0.008], C-reactive protein (CRP) [17.92 (IQR, 3.11– 46.6) vs 3.15 (IQR, 3.11– 23.4), P=0.013] and serum amyloid A (SAA) [99.28 (IQR, 8.85– 300) vs 15.97 (IQR, 5.97– 236.1), P=0.005] in COVID-19 patients with hypertension were significantly higher than in patients without hypertension. Conclusion: It is common for patients with COVID-19 to have the coexistence of hypertension, type 2 diabetes, coronary heart disease and so on, which may exacerbate the severity of COVID-19. Therefore, optimal management of hypertension and other comorbidities is essential for better clinical outcomes. [ABSTRACT FROM AUTHOR]

With the development of distribution networks, large amounts of distribution terminal units (DTU) are gradually integrated into the power system. However, limited numbers of maintenance engineers can hardly cope with the pressure brought about by the substantial increase of DTU devices. As DTU fault would pose a threat to the stable and safe operation of power systems; thus, it is rather significant to reduce the fault incidence of DTU devices and improve the efficiency of fault elimination. In this paper, a DTU fault analysis method using an association rule mining algorithm was proposed. Key factors of DTU fault were analyzed at first. Then, the main concept of the Eclat algorithm was illustrated, and its performance was compared with FP-growth and Apriori algorithms using DTU fault databases of different sizes. Afterwards, a DTU fault analysis method based on the Eclat algorithm was proposed. The practicality of this method was proven by experiment using a realistic DTU fault database. Finally, the application of this method was presented to demonstrate its effectiveness. [ABSTRACT FROM AUTHOR]

Metal Mn powders instead of conventional Mn salts were used for the first time tried as a Mn source to synthesize high-performance LiNi0.5Mn1.5O4 (M-LNMO) using a sol-gel method. Mn powders promote the formation of well-crystallized octahedra with the Fd3 m spinel structure. M-LNMO has a lower stress, a slightly smaller lattice constant, which arises from the lower amount of Mn3+ because of the lower oxygen deficiency, a higher cation ordering, and longer Li−O and shorter Mn (Ni)−O bonds than LiNi0.5Mn1.5O4 synthesized from manganese acetate (MA-LNMO). The well-crystallized octahedra, the low stress, the low amount of Mn3+, and the long Li−O and short Mn (Ni)−O bonds for M-LNMO result in its excellent performance such as a discharge capacity of 68 mAh g−1 at 50 C and a capacity retention of 90 % after 600 cycles at 10 C. This study raises the possibility to promote the synthesis of high-performance LiNi0.5Mn1.5O4 for mass production by using cheap and simple Mn sources such as Mn powders. [ABSTRACT FROM AUTHOR]