Xi'an University of Science and Technology

About: Xi'an University of Science and Technology is a education organization based out in Xi'an, China. It is known for research contribution in the topics: Coal & Coal mining. The organization has 10023 authors who have published 7317 publications receiving 51897 citations.

Nanostructured high-strength molybdenum alloys with unprecedented tensile ductility

Abstract: The high-temperature stability and mechanical properties of refractory molybdenum alloys are highly desirable for a wide range of critical applications. However, a long-standing problem for these alloys is that they suffer from low ductility and limited formability. Here we report a nanostructuring strategy that achieves Mo alloys with yield strength over 800 MPa and tensile elongation as large as ~ 40% at room temperature. The processing route involves a molecular-level liquid-liquid mixing/doping technique that leads to an optimal microstructure of submicrometre grains with nanometric oxide particles uniformly distributed in the grain interior. Our approach can be readily adapted to large-scale industrial production of ductile Mo alloys that can be extensively processed and shaped at low temperatures. The architecture engineered into such multicomponent alloys offers a general pathway for manufacturing dispersion-strengthened materials with both high strength and ductility.

A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility

Abstract: The main purpose of the present study is to use three state-of-the-art data mining techniques, namely, logistic model tree (LMT), random forest (RF), and classification and regression tree (CART) models, to map landslide susceptibility. Long County was selected as the study area. First, a landslide inventory map was constructed using history reports, interpretation of aerial photographs, and extensive field surveys. A total of 171 landslide locations were identified in the study area. Twelve landslide-related parameters were considered for landslide susceptibility mapping, including slope angle, slope aspect, plan curvature, profile curvature, altitude, NDVI, land use, distance to faults, distance to roads, distance to rivers, lithology, and rainfall. The 171 landslides were randomly separated into two groups with a 70/30 ratio for training and validation purposes, and different ratios of non-landslides to landslides grid cells were used to obtain the highest classification accuracy. The linear support vector machine algorithm (LSVM) was used to evaluate the predictive capability of the 12 landslide conditioning factors. Second, LMT, RF, and CART models were constructed using training data. Finally, the applied models were validated and compared using receiver operating characteristics (ROC), and predictive accuracy (ACC) methods. Overall, all three models exhibit reasonably good performances; the RF model exhibits the highest predictive capability compared with the LMT and CART models. The RF model, with a success rate of 0.837 and a prediction rate of 0.781, is a promising technique for landslide susceptibility mapping. Therefore, these three models are useful tools for spatial prediction of landslide susceptibility.

Synchronously improved electromagnetic interference shielding and thermal conductivity for epoxy nanocomposites by constructing 3D copper nanowires/thermally annealed graphene aerogel framework

Abstract: 3D copper nanowires-thermally annealed graphene aerogel (CuNWs-TAGA) framework is firstly prepared by freeze-drying followed by thermal annealing from CuNWs, graphene oxide (GO) and L-ascorbic acid. Epoxy resin is then poured back into the above 3D CuNWs-TAGA framework to fabricate the CuNWs-TAGA/epoxy nanocomposites. CuNWs with average diameter of about 120 nm and length of approximate 10 μm are successfully prepared. When the mass fraction of CuNWs-TAGA is 7.2 wt% (6.0–1.2 wt% CuNWs-TAGA), the thermal conductivity coefficient (λ) value of the CuNWs-TAGA/epoxy nanocomposites reaches the maximum of 0.51 W/mK. Meantime, the CuNWs-TAGA/epoxy nanocomposites exhibit the maximum electromagnetic interference shielding effectiveness (EMI SE) value of 47 dB and electrical conductivity (σ) of 120.8 S/m, ascribed to perfect 3D CuNWs-TAGA conductive network structures. Meanwhile, the corresponding elasticity modulus, hardness, glass transition temperature (Tg) and heat-resistance index (THRI) of the CuNWs-TAGA/epoxy nanocomposites increase to 4.69 GPa, 0.33 GPa, 126.3 °C and 181.7 °C, respectively.

Landslide susceptibility mapping using J48 Decision Tree with AdaBoost, Bagging and Rotation Forest ensembles in the Guangchang area (China)

Abstract: Landslides are a manifestation of slope instability causing different kinds of damage affecting life and property. Therefore, high-performance-based landslide prediction models are useful to government institutions for developing strategies for landslide hazard prevention and mitigation. Development of data mining based algorithms shows that high-performance models can be obtained using ensemble frameworks. The primary objective of this study is to investigate and compare the use of current state-of-the-art ensemble techniques, such as AdaBoost, Bagging, and Rotation Forest, for landslide susceptibility assessment with the base classifier of J48 Decision Tree (JDT). The Guangchang district (Jiangxi province, China) was selected as the case study. Firstly, a landslide inventory map with 237 landslide locations was constructed; the landslide locations were then randomly divided into a ratio of 70/30 for the training and validating models. Secondly, fifteen landslide conditioning factors were prepared, such as slope, aspect, altitude, topographic wetness index (TWI), stream power index (SPI), sediment transport index (STI), plan curvature, profile curvature, lithology, distance to faults, distance to rivers, distance to roads, land use, normalized difference vegetation index (NDVI), and rainfall. Relief-F with the 10-fold cross-validation method was applied to quantify the predictive ability of the conditioning factors and for feature selection. Using the JDT and its three ensemble techniques, a total of four landslide susceptibility models were constructed. Finally, the overall performance of the resulting models was assessed and compared using area under the receiver operating characteristic (ROC) curve (AUC) and statistical indexes. The result showed that all landslide models have high performance (AUC > 0.8). However, the JDT with the Rotation Forest model presents the highest prediction capability (AUC = 0.855), followed by the JDT with the AdaBoost (0.850), the Bagging (0.839), and the JDT (0.814), respectively. Therefore, the result demonstrates that the JDT with Rotation Forest is the best optimized model in this study and it can be considered as a promising method for landslide susceptibility mapping in similar cases for better accuracy.

Psychological symptoms of ordinary Chinese citizens based on SCL-90 during the level I emergency response to COVID-19.

Abstract: The World Health Organization (WHO) has declared that the Corona Virus (COVID-19) has become a global pandemic. This study aimed to investigate the psychological symptoms of ordinary Chinese citizens during the Level I Emergency Response throughout China. From January 31 to February 2 2020, an online questionnaire, Symptom Checklist 90 (SCL-90) was designed, and differences in GSI T-scores among subgroups were examined by ANOVA. Based on a cut-off point of the GSI T-scores of 63, the overall sample was divided into high and low-risk groups. of the 1,060 participants investigated in China, more than 70% of them have moderate and higher level of psychological symptoms specifically elevated scores for obsessive compulsion, interpersonal sensitivity, phobic anxiety, and psychoticism. There were no significant differences between males and females. Those who were of over 50 years old, had an undergraduate education and below, were divorced or widowed, and agricultural workers had significantly more symptoms. However, significantly more minors and medical staff were in the high-risk group. These results show that COVID-19 has a significant adverse socio-psychological influence on ordinary citizens. Therefore, governments should equip psychological health departments and pay attention to the people who are in high-risk groups, providing psychological interventions and assistance.