Observation of room-temperature ferroelectricity in elemental Te nanowires

Ferroelectrics are essential in low-dimensional memory devices for multi-bit storage and high-density integration. A polar structure is a necessary premise for ferroelectricity, mainly existing in compounds. However, it is usually rare in elemental materials, causing a lack of spontaneous electric polarization. Here, we report an unexpected room-temperature ferroelectricity in few-chain Te nanowires. Out-of-plane ferroelectric loops and domain reversal are observed by piezoresponse force microscopy. Through density functional theory, we attribute the ferroelectricity to the ion-displacement created by the interlayer interaction between lone pair electrons. Ferroelectric polarization can induce a strong field effect on the transport along the Te chain, supporting a self-gated field-effect transistor. It enables a nonvolatile memory with high in-plane mobility, zero supply voltage, multilevel resistive states, and a high on/off ratio. Our work provides new opportunities for elemental ferroelectrics with polar structures and paves a way towards applications such as low-power dissipation electronics and computing-in-memory devices

Short-term Exposure to Fine Particles and Risk of Cause-Specific Mortality — China, 2013-2018

What is already known about this topic? Short-term exposure to PM2.5 has been associated with population excess death. This issue is of critical concern in China given its high level of exposure to PM2.5 compared to that of the rest of the world. What is added by this report? Existing studies were conducted from 2013-2015 and have failed to capture the full effects of China’s actions towards cleaner air in recent years, such as the first Action Plan for Air Pollution Prevention and Control issued in 2013. This study uses the longest time series data to date from 2013-2018, provides the latest evidence on PM2.5 and cause-specific death nationwide, and identifies regional patterns of PM2.5-related effects as well as PM2.5-related susceptible populations. What are the implications for public health practice? This study suggests that the development of standards and the implementation of actions to clean the air and protect public health should be tailored to PM2.5-related sensitive diseases, susceptible populations, and regional patterns

Explainable Machine-Learning Predictions for Peak Ground Acceleration

Peak ground acceleration (PGA) prediction is of great significance in the seismic design of engineering structures. Machine learning is a new method to predict PGA and does have some advantages. To establish explainable prediction models of PGA, 3104 sets of uphole and downhole seismic records collected by the KiK-net in Japan were used. The feature combinations that make the models perform best were selected through feature selection. The peak bedrock acceleration (PBA), the predominant frequency (FP), the depth of the soil when the shear wave velocity reaches 800 m/s (D800), and the bedrock shear wave velocity (Bedrock Vs) were used as inputs to predict the PGA. The XGBoost (eXtreme Gradient Boosting), random forest, and decision tree models were established, and the prediction results were compared with the numerical simulation results The influence between the input features and the model prediction results were analyzed with the SHAP (SHapley Additive exPlanations) value. The results show that the R2 of the training dataset and testing dataset reach up to 0.945 and 0.915, respectively. On different site classifications and different PGA intervals, the prediction results of the XGBoost model are better than the random forest model and the decision tree model. Even if a non-integrated algorithm (decision tree model) is used, its prediction effect is better than the numerical simulation methods. The SHAP values of the three machine learning models have the same distribution and densities, and the influence of each feature on the prediction results is consistent with the existing empirical data, which shows the rationality of the machine learning models and provides reliable support for the prediction results

Pore-scale numerical simulation of supercritical CO2-brine two-phase flow based on VOF method

CO2 capture and storage technology is favorable for the reduction of CO2 emissions. In recent years, a great number of research achievements have been obtained on CO2 geological storage from nano scale to oil/gas reservoir scale, but most studies only focus on the flow behaviors in single-dimension porous media. Besides, the physical experiment method is influenced by many uncertain factors and consumes a lot of time and cost. In order to deeply understand the flow behaviors in the process of CO2 geological storage in microscopic view and increase the volume of CO2 geological storage, this paper established 2D and 3D models by using VOF (Volume of Fluid) method which can track the dynamic change of two-phase interface, to numerically simulate supercritical CO2-brine two-phase flow. Then, the distribution characteristics of CO2 clusters and the variation laws of CO2 saturation under different wettability, capillary number and viscosity ratio conditions were compared, and the intrinsic mechanisms of CO2 storage at pore scale were revealed. And the following research results were obtained. First, with the increase of rock wettability to CO2, the sweep range of CO2 enlarged, and the disconnection frequency of CO2 clusters deceased, and thus the volume of CO2 storage increased. Second, with the increase of capillary number, the displacement mode transformed from capillary fingering to stable displacement, and thus the volume of CO2 storage increased. Third, as the viscosity of injected supercritical CO2 gradually approached that of brine, the flow resistance between two-phase fluids decreased, promoting the ''lubricating effect''. As a result, the flow capacity of CO2 phase was improved, and thus the volume of CO2 storage was increased. Fourth, the influence degrees of wettability, capillary number and viscosity ratio on CO2 saturation were different in multi-dimensional porous media models. In conclusion, the CO2-brine two-phase flow simulation based on VOF method revealed the flow mechanisms in the process of CO2 geological storage at pore scale, which is of guiding significance to the development of CCUS technology and provides theoretical guidance and technical support for the study of CO2 geological storage in a larger scale

Force Tracking Control Method for Robotic Ultrasound Scanning System under Soft Uncertain Environment

Robotic ultrasound scanning has excellent potential to reduce physician workload, obtain higher-quality imaging, and reduce costs. However, the traditional admittance control strategy for robotics cannot meet the high-precision force control requirements for robots, which are critical for improving image quality and ensuring patient safety. In this study, an integral adaptive admittance control strategy is proposed for contact force control between an ultrasound probe and human skin to enhance the accuracy of force tracking. First, a robotic ultrasound scanning system is proposed, and the system’s overall workflow is introduced. Second, an adaptive admittance control strategy is designed to estimate the uncertain environmental information online, and the estimated parameters are used to modify the reference trajectory. On the basis of ensuring the stability of the system, an integral controller is then introduced to improve the steady-state response. Subsequently, the stability of the proposed strategy is analysed. In addition, a gravity compensation process is proposed to obtain the actual contact force. Finally, through a simulation analysis, the effectiveness of the strategy is discussed. Simultaneously, a series of experiments are carried out on the robotic ultrasound scanning system, and the results show that the strategy can successfully maintain a constant contact force under soft uncertain environments, which effectively improves the efficiency of scanning

STG-GAN: A spatiotemporal graph generative adversarial networks for short-term passenger flow prediction in urban rail transit systems

Short-term passenger flow prediction is an important but challenging task for better managing urban rail transit (URT) systems. Some emerging deep learning models provide good insights to improve short-term prediction accuracy. However, there exist many complex spatiotemporal dependencies in URT systems. Most previous methods only consider the absolute error between ground truth and predictions as the optimization objective, which fails to account for spatial and temporal constraints on the predictions. Furthermore, a large number of existing prediction models introduce complex neural network layers to improve accuracy while ignoring their training efficiency and memory occupancy, decreasing the chances to be applied to the real world. To overcome these limitations, we propose a novel deep learning-based spatiotemporal graph generative adversarial network (STG-GAN) model with higher prediction accuracy, higher efficiency, and lower memory occupancy to predict short-term passenger flows of the URT network. Our model consists of two major parts, which are optimized in an adversarial learning manner: (1) a generator network including gated temporal conventional networks (TCN) and weight sharing graph convolution networks (GCN) to capture structural spatiotemporal dependencies and generate predictions with a relatively small computational burden; (2) a discriminator network including a spatial discriminator and a temporal discriminator to enhance the spatial and temporal constraints of the predictions. The STG-GAN is evaluated on two large-scale real-world datasets from Beijing Subway. A comparison with those of several state-of-the-art models illustrates its superiority and robustness. This study can provide critical experience in conducting short-term passenger flow predictions, especially from the perspective of real-world applications.Comment: 13 pages, 10 figures, 5 table

Performance Evaluation and Comparison between Direct and Chemical-Assisted Picosecond Laser Micro-Trepanning of Single Crystalline Silicon

The fabrication of micro-holes in silicon substrates that have a proper taper, higher depth-to-diameter ratio, and better surface quality has been attracting intense interest for a long time due to its importance in the semiconductor and MEMS (Micro-Electro-Mechanical System) industry. In this paper, an experimental investigation of the machining performance of the direct and chemical-assisted picosecond laser trepanning of single crystalline silicon is conducted, with a view to assess the two machining methods. The relevant parameters affecting the trepanning process are considered, employing the orthogonal experimental design scheme. It is found that the direct laser trepanning results are associated with evident thermal defects, while the chemical-assisted method is capable of machining micro-holes with negligible thermal damage. Range analysis is then carried out, and the effects of the processing parameters on the hole characteristics are amply discussed to obtain the recommended parameters. Finally, the material removal mechanisms that are involved in the two machining methods are adequately analyzed. For the chemical-assisted trepanning case, the enhanced material removal rate may be attributed to the serious mechanical effects caused by the liquid-confined plasma and cavitation bubbles, and the chemical etching effect provided by NaOH solution

The immunogenicity and protective immunity of multi-epitopes DNA prime-protein boost vaccines encoding Amastin-Kmp-11, Kmp11-Gp63 and Amastin-Gp63 against visceral leishmaniasis.

Visceral leishmaniasis (VL) is the most fatal form of leishmaniasis if left untreated and 50,000 to 90,000 new cases of VL occur worldwide each year. Although various vaccines had been studied in animal models, none of them was eligible to prevent human from infections. In this study, according to the silico analysis of Leishmania Amastin, Kmp-11 and Gp63 protein, dominant epitope sequences of these proteins were selected and linked to construct dominant multi-epitopes DNA and protein vaccines (Amastin-Kmp-11, Amastin-Gp63 and Kmp-11-Gp63) against VL. BALB/c mice were immunized with a DNA prime-protein boost immunization strategy and challenged with a new Leishmania parasite strain isolated from a VL patient. After immunization, the results including specific antibody titers, IL-4 and TNF-α levels, and CD4 and CD8 T cell proportion suggested the potent immunogenicity of the three vaccines. After infection, the results of spleen parasite burdens in the three vaccine groups were significantly lower than those of control groups, and the parasite reduction rates of Amastin-Kmp-11, Amastin-Gp63 and Kmp-11-Gp63 groups were 89.38%, 91.01% and 88.42%, respectively. Spleen smear observation and liver histopathological changes showed that all vaccine groups could produce significant immunoprotection against VL and Amastin-Gp63 vaccine was the best. In conclusion, our work demonstrated that the three dominant multi-epitopes Amastin-Kmp-11, Amastin-Gp63 and Kmp-11-Gp63 DNA prime-protein boost vaccines might be new vaccine candidates for VL, and the Amastin-Gp63 vaccine have best efficacy

High risks of HIV transmission for men who have sex with men--a comparison of risk factors of HIV infection among MSM associated with recruitment channels in 15 cities of China.

While the HIV epidemic varies greatly by region and population group throughout China, the HIV incidence among men who have sex with men (MSM) continues to rise at an alarmingly fast pace. We seek to analyze the risk factors associated with HIV infection among MSM recruited from different channels in large urban centers across China, in an attempt to shed light on the design of future targeted intervention strategies.A total of 33,684 MSM from 14 cities and one province were recruited from July to December 2011. Demographic (e.g. age, marital status, education) and behavioral (e.g. condom use, HIV testing history) data were collected using information collection cards. Blood samples were also collected to test for HIV and Syphilis.Participants were recruited from five different channels, and all demonstrated distinct characteristics. The overall rate of positive HIV screening was 6.27% and the rate of syphilis infection was 6.50%. Participants recruited from bathhouses had the highest HIV (11.80%) and syphilis infection rates (11.20%). Participants who were infected with syphilis had the highest HIV-positive screening rate (13.75%; 95% CI OR, 2.33-3.06). living in the southwest region of the country (11.64%; OR=2.76, 95%CI OR 2.19-3.47), Being >20 years of age (P<0.001), living in the southwest region of the country (OR=2.76, 95%CI 2.19-3.47), not having sex with female over the previous 3 months (OR=1.27, 95%CI 1.09-1.48), no condom use during the last anal intercourse (OR=1.54, 95%CI 1.39-1.70) and other factors were all associated with a higher probability of having an HIV-positive test result.Depending on the way they are recruited, more targeted interventions are required to prevent the spread of HIV/AIDS among MSM with different characteristics and behaviors. Results from this study could provide evidence for researchers to conduct further studies and policy-makers to establish more effective and strategic interventions for MSM in China