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In this project, when the exchange rate of RMB against the U.S. dollar rises in the trade between China and the United States, due to the decline in the income of export goods, the decline in total export value will lead to a decrease in the number of dollars exchanged by China, and on the contrary, the income from imports will increase, so Imports also increase, causing more dollars to be spent. In this way, after a certain period, the total amount of U.S. dollars held by China will decrease, which will lead to an increase in China's demand for U.S. dollars, which will lead to the appreciation of the U.S. dollar, that is, the exchange rate of the RMB against the U.S. dollar will decline.

In high-temperature reverse-bias test of an insulated-gate bipolar transistor module, the problem of self-heating in chip resulting from power loss arises with high frequency. To address this problem, the law of the variation in leakage current with temperature for the reverse-bias state of a device is derived and utilized to establish a temperature calibration curve, with which the online measurement of the insulate-gate bipolar transistor module chip temperature can be implemented directly by real-time monitoring of device leakage current in high-temperature reverse-bias test. The method we proposed solves the problem of large measurement error in chip temperature obtained by traditional thermal resistance calculation method. In addition, real-time chip temperature can be monitored without introducing additional test circuit or HTRB interruption experiment. To demonstrate the effectiveness of the proposed method, the switching small-current temperature measurement method is used to make comparison, and the experiment result indicates that the temperature of the chip in the blocking test can be obtained with high precision by using the leakage current measurement method presented in this work.

Background The volume of COVID-19–related misinformation has long exceeded the resources available to fact checkers to effectively mitigate its ill effects. Automated and web-based approaches can provide effective deterrents to online misinformation. Machine learning–based methods have achieved robust performance on text classification tasks, including potentially low-quality-news credibility assessment. Despite the progress of initial, rapid interventions, the enormity of COVID-19–related misinformation continues to overwhelm fact checkers. Therefore, improvement in automated and machine-learned methods for an infodemic response is urgently needed. Objective The aim of this study was to achieve improvement in automated and machine-learned methods for an infodemic response. Methods We evaluated three strategies for training a machine-learning model to determine the highest model performance: (1) COVID-19–related fact-checked data only, (2) general fact-checked data only, and (3) combined COVID-19 and general fact-checked data. We created two COVID-19–related misinformation data sets from fact-checked “false” content combined with programmatically retrieved “true” content. The first set contained ~7000 entries from July to August 2020, and the second contained ~31,000 entries from January 2020 to June 2022. We crowdsourced 31,441 votes to human label the first data set. Results The models achieved an accuracy of 96.55% and 94.56% on the first and second external validation data set, respectively. Our best-performing model was developed using COVID-19–specific content. We were able to successfully develop combined models that outperformed human votes of misinformation. Specifically, when we blended our model predictions with human votes, the highest accuracy we achieved on the first external validation data set was 99.1%. When we considered outputs where the machine-learning model agreed with human votes, we achieved accuracies up to 98.59% on the first validation data set. This outperformed human votes alone with an accuracy of only 73%. Conclusions External validation accuracies of 96.55% and 94.56% are evidence that machine learning can produce superior results for the difficult task of classifying the veracity of COVID-19 content. Pretrained language models performed best when fine-tuned on a topic-specific data set, while other models achieved their best accuracy when fine-tuned on a combination of topic-specific and general-topic data sets. Crucially, our study found that blended models, trained/fine-tuned on general-topic content with crowdsourced data, improved our models’ accuracies up to 99.7%. The successful use of crowdsourced data can increase the accuracy of models in situations when expert-labeled data are scarce. The 98.59% accuracy on a “high-confidence” subsection comprised of machine-learned and human labels suggests that crowdsourced votes can optimize machine-learned labels to improve accuracy above human-only levels. These results support the utility of supervised machine learning to deter and combat future health-related disinformation.

In this work, we prepared BiCuSeO polycrystalline films by evaporating precursors following with selenization treatment. Their optical and photoelectrical properties were investigated from visible to infrared light. Optical absorption spectrum indicated that the absorption coefficient was 104–105 cm−1 from 0.4 μm to 2 μm. Photo induced voltages were clearly detected when the film surfaces were irradiated by the lasers with various wavelengths and powers. The voltage had a linear dependence with the laser illumination intensity, which indicated the main response mechanism of photo-thermal electric effect in the films. All the results suggested that the BiCuSeO film was a potential material for near infrared detector application.

StakPak insulated gate bipolar transistor (IGBT) modules, which have complex internal structures and double-sided heat dissipation characteristics, represent a complex physical environment with multi-physical field coupling under normal working conditions. This makes it difficult to provide an accurate evaluation of the important reliability parameters of the devices contained in these modules, such as the device thermal resistance and junction temperature. In this paper, ANSYS software is used to model and perform a finite element analysis of single-chip StakPak IGBT modules. The heat flux ratio between the collector and emitter of the IGBT chip is calculated via a simulation; the overall power consumption of the module is then divided into two parts, and the thermal resistance values of the two electrodes, the overall temperature and the stress distribution of the IGBT chip and the StakPak device are simulated and calculated. The relationship between the heat dissipation ratio of the two electrodes and the thermal resistance is studied with respect to pressure, temperature and other factors.

With the wide use of flexible transmission technology, IGBT chip technology is developing rapidly. The stakpak IGBT chip has the advantages of double-sided heat dissipation that the traditional welded IGBT does not have, but the unclear understanding of the multi physical field interaction with it has become the bottleneck of its wide application. In this paper, according to the Fourier expansion formula, the simulation model is built by SolidWorks, and the finite element analysis software ANSYS Workbench is used. The ANSYS Workbench sets the simulation conditions and obtains the double-sided heat flux of the stakpak IGBT sub module, and compares it with the data in the actual working condition to verify the accuracy of the simulation, so as to provide theoretical support for the improvement of the stability and safety of the stakpak IGBT device in operation.

Jammer localization is critical for navigation systems and multi-jammer with unknown number is a more challengeable scene. However, existing methods usually have high computation or time complexity. In this paper, we propose an efficient method just relying on positions and Jam-to-Signal ratio(JSR) changes from crowdsourcing receivers. Jammer signals are discriminated by clustering receivers. In each cluster, we employ JSR changes during jamming to calculate a weighted centroid as the jammer location. With little restriction on devices and no need of much prior knowledge, our method has high localization accuracy and low time complexity on simulation experiments.

The internal anti-parallel Schottky barrier diode threshold voltage of SiC metal-oxide-semiconductor field-effect transistor (MOSFET) modules is less than that of their body diodes, which prevents the use of the body diode voltage drop to measure junction temperature. In this paper, a thermal resistance test of the SiC MOSFET module is proposed based on the onstate voltage drop as a temperature-sensitive electrical parameter. Measurements are performed with the method and its feasibility evaluated. The on-state voltage drop temperature sensitivity under different gate voltages and the repeatability of the on-state voltage drop parameters before and after electric power heating in the thermal resistance tests are studied. The on-state voltage drop is measured under a high gate voltage and the accuracy of junction temperature measurement improves when the grid voltage remains the same during heating and measurement.

Considering the problem of measuring the peak junction temperature of a double-chip module with temperature inhomogeneity, we use a double-chip parallel model to analyze peak junction temperature measurement error for the traditional electrical method. An improved test method is proposed that is based on the small current–voltage drop method—specifically the dual current test method employing an extended-dimension calibration curve library. This method realizes a match of the calibration current with the test current by expanding the dimension database, eliminates errors of temperature measurements, and finally obtains the peak junction temperature of the module. The measurement result is close to the set temperature of the model, thereby verifying its accuracy.

Air separation units (ASUs), as a single industrial equipment item, accounted for a considerable proportion (4.97%) of China’s national total power consumed. Therefore, combining with energy storage technology and implementing demand side management on ASUs can balance the demand on a power grid and bring economic benefits to various enterprises. Air liquefaction is a facet of air separation technology, however, stand-alone liquid air energy storage (LAES) has still issues such as the need for signification investment and a long payback period. To address these issues, we propose a novel air separation unit with energy storage and generation (ASU-ESG) which integrates an ASU, a liquid air storage unit, and an energy release and generation unit. It not only meets the product purity and yield requirements for ASUs, but also realizes the large-scale storage with only one type of device, using a single technology. To assess the performance of ASU-ESG, energy efficiency and economic analyses are conducted, and its effects on power grid balancing are discussed. When no external energy used in ASU-ESG, its electrical round-trip efficiency reaches 53.18%, the electricity cost saves 4.58~6.84% compared with the conventional internal compression ASUs, and the payback period for the LAES system is 2.8~4.2 years. If the ASU-ESG is implemented in China’s ASUs, the annual reduction in the average peak-valley ratio (25.98%) in China is 6.4~7.3%. Considering the widespread application of such ASUs’, for any country and region, its implementation will be of great practical significance to improving the economic benefits of ASUs and balancing the power demand.

Isoimperatorin (QHS) is a phytoconstituent found in the methanolic extracts obtained from the roots of Angelica dahurica, which contains anti-inflammatory, anti-bacterial, analgesic, anti-tumor, and vasodilatory activities. QHS possesses potent antagonistic activity against lipopolysaccharide (LPS)-induced inflammation; however, the mechanism of action remains unclear. In this study, we investigated the anti-inflammatory effect of QHS and explored the underlying mechanisms. The QHS was purchased from Jiangsu Yongjian Pharmaceutical Co., Ltd. (Jiangsu, China). We performed MTT assay, real-time PCR, ELISA, and western blotting experiments to assess the anti-inflammatory activity and the possible mechanism of QHS in vitro. Molecular docking was performed to study the binding of QHS and myeloid differentiation protein-2 (MD-2) and elucidate the possible anti-inflammatory mechanism. QHS had no significant effect on cell viability. Moreover, pre-treatment with QHS significantly decreased the release of inflammatory cytokines and mediators including NO, TNF-α, IL-6, and IL-1β. In addition, real-time PCR showed that QHS decreased the mRNA expressions of iNOS, COX-2 TNF-α, IL-6, and IL-1β. Western blotting indicated that QHS could inhibit the expression of the proteins associated with the LPS-TLR4/MD-2-NF-κB signaling pathway. Lastly, molecular docking revealed a possible binding mechanism between QHS and MD-2. QHS exhibited anti-inflammatory activity when combined with MD-2, regulating the LPS-TLR4/MD-2-NF-κB signaling pathway, and inhibiting the release and expression of inflammatory cytokines and mediators. Furthermore, QHS can be used as a potential TLR4 antagonist, which blocks MD-2 binding, for treating inflammatory responses induced by LPS.

In this work, copper zinc tin sulfide (CZTS) films were deposited by direct current sputtering and the samples were annealed in different oven-set temperatures and atmosphere (Ar and H2S). The surface evolution was investigated carefully by using scanning electron microscopy (SEM), Raman spectroscopy and x-ray photoelectron spectroscopy. The surface of the as-sputtered precursor contained little Cu and large amounts of Zn and Sn. The metallic precursor was continuous and compact without pinholes or cracks. With the increase of the temperature from room temperature to 250°C, Cu atoms diffused to the film surface to form Cu1−x S and covered other compounds. Some small platelets were smaller than 500 nm spreading randomly in the holes of the film surfaces. When the temperature reached 350°C, Zn and Sn atoms began to diffuse to the surface and react with S or Cu1−x S. At 400°C, SEM showed the melting of large particles and small particles with a size from 100 nm to 200 nm in the background of the film surface. Excess Zn segregated towards the surface regions and formed ZnS phase on the surface. In addition, the signal of sodium in the CZTS surface was observed above 400°C. At 600°C, a large amount of regular structures with clear edges and corners were observed in the film surface in SEM images. A clear recrystallized process on the surface was assumed from those observations.

In this work, we reported TEM observations of several CdS thin films prepared by close space submission, sputtering, chemical bath deposition and electronic deposition technologies. The CdS layers prepared by close space submission and sputtering methods had larger grains (50–80 nm), while the CdS layers deposited by chemical bath had very small grains with the size of 5–10 nm. Electro deposited CdS yielded more like an epitaxial film. The optical and chemical properties showed good quality of CdS films prepared by all methods. In this work we proposed a suitable CdS multilayer structure to use it as a window layer in thin film solar cells.

A series of numerical simulations were conducted to study the influences of separated over-fire air (SOFA) distribution, yawing and tilting angles on the flue gas temperature deviation of a 660MW tangentially coal-fired boiler. The turbulent flow, combustion, pollutants and emission characteristics were investigated. The numerical model developed in the study was first validated with field test, which showed good consistence between the numerical and experimental results. Further study indicated that with the increase of SOFA rate, the coal burnout rate, temperature uniformity coefficient and temperature deviation on the temperature detection line (TDL) declined, and the NOx emission dropped. Increase the SOFA yawing angle in reverse tangential direction leads to reduction of high temperature region in the center of lower furnace exit section and in the left of upper furnace exit section, which is positive in reducing gas temperature deviation. Tilting SOFA nozzle upward leads to upward movement of high temperature region in the upper furnace burnout region, increases in gas temperature of upper and lower furnace exits, decreases in temperature distribution uniformity coefficient of furnace exit section and a slight decrease in coal burnout rate, which is negative for reducing gas temperature deviation. (CSPE)

Nacelle wind speed transfer function (NTF) is usually used for power prediction and operational control of a horizontal axis wind turbine. Nacelle wind speed exhibits high instability as it is influenced by both incoming flow and near wake of a wind turbine rotor. Enhanced understanding of the nacelle wind speed characteristics is critical for improving the accuracy of NTF. This paper presents Reynolds-averaged Navier&ndash, Stokes (RANS) simulation results obtained for a multi-megawatt wind turbine under both stable and dynamic incoming flows. The dynamic inlet wind speed varies in the form of simplified sinusoidal and superposed sinusoidal functions. The simulation results are analyzed in time and frequency domains. For a stable inlet flow, the variation of nacelle wind speed is mainly influenced by the blade rotation. The influence of wake flow shows high frequency characteristics. The results with stable inlet flow show that the reduction of the nacelle wind speed with respect to the inlet wind speed is overestimated for low wind speed condition, and underestimated for high wind speed condition. Under time-varing inflow conditions, for the time scale and fluctuation amplitude subject to the International Electrotechnical Commission (IEC) standard, the nacelle wind speed is mainly influenced by the dynamic inflow. The variation of inflow can be recovered by choosing a suitable low pass filter. The work in this paper demonstrates the potential for building accurate NTF based on Computational Fluid Dynamics (CFD) simulations and signal analysis.