In this work, a thin-film transistor gas sensor based on the p-N heterojunction is fabricated by stacking chemical vapor deposition-grown tungsten disulfide (WS2) with a sputtered ...indium–gallium–zinc-oxide (IGZO) film. To the best of our knowledge, the present device has the best NO2 gas sensor response compared to all the gas sensors based on transition-metal dichalcogenide materials. The gas-sensing response is investigated under different NO2 concentrations, adopting heterojunction device mode and transistor mode. High sensing response is obtained of p-N diode in the range of 1–300 ppm with values of 230% for 5 ppm and 18 170% for 300 ppm. On the transistor mode, the gas-sensing response can be modulated by the gate bias, and the transistor shows an ultrahigh response after exposure to NO2, with sensitivity values of 6820% for 5 ppm and 499 400% for 300 ppm. Interestingly, the transistor has a typical ambipolar behavior under dry air, while the transistor becomes p-type as the amount of NO2 increases. The assembly of these results demonstrates that the WS2/IGZO device is a promising platform for the NO2-gas detection, and its gas-modulated transistor properties show a potential application in tunable engineering for two-dimensional material heterojunction-based transistor device.
This paper analyzes the mechanical properties of tungsten disulfide (WS2) by means of multiscale simulation, including density functional theory (DFT), molecular dynamic (MD) analysis, and finite ...element analysis (FEA). We first conducted MD analysis to calculate the mechanical properties (i.e., Young’s modulus and critical stress) of WS2. The influence of different defect types (i.e., point defects and line defects) on the mechanical properties are discussed. The results reveal that WS2 has a high Young’s modulus and high critical stress. Next, the effects of defect density and temperature on the mechanical properties of the material were analyzed. The results show that a lower defect density results in improved performance and a higher temperature results in better ductility, which indicate that WS2 can potentially be a strain sensor. Based on this result, FEA was employed to analyze the WS2 stress sensor and then fabricate and analyze the device for benchmarking. It is found that the FEA model proposed in this work can be used for further optimization of the device. According to the DFT results, a narrower band gap WS2 is found with the existence of defects and the applied strain. The proposed multiscale simulation method can effectively analyze the mechanical properties of WS2 and optimize the design. Moreover, this method can be extended to other 2D/nanomaterials, providing a reference for a rapid and effective systematic design from the nanoscale to macroscale.
As an increasing attention towards sustainable development of energy and environment, the power electronics (PEs) are gaining more and more attraction on various energy systems. The insulated gate ...bipolar transistor (IGBT), as one of the PEs with numerous advantages and potentials for development of higher voltage and current ratings, has been used in a board range of applications. However, the continuing miniaturization and rapid increasing power ratings of IGBTs have remarkable high heat flux, which requires complex thermal management. In this paper, studies of the thermal management on IGBTs are generally reviewed including analyzing, comparing, and classifying the results originating from these researches. The thermal models to accurately calculate the dynamic heat dissipation are divided into analytical models, numerical models, and thermal network models, respectively. The thermal resistances of current IGBT modules are also studied. According to the current products on a number of IGBTs, we observe that the junction-to-case thermal resistance generally decreases inversely in terms of the total thermal power. In addition, the cooling solutions of IGBTs are reviewed and the performance of the various solutions are studied and compared. At last, we have proposed a quick and efficient evaluation judgment for the thermal management of the IGBTs depended on the requirements on the junction-to-case thermal resistance and equivalent heat transfer coefficient of the test samples.
Deep learning theory is widely used in face recognition. Combined with the needs of classroom attendance and students’ learning status monitoring, this article analyzes the YOLO (You Only Look Once) ...face recognition algorithms based on regression method. Aiming at the problem of small target missing detection in the YOLOv3 network structure, an improved YOLOv3 algorithm based on Bayesian optimization is proposed. The algorithm uses deep separable convolution instead of conventional convolution to improve the Darknet-53 basic network, and it reduces the amount of calculation and parameters of the network. A multiscale feature pyramid is built, and an attention guidance module is designed to strengthen multiscale fusion, detecting different sizes of targets. The loss function is improved to solve the imbalance of positive and negative sample distribution and the imbalance between simple samples and difficult samples. The Bayesian function is adopted to optimize the classifier and improve the classification efficiency and accuracy, ensuring the accuracy of small target detection. Five groups of comparative experiments are carried out on public COCO and VOC2012 datasets and self-built datasets. The experimental results show that the proposed improved YOLOv3 model can effectively improve the detection accuracy of multiple faces and small targets. Compared with the traditional YOLOv3 model, the mean mAP of the target is improved by more than 1.2%.
The photovoltaic poverty alleviation project (PPAP), with functions of clean generation and poverty assistance, is regarded as an effective way to promote energy structure transformation and social ...coordinated development. Improper site selection poses a threat to stable profits, which hinders PPAP development. However, existing index systems fail to reflect PPAPs’ characteristics, and traditional decision-making techniques are faced with some problems such as information loss and evaluation bias. Therefore, a hybrid decision-making framework for PPAP site selection is proposed: First, based on 4-dimension searching loop, a comprehensive index system which includes veto indexes and sorting indexes is established; Second, a combined weight determining method is adopted to balance logic importance and decision-making contribution; Third, an extended ranking method is proposed for optimal alternative determination with consideration of intuitionistic fuzzy environment and bounded rationality; Finally, a case of Anhui, China is employed to verify the practicability and the robustness of the proposed framework. Results show that number of households assisted, initial capital and local villager support play a vital role in PPAP site selection. Judging from contributions, this paper can not only provide effective decision-making guidance for project investors, but also enriches the theoretical system of multi-criteria decision-making methods.
In this study, we apply temperature, precipitation, and other data from 66 Chinese meteorological stations including Xining and Lhasa to analyze the extreme climate events and their impacting factors ...over the Qinghai-Tibet Plateau during the period 1961–2007. We focus on the spatial and temporal features of extreme climate events and their long-term changes over five climate zones of alpine grassland, meadow, and desert areas. Results show that, during the past decades, the changes in climate over the Qinghai-Tibet Plateau present trends towards warm and wet conditions. These changes in temperature and precipitation are evident in both seasonal means and extreme events, and the changes in precipitation are apparent in both precipitation amount and number of precipitation days. Clearly, warm and wet events increase, but cold and dry events decrease over the plateau region. Features of the warming climate are relatively consistent in spatial and seasonal distributions, with the most significant changes in winter and autumn and at nighttime. Northern Qinghai exhibits the greatest and most significant decrease in the frequency of extremely low-temperature events. However, the wetting trend shows more distinctive spatial features and is more seasonally dependent. While the trends in both precipitation amount and the number of precipitation days are positive in all climate zones for winter and spring, both positive and insignificant negative trends appear in summer and autumn. The largest decrease in the frequency of severely dry events is found over southeastern Tibet and western Sichuan.
The rapid development of transportation industry has brought some potential safety hazards. Aiming at the problem of driving safety, the application of artificial intelligence technology in safe ...driving behavior recognition can effectively reduce the accident rate and economic losses. Based on the presence of interference signals such as spatiotemporal background mixed signals in the driving monitoring video sequence, the recognition accuracy of small targets such as human eyes is low. In this paper, an improved dual-stream convolutional network is proposed to recognize the safe driving behavior. Based on convolutional neural networks (CNNs), attention mechanism (AM) is integrated into a long short-term memory (LSTM) neural network structure, and the hybrid dual-stream AM-LSTM convolutional network channel is designed. The spatial stream channel uses the CNN method to extract the spatial characteristic value of video image and uses pyramid pooling instead of traditional pooling, normalizing the scale transformation. The time stream channel uses a single-shot multibox detector (SSD) algorithm to calculate the adjacent two frames of video sequence for the detection of small objects such as face and eyes. Then, AM-LSTM is used to fuse and classify dual-stream information. The self-built driving behavior video image set is built. ROC, accuracy rate, and loss function experiments are carried out in the FDDB database, VOT100 data set, and self-built video image set, respectively. Compared with CNN, SSD, IDT, and dual-stream recognition methods, the accuracy rate of this method can be improved by at least 1.4%, and the average absolute error in four video sequences can be improved by more than 2%. On the contrary, in the self-built image set, the recognition rate of doze reaches 68.3%, which is higher than other methods. The experimental results show that this method has good recognition accuracy and practical application value.
The Pichia pastoris fermentation process is with highly nonlinear and time-varying and strong coupling characteristics, traditional control methods such as classic PID cannot satisfy the actual needs ...of the fermentation process control. In order to effectively solve the control problem of the Pichia pastoris fermentation process, a multi-model predictive control method is presented based on the relative error weighting algorithm. First, the prior sample data are divided into multiple training sample sets (sample cluster) by fuzzy C-means clustering algorithm (FCM). Then, the corresponding sub prediction model is obtained by using least square support vector machine (LSSVM) and Improved particle swarm optimization (IPSO) algorithm for each sample cluster. Finally, the control strategy of predictive model is constructed based on multi model relative error weighting algorithm. The simulation in the Pichia pastoris fermentation process shows that the algorithm proposed in this paper could improve the transient response and perform good output tracking in a wide range. It improves the adaptive ability of the model and makes it more accurate to describe the actual state of the nonlinear system.
The incidence of ulcerative colitis (UC) is increasing annually. There are few treatments for UC patients, and some drugs have serious side effects. Sea cucumber peptide (SCP) has anti-inflammatory, ...antioxidant and other biological activities, and various sea cucumber species are in pharmaceutical development. However, relevant studies on the effects of SCP on UC progression are still lacking. In this study, a mouse model of acute colitis was induced by 3% dextran sulfate (DSS), and the effect of 500 mg/kg SCP on colitis was investigated. The results showed that SCP can alleviate DSS-induced colon damage and intestinal barrier damage. SCP significantly inhibited the expression of inflammatory factors and oxidative stress in UC mice. SCP reversed the intestinal microbiota dysregulation induced by DSS, inhibited the growth of Sutterella, Prevotella_9 and Escherichia-Shigella harmful bacteria, and increased the abundance of Lachnospiraceae_NK4A136_group. At the same time, SCP treatment significantly inhibited the LPS-induced polarization of M1 macrophages, which may be mediated by two monopeptides, IPGAPGVP and TGPIGPPGSP, via FPR2. In conclusion, SCP can protect against colitis by modulating the intestinal microbiota composition and the intestinal barrier and inhibiting the polarization of M1 macrophages.
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