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Monolayer 2D transition metal dichalcogenides (TMDs) show high sensitivity to the local dielectric environment, leading to modulation of their optoelectronic properties. Here, we report on the formation of localized trions in a MoS 2 /few-layer graphene van der Waals heterostructure. We performed temperature-dependent photoluminescence and Raman studies down to 80 K, to understand the mechanism for localized charge excitation, which shows contrasting behaviour with MoS 2 /SiO 2 . We attribute trion formation to optically induced charge transfer from few-layer graphene to MoS 2 . Our theoretical analysis and simulations comparing the dielectric screening between MoS 2 /SiO 2 and MoS 2 /few-layer graphene strongly suggest the dominance of excess charge carrier concentration over dielectric screening as the cause of trion formation. The concentration of charge carriers could be tuned actively with excitation power. Our findings provide an efficient approach for trion formation in MoS 2 and explain the mechanism behind charge transfer in the MoS 2 /few-layer graphene heterostructure.

Spin defects embedded in solid-state systems are appealing for quantum sensing of materials and for quantum science and engineering. The spin-sensitive photoluminescence of optically active spin defects in Van der Waals based materials, such as the boron-vacancy (V_{B}^{-}) center in hexagonal boron nitride, enables its application as a quantum sensor to detect weak, spatially localized magnetic static and dynamic fields. However, the utility of V_{B}^{-} centers to probe spin dynamics in magnetic systems has yet to be demonstrated; this is essential to establish the V_{B}^{-} as a modular sensing platform that can be seamlessly integrated with emergent quantum materials to probe a wide range of static and dynamic phenomena. Here, we use V_{B}^{-} centers to experimentally probe uniform mode magnon dynamics and optically perform ferromagnetic resonance spectroscopy on a thin magnetic film.

Spin-orbit torque (SOT)-driven deterministic control of the magnetic state of a ferromagnet with perpendicular magnetic anisotropy is key to next-generation spintronic applications including non-volatile, ultrafast and energy-efficient data-storage devices. However, field-free deterministic switching of perpendicular magnetization remains a challenge because it requires an out-of-plane antidamping torque, which is not allowed in conventional spin-source materials such as heavy metals and topological insulators due to the system's symmetry. The exploitation of low-crystal symmetries in emergent quantum materials offers a unique approach to achieve SOTs with unconventional forms. Here we report an experimental realization of field-free deterministic magnetic switching of a perpendicularly polarized van der Waals magnet employing an out-of-plane antidamping SOT generated in layered WTe 2 , a quantum material with a low-symmetry crystal structure. Our numerical simulations suggest that the out-of-plane antidamping torque in WTe 2 is essential to explain the observed magnetization switching., (© 2022. The Author(s), under exclusive licence to Springer Nature Limited.)

Drowsiness is one of the leading causes of traffic accidents. For those who operate large machinery or motor vehicles, incidents due to lack of sleep can cause property damage and sometimes lead to grave consequences of injuries and fatality. This study aims to design learning models to recognize drowsiness through human facial features. In addition, this work analyzes the attentions of individual neurons in the learning model to understand how neural networks interpret drowsiness. For this analysis, gradient-weighted class activation mapping (Grad-CAM) is implemented in the neural networks to display the attention of neurons. The eye and face images are processed separately to the model for the training process. The results initially show that better results can be obtained by delivering eye images alone. The effect of Grad-CAM is also more reasonable using eye images alone. Furthermore, this work proposed a feature analysis method, K-nearest neighbors Sigma (KNN-Sigma), to estimate the homogeneous concentration and heterogeneous separation of the extracted features. In the end, we found that the fusion of face and eye signals gave the best results for recognition accuracy and KNN-sigma. The area under the curve (AUC) of using face, eye, and fusion images are 0.814, 0.897, and 0.935, respectively.

In November 2019, the coronavirus disease outbreak began, caused by the novel severe acute respiratory syndrome coronavirus 2. In just over two months, the unprecedented rapid spread resulted in more than 10,000 confirmed cases worldwide. This study predicted the infectious spread of coronavirus disease in the contiguous United States using a convolutional autoencoder with long short-term memory and compared its predictive performance with that of the convolutional autoencoder without long short-term memory. The epidemic data were obtained from the World Health Organization and the US Centers for Disease Control and Prevention from January 1st to April 6th, 2020. We used data from the first 366,607 confirmed cases in the United States. In this study, the data from the Centers for Disease Control and Prevention were gridded by latitude and longitude and the grids were categorized into six epidemic levels based on the number of confirmed cases. The input of the convolutional autoencoder with long short-term memory was the distribution of confirmed cases 14 days before, whereas the output was the distribution of confirmed cases 7 days after the date of testing. The mean square error in this model was 1.664, the peak signal-to-noise ratio was 55.699, and the structural similarity index was 0.99, which were better than those of the corresponding results of the convolutional autoencoder. These results showed that the convolutional autoencoder with long short-term memory effectively and reliably predicted the spread of infectious disease in the contiguous United States., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2021 The Authors. Published by Elsevier B.V.)

The early diagnosis of a motor is important. Many researchers have used deep learning to diagnose motor applications. This paper proposes a one-dimensional convolutional neural network for the diagnosis of permanent magnet synchronous motors. The one-dimensional convolutional neural network model is weakly supervised and consists of multiple convolutional feature-extraction modules. Through the analysis of the torque and current signals of the motors, the motors can be diagnosed under a wide range of speeds, variable loads, and eccentricity effects. The advantage of the proposed method is that the feature-extraction modules can extract multiscale features from complex conditions. The number of training parameters was reduced so as to solve the overfitting problem. Furthermore, the class feature map was proposed to automatically determine the frequency component that contributes to the classification using the weak learning method. The experimental results reveal that the proposed model can effectively diagnose three different motor states-healthy state, demagnetization fault state, and bearing fault state. In addition, the model can detect eccentric effects. By combining the current and torque features, the classification accuracy of the proposed model is up to 98.85%, which is higher than that of classical machine-learning methods such as the k-nearest neighbor and support vector machine.

In emergency departments, the most common cause of death associated with suspected infected patients is sepsis. In this study, deep learning algorithms were used to predict the mortality of suspected infected patients in a hospital emergency department. During January 2007 and December 2013, 42,220 patients considered in this study were admitted to the emergency department due to suspected infection. In the present study, a deep learning structure for mortality prediction of septic patients was developed and compared with several machine learning methods as well as two sepsis screening tools: the systemic inflammatory response syndrome (SIRS) and quick sepsis-related organ failure assessment (qSOFA). The mortality predictions were explored for septic patients who died within 72 h and 28 days. Results demonstrated that the accuracy rate of deep learning methods, especially Convolutional Neural Network plus SoftMax (87.01% in 72 h and 81.59% in 28 d), exceeds that of the other machine learning methods, SIRS, and qSOFA. We expect that deep learning can effectively assist medical staff in early identification of critical patients.