Dual-Step Redox Engineering of 2D CoNi-Alloy Embedded B, N-Doped Carbon Layers Toward Tunable Electromagnetic Wave Absorption and Light-Weight Infrared Stealth Heat Insulation Devices.

2D layered metallic graphite composites are promising electromagnetic wave absorption materials (EWAMs) for their combined properties of abundant interlayer free spaces, rich metallic polarized sites, and high conductivity, but the controllable synthesis remains rather challenging. Herein, a dual-step redox engineering strategy is developed by employing cobalt boron imidazolate framework (Co-BIF) to construct 2D CoNi-alloy embedded B, N-doped carbon layers (2D-CNC) as a promising EWAM. In the first step, a chemical etching oxidation process on Co-BIF is used to obtain an optimized 2D-CoNi-layered double hydroxide (2D-CoNi-LDH) intermediate and in the second, high-temperature calcination reduction is implemented to modify graphitization of the degree of the 2D-CNC. The obtained sample delivers superior reflection loss (RLmin) of -60.1 dB and wide effective absorption bandwidth (EAB) of 6.24 GHz. The synergy mechanisms of interfacial/dipole polarization and magnetic coupling are in-depth evidenced by the hologram and Lorentz electron microscopy, revealing its significant contribution on multireflection and impedance matching. Further theoretical evaluation by COMSOL simulation in different fields based on the dynamic loss process toward the test ring reveals the in situ EW attenuation process. This work presents a strategy to develop multifunctional light-weight infrared stealthy aerogel with superior pressure-resistant, anti-corrosion, and heat-insulating properties for future applications.

Video Anomaly Detection Based on Convolutional Recurrent AutoEncoder.

As an essential task in computer vision, video anomaly detection technology is used in video surveillance, scene understanding, road traffic analysis and other fields. However, the definition of anomaly, scene change and complex background present great challenges for video anomaly detection tasks. The insight that motivates this study is that the reconstruction error for normal samples would be lower since they are closer to the training data, while the anomalies could not be reconstructed well. In this paper, we proposed a Convolutional Recurrent AutoEncoder (CR-AE), which combines an attention-based Convolutional Long-Short-Term Memory (ConvLSTM) network and a Convolutional AutoEncoder. The ConvLSTM network and the Convolutional AutoEncoder could capture the irregularity of the temporal pattern and spatial irregularity, respectively. The attention mechanism was used to obtain the current output characteristics from the hidden state of each Covn-LSTM layer. Then, a convolutional decoder was utilized to reconstruct the input video clip and the testing video clip with higher reconstruction error, which were further judged to be anomalies. The proposed method was tested on two popular benchmarks (UCSD ped2 Dataset and Avenue Dataset), and the experimental results demonstrated that CR-AE achieved 95.6% and 73.1% frame-level AUC on two public datasets, respectively.

Detection Anomaly in Video Based on Deep Support Vector Data Description.

Video surveillance systems have been widely deployed in public places such as shopping malls, hospitals, banks, and streets to improve the safety of public life and assets. In most cases, how to detect video abnormal events in a timely and accurate manner is the main goal of social public safety risk prevention and control. Due to the ambiguity of anomaly definition, the scarcity of anomalous data, as well as the complex environmental background and human behavior, video anomaly detection is a major problem in the field of computer vision. Existing anomaly detection methods based on deep learning often use trained networks to extract features. These methods are based on existing network structures, instead of designing networks for the goal of anomaly detection. This paper proposed a method based on Deep Support Vector Data Description (DSVDD). By learning a deep neural network, the input normal sample space can be mapped to the smallest hypersphere. Through DSVDD, not only can the smallest size data hypersphere be found to establish SVDD but also useful data feature representations and normal models can be learned. In the test, the samples mapped inside the hypersphere are judged as normal, while the samples mapped outside the hypersphere are judged as abnormal. The proposed method achieves 86.84% and 73.2% frame-level AUC on the CUHK Avenue and ShanghaiTech Campus datasets, respectively. By comparison, the detection results achieved by the proposed method are better than those achieved by the existing state-of-the-art methods.

Attitudes toward COVID-19 vaccination and willingness to pay: comparison of people with and without mental disorders in China.

BACKGROUND: Acceptance and willingness to pay for the COVID-19 vaccine are unknown. AIMS: We compared attitudes toward COVID-19 vaccination in people suffering from depression or anxiety disorder and people without mental disorders, and their willingness to pay for it. METHOD: Adults with depression or anxiety disorder (n = 79) and healthy controls (n = 134) living in Chongqing, China, completed a cross-sectional study between 13 and 26 January 2021. We used a validated survey to assess eight aspects related to attitudes toward the COVID-19 vaccines. Psychiatric symptoms were assessed by the 21-item Depression, Anxiety and Stress Scale. RESULTS: Seventy-six people with depression or anxiety disorder (96.2%) and 134 healthy controls (100%) reported willingness to receive the COVID-19 vaccine. A significantly higher proportion of people with depression or anxiety disorder (64.5%) were more willing to pay for the COVID-19 vaccine than healthy controls (38.1%) (P ≤ 0.001). After multivariate adjustment, severity of depression and anxiety was significantly associated with willingness to pay for COVID-19 vaccination among psychiatric patients (P = 0.048). Non-healthcare workers (P = 0.039), health insurance (P = 0.003), living with children (P = 0.006) and internalised stigma (P = 0.002) were significant factors associated with willingness to pay for COVID-19 vaccine in healthy controls. CONCLUSIONS: To conclude, psychiatric patients in Chongqing, China, showed high acceptance and willingness to pay for the COVID-19 vaccine. Factors associated with willingness to pay for the COVID-19 vaccine differed between psychiatric patients and healthy controls.

An Asia-Pacific study on healthcare workers' perceptions of, and willingness to receive, the COVID-19 vaccination.

OBJECTIVE: In the fight against COVID-19, vaccination is vital in achieving herd immunity. Many Asian countries are starting to vaccinate frontline workers; however, expedited vaccine development has led to hesitancy among the general population. We evaluated the willingness of healthcare workers to receive the COVID-19 vaccine. METHODS: From 12 to 21 December 2020, we recruited 1720 healthcare workers from 6 countries: China, India, Indonesia, Singapore, Vietnam and Bhutan. The self-administrated survey collected information on willingness to vaccinate, perception of COVID-19, vaccine concerns, COVID-19 risk profile, stigma, pro-socialness scale, and trust in health authorities. RESULTS: More than 95% of the healthcare workers surveyed were willing to vaccinate. These respondents were more likely to perceive the pandemic as severe, consider the vaccine safe, have less financial concerns, less stigmatization regarding the vaccine, higher pro-socialness mindset and trust in health authorities. A high perceived pandemic risk index, low vaccine harm index and high pro-socialness index were independent predictors in multivariable analysis. CONCLUSIONS: The majority of healthcare workers in Asia are willing to receive COVID-19 vaccination. Perceived COVID-19 susceptibility, low potential risk of vaccine harm and pro-socialness are the main drivers. These findings may help formulate vaccination strategies in other countries.

Brief Relaxation Practice Induces Significantly More Prefrontal Cortex Activation during Arithmetic Tasks Comparing to Viewing Greenery Images as Revealed by Functional Near-Infrared Spectroscopy (fNIRS).

BACKGROUND: There is little understanding on how brief relaxation practice and viewing greenery images would affect brain responses during cognitive tasks. In the present study, we examined the variation in brain activation of the prefrontal cortex during arithmetic tasks before and after viewing greenery images, brief relaxation practice, and control task using functional near-infrared spectroscopy (fNIRS). METHOD: This randomized controlled study examined the activation patterns of the prefrontal cortex (PFC) in three groups of research participants who were exposed to viewing greenery images (n = 10), brief relaxation practice (n = 10), and control task (n = 11). The activation pattern of the PFC was measured pre- and post-intervention using a portable fNIRS device and reported as mean total oxygenated hemoglobin (HbO µm). Primary outcome of the study is the difference in HbO µm between post- and pre-intervention readings during a cognitive task that required the research participants to perform arithmetic calculation. RESULTS: In terms of intervention-related differences, there was significant difference in average HbO µm when performing arithmetic tasks before and after brief relaxation practice (p < 0.05). There were significant increases in average HbO µm in the right frontopolar cortex (p = 0.029), the left frontopolar cortex (p = 0.01), and the left orbitofrontal cortex (p = 0.033) during arithmetic tasks after brief relaxation practice. In contrast, there were no significant differences in average HbO µm when performing arithmetic tasks before and after viewing greenery images (p > 0.05) and the control task (p > 0.05). CONCLUSION: Our preliminary findings show that brief relaxation practice but not viewing greenery images led to significant frontal lobe activation during arithmetic tasks. The present study demonstrated, for the first time, that there was an increase in activation in neuroanatomical areas including the combined effort of allocation of attentional resources, exploration, and memory performance after the brief relaxation practice. Our findings suggest the possibility that the right frontopolar cortex, the left frontopolar cortex, and the left orbitofrontal cortex may be specifically associated with the benefits of brief relaxation on the brain.

Bidirectional Discrete Matrix Factorization Hashing for Image Search.

Unsupervised image hashing has recently gained significant momentum due to the scarcity of reliable supervision knowledge, such as class labels and pairwise relationship. Previous unsupervised methods heavily rely on constructing sufficiently large affinity matrix for exploring the geometric structure of data. Nevertheless, due to lack of adequately preserving the intrinsic information of original visual data, satisfactory performance can hardly be achieved. In this article, we propose a novel approach, called bidirectional discrete matrix factorization hashing (BDMFH), which alternates two mutually promoted processes of 1) learning binary codes from data and 2) recovering data from the binary codes. In particular, we design the inverse factorization model, which enforces the learned binary codes inheriting intrinsic structure from the original visual data. Moreover, we develop an efficient discrete optimization algorithm for the proposed BDMFH. Comprehensive experimental results on three large-scale benchmark datasets show that the proposed BDMFH not only significantly outperforms the state-of-the-arts but also provides the satisfactory computational efficiency.