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.

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.

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.

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 ( = 79) and healthy controls ( = 134) living in Chongqing, China, completed a cross-sectional study between 13 and 26 January 2021.

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.

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 ( = 10), brief relaxation practice ( = 10), and control task ( = 11).

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.