Psychological resilience is characterized as the ability to respond to extreme stress or trauma or adverse experience successfully. While the relation between public emergencies and psychological ...distress is well known, research on therelationship between psychological resilience and mental health is very limited during the outbreak of public health emergencies.
This research investigated the relationship between psychological resilience and mental health (depression, anxiety, somatization symptoms) among the general population in China.
Psychological resilience, depression, anxiety, and somatization symptoms of 1770 Chinese citizens were investigated during the epidemic peak of coronavirus disease 2019 (COVID-19) (23rd February 2020 to 2nd March 2020). The analyses were done through the Connor-Davidson Resilience Scale (CD-RISC), the Patient Health Questionnaire-9 (PHQ-9), the Generalized Anxiety Disorder-7 (GAD-7) scale, and the Patient Health Questionnaire-15 (PHQ-15) scale.
The prevalence of depression, anxiety, somatization symptoms was found to be 47.1%, 31.9%, 45.9%, respectively, among all participants. From them, 18.2% showed moderate to severe symptoms of depression, 8.8% showed moderate to severe symptoms of anxiety, and 16.6% showed moderate to severe symptoms of somatization. Psychological resilience was negatively correlated with depression (standardized β = −0.490, P < 0.001), anxiety (standardized β = −0.443, P < 0.001), and somatization symptom scores (standardized β = −0.358, P < 0.001), while controlling for confounding factors. Analysis of the three-factor resilience structure showed that strength and tenacity were correlated with depression (standardized β = −0.256, P < 0.001; standardized β = −0.217, P < 0.001), anxiety (standardized β = −0.268, P < 0.001; standardized β = −0.147, P < 0.001), and somatization symptoms (standardized β = −0.236, P < 0.001; standardized β = −0.126, P < 0.01).
Our results suggest that there is a high prevalence of psychological distresses among the general population at the peak of the COVID-19 epidemic in China, which is negatively correlated with resilience. Psychological resilience represents an essential target for psychological intervention in a public health emergency.
•Investigated mental health and resilience in response to COVID-19.•The study took place at the peak of the COVID-19 epidemic in China.•In total, 18.2% had depression, 8.8% had anxiety, and 16.6% had somatic symptoms.•Resilience negatively correlated with depression, anxiety, and somatic symptoms.•Resilience represents an essential target for psychological interventions.
Aluminum‐gallium‐nitride alloys (Al
x
Ga1–
x
N, 0 ≤ x ≤ 1) can emit light covering the ultraviolet spectrum from 210 to 360 nm. However, these emitters have not fulfilled their full promise to ...replace the toxic and fragile mercury UV lamps due to their low efficiencies. This study demonstrates a promising approach to enhancing the luminescence efficiency of AlGaN multiple quantum wells (MQWs) via the introduction of a lateral‐polarity structure (LPS) comprising both III and N‐polar domains. The enhanced luminescence in LPS is attributed to the surface roughening, and compositional inhomogeneities in the N‐polar domain. The space‐resolved internal quantum efficiency (IQE) mapping shows a higher relative IQE in N‐polar domains and near inversion domain boundaries, providing strong evidence of enhanced radiative recombination efficiency in the LPS. These experimental observations are in good agreement with the theoretical calculations, where both lateral and vertical band diagrams are investigated. This work suggests that the introduction of the LPS in AlGaN‐based MQWs can provide unprecedented tunability in achieving higher luminescence performance in the development of solid state light sources.
The micro‐photoluminescence (µ‐PL) mapping of the lateral‐polarity structure samples with a color bar (left) represents the integrated emission intensity from multiple AlGaN quantum wells. N‐polar wells shows higher PL intensity than III‐polar wells. Right: Atomic resolution scanning transmission electron microscopy images in the N‐polar and III‐polar domains, where polarity can be clearly identified.
Taxi demand can be divided into pick-up demand and drop-off demand, which are firmly related to human’s travel habits. Accurately predicting taxi demand is of great significance to passengers, ...drivers, ride-hailing platforms and urban managers. Most of the existing studies only forecast the taxi demand for pick-up and separate the interaction between spatial correlation and temporal correlation. In this paper, we first analyze the historical data and select three highly relevant parts for each time interval, namely closeness, period and trend. We then construct a multi-task learning component and extract the common spatiotemporal feature by treating the taxi pick-up prediction task and drop-off prediction task as two related tasks. With the aim of fusing spatiotemporal features of historical data, we conduct feature embedding by attention-based long short-term memory (LSTM) and capture the correlation between taxi pick-up and drop-off with 3D ResNet. Finally, we combine external factors to simultaneously predict the taxi demand for pick-up and drop-off in the next time interval. Experiments conducted on real datasets in Chengdu present the effectiveness of the proposed method and show better performance in comparison with state-of-the-art models.
Recently, more and more mobile apps are employed in the marketing field with technical advances. Mobile marketing apps have become a prevalent way for enterprise marketing. Therefore, it has been an ...important and urgent problem to provide personalized and accurate recommendation in mobile marketing, with a large number of items and limited capability of mobile devices. Recommendation have been investigated widely, however, most existing approaches fail to consider the stability or change of users’ behaviors over time. In this paper, we first propose to mine the periodic trends of users’ consuming behavior from historical records by KNN(K-nearest neighbor) and SVR (support vector regression) based time series prediction, and predict the next time when a user re-purchases the item, so that we can recommend the items which users have purchased before at proper time. Second, we aim to find the regularity of users’ purchasing behavior during different life stages and recommend the new items that are needed and proper for their current life stage. In order to solve this, we mine the mapping model from items to user’s life stage first. Based on the model, users’ current life stage can be estimated from their recent behaviors. Finally, users will be recommended with new items which are proper to their estimated life stage. Experimental results show that it has improved the effectiveness of recommendation obviously by mining users’ consuming behaviors with temporal evolution.
Abstract
Recently, object trackers based on Siamese networks have attracted considerable attentions due to their remarkable tracking performance and widespread application. Especially, the ...anchor-based methods exploit the region proposal subnetwork to get accurate prediction of a target and make great performance improvement. However, those trackers cannot capture the spatial information very well and the pre-defined anchors will hinder robustness. To solve these problems, we propose a Siamese-based anchor-free object tracking algorithm with multiscale spatial attentions in this paper. Firstly, we take ResNet-50 as the backbone network to generate multiscale features of both template patch and search regions. Secondly, we propose the spatial attention extraction (SAE) block to capture the spatial information among all positions in the template and search region feature maps. Thirdly, we put these features into the SAE block to get the multiscale spatial attentions. Finally, an anchor-free classification and regression subnetwork is used for predicting the location of the target. Unlike anchor-based methods, our tracker directly predicts the target position without predefined parameters. Extensive experiments with state-of-the-art trackers are carried out on four challenging visual object tracking benchmarks: OTB100, UAV123, VOT2016 and GOT-10k. Those experimental results confirm the effectiveness of our proposed tracker.
With more than 200 million people affected and 4.5 million deaths so far, the coronavirus disease 2019 (COVID-19) pandemic has become one of the greatest disasters in human history. Secondary ...bacterial infections (SBIs) are a known complication of viral respiratory infections, and are significantly associated with poorer outcomes in COVID-19 patients despite antibiotic treatments. The increasing antimicrobial resistance (AMR) in bacteria and the decreasing options available in our antimicrobial armory worsen this crisis and call for alternative treatment options. As natural killers of bacteria, phages are recognized as promising alternatives to antibiotics in treating pulmonary bacterial infections, however, little is known about their use for treating SBIs during virus pandemics such as COVID-19. This review highlights the situation of SBIs in COVID-19 patients, and the distinct strengths and limitations of phage therapy for their containment.
•EEG signals are naturally born with multi modes.•EEG signals can be represented by the high-order multi-way array, tensor.•Tensor of EEG can be exploited by tensor decomposition for multi-way ...analysis.
Electroencephalography (EEG) is one fundamental tool for functional brain imaging. EEG signals tend to be represented by a vector or a matrix to facilitate data processing and analysis with generally understood methodologies like time-series analysis, spectral analysis and matrix decomposition. Indeed, EEG signals are often naturally born with more than two modes of time and space, and they can be denoted by a multi-way array called as tensor. This review summarizes the current progress of tensor decomposition of EEG signals with three aspects. The first is about the existing modes and tensors of EEG signals. Second, two fundamental tensor decomposition models, canonical polyadic decomposition (CPD, it is also called parallel factor analysis-PARAFAC) and Tucker decomposition, are introduced and compared. Moreover, the applications of the two models for EEG signals are addressed. Particularly, the determination of the number of components for each mode is discussed. Finally, the N-way partial least square and higher-order partial least square are described for a potential trend to process and analyze brain signals of two modalities simultaneously.
The fusion of the template and search region features plays a significant role in deep learning-based trackers. In Siamese-based trackers, different cross-correlation operations are commonly used to ...fuse features, which cannot obtain global connections. On the other hand, transformer-based trackers use attention mechanism to fuse features, which cannot suppress the interference of distractors in the background. Furthermore, existing trackers use regression and classification heads with the same structure, which leads to lack a deeper understanding of these two different tasks. To address these problems, we firstly propose a feature enhancement-fusion network (FEFN) based on cross-correlation and transformer, with two Encoders that employ self-attention and a Decoder that removes cross-attention to adapt to the tracking task. Using the FEFN to combine the advantages of Siamese-based and transformer-based trackers, our tracker establishes global connections while effectively suppressing the distractors. We also propose a novel decoupled head, designing a spatial sensitive classification head and a global information sensitive regression head, which helps the context-aware tracker locate the target more accurately. Our proposed tracker obtains 0.710 of AO, 0.814 of SR0.5 and 0.657 of SR0.75 on the GOT-10k test set, and achieves real-time requirement at 36.99FPS.
•Propose a feature enhancement-fusion network based on cross-correlation and transformer.•Adapt the Encoder-Decoder structure to the tracking task.•Propose a context-aware decoupled prediction head.•Achieve excellent performance on five test benchmarks.
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