67 research outputs found
The influence of perceived stress of Chinese healthcare workers after the opening of COVID-19: the bidirectional mediation between mental health and job burnout
ObjectiveTo explore the current status and interaction of perceived stress, job burnout and mental health among healthcare workers after the opening of COVID-19 which occurred in December 2022.MethodsA cross-sectional study of 792 healthcare workers from three tertiary hospitals in Wuxi was conducted from January 2023 to February 2023. Sociodemographic questionnaire, Perceived Stress Scale, Burnout Scale and Mental Health Self-Assessment Questionnaire were used for investigation. SPSS 26.0 was used to conduct data analysis. The significance of mediation was determined by the PROCESS macro using a bootstrap method.ResultsThe results showed that (1) The average scores of the participants for perceived stress, mental health and job burnout were 22.65 (7.67), 3.85 (4.21) and 1.88 (1.03), respectively. (2) The perceived stress score, mental health score and job burnout score of healthcare workers were positively correlated (r = 0.543–0.699, p < 0.05). (3) Mental health partially mediated the relationship between perceived stress and job burnout with a mediating effect of 17.17% of the total effect. Job burnout partially mediated the correlation between perceived stress and mental health with a mediating effect of 31.73% of the total effect.ConclusionThe results of this study suggested that perceived stress had an impact on job burnout and mental health, either directly or indirectly. Healthcare managers should intervene to reduce perceived stress to protect healthcare workers’ mental health, thereby alleviating burnout under the opening COVID-19 pandemic environment
Multiuser Full-Duplex Two-Way Communications via Intelligent Reflecting Surface
Low-cost passive intelligent reflecting surfaces (IRSs) have recently been
envisioned as a revolutionary technology capable of reconfiguring the wireless
propagation environment through carefully tuning reflection elements. This
paper proposes deploying an IRS to cover the dead zone of cellular multiuser
full-duplex (FD) two-way communication links while suppressing user-side
self-interference (SI) and co-channel interference (CI). Based on information
exchanged by the base station (BS) and all users, this approach can potentially
double the spectral efficiency. To ensure network fairness, we jointly optimize
the precoding matrix of the BS and the reflection coefficients of the IRS to
maximize the weighted minimum rate (WMR) of all users, subject to maximum
transmit power and unit-modulus constraints. We reformulate this non-convex
problem and decouple it into two subproblems. Then the optimization variables
in the equivalent problem are alternately optimized by adopting the block
coordinate descent (BCD) algorithm. In order to further reduce the
computational complexity, we propose the minorization-maximization (MM)
algorithm for optimizing the precoding matrix and the reflection coefficient
vector by defining minorizing functions in the surrogate problems. Finally,
simulation results confirm the convergence and efficiency of our proposed
algorithm, and validate the advantages of introducing IRS to improve coverage
in blind areas.Comment: Accepted by IEEE Transactions on Signal Processin
Two-Timescale Transmission Design for Wireless Communication Systems Aided by Active RIS
This paper considers an active reconfigurable intelligent surface (RIS)-aided
communication system, where an M-antenna base station (BS) transmits data
symbols to a single-antenna user via an N-element active RIS. We use
two-timescale channel state information (CSI) in our system, so that the
channel estimation overhead and feedback overhead can be decreased
dramatically. A closed-form approximate expression of the achievable rate (AR)
is derived and the phase shift at the active RIS is optimized. In addition, we
compare the performance of the active RIS system with that of the passive RIS
system. The conclusion shows that the active RIS system achieves a lager AR
than the passive RIS system
Hydrogenated vacancies lock dislocations in aluminium
Due to its high diffusivity, hydrogen is often considered a weak inhibitor or even a promoter
of dislocation movements in metals and alloys. By quantitative mechanical tests in an
environmental transmission electron microscope, here we demonstrate that after exposing
aluminium to hydrogen, mobile dislocations can lose mobility, with activating stress more
than doubled. On degassing, the locked dislocations can be reactivated under cyclic loading
to move in a stick-slip manner. However, relocking the dislocations thereafter requires a
surprisingly long waiting time of ~10Âłs, much longer than that expected from hydrogen
interstitial diffusion. Both the observed slow relocking and strong locking strength can be
attributed to superabundant hydrogenated vacancies, verified by our atomistic calculations.
Vacancies therefore could be a key plastic flow localization agent as well as damage agent in
hydrogen environment
A Fault Diagnosis Scheme for Gearbox Based on Improved Entropy and Optimized Regularized Extreme Learning Machine
The performance of a gearbox is sensitive to failures, especially in the long-term high speed and heavy load field. However, the multi-fault diagnosis in gearboxes is a challenging problem because of the complex and non-stationary measured signal. To obtain fault information more fully and improve the accuracy of gearbox fault diagnosis, this paper proposes a feature extraction method, hierarchical refined composite multiscale fluctuation dispersion entropy (HRCMFDE) to extract the fault features of rolling bearing and the gear vibration signals at different layers and scales. On this basis, a novel fault diagnosis scheme for the gearbox based on HRCMFDE, ReliefF and grey wolf optimizer regularized extreme learning machine is proposed. Firstly, HRCMFDE is employed to extract the original features, the multi-frequency time information can be evaluated simultaneously, and the fault feature information can be extracted more fully. After that, ReliefF is used to screen the sensitive features from the high-dimensional fault features. Finally, the sensitive features are inputted into the optimized regularized extreme learning machine to identify the fault states of the gearbox. Through three different types of gearbox experiments, the experimental results confirm that the proposed method has better diagnostic performance and generalization, which can effectively and accurately identify the different fault categories of the gearbox and outperforms other contrastive methods.</p
Performance analysis and optimization for workflow authorization
Many workflow management systems have been developed to enhance the performance of workflow executions. The authorization policies deployed in the system may restrict the task executions. The common authorization constraints include role constraints, Separation of Duty (SoD), Binding of Duty (BoD) and temporal constraints. This paper presents the methods to check the feasibility of these constraints, and also determines the time durations when the temporal constraints will not impose negative impact on performance. Further, this paper presents an optimal authorization method, which is optimal in the sense that it can minimize a workflow’s delay caused by the temporal constraints. The authorization analysis methods are also extended to analyze the stochastic workflows, in which the tasks’ execution times are not known exactly, but follow certain probability distributions. Simulation experiments have been conducted to verify the effectiveness of the proposed authorization methods. The experimental results show that comparing with the intuitive authorization method, the optimal authorization method can reduce the delay caused by the authorization constraints and consequently reduce the workflows’ response time
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Identifying RFID Tags in Collisions
How to obtain the information from massive tags is a key focus of RFID applications. The occurrence of collisions leads to problems such as reduced identification efficiency in RFID networks. To tackle such challenges, most tag collision arbitration protocols focus on scheduling tag identification with collision avoidance. However, how to effectively identify tags in collisions to improve identification efficiency has not been well explored. In this paper, we propose a group query allocation method to divide the string space into mutually disjoint subsets which contains several strings. Each string can be viewed as a full ID or partial ID of a tag. When multiple string from a subset are sent simultaneously, the reader can identify all of them in a time slot. Based on the group query allocation method, a segment detection based characteristic group query tree (SD-CGQT) protocol is presented for fast tag identification by significantly reducing the collision slots and transmitted bits. Numerous experimental results verify the superiority of the proposed SD-CGQT, compared to prior arts in system efficiency, total identification time, communication complexity and energy consumption
CVPR 2023 Text Guided Video Editing Competition
Humans watch more than a billion hours of video per day. Most of this video
was edited manually, which is a tedious process. However, AI-enabled
video-generation and video-editing is on the rise. Building on text-to-image
models like Stable Diffusion and Imagen, generative AI has improved
dramatically on video tasks. But it's hard to evaluate progress in these video
tasks because there is no standard benchmark. So, we propose a new dataset for
text-guided video editing (TGVE), and we run a competition at CVPR to evaluate
models on our TGVE dataset. In this paper we present a retrospective on the
competition and describe the winning method. The competition dataset is
available at https://sites.google.com/view/loveucvpr23/track4.Comment: Project page: https://sites.google.com/view/loveucvpr23/track
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