Job burnout and associated influencing factors in employees of 7 research and development enterprises in Minhang District of Shanghai

BackgroundJob burnout is an early mental health condition caused by job stress and contributes to many negative effects on work and life. Employees of research and development (R&D) enterprises are exposed to constant pressure from innovation, production speed and sales expansion, and they are prone to burnout symptoms if such factors are not under effective control. ObjectiveTo evaluate the current situation of job burnout among employees of R&D enterprises in Minhang District of Shanghai and explore its influencing factors. MethodsDuring November to December 2021, a cross-sectional study was developed and a convenient sampling method was used to enroll employees from 7 R&D enterprises in Minhang District of Shanghai. On the basis of voluntary participation with informed consent, a survey was conducted by using a self-made questionnaire (collecting data about general demographic characteristics, occupational characteristics, behavior and lifestyle), the Chinese version of the Concise Occupational Stress Questionnaire, and the Chinese version of the Maslach Burnout Inventory-General Survey. Occupational stress and its dimensions (job demand, job control, and social support) were divided into high, medium, and low levels according to tertiles. The positive rate of job burnout was reported according to score categorization (<1.5 refers to no job burnout, ≥1.5 refers to job burnout, where ≥1.5 and <3.5 refer to mild and moderate job burnout, and ≥3.5 refers to severe job burnout). Potential influencing factors of job burnout were evaluated by using one-way ANOVA, chi-square test, forward stepwise regression, and non-conditional binary logistic regression (α=0.05, two-sided test). ResultsA total of 3153 subjects were enrolled and 3014 samples were included in the analysis, with a valid response rate of 95.6%. Among the included subjects, 888 (29.46%) reported no job burnout, 1775 (58.89%) reported mild to moderate job burnout, and 351 (11.64%) reported severe job burnout. The mean of total job burnout score was 2.17±1.12, and the dimentional mean scores were 2.78±1.61 for emotional exhaustion, 1.60±1.60 for cynicism, and 4.05±1.57 for diminished personal accomplishment. Varied categories of sex, age, marital status, working position, sleep status, job demand, job control, and social support groups of workers resulted in significant differences in job burnout score. Compared with the low job demand group, the positive rate of job burnout was elevated in the medium and high job demand groups; the risk of job burnout in the medium job demand group was 1.42 (95%CI: 1.04-1.94) times higher, and that in the high job demand group was 2.64 (95% CI : 2.17-3.22) times higher versus the low job demand group. The risk of job burnout in the medium job control group was 1.35 (95%CI: 1.06-1.72) times higher versus the low job control group. Compared with the low social support group, job burnout was less reported in the other groups, and the OR (95%CI) values of the medium and high social support groups were 0.41 (0.31-0.53) and 0.15 (0.12-0.19) respectively. ConclusionThe rate of reporting positive job burnout in R&D enterprises is high, which deserves sufficient attention. Relieving work pressure, increasing job control and social support, and maintaining adequate sleep are helpful to reduce job burnout

Mediating effect of sleep quality on the association between job stress and health-related productivity loss among workers in R&D enterprises in Shanghai

BackgroundPast research indicates that occupational stress negatively predicts health-related productivity. Simultaneously, sleep problem among workers may stem from job stress, subsequently leading to a decline in sleep quality and resulting in reduced health productivity. Therefore, this study aims to idenitify whether the sleep quality of employees functions as a mediator in the process through which job stress impacts health productivity.ObjectivesThis study aimed to assess the status and analyze differences in quality of sleep, job stress, and health-related productivity loss (HRPL) among workers in research and development (R&amp;D) enterprises in Minhang District, Shanghai. We also assessed the mediating effect of sleep quality on the relationship between job stress and HRPL.MethodsA total of 3,216 workers in R&amp;D firms aged between 18 and 60 years participated in this study (mean age 35.15 years; standard deviation 8.44; male-to-female ratio≈2:1). The Nakata Insomnia Questionnaire, the Chinese version of the Brief Job Stress Questionnaire revised edition, and the Chinese version of the Work Productivity and Activity Impairment Questionnaire were used in this study. And the Kruskal–Wallis test, Hierarchical Multiple Regression Analysis, and Path Analysis were utilized for data analysis in this study.ResultsThere were significant differences in the positive detection rate of insomnia among participants according to age, educational level, marital status, position, length of service, and level of financial difficulties (all P &lt; 0.05). We also found significant differences in the positive detection rate of HRPL among participants according to age, marital status, length of service, and level of financial difficulties (all P &lt; 0.05); participants with insomnia scored higher for HRPL than those without insomnia (6.00 vs. 4.20, P &lt; 0.001). Additionally, participants with job stress problems had higher HRPL than those without these issues (7.00 vs. 4.20, P &lt; 0.001). Our findings suggest that sleep quality plays a mediating role between job stress and HRPL (all P &lt; 0.05).ConclusionsOccupational health professionals must pay particular attention to job stress, sleep quality, and their influencing factors to positively influence the wellbeing of workers while improving productivity

Postoperative high-density lipoprotein cholesterol level: an independent prognostic factor for gastric cancer

ObjectiveThe relationship between serum lipids and prognosis of gastric cancer has not been confirmed. Our purpose in the study was to investigate the associations between preoperative and postoperative serum lipids level and prognosis in patients with gastric cancer.MethodsA retrospective study was performed on 431 patients who received radical (R0) gastrectomy from 2011 to 2013. Preoperative and postoperative serum lipids level were recorded. Clinical-pathological characteristics, oncologic outcomes, disease-free survival (DFS) and overall survival (OS) were collected. The prognostic significance was determined by Kaplan-Meier analysis and Cox proportional hazards regression model.ResultsThere was no significant difference in DFS and OS according to preoperative serum lipids level. Regarding postoperative serum lipids level, compared to normal high-density lipoprotein cholesterol (HDL-C), low postoperative HDL-C level indicated a shorter OS (hazard ratio: 1.76, 99% confidence interval: 1.31–2.38; P=0.000) and a shorter DFS (hazard ratio: 2.06, 99% confidence interval: 1.55–2.73; P=0.000). However, other postoperative serum lipid molecules were not associated with DFS and OS.ConclusionPostoperative HDL-C might be an independent prognostic factor of gastric cancer

Genome editing of a rice CDP-DAG synthase confers multipathogen resistance

Développement d'une infrastructure française distribuée pour la métabolomique dédiée à l'innovatio

A Complexity Reduction Method for VVC Intra Prediction Based on Statistical Analysis and SAE-CNN

Compared with High Efficiency Video Coding (HEVC), the latest video coding standard Versatile Video Coding Standard (VVC), due to the introduction of many novel technologies and the introduction of the Quad-tree with nested Multi-type Tree (QTMT) division scheme in the block division method, the coding quality has been greatly improved. Due to the introduction of the QTMT scheme, the encoder needs to perform rate&ndash;distortion optimization for each division mode during Coding Unit (CU) division, so as to select the best division mode, which also leads to an increase in coding time and coding complexity. Therefore, we propose a VVC intra prediction complexity reduction algorithm based on statistical theory and the Size-adaptive Convolutional Neural Network (SAE-CNN). The algorithm combines the establishment of a pre-decision dictionary based on statistical theory and a Convolutional Neural Network (CNN) model based on adaptively adjusting the size of the pooling layer to form an adaptive CU size division decision process. The algorithm can make a decision on whether to divide CUs of different sizes, thereby avoiding unnecessary Rate&ndash;distortion Optimization (RDO) and reducing coding time. Experimental results show that compared with the original algorithm, our suggested algorithm can save 35.60% of the coding time and only increases the Bj&oslash;ntegaard Delta Bit Rate (BD-BR) by 0.91%

SVM-Based Fast CU Partition Decision Algorithm for VVC Intra Coding

As a new coding standard, Versatile Video Coding (VVC) introduces the quad-tree plus multi-type tree (QTMT) partition structure, which significantly improves coding efficiency compared to High-Efficiency Video Coding (HEVC). The QTMT partition structure further enhances the flexibility of coding unit (CU) partitioning and improves the efficiency of VVC encoding high-resolution video, but introduces an unacceptable coding complexity at the same time. This paper proposes an SVM-based fast CU partition decision algorithm to reduce the coding complexity for VVC. First, the proportion of split modes with different CU sizes is analyzed to explore a method to effectively reduce coding complexity. Then, more reliable correlation features are selected based on the maximum ratio of the standard deviation (SD) and the edge point ratio (EPR) in sub-CUs. Finally, two SVM models are designed and trained using the selected features to provide guidance for deciding whether to divide and the direction of partition. The simulation results indicate that the proposed algorithm can save 54.05% coding time on average with 1.54% BDBR increase compared with VTM7.0

A Complexity Reduction Method for VVC Intra Prediction Based on Statistical Analysis and SAE-CNN

Compared with High Efficiency Video Coding (HEVC), the latest video coding standard Versatile Video Coding Standard (VVC), due to the introduction of many novel technologies and the introduction of the Quad-tree with nested Multi-type Tree (QTMT) division scheme in the block division method, the coding quality has been greatly improved. Due to the introduction of the QTMT scheme, the encoder needs to perform rate–distortion optimization for each division mode during Coding Unit (CU) division, so as to select the best division mode, which also leads to an increase in coding time and coding complexity. Therefore, we propose a VVC intra prediction complexity reduction algorithm based on statistical theory and the Size-adaptive Convolutional Neural Network (SAE-CNN). The algorithm combines the establishment of a pre-decision dictionary based on statistical theory and a Convolutional Neural Network (CNN) model based on adaptively adjusting the size of the pooling layer to form an adaptive CU size division decision process. The algorithm can make a decision on whether to divide CUs of different sizes, thereby avoiding unnecessary Rate–distortion Optimization (RDO) and reducing coding time. Experimental results show that compared with the original algorithm, our suggested algorithm can save 35.60% of the coding time and only increases the Bjøntegaard Delta Bit Rate (BD-BR) by 0.91%

Fast CU Partitioning Algorithm for VVC Based on Multi-Stage Framework and Binary Subnets

VVC is the latest video compression technology available, and while the coding efficiency has improved significantly over the previous generation of standards, it has also led to a dramatic increase in coding complexity. As VVC uses a QTMT division structure, the more flexible division structure also allows for a significant increase in coding time. We have built a multi-stage network framework to solve the above problem by dividing the CU into different stages according to the size of the blocks. The desired features are extracted by dynamically adjusting to the size of the input CU. Secondly, we construct a binary classification subnet to perform the classification task at each stage and can determine the QT and MT division decisions. Finally, the resulting experimental results can demonstrate that our novel two-threshold decision scheme can achieve a balance between RD performance and TS. Our method succeeds in reducing the coding time by 49.08&#x0025; to 52.56&#x0025;, while the complexity of the negligible BD-BR increases by only 1.10&#x0025; to 1.42&#x0025;

Fast CU Partition Decision Algorithm for VVC Intra Coding Using an MET-CNN

The newest video coding standard, the versatile video coding standard (VVC/H.266), came into effect in November 2020. Different from the previous generation standard&mdash;high-efficiency video coding (HEVC/H.265)&mdash;VVC adopts a more flexible block division structure, the quad-tree with nested multi-type tree (QTMT) structure, which improves its coding performance by 24%. However, it also causes a substantial increase in computational complexity. Therefore, this paper first proposes the concept of a stage grid map, which divides the overall division of a 32 &times; 32 coding unit (CU) into four stages and represents it as a structured output. Second, a multi-stage early termination convolutional neural network (MET-CNN) model is devised to predict the full partition information of a CU with a size of 32 &times; 32. Finally, a fast CU partition decision algorithm for VVC intra coding based on an MET-CNN is proposed. The algorithm can predict all partition information of a CU with a size of 32 &times; 32 and its sub-CUs in one run, completely replacing the complex rate-distortion optimization (RDO) process. It also has an early exit mechanism, thereby greatly reducing the encoding time. The experimental results illustrate that the scheme proposed in this paper reduces the encoding time by 49.24% on average, while the Bj&oslash;ntegaard Delta Bit Rate (BDBR) only increases by 0.97%