Substation Safety Awareness Intelligent Model: Fast Personal Protective Equipment Detection using GNN Approach

Federal regulations require employees to protect themselves from electrical hazards when working at substations. Such protections, commonly called personal protective equipment (PPE), vary with the hazard types and nature of exposure or delivery. Over the past decades, personal injuries and fatalities from electrical hazards have remained relatively common despite regular risk assessments and controls. One reason for this is that adequate PPE is not appropriately used. Easy-to-deploy strategies to detect proper use of PPE for electrical hazards are not available. Here, an intelligent detection model is developed to check whether PPE is appropriately worn or not;warning alarms would be triggered when the usage does not follow safety regulations. Arc-flash analysis is employed to determine a reasonable and safe PPE guideline. Eight types of PPE are considered, which cover the major PPE categories utilized in practice, including medical masks recommended for the Covid-19 pandemic. The model's framework utilizes a few-shot based graph neural network (GNN) technique to detect PPE. In contrast to prior data-driven models, only 50 images were collected for each PPE type, a relatively small number compared with state-of-the-art research. The proposed model was trained with diversified samples within multiple environments, resulting in a robust, efficient, intelligent detection model with probability of similarity in the range of 79%- 100%. To tackle the existing issues of computer-vision based PPE detection models, some technical suggestions on preserving personal privacy and PPE labels are provided. IEEE

TrajSGAN: A Semantic-Guiding Adversarial Network for Urban Trajectory Generation

Simulating human mobility contributes to city behavior discovery and decision-making. Although the sequence-based and image-based approaches have made impressive achievements, they still suffer from respective deficiencies such as omitting the depiction of spatial properties or ordinal dependency in trajectory. In this article, we take advantage of the above two paradigms and propose a semantic-guiding adversarial network (TrajSGAN) for generating human trajectories. Specifically, we first devise an attention-based generator to yield trajectory locations in a sequence-to-sequence manner. The encoded historical visits are queried with semantic knowledge (e.g., travel modes and trip purposes) and their important features are enhanced by the multihead attention mechanism. Then, we designate a rollout module to complete the unfinished trajectory sequence and transform it into an image that can depict its spatial structure. Finally, a convolutional neural network (CNN)-based discriminator signifies how “real”the trajectory image looks, and its output is regarded as a reward signal to update the generator by the policy gradient. Experimental results show that the proposed TrajSGAN model significantly outperforms the benchmarks under the MTL-Trajet mobility dataset, with the divergence of spatial-related metrics such as radius of gyration and travel distance reduced by 10%–27%. Furthermore, we apply the real and synthetic trajectories, respectively, to simulate the COVID-19 epidemic spreading under three preventive actions. The coefficient of determination metric between real and synthetic results achieves 91%–98%, indicating that the synthesized data from TrajSGAN can be leveraged to study the epidemic diffusion with an acceptable difference. All of these results verify the superiority and utility of our proposed method. IEEE

COVID-19 cases from the first local outbreak of SARS-CoV-2 B.1.1.7 variant in China presented more serious clinical features: a prospective, comparative cohort study (preprint)

Background: The SARS-CoV-2 B.1.1.7 variant which was first identified in the United Kingdom (U.K.) has increased sharply in numbers worldwide and was reported to be more contagious. On January 17, 2021, a COVID-19 clustered outbreak caused by B.1.1.7 variant occurred in a community in Daxing District, Beijing, China. Three weeks prior, another non-variant (lineage B.1.470) COVID-19 outbreak occurred in Shunyi District, Beijing. This study aimed to investigate the clinical features of B.1.1.7 variant infection. Methods: A prospective cohort study was conducted on COVID-19 cases admitted to Ditan hospital since January 2020. Data of 74 COVID-19 cases from two independent COVID-19 outbreaks in Beijing were extracted as study subjects from a Cloud Database established in Ditan hospital, which included 41 Shunyi cases (Shunyi B.1.470 group) and 33 Daxing cases (Daxing B.1.1.7 group) that have been hospitalized since December 25, 2020 and January 17, 2021, respectively. We conducted a comparison of the clinical characteristics, RT-qPCR results and genomic features between the two groups. Findings: Cases from Daxing B.1.1.7 group (15 [45.5%] male; median age, 39 years [range, 30.5, 62.5]) and cases from Shunyi B.1.470 group (25 [61.0%] male; median age, 31 years [range, 27.5, 41.0]) had a statistically significant difference in median age (P =0.014). Seven clinical indicators of Daxing B.1.1.7 group were significantly higher than Shunyi B.1.470 group including patients having fever over 38 (14/33 [46.43%] in Daxing B.1.1.7 group vs. 9/41 (21.95%) in Shunyi B.1.470 group [P = 0 .015]), C-reactive protein ([CRP, mg/L], 4.30 [2.45, 12.1] vs. 1.80, [0.85, 4.95], [P = 0.005]), Serum amyloid A ([SAA, mg/L], 21.50 [12.50, 50.70] vs. 12.00 [5.20, 26.95], [P = 0.003]), Creatine Kinase ([CK, U/L]), 110.50 [53.15,152.40] vs. 70.40 [54.35,103.05], [P = 0.040]), D-dimer ([DD, mg/L], 0.31 [0.20, 0.48] vs. 0.24 [0.17,0.31], [P = 0.038]), CD4+ T lymphocyte ([CD4+ T, mg/L], [P = 0.003]) , and Ground-glass opacity (GGO) in lung (15/33 [45.45%] vs. 5/41 [12.20%], [P =0.001]). After adjusting for the age factor, B.1.1.7 variant infection was the risk factor for CRP (P = 0.045, Odds ratio [OR] 2.791, CI [1.025, 0.8610]), SAA (0.011, 5.031, [1.459, 17.354]), CK (0.034, 4.34, [0.05, 0.91]), CD4+ T ( 0.029, 3.31, [1.13, 9.71]), and GGO (0.005, 5.418, [1.656, 17.729]) of patients. The median Ct value of RT-qPCR tests of the N-gene target in the Daxing B.1.1.7 group was significantly lower than the Shunyi B.1.470 group (P=0.036). The phylogenetic analysis showed that only 2 amino acid mutations in spike protein were detected in B.1.470 strains while B.1.1.7 strains had 3 deletions and 7 mutations. Interpretation: Clinical features including a more serious inflammatory response, pneumonia and a possible higher viral load were detected in the cases infected with B.1.1.7 SARS-CoV-2 variant. It could therefore be inferred that the B.1.1.7 variant may have increased pathogenicity.

Defining heart disease risk for death in COVID-19 infection.

BACKGROUND: Cardiovascular disease (CVD) was in common in coronavirus disease 2019 (COVID-19) patients and associated with unfavorable outcomes. We aimed to compare the clinical observations and outcomes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-infected patients with or without CVD. METHODS: Patients with laboratory-confirmed SARS-CoV-2 infection were clinically evaluated at Wuhan Seventh People's Hospital, Wuhan, China, from 23 January to 14 March 2020. Demographic data, laboratory findings, comorbidities, treatments and outcomes were collected and analyzed in COVID-19 patients with and without CVD. RESULTS: Among 596 patients with COVID-19, 215 (36.1%) of them with CVD. Compared with patients without CVD, these patients were significantly older (66 vs. 52 years) and had higher proportion of men (52.5% vs. 43.8%). Complications in the course of disease were more common in patients with CVD, included acute respiratory distress syndrome (22.8% vs. 8.1%), malignant arrhythmias (3.7% vs. 1.0%) including ventricular tachycardia/ventricular fibrillation, acute coagulopathy(7.9% vs. 1.8%) and acute kidney injury (11.6% vs. 3.4%). The rate of glucocorticoid therapy (36.7% vs. 25.5%), Vitamin C (23.3% vs. 11.8%), mechanical ventilation (21.9% vs. 7.6%), intensive care unit admission (12.6% vs. 3.7%) and mortality (16.7% vs. 4.7%) were higher in patients with CVD (both P < 0.05). The multivariable Cox regression models showed that older age (≥65 years old) (HR 3.165, 95% CI 1.722-5.817) and patients with CVD (HR 2.166, 95% CI 1.189-3.948) were independent risk factors for death. CONCLUSIONS: CVD are independent risk factors for COVID-19 patients. COVID-19 patients with CVD were more severe and had higher mortality rate, early intervention and vigilance should be taken.

ANALYSIS OF SPORTS BEHAVIOR AND THE CURRENT SITUATION OF YOUNG STUDENTS UNDER THE BACKGROUND OF COVID-19

Introduction: With the repeated covid-19 epidemic, people have gradually realized the importance of physical exercise, so the sports enthusiasm of young students has also been improved to a certain extent. Objective: Analyze the sports behavior and status in adolescent students under the background of covid-19. Methods: A questionnaire survey was used in this paper. The questionnaire design is carried out from three aspects: current exercise status, changes of physical exercise, and sports behavior motivation of young students. Results: Students and parents prefer exercises at home or in the open space of a relatively safe and single community, choosing non-contact sports that can be completed by a single person or are far away from each other. Improvement in both frequency and duration of exercise was observed in the young students, and most had a gain in psychological quality. Conclusion: Physical education teachers must fully match the actual situation of the epidemic’s current development by choosing effective teaching methods to promote the continuous development of young students’ physical quality. Level of evidence II;Therapeutic studies-investigation of treatment results. © 2023, Redprint Editora Ltda. All rights reserved.