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Convergence involving Equipment Vision as well as Burn Electrowriting.
g., age, industry). Nevertheless, most respondents tended to be optimistic about robot applications and dialectical about the ethical issues involved. This is related to the prominent role robots played during the pandemic, the Chinese public's expectations of new technologies and technology-friendly public opinion in China. Exploring the perception and reception of anti-pandemic robots in different countries or cultures is important because it can shed some light on the future applications of robots, especially in the field of infectious disease control.
Close patient contact is an essential component of clinical dental education, which can expose students and faculty to risk of COVID-19 and its sequelae.

The study was a cross-sectional survey conducted among faculty and clinical students at an academic dental hospital in Al Madinah western Saudi Arabia. An online questionnaire was distributed to collect data on prevalence, risk factors, clinical manifestations, and long-term health and socioeconomic complications of COVID-19 infection.

Prevalence of COVID-19 was 19.6% among a total of 316 students and faculty. mTOR cancer Participants cited family and friends as the primary source of infection (40.3%). Among cross-infection control practices, they cited failure to practice distancing as the primary reason for infection transmission (61.3%). The disease was symptomatic in 85.5% of infected personnel. Most frequently reported clinical manifestations were fever, cough, malaise, and diarrhoea (74.1%, 56.5%, 40.3%, 32.3%, respectively). A proportion of 37.1% of infecteof the severity of symptoms encountered during the acute stage of COVID-19 infection, all infected dental healthcare personnel should be followed, especially those who report long COVID. Continuous follow-up and assistance for infected students may be warranted to mitigate the potential academic and mental drawbacks caused by the pandemic. Dental schools should adopt clear policies regarding COVID-19 transmission and prevention and should implement them in their infection-control education and training.Chronic Low Back Pain (LBP) is a symptom that may be caused by several diseases, and it is currently the leading cause of disability worldwide. The increased amount of digital images in orthopaedics has led to the development of methods related to artificial intelligence, and to computer vision in particular, which aim to improve diagnosis and treatment of LBP. In this manuscript, we have systematically reviewed the available literature on the use of computer vision in the diagnosis and treatment of LBP. A systematic research of PubMed electronic database was performed. The search strategy was set as the combinations of the following keywords "Artificial Intelligence", "Feature Extraction", "Segmentation", "Computer Vision", "Machine Learning", "Deep Learning", "Neural Network", "Low Back Pain", "Lumbar". Results The search returned a total of 558 articles. After careful evaluation of the abstracts, 358 were excluded, whereas 124 papers were excluded after full-text examination, taking the number of eligible articles to 76. The main applications of computer vision in LBP include feature extraction and segmentation, which are usually followed by further tasks. Most recent methods use deep learning models rather than digital image processing techniques. The best performing methods for segmentation of vertebrae, intervertebral discs, spinal canal and lumbar muscles achieve Sørensen-Dice scores greater than 90%, whereas studies focusing on localization and identification of structures collectively showed an accuracy greater than 80%. Future advances in artificial intelligence are expected to increase systems' autonomy and reliability, thus providing even more effective tools for the diagnosis and treatment of LBP.At present, a smart city from the perspective of the United Nations Sustainable Development Goals (SDGs) emphasizes the importance of providing citizens with promising health and well-being. However, with the continuous impact of coronavirus disease 2019 (COVID-19) and the increase of city population, the health of citizens is facing new challenges. Therefore, this paper aims to assess the relationship between building, environment, landscape design, art therapy (AT), and therapeutic design (TD) in promoting health within the context of sustainable development. It also summarizes the existing applied research areas and potential value of TD that informs future research. This paper adopts the macro-quantitative and micro-qualitative research methods of bibliometric analysis. The results show that the built environment and AT are related to sustainable development, and closely associated with health and well-being; the application of TD in the environment, architecture, space, and landscape fields promotes the realization of SDGs and lays the foundation for integrating digital technologies such as Building Information Modeling (BIM) into the design process to potentially solve the challenges of TD; and the principle of TD can consider design elements and characteristics from based on people's health needs to better promote human health and well-being.The current academic landscape has overwhelmed faculties and with demands to adopt tech-savvy teaching modes and accelerate scholarly works, administrative duties, and outreach programs. Such demands have deteriorated the health-related quality of life (HRQoL) among university employees. This study aimed to determine the factors associated with HRQoL among university employees in a Malaysian public university. This cross-sectional study was conducted among 397 employees from the Universiti Kebangsaan Malaysia (UKM) between April and June 2019. A self-administered questionnaire that consisted of socio-demographic items, risky health behaviors, health-related information, and validated scales for measuring employees' physical inactivity, psychological states, and HRQoL was utilized. Descriptive and inferential statistics were calculated using SPSS version 23.0. Hierarchical multiple linear regression models were yielded to determine the factors associated with different domains of HRQoL. Mediation analysis was conducted using PROCESS MACRO (Model 4). Statistical significance was set to p  less then 0.05. Physical HRQoL scored the highest, while environmental HRQoL had the lowest score among the employees. Physical HRQoL was influenced by age, service duration, comorbid conditions, BMI, chronic diseases, and anxiety. Factors associated with psychological HRQoL were age, service duration, depression, and stress. Age, service duration, and chronic diseases affected employees' social relationship HRQoL, while environmental HRQoL was associated with age, occupation type, chronic diseases, and depression. Socio-demographics, risky health behaviors, health profiles, and psychological attributes were significantly associated with employees' HRQoL. Age was the only positively correlated factor across all HRQoL domains, while other factors deteriorated employees' HRQoL.Modifiable risk factors are of interest for chronic disease prevention. Few studies have assessed the system of modifiable and mediating pathways leading to diabetes mellitus. We aimed to develop a pathway model for Diabetes Risk with modifiable Lifestyle Risk factors as the start point and Physiological Load as the mediator. As there are no standardised risk thresholds for lifestyle behaviour, we derived a weighted composite for Lifestyle Risk. Physiological Load was based on an index using clinical thresholds. Sociodemographics are non-modifiable risk factors and were specified as covariates. We used structural equation modeling to test the model, first using 2014/2015 data from the Indonesian Family Life Survey. Next, we fitted a smaller model with longitudinal data (2007/2008 to 2014/2015), given limited earlier data. Both models showed the indirect effects of Lifestyle Risk on Diabetes Risk via the mediator of Physiological Load, whereas the direct effect was only supported in the cross-sectional analysis. Specifying Lifestyle Risk as an observable, composite variable incorporates the cumulative effect of risk behaviour and differentiates this study from previous studies assessing it as a latent construct. The parsimonious model groups the multifarious risk factors and illustrates modifiable pathways that could be applied in chronic disease prevention efforts.Mexico is one of the countries most affected by COVID-19. Studies have found that smoking behaviors have been impacted by the pandemic as well; however, results have varied across studies, and it remains unclear what is causing the changes. This study of an open cohort of smokers recruited from a consumer panel (n = 2753) examined changes in cigarettes per day (CPD), daily vs. non-daily smoking, recent quit attempts, perceived stress, depression, and perceived severity of COVID-19 at two points during the pandemic March and July 2020. Differences in CPD between waves were estimated with Poisson regression using generalized estimating equations (GEE). Differences in perceived stress were estimated with linear regression using GEE, and differences in recent quit attempts, depression, and perceived severity of COVID-19 were estimated using separate logistic regression GEE models. Rates of depression were higher in July compared to March (AOR = 1.55, 95% C.I. 1.31-1.85), and the likelihood of recent quit attempt was lower in July compared to March (AOR = 0.85, 95% C.I. 0.75-0.98). There was no statistically significant change in CPD, daily smoking, or perceived stress. Perceived COVID-19 severity for oneself increased significantly (AOR 1.24, 95% C.I. 1.02-1.52); however, the perceived COVID-19 severity for smokers remained constant. Our study suggests that as the COVID-19 pandemic expanded in Mexico, smoking frequency remained stable, and quit attempts decreased, even as adult smokers increasingly perceived infection with COVID-19 for themselves as severe. These results can aid in the development of health communication strategies to educate smokers about their risk for COVID-19, potentially capitalizing on concerns that stem from this syndemic of communicable and smoking-related non-communicable disease.The purpose of the study was (1) to investigate the effects of regular long-term circuit training (once per week) on cardiorespiratory fitness (CRF) in sedentary adults and (2) to compare training progress with the effects of continued exercise participation by regularly active age-matched individuals. Ten sedentary, middle-aged (51 ± 6 years) individuals (sedentary group, SG) of both sexes performed 32 weeks (1 training session/week) of supervised circuit training and 10 weeks of self-managed training. Effects were compared to an age-matched group (51 ± 8 years; n = 10) of regularly active individuals (active group, AG). CRF (expressed as peak oxygen uptake VO2peak; peak power output PPO) and systemic blood pressure (BP) during the incremental test were measured at the start and after the training intervention. CRF decreased significantly within the AG (VO2peak 43.1 ± 7.3 vs. 40.3 ± 6.5 mL/min/kg, p less then 0.05; PPO 3.3 ± 0.6 vs. 3.1 ± 0.6; p less then 0.05) but was maintained in the SG. In addition, significant improvements in restoration of the oxygen level in leg muscles after exercise and reduced systolic BP (180 ± 14 vs.
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