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Treatment adherence (TA) is a critical issue and is under-investigated in hemodialysis patients. A multi-center study was conducted from July 2020 to March 2021 on 972 hemodialysis patients in eight hospitals in Vietnam to explore the factors associated with TA during the COVID-19 pandemic. Data were collected, including socio-demographics, an End-Stage Renal Disease Adherence Questionnaire (ESRD-AQ), 12-item short-form health literacy questionnaire (HLS-SF12), 4-item digital healthy diet literacy scale (DDL), 10-item hemodialysis dietary knowledge scale (HDK), 7-item fear of COVID-19 scale (FCoV-19S), and suspected COVID-19 symptoms (S-COVID19-S). Bivariate and multivariate linear regression models were used to explore the associations. Higher DDL scores were associated with higher TA scores (regression coefficient, B, 1.35; 95% confidence interval, 95%CI, 0.59, 2.12; p = 0.001). Higher FCoV-19S scores were associated with lower TA scores (B, −1.78; 95%CI, −3.33, −0.24; p = 0.023). In addition, patients aged 60–85 (B, 24.85; 95%CI, 6.61, 43.11; p = 0.008) with “very or fairly easy” medication payment ability (B, 27.92; 95%CI, 5.89, 44.95; p = 0.013) had higher TA scores. Patients who underwent hemodialysis for ≥5 years had a lower TA score than those who received

In this work, we use the theory of multi-porosity multi-permeability porothermoelasticity to derive the analytical solutions of pore pressure and stresses for an inclined wellbore, accounting for a rate-dependent mud cake buildup on the wellbore wall. The solutions are further combined with the Hoek-Brown shear failure criterion and a tensile failure criterion to calculate the critical collapse and fracturing mud weights. We choose a naturally fractured rock formation as an example to demonstrate the coupled effects of temperature, mud cake, and the formation's dual-porosity dual-permeability on wellbore stability. We show that drilling mud with a lower temperature than the rock formation results in differential contraction of the solid and pore fluid constituents and leads to significant alteration of pore pressure and stress distributions. Additionally, a low-permeability and thin mud cake can considerably increase effective tangential stress. Natural fractures also alter the pore pressure and effective stress distributions because of fractures’ higher permeability than the rock matrix. Both wellbore cooling and filter cake buildup decrease the collapse mud weight. Natural fractures have significant weakening effects, increasing the collapse mud weight and decreasing the fracturing mud weight. This work provides the first solutions for wellbore stability analysis, considering together the time-dependent filter cake buildup, thermal effects, and formation's multi-porosity multi-permeability.

SummaryEstimation of near-wellbore fracture widths remains central to designing the particle size distribution (PSD) and composition of lost circulation material (LCM) blends. Although elastic rock models are often used for this purpose, they fall short in capturing the substantial effect of pore fluid pressure on the fracture width. The problem is addressed in this paper by incorporating the poroelastic back stress in width estimation of axial fractures nearby an inclined wellbore. The poroelastic back stress is caused by a nonideal drilling fluid filter cake allowing for fluid pressure communication between the wellbore and pore space of the rock surrounding the wellbore. In this aspect, a proper definition of the filter-cake efficiency is made in terms of the wellbore pressure, far-field pore fluid pressure, and pore fluid pressure of the rock surrounding the wellbore. The value of this parameter is estimated from the standard drilling fluid filtration test results, as well as the formation rock permeability. The filter-cake efficiency is next used to develop the long-time, asymptotic analytical solution for the poroelastic stress of an inclined wellbore. By accounting for the obtained poroelastic back stress, an improved estimation of the wellbore tensile limit that depends on the filter-cake efficiency parameter is developed. For wellbore pressures beyond the wellbore tensile limit, the width of the near-wellbore fractures is estimated. The fracture width estimation is made through an analytical, dislocation-based fracture mechanics solution to the integral equation describing the nonlocal stress equilibrium along the fracture faces. The commonly practiced scheme for geometric design of LCM blends is enhanced by using the presented improvement in estimation of the near-wellbore fracture width. A case study is used to demonstrate the substantial effect of drilling fluid filtration properties and the resulting poroelastic back stress on the wellbore tensile limit, estimated fracture width, and consequently, composition of the recommended LCM blend.

Summary Selection of a safe mud weight is crucial in drilling operations to reduce costly wellbore-instability problems. Advanced physics models and their analytical solutions for mud-weight-window computation are available but still demanding in terms of central-processing-unit (CPU) time. This paper presents an artificial-intelligence (AI) solution for predicting time-dependent safe mud-weight windows and very refined polar charts in real time. The AI agents are trained and tested on data generated from a time-dependent coupled analytical solution (poroelastic) because numerical solutions are prohibitively slow. Different AI techniques, including linear regression, decision tree, random forest, extra trees, adaptive neuro fuzzy inference system (ANFIS), and neural networks are evaluated to select the most suitable one. The results show that neural networks have the best performances and are capable of predicting time-dependent mud-weight windows and polar charts as accurately as the analytical solution, with 1/1,000 of the computer time needed, making them very applicable to real-time drilling operations. The trained neural networks achieve a mean squared error (MSE) of 0.0352 and a coefficient of determination (R2) of 0.9984 for collapse mud weights, and an MSE of 0.0072 and an R2 of 0.9998 for fracturing mud weights on test data sets. The neural networks are statistically guaranteed to predict mud weights that are within 5% and 10% of the analytical solutions with probability up to 0.986 and 0.997, respectively, for collapse mud weights, and up to 0.9992 and 0.9998, respectively, for fracturing mud weights. Their time performances are significantly faster and less demanding in computing capacity than the analytical solution, consistently showing three-orders-of-magnitude speedups in computational speed tests. The AI solution is integrated into a deployed wellbore-stability analyzer, which is used to demonstrate the AI’s performances and advantages through three case studies.

Background: The COVID-19 pandemic has serious consequences around the world, especially in the healthcare system. With a crucial role in the pandemic, mental health and quality of life among health care workers (HCWs) needs to be addressed. Purposes: The study aimed to describe mental health and the health-related quality of life among HCWs and explore the relationship between mental health and quality of life among HCWs during the COVID-19 pandemic. Methods: A cross-sectional study was conducted from 6 April to 19 April 2020 at nine hospitals in Vietnam. The questionnaire included participant's characteristics, health-related quality of life (HRQoL), Patient Health Questionnaire (PHQ), and Generalized Anxiety Disorder (GAD). De-pression and anxiety were defined as PHQ score ≥ 10 and GAD score ≥ 8, respectively. Descriptive, bivariate, and multiple regression analyses were used to analyze the data with a significant level at p < 0.05. Results: A total of 3,757 HCWs were enrolled in the study. Proportions of de-pression and anxiety were 4.5% and 3.1%, respectively. The overall mean score of HRQoL was 73.4 ± 14.9 (possible score range from 0 to 100). Protective factors of HRQoL consisted of physical activity, ability to pay for medication, gender, and social status, while its risk factors included suspected COVID-19 symptoms, smoking status. HRQoL was associated with depression (B(95%CI) = -8.15 (-10.76 to -5.55) and anxiety (B(95%CI) = -6.87 (-9.99 to -3.75)), p Conclusion: There was a relationship between mental health and the health-related quality of life among HCWs. Integration of healthy lifestyles and psychological support can help improve mental health and quality of life for HCWs during the pandemic.

During the COVID-19 pandemic, it is essential to evaluate hemodialysis patients’ dietary knowledge, especially among those with COVID-19 related symptoms, in order to identify appropriate strategies in managing their mental health. The study’s purposes were to test the psychometric properties of the hemodialysis dietary knowledge (HDK) scale, and to investigate the modifying impact of HDK on the associations of suspected COVID-19 symptoms (S-COVID-19-S) with anxiety and depression among hemodialysis patients. A cross-sectional study was conducted from July 2020 to March 2021 at eight hospitals across Vietnam. Data of 875 hemodialysis patients were analyzed, including socio-demographic, anxiety (the generalized anxiety disorder scale, GAD-7), depression (the patient health questionnaire, PHQ-9), S-COVID-19-S, HDK, health literacy, and digital healthy diet literacy. Confirmatory factor analysis (CFA) and logistic regression models were used to analyze the data. The HDK scale demonstrates the satisfactory construct validity with good model fit (Goodness of Fit Index, GFI = 0.96; Adjusted Goodness of Fit Index, AGFI = 0.90; Standardized Root Mean Square Residual, SRMR = 0.05; Root Mean Square Error of Approximation, RMSEA = 0.09; Normed Fit Index, NFI = 0.96; Comparative Fit Index, CFI = 0.96, and Parsimony goodness of Fit Index, PGFI = 0.43), criterion validity (as correlated with HL (r = 0.22, p < 0.01) and DDL (r = 0.19, p < 0.01), and reliability (Cronbach alpha = 0.70)). In the multivariate analysis, S-COVID-19-S was associated with a higher likelihood of anxiety (odds ratio, OR, 20.76; 95% confidence interval, 95%CI, 8.85, 48.70; p < 0.001) and depression (OR, 12.95; 95%CI, 6.67, 25.14, p < 0.001). A higher HDK score was associated with a lower likelihood of anxiety (OR, 0.70; 95%CI, 0.64, 0.77; p < 0.001) and depression (OR, 0.72; 95%CI, 0.66, 0.79; p < 0.001). In the interaction analysis, the negative impacts of S-COVID-19-S on anxiety and depression were mitigated by higher HDK scores (p < 0.001). In conclusion, HDK is a valid and reliable tool to measure dietary knowledge in hemodialysis patients. Higher HDK scores potentially protect patients with S-COVID-19-S from anxiety and depression during the pandemic.

We solve analytically and numerically a two-dimensional boundary value problem for the poroelastodynamic responses of a dual-porosity dual-permeability material subjected to harmonic excitation. To derive the analytical solutions, we first apply Helmholtz decomposition to decompose displacement vectors into divergence- and curl-free components. Then we use the diagonalization method to solve a series of coupled partial differential equations. The numerical solution is obtained using the finite element method. We present an example showing that the analytical and numerical solutions are close to each other.

Osteoporosis is a common bone health disorder in hemodialysis patients that is linked with a higher morbidity and mortality rate. While previous studies have explored the associated factors of osteoporosis, there is a lack of studies investigating the impacts of health literacy (HL) and digital healthy diet literacy (DDL) on osteoporosis. Therefore, we aimed to investigate the associations of HL, DDL, and other factors with osteoporosis among hemodialysis patients. From July 2020 to March 2021, a cross-sectional study was conducted on 675 hemodialysis patients in eight hospitals in Vietnam. The data were collected by using the osteoporosis self-assessment tool for Asians (OSTA) and the 12-item short form of the health literacy questionnaire (HLS-SF12) on digital healthy diet literacy (DDL) and hemodialysis dietary knowledge (HDK). In addition, we also collected information about the socio-demographics, the clinical parameters, the biochemical parameters, and physical activity. Unadjusted and adjusted multinomial logistic regression models were utilized in order to investigate the associations. The proportion of patients at low, medium, and high levels of osteoporosis risk was 39.6%, 40.6%, and 19.8%, respectively. In the adjusted models, women had a higher likelihood of osteoporosis risk than men (odds ratio, OR, 3.46; 95% confidence interval, 95% CI, 1.86, 6.44; p < 0.001; and OR, 6.86; 95% CI, 2.96, 15.88; p < 0.001). The patients with rheumatoid arthritis (OR, 4.37; 95% CI, 1.67, 11.52; p = 0.003) and stomach ulcers (OR, 1.95; 95% CI, 1.01, 3.77; p = 0.048) were more likely to have a higher likelihood of osteoporosis risk than those without. The patients who had a higher waist circumference (WC), HL, and DDL were less likely to have a medium level of osteoporosis risk (OR, 0.95; 95% CI, 0.92, 0.98; p = 0.004; OR, 0.92; 95% CI, 0.88, 0.96; p < 0.001; OR, 0.96; 95% CI, 0.93, 0.99; p = 0.017, respectively) and a high level of osteoporosis risk (OR, 0.93; 95% CI, 0.89, 0.97; p = 0.001; OR, 0.89; 95% CI, 0.84, 0.94; p < 0.001; OR, 0.95; 95% CI, 0.91, 0.99; p = 0.008, respectively) compared with a low level of osteoporosis risk and to those with a lower WC, HL, and DDL. In addition, higher levels of hemoglobin (Hb) (OR, 0.79; 95% CI, 0.66, 0.95; p = 0.014), hematocrit (Hct) (OR, 0.95; 95% CI, 0.92, 0.99; p = 0.041), albumin (OR, 0.91; 95% CI, 0.83, 0.99; p = 0.030), and education (OR, 0.37; 95% CI, 0.16, 0.88; p = 0.025) were associated with a lower likelihood of a high level of osteoporosis risk. In conclusion, osteoporosis risk is highly prevalent in hemodialysis patients. Improved HL, DDL, education, WC, albumin, Hb, and Hct levels should be considered in preventing hemodialysis patients from developing osteoporosis.

Background: The COVID-19 pandemic has been disseminating fear in the community, which has affected people’s quality of life, especially those with health problems. Health literacy (HL), eHealth literacy (eHEAL), and digital healthy diet literacy (DDL) may have potential impacts on containing the pandemic and its consequences. This study aimed to examine the association between the fear of COVID-19 scale (FCoV-19S) and the health-related quality of life (HRQoL), and to examine the effect modification by HL, eHEAL, and DDL on this association. Methods: A cross-sectional study was conducted in 11 hospitals across Vietnam from 7 April to 31 May 2020. Data were collected on 4348 outpatients, including demographic characteristics, HL, eHEAL, DDL, FCoV-19S, and HRQoL. Multiple linear regression and interaction models were used to explore associations. Results: Patients with higher FCoV-19S scores had lower HRQoL scores (unstandardized coefficient, B = −0.78, p &lt, 0.001). HL (B = 0.20, p &lt, 0.001), eHEAL (B = 0.24, p &lt, 0.001), and DDL (B = 0.20, p &lt, 0.001) were positively associated with higher HRQoL scores. The negative impact of FCoV-19S on HRQoL was significantly attenuated by higher eHEAL score groups (from one standard deviation (SD) below the mean, B = −0.93, p &lt, 0.001, to the mean, B = −0.85, p &lt, and one SD above the mean, B = −0.77, p &lt, 0.001), and by higher DDL score groups (from one SD below the mean, B = −0.92, p &lt, to the mean, B = −0.82, p &lt, and one SD above the mean, B = −0.72, p &lt, 0.001). Conclusions: eHealth literacy and digital healthy diet literacy could help to protect patients’ health-related quality of life from the negative impact of the fear of COVID-19 during the pandemic.

The COVID-19 pandemic has been influencing people’s psychological health, especially in pregnant women. We aimed to examine associated factors of fear of COVID-19, anxiety and depression among pregnant women during the pandemic where the impacts of healthy eating behaviour (HES) and health literacy (HL) were emphasized. A cross-sectional study was conducted between 14 February 2020 and 31 May 2020 in 18 health centres and hospitals across Vietnam. Data of 518 pregnant women were analysed, including socio-demographics, pregnant-related factors, HES, HL, health-related behaviours, fear of COVID-19 scale (FCoV-19S), anxiety (using the generalized anxiety disorder (GAD-7)) and depression (using the patient health questionnaire with 9 items (PHQ-9)). Regression analysis was utilized to explore the associations. Pregnant women with higher scores of HES and HL had lower likelihood of anxiety (odds ratio, OR, 0.79; 95% confidence interval (95%CI), 0.73, 0.87; p < .001; and OR, 0.94; 95%CI, 0.90, 0.99; p = .018) and depression (OR, 0.84; 95%CI, 0.78, 0.91; p < .001; and OR, 0.96; 95%CI, 0.91, 0.99; p = .044), respectively. Pregnant women being employed had a lower FCoV-19S score (regression coefficient, B, −1.46; 95%CI, −2.51, −0.40; p = .007). Besides, other significant predictors of anxiety were eating healthier during the pandemic, unchanged or more physical activity, elevated gestational age and smoking. Other significant predictors of depression were eating healthier during the pandemic, elevated gestational age and smoking. Among others, HES and HL had positive impacts on protecting pregnant women against anxiety and depression. Improving HES and HL should be addressed as a strategic approach to improve reproductive health during the pandemic.KEY MESSAGEThe COVID-19 pandemic influences antenatal mental disorders with the higher level as opposed to that before the pandemic.Healthy eating behaviour and better health literacy (HL) had critical roles in lowering prenatal anxiety and depression during the COVID-19 crisis.Strategic approaches for improving healthy eating and HL should be recommended for protecting pregnant women from mental health problems during the pandemic. The COVID-19 pandemic influences antenatal mental disorders with the higher level as opposed to that before the pandemic. Healthy eating behaviour and better health literacy (HL) had critical roles in lowering prenatal anxiety and depression during the COVID-19 crisis. Strategic approaches for improving healthy eating and HL should be recommended for protecting pregnant women from mental health problems during the pandemic.

ObjectivesWe examined impacts and interactions of COVID-19 response involvement, health-related behaviours and health literacy (HL) on anxiety, depression, and health-related quality of life (HRQoL) among healthcare workers (HCWs).DesignA cross-sectional study was conducted. Data were collected 6 April to 19 April 2020 using online-based, self-administered questionnaires.Setting19 hospitals and health centres in Vietnam.Participants7 124 HCWs aged 21–60 years.ResultsThe COVID-19 response-involved HCWs had higher anxiety likelihood (OR (95% CI)=4.41 (3.53 to 5.51)), higher depression likelihood (OR(95% CI)=3.31 (2.71 to 4.05)) and lower HRQoL score (coefficient, b(95% CI)=−2.14 (−2.89 to −1.38)), compared with uninvolved HCWs. Overall, HCWs who smoked or drank at unchanged/increased levels had higher likelihood of anxiety, depression and lower HRQoL scores; those with unchanged/healthier eating, unchanged/more physical activity and higher HL scores had lower likelihood of anxiety, depression and higher HRQoL scores. In comparison to uninvolved HCWs who smoked or drank at never/stopped/reduced levels, involved HCWs with unchanged/increased smoking or drinking had lower anxiety likelihood (OR(95% CI)=0.34 (0.14 to 0.83)) or (OR(95% CI)=0.26 (0.11 to 0.60)), and lower depression likelihood (OR(95% CI)=0.33 (0.15 to 0.74)) or (OR(95% CI)=0.24 (0.11 to 0.53)), respectively. In comparison with uninvolved HCWs who exercised at never/stopped/reduced levels, or with those in the lowest HL quartile, involved HCWs with unchanged/increased exercise or with one-quartile HL increment reported lower anxiety likelihood (OR(95% CI)=0.50 (0.31 to 0.81)) or (OR(95% CI)=0.57 (0.45 to 0.71)), lower depression likelihood (OR(95% CI)=0.40 (0.27 to 0.61)) or (OR(95% CI)=0.63 (0.52 to 0.76)), and higher HRQoL scores (b(95% CI)=2.08 (0.58 to 3.58)), or (b(95% CI)=1.10 (0.42 to 1.78)), respectively.ConclusionsPhysical activity and higher HL were found to protect against anxiety and depression and were associated with higher HRQoL. Unexpectedly, smoking and drinking were also found to be coping behaviours. It is important to have strategic approaches that protect HCWs’ mental health and HRQoL.

Background The COVID-19 pandemic has imposed a heavy burden on health care systems and governments. Health literacy (HL) and eHealth literacy (as measured by the eHealth Literacy Scale [eHEALS]) are recognized as strategic public health elements but they have been underestimated during the pandemic. HL, eHEALS score, practices, lifestyles, and the health status of health care workers (HCWs) play crucial roles in containing the COVID-19 pandemic. Objective The aim of this study is to evaluate the psychometric properties of the eHEALS and examine associations of HL and eHEALS scores with adherence to infection prevention and control (IPC) procedures, lifestyle changes, and suspected COVID-19 symptoms among HCWs during lockdown. Methods We conducted an online survey of 5209 HCWs from 15 hospitals and health centers across Vietnam from April 6 to April 19, 2020. Participants answered questions related to sociodemographics, HL, eHEALS, adherence to IPC procedures, behavior changes in eating, smoking, drinking, and physical activity, and suspected COVID-19 symptoms. Principal component analysis, correlation analysis, and bivariate and multivariate linear and logistic regression models were used to validate the eHEALS and examine associations. Results The eHEALS had a satisfactory construct validity with 8 items highly loaded on one component, with factor loadings ranked from 0.78 to 0.92 explaining 76.34% of variance; satisfactory criterion validity as correlated with HL (ρ=0.42); satisfactory convergent validity with high item-scale correlations (ρ=0.80-0.84); and high internal consistency (Cronbach α=.95). HL and eHEALS scores were significantly higher in men (unstandardized coefficient [B]=1.01, 95% CI 0.57-1.45, P Conclusions The eHEALS is a valid and reliable survey tool. Gender, ability to pay for medication, profession, and epidemic containment experience were independent predictors of HL and eHEALS scores. HCWs with higher HL or eHEALS scores had better adherence to IPC procedures, healthier lifestyles, and a lower likelihood of suspected COVID-19 symptoms. Efforts to improve HCWs’ HL and eHEALS scores can help to contain the COVID-19 pandemic and minimize its consequences.

BACKGROUND The COVID-19 pandemic has imposed a heavy burden on health care systems and governments. Health literacy (HL) and eHealth literacy (as measured by the eHealth Literacy Scale [eHEALS]) are recognized as strategic public health elements but they have been underestimated during the pandemic. HL, eHEALS score, practices, lifestyles, and the health status of health care workers (HCWs) play crucial roles in containing the COVID-19 pandemic. OBJECTIVE The aim of this study is to evaluate the psychometric properties of the eHEALS and examine associations of HL and eHEALS scores with adherence to infection prevention and control (IPC) procedures, lifestyle changes, and suspected COVID-19 symptoms among HCWs during lockdown. METHODS We conducted an online survey of 5209 HCWs from 15 hospitals and health centers across Vietnam from April 6 to April 19, 2020. Participants answered questions related to sociodemographics, HL, eHEALS, adherence to IPC procedures, behavior changes in eating, smoking, drinking, and physical activity, and suspected COVID-19 symptoms. Principal component analysis, correlation analysis, and bivariate and multivariate linear and logistic regression models were used to validate the eHEALS and examine associations. RESULTS The eHEALS had a satisfactory construct validity with 8 items highly loaded on one component, with factor loadings ranked from 0.78 to 0.92 explaining 76.34% of variance; satisfactory criterion validity as correlated with HL (ρ=0.42); satisfactory convergent validity with high item-scale correlations (ρ=0.80-0.84); and high internal consistency (Cronbach α=.95). HL and eHEALS scores were significantly higher in men (unstandardized coefficient [B]=1.01, 95% CI 0.57-1.45, PPPPPP=.003), and those with epidemic containment experience (B=1.96, 95% CI 1.56-2.37, PPPPP=.001; OR 1.04, 95% CI 1.02-1.07, P=.002), were more physically active (OR 1.03, 95% CI 1.02-1.03, PPPP CONCLUSIONS The eHEALS is a valid and reliable survey tool. Gender, ability to pay for medication, profession, and epidemic containment experience were independent predictors of HL and eHEALS scores. HCWs with higher HL or eHEALS scores had better adherence to IPC procedures, healthier lifestyles, and a lower likelihood of suspected COVID-19 symptoms. Efforts to improve HCWs’ HL and eHEALS scores can help to contain the COVID-19 pandemic and minimize its consequences.

This paper presents an artificial intelligence (AI) solution for predicting time-dependent safe mud weight windows and polar charts for inclined wellbores. The AI agents, in the form of neural networks, are trained and tested on data generated from a poroelastic analytical solution. The results show that the trained neural networks are capable of predicting time-dependent mud weight windows and polar charts as accurately as the analytical solution. The AI solution achieves a mean squared error (MSE) of 0.107 and a coefficient of determination (R2) of 0.995 for collapse mud weights, and an MSE of 0.043 and an R2 of 0.998 for fracturing mud weights on test datasets. They are also significantly faster and less demanding in computing capacity than the analytical solution, consistently showing three-orders-of-magnitude speed-ups in computational speed tests. Evaluations on two case studies similarly demonstrate the AI solution's capabilities and benefits.

Background: We aimed to examine the impacts of digital healthy diet literacy (DDL) and healthy eating behaviors (HES) on fear of COVID-19, changes in mental health, and health-related quality of life (HRQoL) among front-line healthcare workers (HCWs). Methods: An online survey was conducted at 15 hospitals and health centers from 6–19 April 2020. Data of 2299 front-line HCWs were analyzed—including socio-demographics, symptoms like COVID-19, health literacy, eHealth literacy, DDL, HES, fear of COVID-19, changes in mental health, and HRQoL. Regression models were used to examine the associations. Results: HCWs with higher scores of DDL and HES had lower scores of FCoV-19S (regression coefficient, B, −0.04; 95% confidence interval, 95% CI, −0.07, −0.02; p = 0.001; and B, −0.10; 95% CI, −0.15, −0.06; p < 0.001); had a higher likelihood of stable or better mental health status (odds ratio, OR, 1.02; 95% CI, 1.00, 1.05; p = 0.029; and OR, 1.04; 95% CI, 1.00, 1.07; p = 0.043); and HRQoL (OR, 1.02; 95% CI, 1.01, 1.03; p = 0.006; and OR, 1.04; 95% CI, 1.02, 1.06; p = 0.001), respectively. Conclusions: DDL and HES were found as independent predictors of fear of COVID−19, changes in mental health status, and HRQoL in front-line HCWs. Improving DDL and HES should be considered as a strategic approach for hospitals and healthcare systems.

We introduce the State Classification Problem (SCP) for hybrid systems, and present Neural State Classification (NSC) as an efficient solution technique. SCP generalizes the model checking problem as it entails classifying each state $s$ of a hybrid automaton as either positive or negative, depending on whether or not $s$ satisfies a given time-bounded reachability specification. This is an interesting problem in its own right, which NSC solves using machine-learning techniques, Deep Neural Networks in particular. State classifiers produced by NSC tend to be very efficient (run in constant time and space), but may be subject to classification errors. To quantify and mitigate such errors, our approach comprises: i) techniques for certifying, with statistical guarantees, that an NSC classifier meets given accuracy levels; ii) tuning techniques, including a novel technique based on adversarial sampling, that can virtually eliminate false negatives (positive states classified as negative), thereby making the classifier more conservative. We have applied NSC to six nonlinear hybrid system benchmarks, achieving an accuracy of 99.25% to 99.98%, and a false-negative rate of 0.0033 to 0, which we further reduced to 0.0015 to 0 after tuning the classifier. We believe that this level of accuracy is acceptable in many practical applications, and that these results demonstrate the promise of the NSC approach., ATVA2018 extended version

We present Component-Based Simplex Architecture (CBSA), a new framework for assuring the runtime safety of component-based cyber-physical systems (CPSs). CBSA integrates Assume-Guarantee (A-G) reasoning with the core principles of the Simplex control architecture to allow component-based CPSs to run advanced, uncertified controllers while still providing runtime assurance that A-G contracts and global properties are satisfied. In CBSA, multiple Simplex instances, which can be composed in a nested, serial or parallel manner, coordinate to assure system-wide properties. Combining A-G reasoning and the Simplex architecture is a challenging problem that yields significant benefits. By utilizing A-G contracts, we are able to compositionally determine the switching logic for CBSAs, thereby alleviating the state explosion encountered by other approaches. Another benefit is that we can use A-G proof rules to decompose the proof of system-wide safety assurance into sub-proofs corresponding to the component-based structure of the system architecture. We also introduce the notion of coordinated switching between Simplex instances, a key component of our compositional approach to reasoning about CBSA switching logic. We illustrate our framework with a component-based control system for a ground rover. We formally prove that the CBSA for this system guarantees energy safety (the rover never runs out of power), and collision freedom (the rover never collides with a stationary obstacle). We also consider a CBSA for the rover that guarantees mission completion: all target destinations visited within a prescribed amount of time., Comment: Extended version of a paper to be presented at ACSD 2017, 12 pages, 3 figures, 1 appendix

The popularity of rule-based flocking models, such as Reynolds' classic flocking model, raises the question of whether more declarative flocking models are possible. This question is motivated by the observation that declarative models are generally simpler and easier to design, understand, and analyze than operational models. We introduce a very simple control law for flocking based on a cost function capturing cohesion (agents want to stay together) and separation (agents do not want to get too close). We refer to it as {\textit declarative flocking} (DF). We use model-predictive control (MPC) to define controllers for DF in centralized and distributed settings. A thorough performance comparison of our declarative flocking with Reynolds' model, and with more recent flocking models that use MPC with a cost function based on lattice structures, demonstrate that DF-MPC yields the best cohesion and least fragmentation, and maintains a surprisingly good level of geometric regularity while still producing natural flock shapes similar to those produced by Reynolds' model. We also show that DF-MPC has high resilience to sensor noise., Comment: 7 Pages