Numerical Simulation of Compressible Flows with Interfaces

Compressible interfacial flows exist in a variety of applications: reacting fronts, droplet break up, jets and sprays in high speed, shock passage in foams, etc. These flows behave in a complex multi-scale way including interface deformation, wave interface interaction and complex transport phenomena. In the first section, the interaction of a laminar flame with a compression wave is investigated. More precisely, the evolution of the burning interface is investigated and discussion over different compression waves and their effects on the flame geometry and burning rate are made. In the second part, a numeral framework for simulation of compressible multiphase flows using adaptive wavelet collocation method is developed. This study was originally motivated by the desire for a numerical tool capable of simulating the atomization process during start-up conditions in a supersonic combustor. To model such physics, the solver needs to handle high density ratios, transport terms and capillary effects. The multi-scale behaviour of these flows requires a multi-scale approach. Parallel Adaptive Wavelet Collocation Method (PAWCM) makes use of second generation wavelets to dynamically adapt the grid to localized structures in the flow in time and space. This approach allows the solution to be approximated using a subset of the points that would normally be used with a uniform grid scheme. Thus, computation on this subset is efficient and high levels of data compression is achieved

Intra-articular Along with Subacromial Corticosteroid Injection in Diabetic Patients With Adhesive Capsulitis

Background: To compare intra-articular plus subacromial corticosteroid injection with a single intra-articular injection in diabetics with adhesive capsulitis. Materials and Methods: A total of fifty-four diabetic patients were randomized into corticosteroid injection in both intra-articular and subacromial sites (group A) and one intra-articular injection (group B). Pain by a visual analog scale (VAS), shoulder range of motion, and functional state by the American Shoulder and Elbow Score was assessed before injection, and at follow-up months. Results: The pain VAS scores of group A were considerably lower than group B at the first-month follow-up visit (P=0.01). The range of motion in forward-elevation and internal rotation at three-month follow-up visits was significantly higher in group A than in group B (P=0.035, P=0.04, respectively). No notable differences in the range of motion in forward-elevation, internal rotation, and external rotation between groups at the final follow-up visit were seen. Though a significant difference in the ASES between groups at the third-month follow-up visit (P=0.03), the ASES score at the final sixth-month follow-up was similar in both groups (P=0.7). Conclusion: In diabetic adhesive capsulitis of the shoulder, subacromial combined with intra-articular steroid injections has superior subjective outcomes compared to single intra-articular corticosteroid injection

Principal Component Neural Networks for Modeling, Prediction, and Optimization of Hot Mix Asphalt Dynamics Modulus

The dynamic modulus of hot mix asphalt (HMA) is a fundamental material property that defines the stress-strain relationship based on viscoelastic principles and is a function of HMA properties, loading rate, and temperature. Because of the large number of efficacious predictors (factors) and their nonlinear interrelationships, developing predictive models for dynamic modulus can be a challenging task. In this research, results obtained from a series of laboratory tests including mixture dynamic modulus, aggregate gradation, dynamic shear rheometer (on asphalt binder), and mixture volumetric are used to create a database. The created database is used to develop a model for estimating the dynamic modulus. First, the highly correlated predictor variables are detected, then Principal Component Analysis (PCA) is used to first reduce the problem dimensionality, then to produce a set of orthogonal pseudo-inputs from which two separate predictive models were developed using linear regression analysis and Artificial Neural Networks (ANN). These models are compared to existing predictive models using both statistical analysis and Receiver Operating Characteristic (ROC) Analysis. Empirically-based predictive models can behave differently outside of the convex hull of their input variables space, and it is very risky to use them outside of their input space, so this is not common practice of design engineers. To prevent extrapolation, an input hyper-space is added as a constraint to the model. To demonstrate an application of the proposed framework, it was used to solve design-based optimization problems, in two of which optimal and inverse design are presented and solved using a mean-variance mapping optimization algorithm. The design parameters satisfy the current design specifications of asphalt pavement and can be used as a first step in solving real-life design problems

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

Numerical Simulation of Compressible Flows with Interfaces

Compressible interfacial flows exist in a variety of applications: reacting fronts, droplet break up, jets and sprays in high speed, shock passage in foams, etc. These flows behave in a complex multi-scale way including interface deformation, wave interface interaction and complex transport phenomena. In the first section, the interaction of a laminar flame with a compression wave is investigated. More precisely, the evolution of the burning interface is investigated and discussion over different compression waves and their effects on the flame geometry and burning rate are made. In the second part, a numeral framework for simulation of compressible multiphase flows using adaptive wavelet collocation method is developed. This study was originally motivated by the desire for a numerical tool capable of simulating the atomization process during start-up conditions in a supersonic combustor. To model such physics, the solver needs to handle high density ratios, transport terms and capillary effects. The multi-scale behaviour of these flows requires a multi-scale approach. Parallel Adaptive Wavelet Collocation Method (PAWCM) makes use of second generation wavelets to dynamically adapt the grid to localized structures in the flow in time and space. This approach allows the solution to be approximated using a subset of the points that would normally be used with a uniform grid scheme. Thus, computation on this subset is efficient and high levels of data compression is achieved.</p

The Effect of Impostor Syndrome on Job Involvement with Mediating Role of Structural Empowerment regarding the Employees of Isfahan University of Medical Sciences

Background: Empowerment allows health managers to make a conscious choice to improve the quality of care. On the other hand, one of the factors that may affect psychological aspects of employees and decrease job participation of employees is imposter syndrome. The main goal of this research was to determine the effect of imposter syndrome on job involvement with the mediating role of structural empowerment of employees. Methods: This was a descriptive-analytical study conducted cross-sectionally in 2021. The statistical population of the present study included all employees (1300) working in Isfahan University of Medical Sciences. The sample size was estimated to be 297 people through Cochran method and stratified random sampling method. Data collection tools included the standard imposter questionnaires of Klans and Ames (1987), Shafli et al.'s job involvement questionnaire (2006), and Maleki et al.'s structural empowerment questionnaire (2012). Face validity was confirmed by professors and experts, convergent validity with a mean variance of greater than 0.5, and divergent validity was confirmed using Fornell and Larker methods. Also, the reliability of the instrument was confirmed with the Cronbach's alpha of greater than 0.7. Data analysis was done with descriptive and inferential statistical tests using structural equation method and SPSS 22 and PLS 3 statistical software. Results: Based on the analysis of research variables, imposter syndrome had a negative and significant effect (P < 0.001) on job involvement and structural empowerment of employees with path coefficients of - 0.349 and - 0.856, respectively. There was a positive and significant correlation (P < 0.001) between structural empowerment of employees' job involvement and a path coefficient of 0.452. Finally, self-destructive syndrome with the mediating role of structural empowerment had a significant and negative effect (P < 0.001) on job involvement of employees with a path coefficient of - 0.386. Conclusion: Structural empowerment is associated with higher work efficiency and engagement among employees. Accordingly, structural empowerment may increase work engagement by stimulating employees' intrinsic and extrinsic motivation. But the presence of employees with imposter syndrome leads to the opposite effect

Classification of Weather Conditions Based on Supervised Learning for Swedish Cities

Weather forecasting has always been challenging due to the atmosphere’s complex and dynamic nature. Weather conditions such as rain, clouds, clear skies, and sunniness are influenced by several factors, including temperature, pressure, humidity, wind speed, and direction. Physical and complex models are currently used to determine weather conditions, but they have their limitations, particularly in terms of computing time. In recent years, supervised machine learning methods have shown great potential in predicting weather events accurately. These methods use historical weather data to train a model, which can then be used to predict future weather conditions. This study enhances weather forecasting by employing four supervised machine learning techniques—artificial neural networks (ANNs), support vector machines (SVMs), random forest (RF), and k-nearest neighbors (KNN)—on three distinct datasets obtained from the Weatherstack database. These datasets, with varying temporal spans and uncertainty levels in their input features, are used to train and evaluate the methods. The results show that the ANN has superior performance across all datasets. Furthermore, when compared to Weatherstack’s weather prediction model, all methods demonstrate significant improvements. Interestingly, our models show variance in performance across different datasets, particularly those with predicted rather than observed input features, underscoring the complexities of handling data uncertainty. The study provides valuable insights into the use of supervised machine learning techniques for weather forecasting and contributes to the development of more precise prediction models

Principal Component Neural Networks for Modeling, Prediction, and Optimization of Hot Mix Asphalt Dynamics Modulus

The dynamic modulus of hot mix asphalt (HMA) is a fundamental material property that defines the stress-strain relationship based on viscoelastic principles and is a function of HMA properties, loading rate, and temperature. Because of the large number of efficacious predictors (factors) and their nonlinear interrelationships, developing predictive models for dynamic modulus can be a challenging task. In this research, results obtained from a series of laboratory tests including mixture dynamic modulus, aggregate gradation, dynamic shear rheometer (on asphalt binder), and mixture volumetric are used to create a database. The created database is used to develop a model for estimating the dynamic modulus. First, the highly correlated predictor variables are detected, then Principal Component Analysis (PCA) is used to first reduce the problem dimensionality, then to produce a set of orthogonal pseudo-inputs from which two separate predictive models were developed using linear regression analysis and Artificial Neural Networks (ANN). These models are compared to existing predictive models using both statistical analysis and Receiver Operating Characteristic (ROC) Analysis. Empirically-based predictive models can behave differently outside of the convex hull of their input variables space, and it is very risky to use them outside of their input space, so this is not common practice of design engineers. To prevent extrapolation, an input hyper-space is added as a constraint to the model. To demonstrate an application of the proposed framework, it was used to solve design-based optimization problems, in two of which optimal and inverse design are presented and solved using a mean-variance mapping optimization algorithm. The design parameters satisfy the current design specifications of asphalt pavement and can be used as a first step in solving real-life design problems