Acausal Powertrain Modelling with Application to Model-based Powertrain Control

The automotive industry has long been searching for efficient ways to improve vehicle performance such as drivability, fuel consumption, and emissions. Researchers in the automotive industry have tried to develop methods to improve fuel consumption and reduce the emission gases of a vehicle, while satisfying drivability and ride comfort issues. Today, by developing computer/software technologies, automotive manufacturers are moving more and more towards modelling a real component (prototype) in a software domain (virtual prototype). For instance, modelling the components of a vehicle's powertrain (driveline) in the software domain helps the designers to iterate the model for different operating conditions and scenarios to obtain better performance without any cost of making a real prototype. The objective of this research is to develop and validate physics-based powertrain models with sufficient fidelity to be useful to the automotive industry for rapid prototyping. Developing a physics-based powertrain model that can accurately simulate real phenomenon in the powertrain components is of great importance. For instance, a high-fidelity simulation of the combustion phenomenon in the internal combustion (IC) engine with detailed physical and chemical reactions can be used as a virtual prototype to estimate physical prototype characteristics in a shorter time than it would take to build a physical prototype. Therefore, the powertrain design can be explored and validated virtually in the software domain to reduce the cost and time of product development. The main focus of this thesis is on development of an internal combustion engine model, four-cylinder spark ignition engine, and a hydrodynamic torque converter model. Then, the models are integrated along with the rest of a powertrain's components (e.g. vehicle longitudinal dynamics model) through acausal connections, which represents a more feasible physics-based powertrain model for model-based control design. The powertrain model can be operated at almost all operating conditions (e.g. wide range of the engine speeds and loads), and is able to capture some transient behaviour of the powertrain as well as the steady state response. Moreover, the parametric formulation of each component in the proposed powertrain model makes the model more efficient to simulate different types of powertrain (e.g. for a passenger car or truck)

Using Adaptive Pole Placement Control Strategy for Active Steering Safety System

This paper studies the design of an adaptive control strategy to tune an active steering system for better drivability and maneuverability. In the first step, adaptive control strategy is applied to estimate the uncertain parameters on-line (e.g. cornering stiffness), then the estimated parameters are fed into the pole placement controller to generate corrective feedback gain to improve the steering system dynamic's characteristics. The simulations are evaluated for three types of road conditions (dry, wet, and icy), and the performance of the adaptive pole placement control (APPC) are compared with pole placement control (PPC) and a passive system. The results show that the APPC strategy significantly improves the yaw rate and side slip angle of a bicycle plant model

Synthesis of hybridized benzylthio-1,3,4-thiadiazol-isatin derivatives and in vitro cytotoxicity evaluation

Introduction: In this research synthesis of hybridized benzylthio-thiadiazol-isatin derivatives has been reported and then the effects of the synthesized compounds were  investigated on cancer cell lines and molecular docking was also studied on proposed receptor. Methods and Results: This project was done in 2 steps that includes the synthesis of new hybrids of thiadiazole-isatin derivatives and characterized by various spectroscopy methods such as "Mass spectroscopy, Infrared spectroscopy, and 1H NMR". To study cytotoxic effects of the compounds, different concentrations of synthesized derivatives were  prepared and tested on the three rank 7 cellular MCF-7 "breast cancer", PC3 "Prostate carcinoma", and SKNMC "Norobelastoma". The method used was MTT that after various stages of the solution and added MTT, the color was measured by the producted formazan during measurements suitable wave. The color ratio was  as equal as  the number of living cells. For comparing the  cytotoxicity we  used doxorubicin as control drug. Conclusions: The most potent of the compounds were 3b, 4c, and 4d against MCF7 cell line, 3b, 4h against PC3 cell line, and 3b,4f, and 4h against SKNMC cell line which seems to be the best ones relative to the control drug. Also we found that treatment with 3b led to  decrease in IC50 and significantly increased cytotoxicity effects of the compound in PC3, SKNMC and MCF7 cells lines

Parametric Importance Analysis and Design Optimization of a Torque Converter Model Using Sensitivity Information

Replicated with permission by SAE Copyright © 2017 SAE International. Further distribution of this material is not permitted without prior permission from SAE.Torque converters are used as coupling devices in automobile powertrains involving automatic transmissions. Efficient modeling of torque converters capturing various modes of operation is important for powertrain design and simulation, (Hroval and Tobler 1, Ishihara and Emori 2) optimization and control applications. Models of torque converters are available in various commercial simulation packages, Hadi et. al. 3. The information about the effect of model parameters on torque converter performance is valuable for any design operation. In this paper, a symbolic sensitivity analysis of a torque converter model will be presented. Direct differentiation (Serban and Freeman 4) is used to generate the sensitivity equations which results in equations in symbolic form. By solving the sensitivity equations, the effect of a perturbation of the model parameters on the behavior of the system is determined. A parametric importance analysis is performed on the model: the model parameters are arranged according to their effect on the amount loss of energy during the operation of the torque converter. The radii of the pump, turbine and stator, the density of the hydraulic fluid and the exit angle of the vanes of the stator were found to have the most significant effects on the model. Using the sensitivity information, a design optimization problem is defined and solved to obtain a set of parameter values that minimizes the energy lost during the torque converter operation.Financial support for this work has been provided by the Natural Sciences and Engineering Research Council of Canada (NSERC), Toyota, and Maplesoft

Synthesis and investigation of antioxidant activities of 2-benzylidene-3-coumaranones

      A number of 6-hydroxy-2-benzylidene-3-coumaranones were synthesized from condensation of 6-hydroxy-3-coumaranone with appropriate aldehydes and were evaluated for their antioxidant activities. The antioxidant activity was assessed using two methods, including, 1,1-biphenyl-2-picrylhydrazyl (DPPH) radical scavenging, and reducing power assays. Some of the benzylidene coumaranones showed antioxidant activity more than Trolox as reference antioxidant

Math-based spark ignition engine modelling including emission prediction for control applications

Originally published by Inderscience: Asl, H. A., Fraser, R. A., & McPhee, J. (2015). Math-based spark ignition engine modelling including emission prediction for control applications. International Journal of Vehicle Systems Modelling and Testing, 10(2), 148. doi:10.1504/ijvsmt.2015.068977A complete spark ignition (SI) engine model is a multi-domain model including fluid dynamics, thermodynamics, combustion, electrical, and mechanical sub-models. The complexity of these models depends on the type of analysis used for model development, which may vary from highly detailed computational fluid dynamics (CFD) analysis (multi-dimensional model) to simpler data-based analysis in which the data is obtained from experiments (zero-dimensional model). The main objective of our research is to develop a math-based SI engine model for control application and real time simulation. The model must be accurate enough to capture the combustion characteristics (e.g., combustion temperature) and predict emission gases, while being fast enough for real time simulation purposes. In this paper, a physics-based model of an SI engine is presented which consists of different sub-models including: throttle body and manifold model, four-stroke quasi-dimensional thermodynamic model of gas exchange and power cycles, two-zone combustion and flame propagation model, emission gases model based on the chemical kinetics equations, and mechanical torque model. Moreover, part of the simulation results is validated against the GT-Power simulation results. The math-based model is created in the MapleSim environment. The symbolic nature of MapleSim significantly shortens the simulation time and also enables parametric sensitivity analysis

Chemometrical-electrochemical investigation for comparing inhibitory effects of quercetin and its sulfonamide derivative on human carbonic anhydrase II: Theoretical and experimental evidence

This paper reports results of a valuable study on investigation of inhibitory effects of the sulfonamide derivative of quercetin (QD) on human carbonic anhydrase II (CA-II) by electrochemical and chemometrical approaches. To achieve this goal, a glassy carbon electrode (GCE) was chosen as the sensing platform and different electrochemical techniques such as cyclic voltammetry (CV), differential pulse voltammetry (DPV), linear sweep voltammetry (LSV) and electrochemical impedance spectroscopy (EIS) were used to investigate and comparing inhibitory effects of quercetin (Q) and QD on CA-II. By the use of EQUISPEC, SPECFIT, SQUAD and REACTLAB as efficient hard-modeling algorithms, bindings of Q and QD with CA-II were investigated and the results confirmed that the QD inhibited the CA-II stronger than Q suggesting a highly relevant role of QD's-SO2NH2 group in inhibiting activity and also was confirmed by docking studies. Finally, a novel EIS technique based on interaction of Q and CA-II was developed for sensitive electroanalytical determination of CA-II and in this section of our study, the sensitivity of the developed electroanalytical methodology was improved by the modification of the GCE was with multi-walled carbon nanotubes-ionic liquid.Fil: Khodarahmi, Reza. Kermanshah University of Medical Sciences; IránFil: Khateri, Shaya. Kermanshah University of Medical Sciences; IránFil: Adibi, Hadi. Kermanshah University of Medical Sciences; IránFil: Nasirian, Vahid. State University of Louisiana; Estados UnidosFil: Hedayati, Mehdi. Kermanshah University of Medical Sciences; IránFil: Faramarzi, Elahe. Kermanshah University of Medical Sciences; IránFil: Soleimani, Shokoufeh. Kermanshah University of Medical Sciences; IránFil: Goicoechea, Hector Casimiro. Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas. Laboratorio de Desarrollo Analítico y Quimiometría; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste; ArgentinaFil: Jalalvand, Ali Reza. Kermanshah University Of Medical Sciences; Irá

Depression, anxiety and stress, comorbidity evaluation among a large sample of general adults: results from SEPAHAN study

Depression, anxiety and stress are common psychological disorders (PDs). This study aimed to assess the odds of co-occurrence of mentioned PDs in total sample and different levels of socio-demographic characteristics, specifically among a large sample of general adults. In a cross-sectional, community-based study conducted among 4763 Iranian adults, depression and anxiety were assessed with Hospital Anxiety and Depression Scale (HADS) and stress with General Health Questionnaire (GHQ). The loglinear analysis was applied to investigate their comorbidities. Based on selected models with pair-comorbidity of anxiety with stress, depression with stress, and anxiety with depression, the results showed the odds of comorbidity between anxiety and depression (odds ratio (OR) =12.29, 95%CI: 9.58-15.80), depression and stress (OR = 7.80, 95%CI: 6.55-10.18), and stress and anxiety (OR = 4.62, 95%CI: 3.71-5.75). Also, ORs of pair-comorbidities were the same, except between stress and anxiety for men compared to women (adjusted-OR = 6.47, 95%CI: 4.44-9.49 versus 3.85, 95%CI: 2.95-5.00) and comorbidity between stress and depression for the participants with lower than 40 years compared to others (adjusted-OR = 9.03, 95%CI: 7.17-11.36 versus 6.41, 95%CI: 4.90-8.41), p< 0.05. Stress comorbidity with depression was higher level than other pair-comorbidities. Obvious discrepancies were also observed in terms of ORs of pair-comorbidities between three mentioned disorders in different levels of SDCs

Global burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

Background: Understanding the health consequences associated with exposure to risk factors is necessary to inform public health policy and practice. To systematically quantify the contributions of risk factor exposures to specific health outcomes, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 aims to provide comprehensive estimates of exposure levels, relative health risks, and attributable burden of disease for 88 risk factors in 204 countries and territories and 811 subnational locations, from 1990 to 2021. Methods: The GBD 2021 risk factor analysis used data from 54 561 total distinct sources to produce epidemiological estimates for 88 risk factors and their associated health outcomes for a total of 631 risk–outcome pairs. Pairs were included on the basis of data-driven determination of a risk–outcome association. Age-sex-location-year-specific estimates were generated at global, regional, and national levels. Our approach followed the comparative risk assessment framework predicated on a causal web of hierarchically organised, potentially combinative, modifiable risks. Relative risks (RRs) of a given outcome occurring as a function of risk factor exposure were estimated separately for each risk–outcome pair, and summary exposure values (SEVs), representing risk-weighted exposure prevalence, and theoretical minimum risk exposure levels (TMRELs) were estimated for each risk factor. These estimates were used to calculate the population attributable fraction (PAF; ie, the proportional change in health risk that would occur if exposure to a risk factor were reduced to the TMREL). The product of PAFs and disease burden associated with a given outcome, measured in disability-adjusted life-years (DALYs), yielded measures of attributable burden (ie, the proportion of total disease burden attributable to a particular risk factor or combination of risk factors). Adjustments for mediation were applied to account for relationships involving risk factors that act indirectly on outcomes via intermediate risks. Attributable burden estimates were stratified by Socio-demographic Index (SDI) quintile and presented as counts, age-standardised rates, and rankings. To complement estimates of RR and attributable burden, newly developed burden of proof risk function (BPRF) methods were applied to yield supplementary, conservative interpretations of risk–outcome associations based on the consistency of underlying evidence, accounting for unexplained heterogeneity between input data from different studies. Estimates reported represent the mean value across 500 draws from the estimate's distribution, with 95% uncertainty intervals (UIs) calculated as the 2·5th and 97·5th percentile values across the draws. Findings: Among the specific risk factors analysed for this study, particulate matter air pollution was the leading contributor to the global disease burden in 2021, contributing 8·0% (95% UI 6·7–9·4) of total DALYs, followed by high systolic blood pressure (SBP; 7·8% [6·4–9·2]), smoking (5·7% [4·7–6·8]), low birthweight and short gestation (5·6% [4·8–6·3]), and high fasting plasma glucose (FPG; 5·4% [4·8–6·0]). For younger demographics (ie, those aged 0–4 years and 5–14 years), risks such as low birthweight and short gestation and unsafe water, sanitation, and handwashing (WaSH) were among the leading risk factors, while for older age groups, metabolic risks such as high SBP, high body-mass index (BMI), high FPG, and high LDL cholesterol had a greater impact. From 2000 to 2021, there was an observable shift in global health challenges, marked by a decline in the number of all-age DALYs broadly attributable to behavioural risks (decrease of 20·7% [13·9–27·7]) and environmental and occupational risks (decrease of 22·0% [15·5–28·8]), coupled with a 49·4% (42·3–56·9) increase in DALYs attributable to metabolic risks, all reflecting ageing populations and changing lifestyles on a global scale. Age-standardised global DALY rates attributable to high BMI and high FPG rose considerably (15·7% [9·9–21·7] for high BMI and 7·9% [3·3–12·9] for high FPG) over this period, with exposure to these risks increasing annually at rates of 1·8% (1·6–1·9) for high BMI and 1·3% (1·1–1·5) for high FPG. By contrast, the global risk-attributable burden and exposure to many other risk factors declined, notably for risks such as child growth failure and unsafe water source, with age-standardised attributable DALYs decreasing by 71·5% (64·4–78·8) for child growth failure and 66·3% (60·2–72·0) for unsafe water source. We separated risk factors into three groups according to trajectory over time: those with a decreasing attributable burden, due largely to declining risk exposure (eg, diet high in trans-fat and household air pollution) but also to proportionally smaller child and youth populations (eg, child and maternal malnutrition); those for which the burden increased moderately in spite of declining risk exposure, due largely to population ageing (eg, smoking); and those for which the burden increased considerably due to both increasing risk exposure and population ageing (eg, ambient particulate matter air pollution, high BMI, high FPG, and high SBP). Interpretation: Substantial progress has been made in reducing the global disease burden attributable to a range of risk factors, particularly those related to maternal and child health, WaSH, and household air pollution. Maintaining efforts to minimise the impact of these risk factors, especially in low SDI locations, is necessary to sustain progress. Successes in moderating the smoking-related burden by reducing risk exposure highlight the need to advance policies that reduce exposure to other leading risk factors such as ambient particulate matter air pollution and high SBP. Troubling increases in high FPG, high BMI, and other risk factors related to obesity and metabolic syndrome indicate an urgent need to identify and implement interventions

Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

Background: Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020–21 COVID-19 pandemic period. Methods: 22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution. Findings: Global all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5–65·1] decline), and increased during the COVID-19 pandemic period (2020–21; 5·1% [0·9–9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98–5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50–6·01) in 2019. An estimated 131 million (126–137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7–17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8–24·8), from 49·0 years (46·7–51·3) to 71·7 years (70·9–72·5). Global life expectancy at birth declined by 1·6 years (1·0–2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67–8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4–52·7]) and south Asia (26·3% [9·0–44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations. Interpretation: Global adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic