Dynamic suspension modeling of an eddy-current device : an application to Maglev

When a magnetic source is simultaneously oscillated and translationally moved above a linear conductive passive guideway such as aluminum, eddy-currents are induced that give rise to a time-varying opposing field in the air-gap. This time-varying opposing field interacts with the source field, creating simultaneously suspension, propulsion or braking and lateral forces that are required for a Maglev system. The 2-D analytic based transient eddy-current force and power loss equations are derived for the case when an arbitrary magnetic source is moving and oscillating above a conductive guideway. These general equations for force and power loss are derived using a spatial Fourier transform and temporal Laplace transform technique. The derived equations are capable of accounting for step changes in the input parameters, in addition to arbitrary continuous changes in the input conditions. The equations have been validated for both step changes as well as continuous changes in the input conditions using a 2-D transient finite-element model. The dynamics of an EDW Maglev is investigated by using both steady-state and transient eddy-current models. The analytic equations for the self as well as mutual damping and stiffness coefficients of an EDW Maglev are derived using the 2-D analytic steady-state eddy-current force equations. It is shown that the steady-state eddy-current model in which the heave velocity is included in the formulation can accurately predict the dynamic behavior of a 2-degree of freedom EDW Maglev vehicle. The 2-D EDW Maglev vehicle has been built using Matlab/SimMechanics™. A 1-degree of freedom pendulum setup of an EDW Maglev has been built in order to investigate the dynamics of an EDW Maglev. The dynamic model of an EDW Maglev has been validated using this pendulum setup. A multi-degree of freedom Maglev vehicle prototype has been constructed using four EDWs. The dynamics of the prototype Maglev has been investigated using the Matlab simulations. This prototype setup will be used to investigate the dynamic behavior of EDW Maglev in the future

Do cortisol affects the brain electrical activity (EEG powers)?

Background: Glucocorticoids at very low/high levels are detrimental for memory performance. But how electroencephalogram (EEG) activity correlates with the cortisol in high and low efficient brains are still controversial. Objective: To find the association of cortisol with EEG powers in high and low cognitive brains at the time of examination preparation. Method: The EEG was recorded in an eye-closed state for 5-minutes in high (n-59) and low (n-24) cognitive individuals. Their salivary cortisol was estimated and correlated with the EEG activity by Spearman correlation test (p<0.05). The cortisol level between two groups was compared by Mann-Whitney U test. Result: Cortisol (ng/ml) was high in low cognitive group (1.36) than to the other group (1.32).There was a negative association of cortisol with EEG powers (r= -0.41 to -0.5) in central (beta, alpha2), frontal (alpha2) and left-temporal (alpha2) regions of the low cognitive brains. In high cognitive brains, cortisol was negatively associated with beta activity in right-temporal (r=-0.27) but positively associated with theta activity in mid-frontal (r=0.33) brain area. Conclusion: The less efficient brain has high cortisol level during preparation for their examination. This might have decreased the alpha2 activity in them that will impair the processing of long term memory. However, these individuals seem to manage the examination stress by decreasing the firing of the beta activity. Conversely, in the high cognitive brain, the rise in cortisol level seemed to increase the mid-frontal theta activity that might improve the attention and encoding of the information in these individuals

Prevalence of Hypertension, Obesity, Diabetes, and Metabolic Syndrome in Nepal

Background. This study was carried out to establish the prevalence of cardiovascular risks such as hypertension, obesity, and diabetes in Eastern Nepal. This study also establishes the prevalence of metabolic syndrome (MS) and its relationships to these cardiovascular risk factors and lifestyle. Methods. 14,425 subjects aged 20–100 (mean 41.4 ± 15.1) were screened with a physical examination and blood tests. Both the International Diabetic Federation (IDF) and National Cholesterol Education Programme's (NCEP) definitions for MS were used and compared. Results. 34% of the participants had hypertension, and 6.3% were diabetic. 28% were overweight, and 32% were obese. 22.5% of the participants had metabolic syndrome based on IDF criteria and 20.7% according to the NCEP definition. Prevalence was higher in the less educated, people working at home, and females. There was no significant correlation between the participants' lifestyle factors and the prevalence of MS. Conclusion. The high incidence of dyslipidemia and abdominal obesity could be the major contributors to MS in Nepal. Education also appears to be related to the prevalence of MS. This study confirms the need to initiate appropriate treatment options for a condition which is highly prevalent in Eastern Nepal

A Deep Learning Tool for the Assessment of Pavement Smoothness and Aggregate Segregation during Construction

Pavement construction monitoring and quality assurance (QA) practices are mostly based on costly, discrete, and destructive methods. Most quality assurance programs are based on pavement construction procedures encompassing in-situ coring for layer thickness determination, density measurements, laboratory testing to measure volumetric properties, and smoothness measurements in case of the availability of a profiler. The main objective of this study was to develop a machine learning-based classifier for predicting pavement roughness and aggregate segregation based on digital image analysis, image recognition, and deep learning machine models. The developed Convolution Neural Networks (CNN) models were trained, tested, and validated using 600-pavement surface images extracted from the Louisiana Department of Transportation and Development (LaDOTD) Pavement Management System (PMS) and 129 pavement images collected from three construction sites a few days after paving. These images were randomly divided into 70%, 15%, and 15% for the training, testing, and validation phases, respectively. The roughness model achieved 93.8% and 92.6% accuracy in the training and validation stages; respectively, and predicted the International Roughness Index (IRI) values with a coefficient of determination R2 of 0.98 and a Root-Mean Square Error (RMSE) of 3.5%. In addition, the developed image-processing model for the detection of aggregate segregation achieved adequate accuracy. Furthermore, the developed segregation detection procedure adequately described the relationship between mix density and segregation

A Deep Learning Tool for the Assessment of Pavement Smoothness and Aggregate Segregation during Construction

Pavement construction monitoring and quality assurance (QA) practices are mostly based on costly, discrete, and destructive methods. Most quality assurance programs are based on pavement construction procedures encompassing in-situ coring for layer thickness determination, density measurements, laboratory testing to measure volumetric properties, and smoothness measurements in case of the availability of a profiler. The main objective of this study was to develop a machine learning-based classifier for predicting pavement roughness and aggregate segregation based on digital image analysis, image recognition, and deep learning machine models. The developed Convolution Neural Networks (CNN) models were trained, tested, and validated using 600-pavement surface images extracted from the Louisiana Department of Transportation and Development (LaDOTD) Pavement Management System (PMS) and 129 pavement images collected from three construction sites a few days after paving. These images were randomly divided into 70%, 15%, and 15% for the training, testing, and validation phases, respectively. The roughness model achieved 93.8% and 92.6% accuracy in the training and validation stages; respectively, and predicted the International Roughness Index (IRI) values with a coefficient of determination R2 of 0.98 and a Root-Mean Square Error (RMSE) of 3.5%. In addition, the developed image-processing model for the detection of aggregate segregation achieved adequate accuracy. Furthermore, the developed segregation detection procedure adequately described the relationship between mix density and segregation

Antibiotic utilization, sensitivity, and cost in the medical intensive care unit of a tertiary care teaching hospital in Nepal

Background:High utilization and irrational use of antibiotics in an intensive care unit increases microbial resistance, morbidity, mortality, and costs.Objective:This study aimed to evaluate the utilization, sensitivity and cost analysis of antibiotics used in the medical intensive care unit of a tertiary care teaching hospital of Nepal.Methods:A prospective cohort study was conducted on patients admitted to the medical intensive care unit at a tertiary care teaching hospital in central Nepal from July to September 2016. Antibiotic utilization, defined daily dose per 100 bed-days and the cost of antibiotics per patient were calculated. Descriptive statistics were performed using IBM-SPSS 20.0.Results:A total of 365 antibiotics were prescribed in 157 patients during the study period, with an average of 2.34 prescriptions per patient. Total antibiotic utilization in terms of defined daily dose per 100 bed-days was 49.5. Piperacillin/tazobactam (45.2%) was the most commonly prescribed antibiotic, and meropenem was the most expensive antibiotics (US$4440.70). The median (interquartile range) cost of antibiotics used per patient was US$47.67 (US$63.73). Escherichia coli, Acinetobacter, and Pseudomonas sp. were the common organisms isolated and were found to be resistant to some of the commonly used antibiotics.Conclusion:This study suggests that the utilization and cost of antibiotics are high in medical intensive care unit of the hospital and E. coli was resistant to multiple antibiotics. The findings highlight an urgent need for the implementation of antibiotic stewardship program in order to improve antibiotic utilization in such hospital settings