Association of Cigarette Smoking with Thickness of Intimal Layer of Carotid Arteries on Color Doppler Ultrasound Study and Its Surgical Management

Objectives:  Aim of this study is to evaluate the intimal vessel wall thickness in smoker and their co-relation with non-smoker and also surgical management of stenosis. Material and Methods:  It is a prospective study of 55 cases. Study span and follow up duration were 4 months. Our patients were presented with the history of CVA (Cerebrovascular accident), hypertension, diabetes, and headache. Results:  In all patients, the carotid doppler ultrasound was done and their intimal vessel wall thickness was noted. Our 78% patients were smoker and non-smoker was 22%. In our study, 52% patients had CVA, 41% patients had hypertension, 30% patients had headache and 9% patients were also obese. Forty three smokers used to take 15 – 25 cigarettes daily. Among 43 patients, 5 patients were females. In carotid Doppler study, intimal thickness was increased in 87%, the plaque was observed in 49% and stenosis was observed in 38% cases. In 18 % patients, who had stenosis > 70%, carotid endarterectomy was performed and in rest of the patients medical treatment done.Patients who were chronic smokers and had medical co-morbidities showed greater thickness of intimal layer of vessels on carotid Doppler. In 6 patients, post-operative headache occurred. Conclusion:  Smokers had more thickness of intimal layer of carotid vessels. Carotid endarterectomy yields good results in case of stenosis more than 70%. Keywords:  Cerebrovascular accident, Intimal layer thickness, Carotid doppler ultrasound, Cigarette smokin

Robust detection of outliers in both response and explanatory variables of the simple circular regression model

It is very important to make sure that a statistical data is free from outliers before making any kind of statistical analysis. This is due to the fact that outliers have an unduly affect on the parameter estimates. Circular data which can be used in many scientific fields are not guaranteed to be free from outliers. Often, the relationship between two circular variables is represented by the simple circular regression model. In this respect, outliers might occur in the both response and explanatory variables of the circular model. In circular literature, some researchers show interest to identify outliers only in the response variable. However, to the best of our knowledge, no one has proposed a method which can detect outliers in both the response and explanatory variables of the circular linear model. Thus, in this article, an attempt has been made to propose a new method which can detect outliers in both variables of the simple circular linear model. The proposed method depends on the robust circular distance between the response and the explanatory variables in the model. Results from the simulations and real data example show the merit of our proposed method in detecting outliers in simple circular model

Effects of lubricated surface in the stagnation point flow of a micropolar fluid

In this investigation, we have considered a steady, two-dimensional flow of a micropolar fluid towards a stagnation point over a lubricated plate. A power law fluid is utilized for the purpose of lubrication. To derive the slip condition in the present flow situation, continuity of shear stress and velocity has been imposed at the fluid lubricant interface. The set of nonlinear coupled ordinary differential equations subject to boundary conditions is solved by a powerful numerical technique called the Keller-box method. Some important flow features have been analyzed and discussed under the influence of slip parameter , material parameter and ratio of micro-rotation to the skin friction parameter . The main purpose of the present article is to analyze the reduction in the shear stress and couple stress effects in the presence of lubrication as compared to the viscous fluid that may be beneficial during polymeric processing

Detection of Outliers in Univariate Circular Data using Robust Circular Distance

A robust statistic to detect single and multi-outliers in univariate circular data is proposed. The performance of the proposed statistic was tested by applying it to a simulation study and to three real data sets, and was demonstrated to be robust

Adjusting outliers in univariate circular data

Circular data analysis is a particular branch of statistics that sits somewhere between the analysis of linear data and the analysis of spherical data. Circular data are used in many scientific fields. The efficiency of the statistical methods that are applied depends on the accuracy of the data in the study. However, circular data may have outliers that cannot be deleted. If this is the case, we have two ways to avoid the effect of outliers. First, we can apply robust methods for statistical estimations. Second, we can adjust the outliers using the other clean data points in the dataset. In this paper, we focus on adjusting outliers in circular data using the circular distance between the circular data points and the circular mean direction. The proposed procedure is tested by applying it to a simulation study and to real data sets. The results show that the proposed procedure can adjust outliers according to the measures used in the paper

Region of Interest Extraction in 3D Face Using Local Shape Descriptor

Recently, numerous efforts were focused on 3D face models due to its geometrical information and its reliability against pose estimation and identification problems. The major objective of this work is to reduce the massive amount of information contained the entire 3D face image into a distinctive and informative subset interested regions based 3D face analysis systems. The interested regions are represented by nose and eyes regions of frontal and profile 3D images. These regions are detected based on distance to local plan descriptor only which is copes well with profile views of 3D images. The statistical distribution of distance to local plane descriptor is predicted using Gaussian distribution. The framework of the proposed approach involves two modes: training mode and testing mode. In the training mode, a learning process for local shape descriptor related to the interested regions is carried out. The interested regions (nose and eyes) are extracted automatically in the testing mode. The performance evaluation of the proposed approach has been conducted using 3D images taken from GAVADB 3D face database which consists of both frontal and profile views. The proposed approach achieved high detection rate of interested regions for both frontal and profile views

A Multidimensional Approach to Measure Poverty in Rural Bangladesh

Poverty is increasingly being understood as a multidimensional phenomenon. Other than income-consumption, which has been extensively studied in the past, health, education, shelter, and social involvement are among the most important dimensions of poverty. The present study attempts to develop a simple tool to measure poverty in its multidimensionality where it views poverty as an inadequate fulfillment of basic needs, such as food, clothing, shelter, health, education, and social involvement. The scale score ranges between 72 and 24 and is constructed in such a way that the score increases with increasing level of poverty. Using various techniques, the study evaluates the poverty-measurement tool and provides evidence for its reliability and validity by administering it in various areas of rural Bangladesh. The reliability coefficients, such as test-retest coefficient (0.85) and Cronbach's alpha (0.80) of the tool, were satisfactorily high. Based on the socioeconomic status defined by the participatory rural appraisal (PRA) exercise, the level of poverty identified by the scale was 33% in Chakaria, 26% in Matlab, and 32% in other rural areas of the country. The validity of these results was tested against some traditional methods of identifying the poor, and the association of the scores with that of the traditional indicators, such as ownership of land and occupation, asset index (r=0.72), and the wealth ranking obtained from the PRA exercise, was consistent. A statistically significant inverse relationship of the poverty scores with the socioeconomic status was observed in all cases. The scale also allowed the absolute level of poverty to be measured, and in the present study, the highest percentage of absolute poor was found in terms of health (44.2% in Chakaria, 36.4% in Matlab, and 39.1% in other rural areas), followed by social exclusion (35.7% in Chakaria, 28.5% in Matlab, and 22.3% in other rural areas), clothing (6.2% in Chakaria, 8.3% in Matlab, and 20% in other rural areas), education (14.7% in Chakaria, 8% in Matlab, and 16.8% in other rural areas), food (7.8% in Chakaria, 2.9% in Matlab and 3% in other rural areas), and shelter (0.8% in Chakaria, 1.4% in Matlab, and 3.7% in other rural areas). This instrument will also prove itself invaluable in assessing the individual effects of poverty-alleviation programmes or policies on all these different dimensions

Robust circular distance and its application in the identification of outliers in the simple circular regression model

Abstract: Background and Objective: The existence of outliers in any type of data influences the efficiency of an estimator. Few methods for detecting outliers in a simple circular regression model have been proposed in the study but it suspected that they are not very successful in the presence of multiple outliers in a data set. This study aimed to investigate new statistic to identify multiple outliers in the response variable in a simple circular regression model. Materials and Methods: The proposed statistic is based on calculating robust circular distance between circular residuals and circular location parameter. The performance of the proposed statistic is evaluated by the proportion of detected outliers and the rate of masking and swamping. The simulation study is applied for different sample sizes at 10 and 20% ratios of contamination. Results: The results from simulated data showed that the proposed statistic has the highest proportion of outliers and the lowest rate of masking comparing with some existing methods. Conclusion: The proposed statistic is very successful in detecting outliers with negligible amount of masking and swamping rates

Middle Miocene Evaporites from Northern Iraq: Petrography, Geochemistry, and Cap Rock Efficiency

Evaporites (gypsum and anhydrite) of the middle Miocene age (Fat’ha Formation) form one of the main sulfate cap rocks in the Middle East oilfields. Detailed petrographic and diagenetic investigations accompanied with geochemical analysis of these evaporite rocks in Mosul and Kirkuk areas of northern Iraq have revealed that nodular gypsum is the dominant type, whereas laminated, structureless, and secondary (selenite and satin spar) also are present. Nodular gypsum was deposited in a very shallow, arid, and semi-restricted lagoonal environment which has undergone influx and reflux processes, while laminated gypsum may represent pulses of freshwater into the lagoonal basin of Fat’ha Formation. Low strontium values of the secondary and laminated gypsum may attribute to their secondary origin by hydration processes from the original anhydrite. Based on petrographic, diagenetic, and petrophysical (porosity and permeability) properties, it appears that the efficiency of the Fat’ha sulfates as petroleum cap rocks increases with increasing nodular growth and compaction degree. The occasional presence of bitumen inclusions with both nodular gypsum and host materials relates to early leakage of the hydrocarbons which were being halt due to the growing and packing of nodules and host materials

Detection of outliers in the response and explanatory variables of the simple circular regression model

The circular regression model may contain one or more data points which appear to be peculiar or inconsistent with the main part of the model. This may be occur due to recording errors, sudden short events, sampling under abnormal conditions etc. The existence of these data points “outliers” in the data set cause lot of problems in the research results and the conclusions. Therefore, we should identify them before applying statistical analysis. In this article, we aim to propose a statistic to identify outliers in the both of the response and explanatory variables of the simple circular regression model. Our proposed statistic is robust circular distance RCDxy and it is justified by the three robust measurements such as proportion of detection outliers, masking and swamping rates