Search results for: interval regression

Authors: Muhammad Saleem Akhtar, Mushtaq Hussain Lashari, Ejaz Ahmad, Tanveer Ahmad, Laeeq Akbar Lodhi, Ijaz Ahmad, Masood Akhtar

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The objective of this study was to determine the effect of bull exposure continuously or intermittently or its excretory products after calving on postpartum interval to estrus, in Nili-Ravi buffalo. Forty-eight buffaloes of Nili-Ravi breed were allocated one of the four treatments in a totally randomized plan using a 4 x 1 factorial design. The four treatment groups were BEC (Bull Exposed Continuously), BEI (Bull Exposed Intermittently), EPB (Excretory Products of Bull) and BNE (Bull Not Exposed). BEC; buffaloes (n = 12) were exposed continuously to the physical presence of a bull whereas in BEI; buffaloes (n = 12) were exposed intermittently to the physical presence of bull. EPB; buffaloes (n = 12) were exposed to discharge waste (urine and feces) of bull and BNE buffaloes (n = 12) were not exposed to a bull or discharge waste of bulls. Buffaloes were exposed on day 15 after parturition. Day 15 postpartum represented d 0 for each treatment. The postpartum interval from calving to first behavioural estrus was 66.88 days in BEC, 75.12 days in BEI, 77.28 days in EPB and 76.5 days in BNE treatments. Postpartum interval to first behavioural estrus was shorter in BEC than BEI, EPB, and BNE treatments. There was no significant difference in postpartum interval to estrus between BEI, EPB and BNE treatments. In present study, the percentage of buffaloes showing estrus during experimental period was 75.0%, 66.66%, 66.66% and 58.33% in BEC, BEI, EPB and BNE treatments, respectively. The mean serum progesterone concentration did not differ significantly between BEC and other (BEI, EPB, and BNE) treatments. It was concluded that presence of bull has positive effect in reducing calving interval in Nili Ravi buffalo.

Keywords: calving interval, biostimulation, buffalo, bull exposure

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Authors: Yi-Cheng Tian, Miin-Shen Yang

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The mixture likelihood approach to clustering is a popular clustering method where the expectation and maximization (EM) algorithm is the most used mixture likelihood method. In the literature, the EM algorithm had been used for mixture regression models. However, these EM mixture regression algorithms are sensitive to initial values with a priori number of clusters. In this paper, to resolve these drawbacks, we construct a learning-based schema for the EM mixture regression algorithm such that it is free of initializations and can automatically obtain an approximately optimal number of clusters. Some numerical examples and comparisons demonstrate the superiority and usefulness of the proposed learning-based EM mixture regression algorithm.

Keywords: clustering, EM algorithm, Gaussian mixture model, mixture regression model

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Authors: Hakan Arslan, Deniz Ekinci, Alper Gungor, Gurkan Bilir, Omer Tas, Mehmet Altun

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Drought is one of the major abiotic stresses affecting the agricultural production worldwide. In this study, Leaf samples were taken from the carrot plants grown under drought stress conditions during the harvesting period. The plants were irrigated in three irrigation interval (4, 6 and 8 days) and Irrigation water regime was set up in pots. The changes in activities of antioxidant enzymes such as glutathione reductase (GR), glutathione s-transferase (GST), superoxide dismutase (SOD)) in leaves of black carrot were investigated. The activities of antioxidant enzymes (GR, GST, SOD) were varied significantly with irrigation intervals. The highest value of GR, GST and SOD were determined in the irrigation interval of 6 days. All antioxidant activity values were decreased in 8 days of irrigation interval. As a result of the study, it has been suggested that optimum irrigation intervals for plants can be used in antioxidant enzymes.

Keywords: antioxidant enzyme, carrot, drought, irrigation interval

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Authors: Ali Golestani, Vahid Naseri, Hossein Taheri

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Recently, some of studies examined the effect of exercise on neurotrophic factors influencing the growth, protection, plasticity and function in central and peripheral nerve cells. The aim of this study was to investigate the effects of continuous and interval aerobic exercises with moderate intensity on serum levels of glial cell line-derived neurotrophic factor (GDNF) and aerobic capacity in obese children. 21 obese students with an average age of 13.6 ± 0.5 height 171 ± 5 and BMI 32 ± 1.2 were divided randomly to control, continuous aerobic and interval aerobic groups. Training protocol included continuous or interval aerobic exercises with moderate intensity 50-65%MHR, three times per week for 10 weeks. 48 hours before and after executing of protocol, blood samples were taken from the participants and their GDNF serum levels were measured by ELISA. Aerobic power was estimated using Shuttle-run test. T-test results indicated a small increase in their GDNF serum levels, which was not statistically significant (p =0.11). In addition, the results of ANOVA did not show any significant difference between continuous and interval aerobic training on the serum levels of their GDNF but their aerobic capacity significantly increased (p =0.012). Although continuous and interval aerobic exercise improves aerobic power in obese children, they had no significant effect on their serum levels of GDNF.

Keywords: aerobic power, continuous aerobic training, glial cell line-derived neurotrophic factor (GDNF), interval aerobic training, obese children

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Authors: Sonal Budhiraja, Biswabrata Pradhan

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This work considers statistical inference based on progressive Type-I interval censored data with random removal. The scheme of progressive Type-I interval censoring with random removal can be described as follows. Suppose n identical items are placed on a test at time T0 = 0 under k pre-fixed inspection times at pre-specified times T1 < T2 < . . . < Tk, where Tk is the scheduled termination time of the experiment. At inspection time Ti, Ri of the remaining surviving units Si, are randomly removed from the experiment. The removal follows a binomial distribution with parameters Si and pi for i = 1, . . . , k, with pk = 1. In this censoring scheme, the number of failures in different inspection intervals and the number of randomly removed items at pre-specified inspection times are observed. Asymptotic properties of the maximum likelihood estimators (MLEs) are established under some regularity conditions. A β-content γ-level tolerance interval (TI) is determined for two parameters Weibull lifetime model using the asymptotic properties of MLEs. The minimum sample size required to achieve the desired β-content γ-level TI is determined. The performance of the MLEs and TI is studied via simulation.

Keywords: asymptotic normality, consistency, regularity conditions, simulation study, tolerance interval

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Authors: Kisan Sarda, Bhavika Shingote

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This paper aims to evaluate regression modelling for prediction of Energy storage of solar photovoltaic (PV) system using Semi parametric regression techniques because there are some parameters which are known while there are some unknown parameters like humidity, dust etc. Here irradiation of solar energy is different for different places on the basis of Latitudes, so by finding out areas which give more storage we can implement PV systems at those places and our need of energy will be fulfilled. This regression modelling is done for daily, monthly and seasonal prediction of solar energy storage. In this, we have used R modules for designing the algorithm. This algorithm will give the best comparative results than other regression models for the solar PV cell energy storage.

Keywords: semi parametric regression, photovoltaic (PV) system, regression modelling, irradiation

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Authors: Abdolrasoul Daneshjoo, Fatemeh Akbari Ghara

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Background and Purpose: Obesity is an increasing factor in cardiovascular disease and serum levels of cellular adhesion molecule. It plays an important role in predicting risk for coronary artery disease. The purpose of this research was to study the effect of eight weeks moderate intensity interval training and curcumin intake on ICAM-1 & VCAM-1 levels of menopausal fat rats. Materials and methods: in this study, 28 Wistar Menopausal fat rats aged 6-8 weeks with an average weight of 250-300 (gr) were randomly divided into four groups: control, curcumin supplement, moderate intensity interval training and moderate intensity interval training + curcumin supplement. (7 rats each group). The training program was planned as 8 weeks and 3 sessions per week. Each session consisted of 10 one-min sets with 50 percent intensity and the 2-minutes interval between sets in the first week. Subjects started with 14 meters per minute, and 2 (m/min) was added to increase their speed weekly until the speed of 28 (m/min) in the 8th week. Blood samples were taken 48 hours after the last training session, and ICAM-1 A and VCAM-1 levels were measured. SPSS software, one-way analysis of variance (ANOVA) and Pearson correlation coefficient were used to assess the results. Results: The results showed that eight weeks of training and taking curcumin had significant effects on ICAM-1 levels of the rats (p ≤ 0.05). However, it had no significant effect on VCAM-1 levels in menopausal obese rates (p ≥ 0.05). There was no significant correlation between the levels of ICAM-1 and VCAM-1 in eight weeks training and taking curcumin. Conclusion: Implementation of moderate intensity interval training and the use of curcumin decreased ICAM-1 significantly.

Keywords: curcumin, interval training , ICMA, VCAM

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Authors: Nitaya Buntao

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This paper considers the problems of interval estimation for rainfall mean of the lognormal distribution and the delta-lognormal distribution in Northeastern Thailand. We present here the modified generalized pivotal approach (MGPA) compared to the modified method of variance estimates recovery (MMOVER). The performance of each method is examined in term of coverage probabilities and average lengths by Monte Carlo simulation. An extensive simulation study indicates that the MMOVER performs better than the MGPA approach in terms of the coverage probability; it results in highly accurate coverage probability.

Keywords: rainfall mean, interval estimation, lognormal distribution, delta-lognormal distribution

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Authors: Suparman

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Piecewise linear regression models are very flexible models for modeling the data. If the piecewise linear regression models are matched against the data, then the parameters are generally not known. This paper studies the problem of parameter estimation of piecewise linear regression models. The method used to estimate the parameters of picewise linear regression models is Bayesian method. But the Bayes estimator can not be found analytically. To overcome these problems, the reversible jump MCMC algorithm is proposed. Reversible jump MCMC algorithm generates the Markov chain converges to the limit distribution of the posterior distribution of the parameters of picewise linear regression models. The resulting Markov chain is used to calculate the Bayes estimator for the parameters of picewise linear regression models.

Keywords: regression, piecewise, Bayesian, reversible Jump MCMC

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Authors: Idrissa Kayijuka

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The logistic regression and Cox regression models (proportional hazard model) at present are being employed in the analysis of prospective epidemiologic research looking into risk factors in their application on chronic diseases. However, a theoretical relationship between the two models has been studied. By definition, Cox regression model also called Cox proportional hazard model is a procedure that is used in modeling data regarding time leading up to an event where censored cases exist. Whereas the Logistic regression model is mostly applicable in cases where the independent variables consist of numerical as well as nominal values while the resultant variable is binary (dichotomous). Arguments and findings of many researchers focused on the overview of Cox and Logistic regression models and their different applications in different areas. In this work, the analysis is done on secondary data whose source is SPSS exercise data on BREAST CANCER with a sample size of 1121 women where the main objective is to show the application difference between Cox regression model and logistic regression model based on factors that cause women to die due to breast cancer. Thus we did some analysis manually i.e. on lymph nodes status, and SPSS software helped to analyze the mentioned data. This study found out that there is an application difference between Cox and Logistic regression models which is Cox regression model is used if one wishes to analyze data which also include the follow-up time whereas Logistic regression model analyzes data without follow-up-time. Also, they have measurements of association which is different: hazard ratio and odds ratio for Cox and logistic regression models respectively. A similarity between the two models is that they are both applicable in the prediction of the upshot of a categorical variable i.e. a variable that can accommodate only a restricted number of categories. In conclusion, Cox regression model differs from logistic regression by assessing a rate instead of proportion. The two models can be applied in many other researches since they are suitable methods for analyzing data but the more recommended is the Cox, regression model.

Keywords: logistic regression model, Cox regression model, survival analysis, hazard ratio

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Authors: Olivia Rachel Hwang, Yu-Hsuan Joni Shao

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Androgen Deprivation Therapy (ADT) has been a primary treatment for patients with advanced prostate cancer. However, it is associated with numerous adverse effects related to Metabolic Syndrome (MetS), including hypertension, diabetes, hyperlipidaemia, heart diseases and ischemic strokes. However, complications associated with ADT for prostate cancer in Taiwan is not well documented. The purpose of this study is to utilize the data from NHIRD (National Health Insurance Research Database) to examine the trajectory changes of MetS-related complications in men receiving ADT. The risks of developing complications after the treatment were analyzed with multivariate Cox regression model. Covariates including in the model were the complications before the diagnosis of prostate cancer, the age, and the year at cancer diagnosis. A total number of 17268 patients from 1997-2013 were included in this study. The exclusion criteria were patients with any other types of cancer or with the existing MetS-related complications. Changes in MetS-related complications were observed among two treatment groups: 1) ADT (n=9042), and 2) non-ADT (n=8226). The ADT group appeared to have an increased risk in hypertension (hazard ratio 1.08, 95% confidence interval 1.03-1.13, P = 0.001) and hyperlipidemia (hazard ratio 1.09, 95% confidence interval 1.01-1.17, P = 0.02) when compared with non-ADT group in the multivariate Cox regression analyses. In the risk of diabetes, heart diseases, and ischemic strokes, ADT group appeared to have an increased but not significant hazard ratio. In conclusion, ADT was associated with an increased risk in hypertension and hyperlipidemia in prostate cancer patients in Taiwan. The risk of hypertension and hyperlipidemia should be considered while deciding on ADT, especially those with the known history of hypertension and hyperlipidemia.

Keywords: androgen deprivation therapy, ADT, complications, metabolic syndrome, MetS, prostate cancer

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Authors: Masahiro Ohmura, Koh Kakusho, Takeshi Okadome

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This paper presents a regression model with autocorrelated errors in which the inputs are social moods obtained by analyzing the adjectives in Twitter posts using a document topic model. The regression model predicts Dow Jones Industrial Average (DJIA) more precisely than autoregressive moving-average models.

Keywords: stock market prediction, social moods, regression model, DJIA

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Authors: Shiwei Deng, Yang Bao

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In this paper, we present a model-based regression test suite reducing approach that uses EFSM model dependence analysis and probability-driven greedy algorithm to reduce software regression test suites. The approach automatically identifies the difference between the original model and the modified model as a set of elementary model modifications. The EFSM dependence analysis is performed for each elementary modification to reduce the regression test suite, and then the probability-driven greedy algorithm is adopted to select the minimum set of test cases from the reduced regression test suite that cover all interaction patterns. Our initial experience shows that the approach may significantly reduce the size of regression test suites.

Keywords: dependence analysis, EFSM model, greedy algorithm, regression test

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Authors: Suparman

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Piecewise polynomial regression model is very flexible model for modeling the data. If the piecewise polynomial regression model is matched against the data, its parameters are not generally known. This paper studies the parameter estimation problem of piecewise polynomial regression model. The method which is used to estimate the parameters of the piecewise polynomial regression model is Bayesian method. Unfortunately, the Bayes estimator cannot be found analytically. Reversible jump MCMC algorithm is proposed to solve this problem. Reversible jump MCMC algorithm generates the Markov chain that converges to the limit distribution of the posterior distribution of piecewise polynomial regression model parameter. The resulting Markov chain is used to calculate the Bayes estimator for the parameters of piecewise polynomial regression model.

Keywords: piecewise regression, bayesian, reversible jump MCMC, segmentation

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Authors: Shih-Pin Chen, Shih-Syuan You

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Fuzzy regression models are useful for investigating the relationship between explanatory variables and responses in fuzzy environments. To overcome the deficiencies of previous models and increase the explanatory power of fuzzy data, the graded mean integration (GMI) representation is applied to determine representative crisp regression coefficients. A fuzzy regression model is constructed based on the modified dissemblance index (MDI), which can precisely measure the actual total error. Compared with previous studies based on the proposed MDI and distance criterion, the results from commonly used test examples show that the proposed fuzzy linear regression model has higher explanatory power and forecasting accuracy.

Keywords: dissemblance index, fuzzy linear regression, graded mean integration, mathematical programming

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Authors: Ahmed Guerine, Ali El Hafidi, Bruno Martin, Philippe Leclaire

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In this paper, we propose a method for the dynamic response of multi-storey structures with uncertain-but-bounded parameters. The effectiveness of the proposed method is demonstrated by a numerical example of three-storey structures. This equation is integrated numerically using Newmark’s method. The numerical results are obtained by the proposed method. The simulation accounting the interval analysis method results are compared with a probabilistic approach results. The interval analysis method provides a mean curve that is between an upper and lower bound obtained from the probabilistic approach.

Keywords: multi-storey structure, dynamic response, interval analysis method, random parameters

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Authors: Kreangkri Ratchagit

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This paper is concerned with exponential stability of switched linear systems with interval time-varying delays. The time delay is any continuous function belonging to a given interval, in which the lower bound of delay is not restricted to zero. By constructing a suitable augmented Lyapunov-Krasovskii functional combined with Leibniz-Newton’s formula, a switching rule for the exponential stability of switched linear systems with interval time-varying delays and new delay-dependent sufficient conditions for the exponential stability of the systems are first established in terms of LMIs. Finally, some examples are exploited to illustrate the effectiveness of the proposed schemes.

Keywords: exponential stability, hybrid systems, timevarying delays, Lyapunov-Krasovskii functional, Leibniz-Newton’s formula

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Authors: F. Hamidi, N. Amiri, H. Mishmast Nehi

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The Bilevel Programming (BP) model has been presented for a decision making process that consists of two decision makers in a hierarchical structure. In fact, BP is a model for a static two person game (the leader player in the upper level and the follower player in the lower level) wherein each player tries to optimize his/her personal objective function under dependent constraints; this game is sequential and non-cooperative. The decision making variables are divided between the two players and one’s choice affects the other’s benefit and choices. In other words, BP consists of two nested optimization problems with two objective functions (upper and lower) where the constraint region of the upper level problem is implicitly determined by the lower level problem. In real cases, the coefficients of an optimization problem may not be precise, i.e. they may be interval. In this paper we develop an algorithm for solving interval bilevel linear fractional programming problems. That is to say, bilevel problems in which both objective functions are linear fractional, the coefficients are interval and the common constraint region is a polyhedron. From the original problem, the best and the worst bilevel linear fractional problems have been derived and then, using the extended Charnes and Cooper transformation, each fractional problem can be reduced to a linear problem. Then we can find the best and the worst optimal values of the leader objective function by two algorithms.

Keywords: best and worst optimal solutions, bilevel programming, fractional, interval coefficients

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Authors: Jan Henrik Wosnitza

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The logistic regression has developed into a standard approach for estimating conditional probabilities in a wide range of applications including credit risk prediction. The article at hand contributes to the current literature on logistic regression fourfold: First, it is demonstrated that the binary logistic regression automatically meets its model assumptions under very general conditions. This result explains, at least in part, the logistic regression's popularity. Second, the requirement of homoscedasticity in the context of binary logistic regression is theoretically substantiated. The variances among the groups of defaulted and non-defaulted obligors have to be the same across the level of the aggregated default indicators in order to achieve linear logits. Third, this article sheds some light on the question why nonlinear logits might be superior to linear logits in case of a small amount of data. Fourth, an innovative methodology for estimating correlations between obligor-specific log-odds is proposed. In order to crystallize the key ideas, this paper focuses on the example of credit risk prediction. However, the results presented in this paper can easily be transferred to any other field of application.

Keywords: correlation, credit risk estimation, default correlation, homoscedasticity, logistic regression, nonlinear logistic regression

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Authors: Aswan Aswan

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Ichnofossils were examined based on two wells cores of Jurassic sediment from Bintuni Basin, West Papua, Indonesia. The cores are the Jurassic interval and known as the potential reservoir interval in this area. Representative of 18 ichnogenera was recorded including forms assigned to Arenicolites, Asterosoma, Bergaueria, Chondrites, cryptic bioturbation, Glossifungites, Lockeia, Ophiomorpha, Palaeophycus, Phycosiphon, Planolites, Rhizocorallium, Rosselia, root structure, Skolithos, Teichicnus, Thalassinoides, and Zoophycos. The two cores represent a depositional system that is dominated by tidal flat, shallow marine shelf continuum possibly crossed by estuaries or tidal shoals channels. From the first core identified two deepening cycles. The shallow one is a shallow marine with tidal influence while the deeper one attached to the shelf. Shallow interval usually indicates by appearances of Ophiomorpha and Glossifungites while the deeper shallow marine interval signs by the abundance of Phycosiphon. The second core reveals eight deepening cycles.

Keywords: ichnofossil, Jurassic, sediment, reservoir, Bintuni, Indonesia, West Papua

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Authors: Getachew Belay Kassahun

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Background: Aspartate aminotransferase to Platelet Ratio Index (APRI) currently becomes a biomarker for screening liver fibrosis since liver biopsy procedure is invasive and variation in pathological interpretation. Clinical Laboratory Standard Institute recommends establishing age, sex and environment specific reference interval for biomarkers in a homogenous population. The current study was aimed to derive community based reference interval of APRI aged between 12 and 60 years old in Mekelle city Tigrai, Northern Ethiopia. Method: Six hundred eighty eight study participants were collected from three districts in Mekelle city. The 3 districts were selected through random sampling technique and sample size to kebelles (small administration) were distributed proportional to household number in each district. Lottery method was used at household level if more than 2 study participants to each age partition were found. A community based cross sectional in a total of 534 study participants, 264 male and 270 females, were included in the final laboratory and data analysis but around 154 study participants were excluded through exclusion criteria. Aspartate aminotransferase was analyzed through Biosystem chemistry analyzer and Sysmix machine was used to analyze platelet. Man Whitney U test non parametric stastical tool was used to appreciate stastical difference among gender after excluding the outliers through Box and Whisker. Result: The study appreciated stastical difference among gender for APRI reference interval. The combined, male and female reference interval in the current study was 0.098-0.390, 0.133-0.428 and 0.090-0.319 respectively. The upper and lower reference interval of males was higher than females in all age partition and there was no stastical difference (p-value (<0.05)) between age partition. Conclusion: The current study showed using sex specific reference interval is significant to APRI biomarker in clinical practice for result interpretation.

Keywords: reference interval, aspartate aminotransferase to platelet ratio Index, Ethiopia, tigray

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Authors: Grienggrai Rajchakit

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This paper is concerned with the exponential stability of switched linear systems with interval time-varying delays. The time delay is any continuous function belonging to a given interval, in which the lower bound of delay is not restricted to zero. By constructing a suitable augmented Lyapunov-Krasovskii functional combined with Leibniz-Newton's formula, a switching rule for the exponential stability of switched linear systems with interval time-varying delays and new delay-dependent sufficient conditions for the exponential stability of the systems are first established in terms of LMIs. Finally, some examples are exploited to illustrate the effectiveness of the proposed schemes.

Keywords: exponential stability, hybrid systems, time-varying delays, lyapunov-krasovskii functional, leibniz-newton's formula

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Authors: Arunee Wongkhao, Suparat Niwitpong, Sa-aat Niwitpong

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In this paper, we propose two new confidence intervals for the difference between the inverse of normal means with known coefficients of variation. One of these two confidence intervals for this problem is constructed based on the generalized confidence interval and the other confidence interval is constructed based on the closed form method of variance estimation. We examine the performance of these confidence intervals in terms of coverage probabilities and expected lengths via Monte Carlo simulation.

Keywords: coverage probability, expected length, inverse of normal mean, coefficient of variation, generalized confidence interval, closed form method of variance estimation

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Authors: Zhou Jianhong

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Model averaging is a desirable approach to deal with model uncertainty, which, however, has rarely been explored for Poisson regression. In this paper, we propose a model averaging procedure based on an unbiased estimator of the expected Kullback-Leibler distance for the Poisson regression. Simulation study shows that the proposed model average estimator outperforms some other commonly used model selection and model average estimators in some situations. Our proposed methods are further applied to a real data example and the advantage of this method is demonstrated again.

Keywords: model averaging, poission regression, Kullback-Leibler distance, statistics

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Authors: L. Q. Yuan, J. Yang, A. Siddiqui

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A new regression uncertainty analysis methodology was applied to determine the uncertainties of the critical heat flux (CHF) power correlation for an advanced 43-element bundle design, which was developed by Canadian Nuclear Laboratories (CNL) to achieve improved economics, resource utilization and energy sustainability. The new methodology is considered more appropriate than the traditional methodology in the assessment of the experimental uncertainty associated with regressions. The methodology was first assessed using both the Monte Carlo Method (MCM) and the Taylor Series Method (TSM) for a simple linear regression model, and then extended successfully to a non-linear CHF power regression model (CHF power as a function of inlet temperature, outlet pressure and mass flow rate). The regression uncertainty assessed by MCM agrees well with that by TSM. An equation to evaluate the CHF power regression uncertainty was developed and expressed as a function of independent variables that determine the CHF power.

Keywords: CHF experiment, CHF correlation, regression uncertainty, Monte Carlo Method, Taylor Series Method

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Authors: Noor F. Ilias, Mazlifah Omar, Hashbullah Ismail

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Exercise training has been shown to improve functional capacity and is recommended as a therapy for management of blood pressure. Our purpose was to establish whether different exercise capacity produces different effect size for Cardiorespiratory Fitness (CRF), systolic (SBP) and diastolic (DBP) blood pressure in patients with hypertension. Exercise characteristic is required in order to have optimal benefit from the training, but optimal exercise capacity is still unwarranted. A MEDLINE search (1985 to 2015) was conducted for exercise based rehabilitation trials in hypertensive patients. Thirty-seven studies met the selection criteria. Of these, 31 (83.7%) were aerobic exercise and 6 (16.3%) aerobic with additional resistance exercise, providing a total of 1318 exercise subjects and 819 control, the total of subjects was 2137. We calculated exercise volume and energy expenditure through the description of exercise characteristics. 4 studies (18.2%) were 451kcal - 900 kcal, 12 (54.5%) were 900 kcal – 1350 kcal and 6 (27.3%) >1351kcal per week. Peak oxygen consumption (peak VO2) increased by mean difference of 1.44 ml/kg/min (95% confidence interval [CI]: 1.08 to 1.79 ml/kg/min; p = 0.00001) with weighted mean 21.2% for aerobic exercise compare to aerobic with additional resistance exercise 4.50 ml/kg/min (95% confidence interval [CI]: 3.57 to 5.42 ml/kg/min; p = 0.00001) with weighted mean 14.5%. SBP was clinically reduce for both aerobic and aerobic with resistance training by mean difference of -4.66 mmHg (95% confidence interval [CI]: -5.68 to -3.63 mmHg; p = 0.00001) weighted mean 6% reduction and -5.06 mmHg (95% confidence interval [CI]: -7.32 to -2.8 mmHg; p = 0.0001) weighted mean 5% reduction respectively. Result for DBP was clinically reduce for aerobic by mean difference of -1.62 mmHg (95% confidence interval [CI]: -2.09 to -1.15 mmHg; p = 0.00001) weighted mean 4% reduction and aerobic with resistance training reduce by mean difference of -3.26 mmHg (95% confidence interval [CI]: -4.87 to -1.65 mmHg; p = 0.0001) weighted mean 6% reduction. Optimum exercise capacity for 451 kcal – 900 kcal showed greater improvement in peak VO2 and SBP by 2.76 ml/kg/min (95% confidence interval [CI]: 1.47 to 4.05 ml/kg/min; p = 0.0001) with weighted mean 40.6% and -16.66 mmHg (95% confidence interval [CI]: -21.72 to -11.60 mmHg; p = 0.00001) weighted mean 9.8% respectively. Our data demonstrated that aerobic exercise with total volume of 451 kcal – 900 kcal/ week energy expenditure may elicit greater changes in cardiorespiratory fitness and blood pressure in hypertensive patients. Higher exercise capacity weekly does not seem better result in management hypertensive patients.

Keywords: blood Pressure, exercise, hypertension, peak VO2

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Authors: Aya Gamal Khattab

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Chronic infection with the hepatitis C virus (HCV) is now the leading cause of liver-related morbidity and mortality; Currently, alanine aminotransferase ALT measurement is not only widely used in detecting the incidence, development, and prognosis of liver disease with obvious clinical symptoms, but also provides reference on screening the overall health status during health check-ups. Exercise is a low-cost, reliable and sustainable therapy for many chronic diseases. Low-volume high intensity interval training HIT is time efficient while also having wider application to different populations including people at risk for chronic inflammatory diseases. Purpose of this study was to investigate the effect of low volume high intensity interval training on ALT, AST in HCV patients. All practical work was done in outpatient physiotherapy clinic of Suez Canal Authority Hospitals. Forty patients both gender (27 male, 13 female), age ranged (40-60) years old submitted to low volume high intensity interval training on treadmill for two months three sessions per week. Each session consisting of five min warming up, two bouts for 10 min each bout consisting of 30 sec - 1 min of high intensity (75%-85%) HRmax then two to four min active recovery at intensity (40%-60%) HRmax, so the sum of high intensity intervals was one to two min for each session and four to eight min active recovery, and ends with five min cooling down. ALT and AST were measured before starting exercise session and 2 months later after finishing the total exercise sessions through blood samples. Results showed significant decrease in ALT, AST with improvement percentage (18.85%), (23.87%) in the study, so the study concluded that low volume high intensity interval training had a significant effect in lowering the level of circulating liver enzymes (ALT, AST) which means protection of hepatic cells and restoration of its function.

Keywords: alanine aminotransferase (ALT), aspartate aminotransferase (AST), hepatitis C (HCV), low volume high intensity interval training

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Authors: Mohamed M. A. Rahoma

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This paper presents the results of my sedimentological and petrographical study of the Nubian Formation in the north Gialo area in the Sirte basin in Libya that was used for identifying and recognizing the facies type and their changes through the studied interval. It also helped me to interpret the depositional processes and the depositional environments and describe the textural characteristics, detrital mineralogy, Authigenic mineralogy and porosity characteristics of the rocks within the cored interval. Thus, we can identify the principal controls on porosity and permeability within the reservoir sections for the studied interval. To achieve this study, I described the cores studied well and marked all features represented in color, grain size, lithology, and sedimentary structures and used them to identify the facies. Then, I chose a number of samples according to a noticeable change in the facies through the interval for microscopic investigation. The results of the microscopic investigation showed that the authigenic clays and the authigenic types of cement have an important influence on the reservoir quality by converting intergranular macropores to microporosity and reducing permeability. It is recommended to give these authigenic minerals more investigation in future studies since they have an essential influence on the potential of sandstones reservoirs.

Keywords: diagenesis processes, authigenic minerals, Nubian Sandstone, reservoir quality

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Authors: Jude Opara, Esemokumo Perewarebo Akpos

Abstract:

This paper is on non-parametric linear regression over its parametric counterparts with large sample size. Data set on anthropometric measurement of primary school pupils was taken for the analysis. The study used 50 randomly selected pupils for the study. The set of data was subjected to normality test, and it was discovered that the residuals are not normally distributed (i.e. they do not follow a Gaussian distribution) for the commonly used least squares regression method for fitting an equation into a set of (x,y)-data points using the Anderson-Darling technique. The algorithms for the nonparametric Theil’s regression are stated in this paper as well as its parametric OLS counterpart. The use of a programming language software known as “R Development” was used in this paper. From the analysis, the result showed that there exists a significant relationship between the response and the explanatory variable for both the parametric and non-parametric regression. To know the efficiency of one method over the other, the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) are used, and it is discovered that the nonparametric regression performs better than its parametric regression counterparts due to their lower values in both the AIC and BIC. The study however recommends that future researchers should study a similar work by examining the presence of outliers in the data set, and probably expunge it if detected and re-analyze to compare results.

Keywords: Theil’s regression, Bayesian information criterion, Akaike information criterion, OLS

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Authors: Sujay Bisht

Abstract:

The purpose of the study was to evaluate and find out the effect of Hill interval training on VO₂ MAX among field Hockey players. Thirty male field hockey players were selected from LNIPE, Guwahati who were studied in B.P.Ed course. The selected subjects were aged between 18 to 23 years. The VO₂ MAX was calculated and they were divided into two group. One group (N=15) considered as control group that did not participated in any special training apart from regular scheduled/curriculum and another group (N=15) considered as an experimental group which underwent four week Hill Training program. The selected criterion variable such VO₂ Max was measured by the cooper 12min/run/walk test and scores was recorded in ml/kg/min. The subjects were tested on selected criterion variable such as VO₂ Max prior and immediately after the training program. The pretest and posttest data were evaluate by the Analysis of Covariance (ANCOVA) to find out the significance difference if any between the experimental and control group on selected criterion variable. The level of significance was set at 0.05 level of confidence. After applied ANCOVA it was revealed that there was a significant different among the experimental and control group on VO₂ Max. Finally it was concluded that 4 week of Hill interval training effect the VO₂ max performance of field hockey players.

Keywords: VO₂ max, hill interval training, ANCOVA, experimental group

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