ABSTRACT

The objective of the study was to evaluate the effect of Diabetes in elderly females.

Oxidative stress is regarded as a main causal factor for natural ageing. This study tested

the hypothesis that healthy elderly people show higher oxidative DNA damage levels and

lower antioxidative enzymatic defense capacities. In type II diabetic patients, persistence

of hyperglycemia is one of the causes of increased in oxygen free radicals. This increase

oxidative stress and makes the life threatening cardiovascular complications. Blood

samples were drawn from selective 100 elderly females (with and without diabetes). In

elderly females having menopause, there occur hypercholesterolemia,

hypertriglyceridemia, and hyperlipoproteinemia, these on the body, escort an increase in

free radicals, with mounting oxidative stress. In diabetes the body suffers with a poor or

negligible glycemic control. All diabetic post menopausal females with CVD have been

reported a significant high levels of Fasting Blood Sugar, Total Cholesterol, Triglycerides,

LDL-C, VLDL-C, CAT, NT-proBNP and MDA and significantly lower levels of HDL-C, GPx,

SOD as compared to control subjects. In type-II diabetes mellitus elderly female patients

during the postmenopause, the probability of secondary complications of cardiovascular

diseases increases.

This paper proposes an estimation method based on the reflection of the (manifest) explanatory variable to estimate the parameters of a simple linear regression model when both response and explanatory variables are subject to measurement error (ME). The reflection method (RM) uses all observed data points, and does not exclude or ignore part of the data or replace them by their ranks. The RM is straightforward, and easy to implement. We show that the RM is equivalent or asymptotically equivalent to the orthogonal regression (OR) method. Simulation studies show that the RM produces estimators that are nearly asymptotically unbiased and efficient under the assumption that the ratio of the error variances equals one. Moreover, it allows to define the sum of squares of errors uniquely, the same way as in the case of no measurement error. Simulation based numerical comparisons of the RM with the ordinary least square (OLS) and OR methods are also included.

The classic problem for a logistically evolving single species population being harvested involves three parameters: rate constant, carrying capacity and harvesting rate, which are taken to be positive constants. However, in real world situations, these parameters may vary with time. This paper considers the situation where these vary on a time scale much longer than that intrinsic to the population evolution itself. Application of a multiple time scale approach gives approximate explicit closed form expressions for the changing population, that compare favorably with those generated from numerical solutions.

This paper presents a new approach using Witness Simulation in combating issues related to maintenance within the existing manufacturing facility. Currently, maintenance is a key issue as many machines breakdown due to every day wear and tear as well as the age of the machine itself. This causes bottlenecks within the entire production system as the schedules of production is disturbed and further backlogs are developed. Also, when machines breakdown there seems to be very little communication from one end to another ie parts are needed for the repair of many of the machines, for example, it is very common for the rotary kiln to come to a halt due to increased temperature and different quantities of a mixture of materials. In order to fix the problem and move back on schedule the maintenance team uses certain materials ie same materials, all the time. However, due to not having an adequate stock control system and the lack of communication many a times, parts are not readily available and hence the team wait further until parts arrive at the facility. This paper will demonstrate how all of the above problem areas can be adhered to via the use of adopting a simple automated response system using Witness Simulation. The demonstration will be based on a single machine for testing purposes to ensure efficiency that can later be applied to the entire system as a whole.

This paper explores the use of Bayesian network modeling of machine breakdowns within a cement manufacturing plant. The Bayesian network modeling is introduced using Hugin software and then implemented into a Witness Simulation model using historical data, expert knowledge and opinions. The models simulate 3 parameters of the machine based on life consumption and usage of each parameter, the developed Witness model produces a probability failure rate based on these parameter usages. The failure probability developed by Witness is implemented using the Chain Rule; this is compared with the probability for failure based on the Bayesian network model. This enables the user to see the probability of failure developed by the implementation of the chain rule and Witness based on the parameter usage, on a live streaming fashion as the model is running. This can be used as a decision making tool for management to consider machine maintenance affectively based on parameter usage.

This paper explores the value of stochastic simulation as a tool for predicting current or future reliability of machinery, it helps to understand machine failures by using confidence levels developed through replications and exploring the changes that occur. A stochastic simulation model has been constructed representing a crusher machine with in a cement manufacturing plant. The crusher machine has three parameters; namely those are drill head, dusting and lubrication. The consumption of these parameters results in the development of a probability of failure for the machine using Bayes theory.

.

Olive oil cake is a by-product from the olive oil processing industry and can be used for the lipase and protease production by Candida utilis in solid state fermentation. Different carbon and nitrogen sources were evaluated, and the results showed that the supplementation of the substrate with maltose and starch as carbon sources and yeast extract as a nitrogen source significantly increased the lipase production. The best results were obtained with maltose, whereas rather low lipase and protease activities were found with glucose and oleic acid. Response surface methodology and a five-level-three-factor central composite rotatable design were used to evaluate the effects of the initial moisture content, inoculum size and fermentation time on both lipase and protease activity levels. A lipase activity value of ≈25 U g(-1) and a protease activity value of 110 U g(-1) were obtained under the optimized fermentation conditions. An alkaline treatment of the substrate appeared to be efficient, leading to increases of 39% and 133% in the lipase and protease production, respectively. The results showed that the olive cake could be a good source for enzyme production by solid state fermentation.