This paper looks into how simulation can be used and developed to combat and aid integrated maintenance and cost optimisation of Libyan cement factory by using simulation as an intelligent maintenance tool to enable intelligent decisions to be made by maintenance management. The cost of preventive maintenance against corrective maintenance, the loss of production and the delay in schedule. Also this paper presents a methodology by integrating maintenance, repair and replacement decisions in bridge management based on reliability, optimization, and life-cycle cost. The overall total cost incurred per minute due to machine failure for more intelligent maintenance decision making. The end result of the model identifies a true to life cost of preventive and corrective maintenance incurred by organisation due to failures.

This study investigated the impact of textual cohesive conjunctions on the reading comprehension of Libyan university students studying English as a foreign language. The study assumed that conjunctions have a positive effect on reading comprehension if students are explicitly taught these items. Data were collected through using a self-completion questionnaire, and the application of two intervention programmes in Gharian and Sabrata English Departments. A hundred students participated in the programmes that included the administration of pre-and post-test and only post-test experiments. As a part of the experiments, participants who were assigned randomly to the treatment groups attended about thirty hours of explicit teaching of conjunctions. The results revealed that conjunctives facilitate the reading comprehension of the fourth-year English department students in two Libyan universities. These findings could have important pedagogical implications, which may require explicit teaching of conjunctions in order to improve reading comprehension.

Key words: conjunctions, cohesion, coherence, reading comprehension.

With the development of the internet and multimedia technology digital video encryption has attracted a great deal of

research interest in the recent few years in applications. In this paper, we propose a method to encrypt video data. The proposed

algorithm is based on the MPEG video coding standard. It selectively encrypts some DCT coefficients in the I frame, B frame and P

frame in MPEG video compression by using chaotic systems. The key in this paper is chaotic sequence based on logistic mapping. It

can produce the pseudo-random sequences with good randomness. The experimental results based on chaotic maps prove the

effectiveness of the proposed method, showing advantages of large key space and high-level security. The proposed algorithm was

measured through a series of tests and achieved good results. The results indicate that the algorithm can be implemented for video

encryption efficiently and it provides considerable levels of security

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.