Sentiment analysis is the computational study of people’s opinion or feedback, attitudes, and emotions toward entities, individuals, issues, events, topics and their attributes. There are many research conducted for other languages such as English, Spanish, French, and German. However, lack of research is conducted to harvest the information in Malay words and structure them into a meaningful data. The objective of this paper is to introduce a lexical based method in analysing sentiment of Facebook comments in Malay. Three types of lexical based techniques are implemented in order to identify the sentiment of Facebook comments. The techniques used are term counting, term score summation and average on comments. The comparison of accuracy, precision and recall for all techniques are computed. The result shows that the average on comments method outperforms the other two techniques.

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The paper aims to provide an insight into the significance of having a simulation model to forecast the supply of registered nurses for health workforce planning policy using System Dynamics. A model is highly in demand to predict the workforce demand for nurses in the future, which it supports for complete development of a needs-based nurse workforce projection using Malaysia as a case study. The supply model consists of three sub-models to forecast the number of registered nurses for the next 15 years: training model, population model and Full Time Equivalent (FTE) model. In fact, the training model is for predicting the number of newly registered nurses after training is completed. Furthermore, the population model is for indicating the number of registered nurses in the nation and the FTE model is useful for counting the number of registered nurses with direct patient care. Each model is described in detail with the logical connection and mathematical governing equation for accurate forecasting. The supply model is validated using error analysis approach in terms of the root mean square percent error and the Theil inequality statistics, which is mportant for evaluating the simulation results. Moreover, the output of simulation results provides a useful insight for policy makers as a what-if analysis is conducted. Some recommendations are proposed in order to deal with the nursing deficit. It must be noted that the results from the simulation model will be used for the next stage of the Needs-Based Nurse Workforce projection project. The impact of this study is that it provides the ability for greater planning and policy making with better predictions.

Biomass, substrate or metabolite concentrations are difficult to measure online in fermentation processes because of the lack of reliable, cheap and sterilizable transducers. Currently, many of the measurements required may be determined through offline analysis, which is costly and time consuming. Furthermore, the specific growth rate conditions involved in the fermentation are typically non-linear and uncertain. In this paper, a new variable, the substrate consumption rate, consisting of a combination of substrate concentration, biomass concentration, specific growth rate and yield production coefficient, is introduced to overcome these problems and simplify the non-linear differential equations of the fermentation process. A sliding mode observer, which only requires measurement of substrate concentration, is then developed to solve the estimation problem, providing a soft sensor to estimate the substrate consumption rate. It is shown that the sliding mode exhibited by the corresponding observer error dynamics is exponentially stable. This parameterization and the resulting estimate of biomass concentration are then utilized within a feedback control strategy. Non-linear simulation results in the presence of both parameter uncertainties and external disturbances illustrate the approach.

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