The objectives of this research are to propose the mode estimation method of Inverse Gaussian data and to compare three mode estimation methods. Those methods are simple method adjusted mode method with term (n+1)/n and adjusted mode method with term (n+2)/n. The performance of each method can be measured by the absolute bias (|Bias|) and the mean square errors (MSE). This study was performed by using different sample sizes (n) of 5 10 20 30 and 50 whereas the parameter  is fixed to be 1 and parameters  are fixed to be 1 3 5 10 15 and 20. This research used the Monte Carlo Simulation technique. The experiment was repeated 50000 times for each condition. The results showed that the adjusted mode method with term (n+2)/n gave the lowest value of |Bias| and MSE in most of the criterions of sample sizes and parameter values.

The results revealed that the manager๛s opinions towards operation of quality control and inspection of auto-parts at a high level were lineworkers in car accessory production plant and other production plant. The opinions of the office workers towards operation of quality control and inspection of auto parts at a high level were lineworkers in car body production plant car accessory production plant and other production plant. When comparing the operation level of quality control and inspection of plants classified by types of vendors it was found that there was a significant difference at .05 level. The collected data were analyzed by SPSS program into percentage mean standard deviation t-test one-way ANOVA and multiple comparison.

The objectives of this research were to investigate the problems advantages and satisfaction of graduate students of King Mongkut’s University of Technology Thonburi over the internet network. This survey research was conducted by means of questionnaires which were completed by 1077 samples group of graduate students of King Mongkut’s University of Technology Thonburi inquiring about the problems advantages and satisfaction of the internet access. The statistics used were mean and standard error. This research demonstrates that the problems resulted from obsolete and unfuctional hardware (4.0950). Most advantages of internet presented in this finding were on the grounds that the users can search a variety of knowledge (4.2119). In view of users the satisfaction gained from the internet was based on the fact that the users can search and easily access the information for academic purposes (4.0380).

This study surveyed seventeen wastewater treatment plants in concentrated latex rubber and seafood industries in southern Thailand. The researchers concerned mainly in the bulking sludge problems caused by filamentous bacteria. Wastewaters from those factories contained high organic contents high BOD5 and TKN concentrations. All of the existing activated sludge process were equipped with other pre- and post-treatments. Sixteen of them were concerned with only carbon and suspended removal. They were operated with high hydraulic retention time (HRT) and low food per microorganism ratio (F/M ratio) which are the main causes of bulking sludge problem. Most operators faced up with this problem but could not realize the reasons and solutions. This study observed their settle ability and presented filamentous bacteria in each plant. It can be evidently seen that some filamentous bacteria such as type 0041 and Nostocoida limicola II appeared at seafood treatment plants only and type 1863 found in rubber treatment plants only. This can be affirmed to the nature of influent composition of wastewater.

Presenting information to an e-learning environment is a challenge mostly because of the hypertext/hypermedia nature and the richness of the context and information provides. This paper proposes a dynamic semantic model for e-learning system based on the psycholinguistic theories of human memory Spreading Activation Network (SAN). This work employs a SAN as a technique to provide the interfaces action selection mechanism in an uncertain environment. The paper combines the SAN with the temporal logic to provide an e-learning system that a learning activity level evolves according to their expected contextual relevance. The system differs from the other e-learning by representing dynamic associations between learning activities and the relevance subjects. This system equipped with an Event-Triggered learning interface (context) adaptation component. This component provides multiple parallel processes for perception. These processes provide context screen selection and learning task operation based upon the user current situation. The SAN attempts to achieve a number of goals in an unpredictable complex dynamic environment. Spreading activation explains the predictive top-down effect of knowledge. It supports general heuristics which may be used as the first step of more elaborated methods. This model is suited to deal with the interaction between semantic and episodic memories as well as many other practical issues regarding e-learning including the retroactive effect of semantics over perception. The system uses the SAN to activate the most suitable interface screen (context) in response to the current conditions (learning activities) while the system continues working towards the learning objective goal. The paper presents our efforts to realize such e-learning system. The proposed paradigm has been implemented to develop a prototypical system and the experiments also illustrate the robustness of such an e-learning framework.