This research objective was to apply thermoelectric module model TEC12710 as a preheater and dehumidifier for red chilies dryer. The heat side and cool side of Thermoelectric module affected by supplied electricity were used to produces the low humidity and warm air in preheater before charging to the main electric heater of dryer. Fresh red chilies were selected as a sample in the prototype testing during the use of preheater assisted main heater and using only main heater. The test results are compared with the sample dried under the natural sun drying. The experimental results show that the preheater can condense the moisture in air with the highest MER of 0.083 kgwaterh-1 at cold side of thermoelectric module and can heat the air up in 10°C of temperature with hot side of thermoelectric module. The final moisture content of sample is only 10%wb with the slightly changed in color when compared with the sample from sun drying. The using of preheater with main heater gives a higher specific moisture extraction rate lower specific energy consumption 14 MJ energy used and can dry faster than 10 h when compared with the using of only main heater.

The purpose of the special project education about the mobiles applied from Manora-petterned beads is to study the procession of making the hanging made from the Manorah beads pattern and study the satisfaction of the target groupson the mobiles applied from Manora-petterned beads.The students who studied about the special project has studied andgathers relevant information all information from 3 stylesto present to 5 expertsand let them choose the best one with their suggestions to improve the draft to create a set of the mobiles applied from Manora-petterned beads and bring this creation to survey the satisfaction to the target groups (50 persons) and use this information to analyze by using the freq the percentage the average and found that. 1. Most experts chose the draft of style B with the reason that it has clear fine and beautiful pattern and most of the expertschose colour tone with the reason that the colour tone green colour is in cool tone and it looked fresh and bright outstanding and beautiful. 2. The satisfaction of the targets in the product side found that the satisfaction of the beauty is in the most level. The average is 4 .7 6 . In the material side the satisfaction in the suitability of the material is in the most level. The average is 4.54. In the using benefit the satisfaction in using for Instruction media is in the most level. The average is 4.46. And the satisfaction in distribution by the web site is in the most level. The average is 4.60

Bicomponent fiber is a specific type of synthetic fibers with two different properties of polymers joined together in the same filament during spinning process. Bicomponent fibers have distinctive properties from monocomponent fibers. The properties of fibers are outcomes of type of cross section properties of composed polymers fiber spinning process condition and after-treatment process. The primary applications of bicomponent fibers are self-crimping (or self-bulkiness) fibers thermal binder fibers microfibers and special property fibers which can be added into the shealth of fiber. The fiber processing types and applications of bicomponent fibers are summerised and stated in this paper.

This research proposes a Bayesian logistic regression model which is applied to the data from autoparts manufacturing machines. Factors related to defective and bad products are investigated. The proposed model is compared with the logistic regression using maximum likelihood method for parameter estimation. The data were collected from 132 machines in an autoparts manufacturing factory. The research found that useful life machine type 6 worker group 3 and 4 working step 1 and 2 influence to the risk of producing defective and bad products. When the useful life is increased by 1 month the risk of producing defective and bad products will be increased by 2.2%. The risk that the machine type 6 will produce defective and bad products is 4.078 times greater than the risk that the machine type will do. The risk that the worker group 3 will produce defective and bad products is 61.7% less than the risk that the worker group 12 will do. The risk that the worker group 4 will produce defective and bad products is 61.5% less than the risk that the worker group 12 will do. The risk that the working step 1 will produce defective and bad products is 2.831 times greater than the risk that the working step 4 will do. The risk that the working step 2 will produce defective and bad products is 13.8 % greater than the risk that the working step 4 will do. The parameter estimates from the Bayesian logistic regression are very close to the ones from the logistic regression using maximum likelihood method for parameter estimation