This research aimed to develop paper from oil palm empty fruit bunch (Fb) oil palm mesocarp (M) and paper mulberry (P) with chitosan coating to be used as single-use paper bag for dry food products. The optimal pulp preparation conditions for Fb M and P fibers were boiling in 25 25 and 15% NaOH solution at 170 170 and 100°C for 3 hours respectively. The optimal H2O2 concentrations for bleaching of Fb M and P pulps were 40% 40% and 0.2% respectively. The obtained pulps were used to prepare papers with the basis weight of 109 g/m2 . The papers consisted of Fb M and P pulps at ratios of 70:0:30 (FbP) 0:70:30 (MP) and 35:35:30 (FbMP). It was found that FbP paper showed superior physical and/or mechanical characteristics than those of MP and FbMP. FbP papers were then coated with chitosan solutions (0.2-0.6%) to further improve their mechanical properties and reduce their water absorption characteristics. The optimal concentration of chitosan was 0.4%. This study showed that all three types of pulps could be used to produce papers that had equal or superior mechanical- and water adsorption properties as compared to those of commercially available paper bags made from wood pulp.

The objectives of this research were 1) to study attitude and advertising media exposure of the BTS passengers; 2) to compare the relationship between the BTS passengers’ demographic characteristics and their service behaviors; 3) to compare the differences of the BTS passengers’ demographic characteristics towards their advertising media exposure; and 4) to study the relationship of the passengers’ attitudes on the BTS in-trained advertisement with the BTS passengers’ advertising media exposure. The researcher randomly selected 400 participants through the Cluster sampling method by using the questionnaire as a data collection tool. The analytical statistics included standard deviation mean average and frequency distribution. The hypothesis testing included Chi-square T-Test F-Test (ANOVA) values testing and Pearson Prodcut Moment correlation coefficient.

The purpose of this project is to reuse the wasted cloth into a street wear style cloth. We will use a 100 people as our target group. The materials for the project are : 4 types of reused street wear cloths and a survey. From the study we have sum up to a conclusion. We have prepared 4 street wear cloths targeting ladies age from 21-23 and 25. These participants needs to have at least a under grads Degree with an incomes of 15000 bath or lower per month. The question in our survey consist of : That these reuse street wear cloth can reduce the amount of wasted cloth and it can also be brought Into a main line of cloth wearing. It can also be also be worn on a daily bases. It is also quite pretty and Attractive. It is useful and it can help to give more value to those wasted cloths. This type of clothing is suited for ladies from 21-25 years of age. And it can also benefit them. Does it have a modernized way of Decorations? And will it benefit to those who live around us. and the results are all came in as a High rating from 4.4 4.42 4.35 4.34 4.33 4.29 4.28 4.27 and so on. The detailed conclusion to the street wear cloths are : the design sample 1 sample 2 sample4 Sample3 are scored accordingly 4.34 4.25 4 3.54 in orderly fashion. The cutting sample 1 sample 2 sample 4 sample 3 came as a high rating scoring from 4.28 4.25 4.19 3.7 accordingly. The decorations sample 1 sample 2 and sample 4 came in as high rating scoring from 4.31 4.24 3.99 Aecordingly. The sample 3 came in as medium score of 3.46. the patching sample 1 sample 2 sample 4 sample 3 came as a high score rating 4.38 4.36 4.04 3.76 accordingly the coloring sample 1 sample 2 sample 4 came in as high score from 4.4 4.33 4.05. but sample 3 have only received a Medium score 3.5. for over all scoring sample 1 sample 2 sample 4 sample 3 all got high scores of 4.34 4.29 4.05 3.59 accordingly.

This paper presents an Information system for the management of the building and location. By the building Class room Timetable Schedule Photos building and Photo structure for building put on to collect the necessary information and can be easy accessed through the presentation with a web browser making agencies can be useful to use it is the And can be checked The system can allocate a code to bring the teaching of information management department is properly registered. And data to help executives make decisions about planning and building for the future. The assessment of the system’s quality was performed by 6 experts which discovered the system had a good level of satisfaction. The result of the evaluation from experts found mean at 4.00 with standard deviation at 0.36 In conclusion this system achieved all goals and gained a high level of satisfaction from all user

The surveillance and reports on accidents for the current traffic management are carried out by viewing and inspecting CCTV footage. This method is slow to detect incidents and requires a lot of manpower. Since nowadays communication technology and social media play a role in reporting such incidents this research has been conducted to investigate accidents of interest from text messages reported on social media in Thai. There is currently no research or platform that supports this type of work clearly. This research presented the development of a neuron network memory-learning model to solve the problem of incidence pattern classification and incidence severity identification from social media messages in Thai. Using deep learning models such as MLP CNN Bi-LSTM and LSTM+CNN the study was designed and divided into three experimental patterns. This included examining patterns to identify traffic incidents that may be reported as general news or traffic reporting; examining patterns to indicate the type of incidents such as traffic accidents disasters damaged roads or others other than those mentioned; and examining patterns to specify the severity levels of the incidents such as normal intermediate traffic lane blocking or immovable. The experiments demonstrated the capability of CNN+LSTM learning models with the best incidence detection result at 93.44 %. The CNN model gave the best result in identifying a pattern of incidence at 85.29 % and the LSTM model was best able to determine the severity levels of the incidence at 88.53 %.