18 research outputs found
Environmental and Economic of Flooring Building Materials
Green building design requires use of building materials that minimize environmental impact, necessitating selection of building materials by their environmental profile as well as economic cost-benefit considerations. The objective of this research is to determine the environmental impacts per square meter of three flooring materials; ceramic tiles, marble tiles, and parquet produced in Thailand. Life cycle cost (LCC) of the three materials are determined and compared. The study finds that ceramic tiles cause the greatest environmental impact, especially during the material extraction phase. When calculating all costs incurred throughout the life-cycle, the cost of untreated solid wood parquet is highest
Suitable Low Income Flood Resilient Housing
Climate change is a critical issue for all of humanity. It is predicted that Thailand is likely to have an increasing frequency and intensity of rainfall and storms which, will result in a more severe flash flood problem. Slum residents in Chiang Mai are one of the groups of people who are vulnerable to flooding impacts of climate change. The objective of this study is to analyze the flood-resilient housing style of low-income people. Data from 3 slums (146 households) which encounter different kinds of floods every year, i.e. drainage floods (Ban Sanku: 21 households), river floods (Kampang Ngam: 64 households) and flash floods (Samunkee Pattana: 61 households), were collected. The study found that flood frequency, duration, depth and flow velocity caused damage to the houses, but only flood frequency, duration, and flow velocity were factors affecting the housing structure. If considering only damage to slums which frequently face shallow water depth, slow flow velocity and short duration, all 8 low-income housing styles (A-H) can be built. The high platform house with open space under the house is appropriate for slums located in flooding area where high-level, slow flow velocity floods occur frequently but for a short duration. It may be a permanent, semi-permanent or temporary structure (D-F). For the other slums facing high flood levels with high flow velocities for a short duration, all permanent housing styles are appropriate. If the objective is not only damage prevention but also living during a flood, permanent high platform houses with open spaces under the houses are recommended for all slums
āđāļāļāļāđāļēāļĨāļāļāđāļāļīāļāļŠāļēāđāļŦāļāļļāļāļ§āļēāļĄāļŠāđāļēāđāļĢāđāļāđāļāļāļēāļāļĩāļāļāļāļāļāļąāļāļāļīāļāļŠāļŦāļāļīāļāļĻāļķāļāļĐāļē
The purposes of this research were to develop and to examine the model validity of the causal model of career success for cooperative education graduates with the empirical data. The research samples consisted of 1,861 Suranaree University of Technology (SUT)âs cooperative education graduates from batch 1 to batch 20. Questionnaires were utilized for the data collection. Data were analyzed by using descriptive statistics and structural equation model. The major findings were as follows: 1) the causal model of career success for cooperative education graduates consisted of 1 endogenous latent variable: career success, and 4 exogenous latent variables: perceived self-efficacy, cooperative education experience, organizational support, and work-family balance; and 2) the causal model of career success for cooperative education graduates had a good fit to the empirical data (2=15.01, df=15, p=.45, x2/df=1.00, GFI=1.00, RMSEA=.00, RMR=.00, NFI=1.00, RFI=1.00, GFI=1.00). Organizational support, perceived self-efficacy, and cooperative education experience had a direct effects on the career success at the significant levels of .01, .01, and .05, respectively, along with the positive effect sizes of .66, .31, and .05, respectively. All of the causal variables in the model accounted for 77% of the variance in career success
āļāļēāļĢāļāļēāļāļāļēāļĢāļāđāļāļļāļāļŦāļ āļđāļĄāļīāļāļ·āđāļāļāļīāļ§āļĢāļ°āļāļąāļāļāđāļāļāļāļīāđāļāđāļāļāļąāļāļŦāļ§āļąāļāđāļāļĩāļĒāļāđāļŦāļĄāđāļ āļēāļĒāđāļāđāļāđāļāļĄāļđāļĨāļ āļēāļāļāļēāļĒāļāļēāļĢāđāļāļĨāļĩāđāļĒāļāđāļāļĨāļāļ āļđāļĄāļīāļāļēāļāļēāļĻPrediction of Future Surface Temperature at Local Scale in Chiang Mai Province Under Climate Change Scenarios
āļāļēāļāļāļąāļāļŦāļēāļāļēāļĢāđāļāļĨāļĩāđāļĒāļāđāļāļĨāļāļ āļđāļĄāļīāļāļēāļāļēāļĻāļŠāđāļāļāļĨāļāļģāđāļŦāđāđāļāļīāļāļāļēāļĢāđāļāļĨāļĩāđāļĒāļāđāļāļĨāļāļŦāļĨāļēāļĒāļāļĢāļ°āļāļēāļĢāļĢāļ§āļĄāļāļąāđāļāļāļēāļĢāđāļāļĨāļĩāđāļĒāļāđāļāļĨāļāļāļļāļāļŦāļ āļđāļĄāļīāļĄāļĩāļāļēāļāļ§āļīāļāļąāļĒāļĄāļēāļāļĄāļēāļĒāļāļĩāđāļāļĒāļēāļĒāļēāļĄāļāļēāļāļāļēāļĢāļāđāļāļļāļāļŦāļ āļđāļĄāļīāļāļēāļāļēāļĻāđāļāļāļāļēāļāļ āđāļāđāļĒāļąāļāļāļāđāļāđāļāļāļēāļĢāļāļēāļāļāļēāļĢāļāđāļāļļāļāļŦāļ āļđāļĄāļīāđāļāļĨāļĩāđāļĒāļŠāļģāļŦāļĢāļąāļāļāļ·āđāļāļāļĩāđāļāļāļēāļāđāļŦāļāđāļĢāļ°āļāļąāļāļāļĢāļ°āđāļāļĻāļŦāļĢāļ·āļāļĢāļ°āļāļąāļāļ āļđāļĄāļīāļ āļēāļ āļāļķāđāļāđāļāļāļ§āļēāļĄāđāļāđāļāļāļĢāļīāļāđāļĨāđāļ§āļāļļāļāļŦāļ āļđāļĄāļīāļĢāļ°āļāļąāļāļāđāļāļāļāļīāđāļāļāļēāļāļŠāļđāļāļāļ§āđāļēāļŦāļĢāļ·āļāļāđāļģāļāļ§āđāļēāļāđāļēāļāļļāļāļŦāļ āļđāļĄāļīāļāļĩāđāđāļāđāļāļēāļāļāļēāļĢāļāļēāļāļāļēāļĢāļāđāļāļļāļāļŦāļ āļđāļĄāļīāđāļāļĨāļĩāđāļĒāļĢāļ°āļāļąāļāļāļĢāļ°āđāļāļĻāļŦāļĢāļ·āļāļĢāļ°āļāļąāļāļ āļđāļĄāļīāļ āļēāļ āļāļķāļāđāļāļīāļāļāļģāļāļēāļĄāļ§āđāļēāļŦāļēāļāļāđāļāļāļāļēāļĢāļāļĢāļēāļāļāđāļāļĄāļđāļĨāļāļļāļāļŦāļ āļđāļĄāļīāđāļāļāļāļēāļāļāđāļāļāļ·āđāļāļāļĩāđāļāļāļēāļāđāļĨāđāļāļĢāļ°āļāļąāļāļāđāļāļāļāļīāđāļāļāļ°āļŠāļēāļĄāļēāļĢāļāļāļģāđāļāđāļāļĒāđāļēāļāđāļĢ āļāļēāļāļ§āļīāļāļąāļĒāļāļĩāđāļāļķāļāļāļĢāļ°āļĒāļļāļāļāđāđāļāđāļāđāļāļĄāļđāļĨāļāļēāļĢāđāļāļĨāļĩāđāļĒāļāđāļāļĨāļāļāļļāļāļŦāļ āļđāļĄāļīāļāļēāļāļāļēāļĢāļāļēāļāļāļēāļĢāļāđāļāļēāļĢāđāļāļĨāļĩāđāļĒāļāđāļāļĨāļāļ āļđāļĄāļīāļāļēāļāļēāļĻāļāļāļāļāļĢāļ°āđāļāļĻāđāļāļĒāļāļĩāđāđāļāđāđāļāļāļāļģāļĨāļāļāļ āļđāļĄāļīāļāļēāļāļēāļĻāđāļĨāļ ECHAM4 āļāļĩāđāļĄāļĩāļāļĒāļđāđāļĢāđāļ§āļĄāļāļąāļāļ§āļīāļāļĩāļāļēāļĢāļŠāļģāļĢāļ§āļāļĢāļ°āļĒāļ°āđāļāļĨ āđāļāļ·āđāļāđāļŦāđāđāļāđāļāđāļāļĄāļđāļĨāļāļļāļāļŦāļ āļđāļĄāļīāđāļāļāļāļēāļāļāļĢāļ°āļāļąāļāļāđāļāļāļāļīāđāļ āđāļāļĒāđāļāđāļāļ·āđāļāļāļĩāđāļĻāļķāļāļĐāļēāļāļģāļāļ§āļ 3 āļāļģāđāļ āļāđāļāļāļąāļāļŦāļ§āļąāļāđāļāļĩāļĒāļāđāļŦāļĄāđ āđāļāđāđāļāđ āļāļģāđāļ āļāđāļĄāļ·āļāļāđāļāļĩāļĒāļāđāļŦāļĄāđ āļāļģāđāļ āļāļŠāļąāļāļāļĢāļēāļĒ āđāļĨāļ°āļāļģāđāļ āļāļāļāļĒāļŠāļ°āđāļāđāļ āđāļāđāļāļāļ·āđāļāļāļĩāđāļĻāļķāļāļĐāļēāļāļģāļĢāđāļāļāļŠāļģāļŦāļĢāļąāļāļāļēāļāļ§āļīāļāļąāļĒāļāļĩāđāļāđāļāļ āļāļĨāļāļēāļāļāļēāļĢāļĻāļķāļāļĐāļēāļāļģāđāļŦāđāđāļāđāļāđāļāļĄāļđāļĨāļāļļāļāļŦāļ āļđāļĄāļīāļĢāļ°āļāļąāļāļāđāļāļāļāļīāđāļāļŠāļđāļāļŠāļļāļāđāļāđāļāđāļĨāļ°āļāļĩāļĢāļ°āļŦāļ§āđāļēāļāļāļĩ āļ.āļĻ. 2560â2592 āļāļĩāđāļĄāļĩāļāļāļēāļāļāļāļēāļāļāļļāļāļāļĢāļīāļ 30Ã30 āđāļĄāļāļĢ āļāļģāļāļ§āļ 3 āļāļļāļāļāđāļāļĄāļđāļĨ āđāļāđāđāļāđ āļāļļāļāļāđāļāļĄāļđāļĨāļāļļāļāļŦāļ āļđāļĄāļīāļĢāļ°āļāļąāļāļāđāļāļāļāļīāđāļāļāļēāļāļ āļēāļāļāļēāļĒ ECHAM4 A2, ECHAM4 B2 āđāļĨāļ° ECHAM5 A1B āļāļķāđāļāļĄāļĩāđāļāļ·āđāļāļāđāļāđāļĨāļ°āļāļĩāđāļĄāļēāđāļāļāļēāļĢāļŠāļĢāđāļēāļāļ āļēāļāļāļēāļĒāļāļĩāđāđāļāļāļāđāļēāļāļāļąāļ āđāļĨāļ°āļāļāļ§āđāļēāļāđāļāļĄāļđāļĨāđāļāļĢāļ°āļāļąāļāļāđāļāļāļāļīāđāļāđāļāđāļāđāļĨāļ°āļāļĩāļĄāļĩāļāļ§āļēāļĄāđāļāļāļāđāļēāļāļāļąāļāļāđāļāļāļāđāļēāļāļĄāļēāļāđāļāļĨāļĩāđāļĒāļāļĢāļ°āļĄāļēāļ 20°C āļāļģāđāļŦāđāđāļŦāđāļāđāļāđāļ§āđāļēāļāļēāļĢāļāļīāļāļēāļĢāļāļēāļāđāļāļĄāļđāļĨāļāļēāļāļēāļĻāļĢāļ°āļāļąāļāļāđāļāļāļāļīāđāļāļĄāļĩāļāļ§āļēāļĄāļŠāļģāļāļąāļāļāđāļāļāļēāļĢāļ§āļēāļāđāļāļāđāļāļāđāļēāļāļāđāļēāļāđ āļāļĩāđāđāļāļāļēāļ°āđāļāļēāļ°āļāļāđāļāđāļ āļāļēāļĢāļāļĨāļđāļāļāļ·āļ āļāļēāļĢāđāļĨāļĩāđāļĒāļāļŠāļąāļāļ§āđ āļāļĩāđāđāļĄāđāļāļēāļāđāļāđāļāđāļāļĄāļđāļĨāļāļļāļāļŦāļ āļđāļĄāļīāđāļāļĨāļĩāđāļĒāļāļāļāļāļ·āđāļāļāļĩāđāļāļąāđāļāļāļąāļāļŦāļ§āļąāļāđāļāđ āļāļāļāļāļēāļāļāļĩāđāļĒāļąāļāļāļāļ§āđāļēāđāļāļāļāļēāļāļāļāļļāļāļŦāļ āļđāļĄāļīāļĢāļ°āļāļąāļāļāđāļāļāļāļīāđāļāļāļāļāļāļąāđāļ 3 āļāļģāđāļ āļ āđāļāļāļąāļāļŦāļ§āļąāļāđāļāļĩāļĒāļāđāļŦāļĄāđāļĄāļĩāđāļāļ§āđāļāđāļĄāļāļēāļĢāđāļāļĨāļĩāđāļĒāļāđāļāļĨāļāļāļļāļāļŦāļ āļđāļĄāļīāļŠāļđāļāļāļķāđāļ āđāļĨāļ°āļāļāļ§āđāļēāđāļāļ§āđāļāđāļĄāļāļļāļāļŦāļ āļđāļĄāļīāļāđāļāļāļāļīāđāļāđāļāļāļāļēāļāļāļāļĩāđāđāļāđāļāļēāļāļ āļēāļāļāļēāļĒ ECHAM5 A1B āļĄāļĩāđāļāļ§āđāļāđāļĄāļāļ°āļĄāļĩāļāļļāļāļŦāļ āļđāļĄāļīāļŠāļđāļāļŠāļļāļāļŠāļđāļāļāļ§āđāļēāļāļļāļāļāđāļāļĄāļđāļĨāļāļļāļāļŦāļ āļđāļĄāļīāļāļĩāđāđāļāđāļāļēāļāļāļēāļāļ āļēāļāļāļēāļĒ ECHAM4 A2 āđāļĨāļ° ECHAM4 B2 āļāļąāļāļāļąāđāļāļāļąāļ§āđāļāļĢāļāļĩāđāļŠāļģāļāļąāļāļāļĒāđāļēāļāļŦāļāļķāđāļāļāļ·āļāļāļēāļĢāđāļĨāļ·āļāļāđāļāđāļ āļēāļāļāļēāļĒāļāļāļēāļāļāļāļĩāđāļĄāļĩāđāļāļ·āđāļāļāđāļāļāļĩāđāļŠāļāļāļāļĨāđāļāļāļāļąāļāļāļ·āđāļāļāļĩāđāđāļāļ·āđāļāđāļŦāđāļŠāļēāļĄāļēāļĢāļāļŦāļēāđāļāļ§āļāļēāļāļāđāļāļāļāļąāļāļāļĩāđāļāļĢāļāļāļāļĨāļļāļĄāļāļēāļĢāđāļāļĨāļĩāđāļĒāļāđāļāļĨāļāļ āļđāļĄāļīāļāļēāļāļēāļĻāļĄāļēāļāļāļĩāđāļŠāļļāļThe climate change problem results in several changes including the temperature. There have been many studies on the future climate forecast but only providing the future surface temperature of countrywide area or at the regional scale. In fact, the surface temperature of a local area might be lower or higher than that. This raises the question on how the future surface temperature at local scale can be predicted. This study applies the available data on Thailandâs existing climate change forecast using an existing world climate model ECHAM4 together with the remote sensing to predict the future surface temperature at the local scale. The 3 districts in Chiang Mai, i.e. Muang Chiang Mai, San Sai, and Doi Saket were used as a pilot study for this research. The results provided the annual maximum surface temperature during 2560BE and 2592BE of 30Ã30 meter grid spacing. The 3 sets of local temperature include ECHAM4 A2, ECHAM4 B2, and ECHAM5 A1B projects with different conditions and sources of projecting. In addition, there is a significant difference the temperature among each local data and in each year around 20 deg Celsius. Therefore, it can be seen that only the local temperature forecast is very important for planning on specific areas, e.g. annual cropping, cattle, etc. rather than using the average temperature of large provincial area. It was also found that the future climate change of the 3 districts tends to be higher. Moreover, the future local surface temperature from the ECHAM5 A1B project tends to be higher than those of ECHAM4 A2 and ECHAM4 B2. As a consequence, it is important to choose the suitable model of climate change scenarios in order to find the best guideline to solve the problem on the climate change in certain local area