18 research outputs found

    Environmental and Economic of Flooring Building Materials

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    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

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    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

    āđāļšāļšāļˆāđāļēāļĨāļ­āļ‡āđ€āļŠāļīāļ‡āļŠāļēāđ€āļŦāļ•āļļāļ„āļ§āļēāļĄāļŠāđāļēāđ€āļĢāđ‡āļˆāđƒāļ™āļ­āļēāļŠāļĩāļžāļ‚āļ­āļ‡āļšāļąāļ“āļ‘āļīāļ•āļŠāļŦāļāļīāļˆāļĻāļķāļāļĐāļē

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    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)’s 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

    āļāļēāļĢāļ„āļēāļ”āļāļēāļĢāļ“āđŒāļ­āļļāļ“āļŦāļ āļđāļĄāļīāļžāļ·āđ‰āļ™āļœāļīāļ§āļĢāļ°āļ”āļąāļšāļ—āđ‰āļ­āļ‡āļ–āļīāđˆāļ™āđƒāļ™āļˆāļąāļ‡āļŦāļ§āļąāļ”āđ€āļŠāļĩāļĒāļ‡āđƒāļŦāļĄāđˆāļ āļēāļĒāđƒāļ•āđ‰āļ‚āđ‰āļ­āļĄāļđāļĨāļ āļēāļžāļ‰āļēāļĒāļāļēāļĢāđ€āļ›āļĨāļĩāđˆāļĒāļ™āđāļ›āļĨāļ‡āļ āļđāļĄāļīāļ­āļēāļāļēāļĻPrediction of Future Surface Temperature at Local Scale in Chiang Mai Province Under Climate Change Scenarios

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    āļˆāļēāļāļ›āļąāļāļŦāļēāļāļēāļĢāđ€āļ›āļĨāļĩāđˆāļĒāļ™āđāļ›āļĨāļ‡āļ āļđāļĄāļīāļ­āļēāļāļēāļĻāļŠāđˆāļ‡āļœāļĨāļ—āļģāđƒāļŦāđ‰āđ€āļāļīāļ”āļāļēāļĢāđ€āļ›āļĨāļĩāđˆāļĒāļ™āđāļ›āļĨāļ‡āļŦāļĨāļēāļĒāļ›āļĢāļ°āļāļēāļĢāļĢāļ§āļĄāļ—āļąāđ‰āļ‡āļāļēāļĢāđ€āļ›āļĨāļĩāđˆāļĒāļ™āđāļ›āļĨāļ‡āļ­āļļāļ“āļŦāļ āļđāļĄāļīāļĄāļĩāļ‡āļēāļ™āļ§āļīāļˆāļąāļĒāļĄāļēāļāļĄāļēāļĒāļ—āļĩāđˆāļžāļĒāļēāļĒāļēāļĄāļ„āļēāļ”āļāļēāļĢāļ“āđŒāļ­āļļāļ“āļŦāļ āļđāļĄāļīāļ­āļēāļāļēāļĻāđƒāļ™āļ­āļ™āļēāļ„āļ• āđāļ•āđˆāļĒāļąāļ‡āļ„āļ‡āđ€āļ›āđ‡āļ™āļāļēāļĢāļ„āļēāļ”āļāļēāļĢāļ“āđŒāļ­āļļāļ“āļŦāļ āļđāļĄāļīāđ€āļ‰āļĨāļĩāđˆāļĒāļŠāļģāļŦāļĢāļąāļšāļžāļ·āđ‰āļ™āļ—āļĩāđˆāļ‚āļ™āļēāļ”āđƒāļŦāļāđˆāļĢāļ°āļ”āļąāļšāļ›āļĢāļ°āđ€āļ—āļĻāļŦāļĢāļ·āļ­āļĢāļ°āļ”āļąāļšāļ āļđāļĄāļīāļ āļēāļ„ āļ‹āļķāđˆāļ‡āđƒāļ™āļ„āļ§āļēāļĄāđ€āļ›āđ‡āļ™āļˆāļĢāļīāļ‡āđāļĨāđ‰āļ§āļ­āļļāļ“āļŦāļ āļđāļĄāļīāļĢāļ°āļ”āļąāļšāļ—āđ‰āļ­āļ‡āļ–āļīāđˆāļ™āļ­āļēāļˆāļŠāļđāļ‡āļāļ§āđˆāļēāļŦāļĢāļ·āļ­āļ•āđˆāļģāļāļ§āđˆāļēāļ„āđˆāļēāļ­āļļāļ“āļŦāļ āļđāļĄāļīāļ—āļĩāđˆāđ„āļ”āđ‰āļˆāļēāļāļāļēāļĢāļ„āļēāļ”āļāļēāļĢāļ“āđŒāļ­āļļāļ“āļŦāļ āļđāļĄāļīāđ€āļ‰āļĨāļĩāđˆāļĒāļĢāļ°āļ”āļąāļšāļ›āļĢāļ°āđ€āļ—āļĻāļŦāļĢāļ·āļ­āļĢāļ°āļ”āļąāļšāļ āļđāļĄāļīāļ āļēāļ„ āļˆāļķāļ‡āđ€āļāļīāļ”āļ„āļģāļ–āļēāļĄāļ§āđˆāļēāļŦāļēāļāļ•āđ‰āļ­āļ‡āļāļēāļĢāļ—āļĢāļēāļšāļ‚āđ‰āļ­āļĄāļđāļĨāļ­āļļāļ“āļŦāļ āļđāļĄāļīāđƒāļ™āļ­āļ™āļēāļ„āļ•āđƒāļ™āļžāļ·āđ‰āļ™āļ—āļĩāđˆāļ‚āļ™āļēāļ”āđ€āļĨāđ‡āļāļĢāļ°āļ”āļąāļšāļ—āđ‰āļ­āļ‡āļ–āļīāđˆāļ™āļˆāļ°āļŠāļēāļĄāļēāļĢāļ–āļ—āļģāđ„āļ”āđ‰āļ­āļĒāđˆāļēāļ‡āđ„āļĢ āļ‡āļēāļ™āļ§āļīāļˆāļąāļĒāļ™āļĩāđ‰āļˆāļķāļ‡āļ›āļĢāļ°āļĒāļļāļāļ•āđŒāđƒāļŠāđ‰āļ‚āđ‰āļ­āļĄāļđāļĨāļāļēāļĢāđ€āļ›āļĨāļĩāđˆāļĒāļ™āđāļ›āļĨāļ‡āļ­āļļāļ“āļŦāļ āļđāļĄāļīāļˆāļēāļāļāļēāļĢāļ„āļēāļ”āļāļēāļĢāļ“āđŒāļāļēāļĢāđ€āļ›āļĨāļĩāđˆāļĒāļ™āđāļ›āļĨāļ‡āļ āļđāļĄāļīāļ­āļēāļāļēāļĻāļ‚āļ­āļ‡āļ›āļĢāļ°āđ€āļ—āļĻāđ„āļ—āļĒāļ—āļĩāđˆāđƒāļŠāđ‰āđāļšāļšāļˆāļģāļĨāļ­āļ‡āļ āļđāļĄāļīāļ­āļēāļāļēāļĻāđ‚āļĨāļ ECHAM4 āļ—āļĩāđˆāļĄāļĩāļ­āļĒāļđāđˆāļĢāđˆāļ§āļĄāļāļąāļšāļ§āļīāļ˜āļĩāļāļēāļĢāļŠāļģāļĢāļ§āļˆāļĢāļ°āļĒāļ°āđ„āļāļĨ āđ€āļžāļ·āđˆāļ­āđƒāļŦāđ‰āđ„āļ”āđ‰āļ‚āđ‰āļ­āļĄāļđāļĨāļ­āļļāļ“āļŦāļ āļđāļĄāļīāđƒāļ™āļ­āļ™āļēāļ„āļ•āļĢāļ°āļ”āļąāļšāļ—āđ‰āļ­āļ‡āļ–āļīāđˆāļ™ āđ‚āļ”āļĒāđƒāļŠāđ‰āļžāļ·āđ‰āļ™āļ—āļĩāđˆāļĻāļķāļāļĐāļēāļˆāļģāļ™āļ§āļ™ 3 āļ­āļģāđ€āļ āļ­āđƒāļ™āļˆāļąāļ‡āļŦāļ§āļąāļ”āđ€āļŠāļĩāļĒāļ‡āđƒāļŦāļĄāđˆ āđ„āļ”āđ‰āđāļāđˆ āļ­āļģāđ€āļ āļ­āđ€āļĄāļ·āļ­āļ‡āđ€āļŠāļĩāļĒāļ‡āđƒāļŦāļĄāđˆ āļ­āļģāđ€āļ āļ­āļŠāļąāļ™āļ—āļĢāļēāļĒ āđāļĨāļ°āļ­āļģāđ€āļ āļ­āļ”āļ­āļĒāļŠāļ°āđ€āļāđ‡āļ” āđ€āļ›āđ‡āļ™āļžāļ·āđ‰āļ™āļ—āļĩāđˆāļĻāļķāļāļĐāļēāļ™āļģāļĢāđˆāļ­āļ‡āļŠāļģāļŦāļĢāļąāļšāļ‡āļēāļ™āļ§āļīāļˆāļąāļĒāļ™āļĩāđ‰āļāđˆāļ­āļ™ āļœāļĨāļˆāļēāļāļāļēāļĢāļĻāļķāļāļĐāļēāļ—āļģāđƒāļŦāđ‰āđ„āļ”āđ‰āļ‚āđ‰āļ­āļĄāļđāļĨāļ­āļļāļ“āļŦāļ āļđāļĄāļīāļĢāļ°āļ”āļąāļšāļ—āđ‰āļ­āļ‡āļ–āļīāđˆāļ™āļŠāļđāļ‡āļŠāļļāļ”āđƒāļ™āđāļ•āđˆāļĨāļ°āļ›āļĩāļĢāļ°āļŦāļ§āđˆāļēāļ‡āļ›āļĩ āļž.āļĻ. 2560–2592 āļ—āļĩāđˆāļĄāļĩāļ‚āļ™āļēāļ”āļ‚āļ™āļēāļ”āļˆāļļāļ”āļāļĢāļīāļ” 30×30 āđ€āļĄāļ•āļĢ āļˆāļģāļ™āļ§āļ™ 3 āļŠāļļāļ”āļ‚āđ‰āļ­āļĄāļđāļĨ āđ„āļ”āđ‰āđāļāđˆ āļŠāļļāļ”āļ‚āđ‰āļ­āļĄāļđāļĨāļ­āļļāļ“āļŦāļ āļđāļĄāļīāļĢāļ°āļ”āļąāļšāļ—āđ‰āļ­āļ‡āļ–āļīāđˆāļ™āļˆāļēāļāļ āļēāļžāļ‰āļēāļĒ ECHAM4 A2, ECHAM4 B2 āđāļĨāļ° ECHAM5 A1B āļ‹āļķāđˆāļ‡āļĄāļĩāđ€āļ‡āļ·āđˆāļ­āļ™āđ„āļ‚āđāļĨāļ°āļ—āļĩāđˆāļĄāļēāđƒāļ™āļāļēāļĢāļŠāļĢāđ‰āļēāļ‡āļ āļēāļžāļ‰āļēāļĒāļ—āļĩāđˆāđāļ•āļāļ•āđˆāļēāļ‡āļāļąāļ™ āđāļĨāļ°āļžāļšāļ§āđˆāļēāļ‚āđ‰āļ­āļĄāļđāļĨāđƒāļ™āļĢāļ°āļ”āļąāļšāļ—āđ‰āļ­āļ‡āļ–āļīāđˆāļ™āđƒāļ™āđāļ•āđˆāļĨāļ°āļ›āļĩāļĄāļĩāļ„āļ§āļēāļĄāđāļ•āļāļ•āđˆāļēāļ‡āļāļąāļ™āļ„āđˆāļ­āļ™āļ‚āđ‰āļēāļ‡āļĄāļēāļāđ€āļ‰āļĨāļĩāđˆāļĒāļ›āļĢāļ°āļĄāļēāļ“ 20°C āļ—āļģāđƒāļŦāđ‰āđ€āļŦāđ‡āļ™āđ„āļ”āđ‰āļ§āđˆāļēāļāļēāļĢāļžāļīāļˆāļēāļĢāļ“āļēāļ‚āđ‰āļ­āļĄāļđāļĨāļ­āļēāļāļēāļĻāļĢāļ°āļ”āļąāļšāļ—āđ‰āļ­āļ‡āļ–āļīāđˆāļ™āļĄāļĩāļ„āļ§āļēāļĄāļŠāļģāļ„āļąāļāļ•āđˆāļ­āļāļēāļĢāļ§āļēāļ‡āđāļœāļ™āđƒāļ™āļ”āđ‰āļēāļ™āļ•āđˆāļēāļ‡āđ† āļ—āļĩāđˆāđ€āļ‰āļžāļēāļ°āđ€āļˆāļēāļ°āļˆāļ‡āđ€āļŠāđˆāļ™ āļāļēāļĢāļ›āļĨāļđāļāļžāļ·āļŠ āļāļēāļĢāđ€āļĨāļĩāđ‰āļĒāļ‡āļŠāļąāļ•āļ§āđŒ āļ—āļĩāđˆāđ„āļĄāđˆāļ­āļēāļˆāđƒāļŠāđ‰āļ‚āđ‰āļ­āļĄāļđāļĨāļ­āļļāļ“āļŦāļ āļđāļĄāļīāđ€āļ‰āļĨāļĩāđˆāļĒāļ‚āļ­āļ‡āļžāļ·āđ‰āļ™āļ—āļĩāđˆāļ—āļąāđ‰āļ‡āļˆāļąāļ‡āļŦāļ§āļąāļ”āđ„āļ”āđ‰ āļ™āļ­āļāļˆāļēāļāļ™āļĩāđ‰āļĒāļąāļ‡āļžāļšāļ§āđˆāļēāđƒāļ™āļ­āļ™āļēāļ„āļ•āļ­āļļāļ“āļŦāļ āļđāļĄāļīāļĢāļ°āļ”āļąāļšāļ—āđ‰āļ­āļ‡āļ–āļīāđˆāļ™āļ‚āļ­āļ‡āļ—āļąāđ‰āļ‡ 3 āļ­āļģāđ€āļ āļ­ āđƒāļ™āļˆāļąāļ‡āļŦāļ§āļąāļ”āđ€āļŠāļĩāļĒāļ‡āđƒāļŦāļĄāđˆāļĄāļĩāđāļ™āļ§āđ‚āļ™āđ‰āļĄāļāļēāļĢāđ€āļ›āļĨāļĩāđˆāļĒāļ™āđāļ›āļĨāļ‡āļ­āļļāļ“āļŦāļ āļđāļĄāļīāļŠāļđāļ‡āļ‚āļķāđ‰āļ™ āđāļĨāļ°āļžāļšāļ§āđˆāļēāđāļ™āļ§āđ‚āļ™āđ‰āļĄāļ­āļļāļ“āļŦāļ āļđāļĄāļīāļ—āđ‰āļ­āļ‡āļ–āļīāđˆāļ™āđƒāļ™āļ­āļ™āļēāļ„āļ•āļ—āļĩāđˆāđ„āļ”āđ‰āļˆāļēāļāļ āļēāļžāļ‰āļēāļĒ ECHAM5 A1B āļĄāļĩāđāļ™āļ§āđ‚āļ™āđ‰āļĄāļˆāļ°āļĄāļĩāļ­āļļāļ“āļŦāļ āļđāļĄāļīāļŠāļđāļ‡āļŠāļļāļ”āļŠāļđāļ‡āļāļ§āđˆāļēāļŠāļļāļ”āļ‚āđ‰āļ­āļĄāļđāļĨāļ­āļļāļ“āļŦāļ āļđāļĄāļīāļ—āļĩāđˆāđ„āļ”āđ‰āļˆāļēāļāļˆāļēāļāļ āļēāļžāļ‰āļēāļĒ ECHAM4 A2 āđāļĨāļ° ECHAM4 B2 āļ”āļąāļ‡āļ™āļąāđ‰āļ™āļ•āļąāļ§āđāļ›āļĢāļ—āļĩāđˆāļŠāļģāļ„āļąāļāļ­āļĒāđˆāļēāļ‡āļŦāļ™āļķāđˆāļ‡āļ„āļ·āļ­āļāļēāļĢāđ€āļĨāļ·āļ­āļāđƒāļŠāđ‰āļ āļēāļžāļ‰āļēāļĒāļ­āļ™āļēāļ„āļ•āļ—āļĩāđˆāļĄāļĩāđ€āļ‡āļ·āđˆāļ­āļ™āđ„āļ‚āļ—āļĩāđˆāļŠāļ­āļ”āļ„āļĨāđ‰āļ­āļ‡āļāļąāļšāļžāļ·āđ‰āļ™āļ—āļĩāđˆāđ€āļžāļ·āđˆāļ­āđƒāļŦāđ‰āļŠāļēāļĄāļēāļĢāļ–āļŦāļēāđāļ™āļ§āļ—āļēāļ‡āļ›āđ‰āļ­āļ‡āļāļąāļ™āļ—āļĩāđˆāļ„āļĢāļ­āļšāļ„āļĨāļļāļĄāļāļēāļĢāđ€āļ›āļĨāļĩāđˆāļĒāļ™āđāļ›āļĨāļ‡āļ āļđāļĄāļīāļ­āļēāļāļēāļĻāļĄāļēāļāļ—āļĩāđˆāļŠāļļāļ”The 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 Thailand’s 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 30×30 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
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