Search

Writing is one of the essential skills that EFL students, specifically in Thailand, need to achieve while their learning English during tertiary education. However, Thai EFL students have few chances to practice writing skills while learning. This study was conducted to develop an instructional design model for assisting students in learning collaboratively using Facebook groups to enhance their English writing skills at the beginning stage of their university education. In this study, collaborative learning and writing, the theory of instructional design, and five previous instructional design models were analyzed, and synthesized. In addition, the seven steps model for designing an instructional model by Brahmawong and Vate-U-Lan (2009) was adapted to develop the instructional design model. Experts in the fields of technology and English Language Teaching then evaluated the model. The results of the study showed that the elements of the FBCL Model was satisfactory and appropriate for giving EFL writing instruction in Facebook groups. The FBCL Model may also be beneficial in providing an instructional framework to EFL writing instructors and instructional designers.

We examine the roles of land and labor diversification in mitigating the effects of covariate and idiosyncratic shocks in the two middle-income countries Thailand and Vietnam. We use an unbalanced panel dataset of rural households obtained from five survey waves during 2007–2016 (9291 households for Thailand and 9255 households for Vietnam). We employ the System-Generalized Method of Moments estimators to control for endogeneity. Our study finds that (i) rural households in both countries are able to maintain per capita consumption in the face of idiosyncratic shocks but not covariate shocks; (ii) labor diversification in Thailand and land diversification in Vietnam are used as ex-post coping strategies against covariate shocks but their shock-mitigating roles are insignificant; and (iii) land diversification in Thailand and labor diversification in Vietnam are helpful in improving per capita consumption when households face covariate shocks. Our findings suggest that facilitating access to credit, enhancing farm mechanization, and improving road quality in Thailand as well as promoting the development of local rural nonfarm sectors in Vietnam would benefit rural households in dealing with covariate shocks. [ABSTRACT FROM AUTHOR]

In this paper, we deal with the backward problem for nonlinear parabolic equations involving a pseudo-differential operator in the n-dimensional space. We prove that the problem is ill-posed in the sense of Hadamard, i.e., the solution, if it exists, does not depend continuously on the data. To regularize the problem, we propose two modified versions of the so-called optimal filtering method of Seidman [T.I. Seidman, Optimal filtering for the backward heat equation, SIAM J. Numer. Anal., 33 (1996), 162–170]. According to different a priori assumptions on the regularity of the exact solution, we obtain some sharp optimal estimates of the Hölder-Logarithmic type in the Sobolev space Hq(Rn). [ABSTRACT FROM AUTHOR]

The production of banana—one of the highly consumed fruits—is highly affected due to loss of certain number of banana plants in an early phase of vegetation. This affects the ability of farmers to forecast and estimate the production of banana. In this paper, we propose a deep learning (DL) based method to precisely detect and count banana plants on a farm exclusive of other plants, using high resolution RGB aerial images collected from Unmanned Aerial Vehicle (UAV). An attempt to detect the plants on the normal RGB images resulted less than 78.8% recall for our sample images of a commercial banana farm in Thailand. To improve this result, we use three image processing methods—Linear Contrast Stretch, Synthetic Color Transform and Triangular Greenness Index—to enhance the vegetative properties of orthomosaic, generating multiple variants of orthomosaic. Then we separately train a parameter-optimized Convolutional Neural Network (CNN) on manually interpreted banana plant samples seen on each image variants, to produce multiple results of detection on our region of interest. 96.4%, 85.1% and 75.8% of plants were correctly detected on three of our dataset collected from multiple altitude of 40, 50 and 60 meters, of same farm. Further discussion on results obtained from combination of multiple altitude variants are also discussed later in the research, in an attempt to find better altitude combination for data collection from UAV for the detection of banana plants. The results showed that merging the detection results of 40 and 50 meter dataset could detect the plants missed by each other, increasing recall upto 99%. [ABSTRACT FROM AUTHOR]