S U M M A R YIn this paper, we start with the implementation of a data space conjugate gradient (DCG) method for 3-D magnetotelluric (MT) data. This code will be referred to as WSDCG3DMT. It is an extension of the 2-D method previously developed. Several experiments on both synthetic and real data sets show that WSDCG3DMT usually needs more computational time than the data space Occam's inversion (OCCAM) for which the corresponding code is referred to as WSINV3DMT. However, the memory requirement of WSDCG3DMT is only a fraction of that of WSINV3DMT. Based on the knowledge gained from several studies of both codes, we have created a new hybrid scheme called the DCG Occam's inversion (DCGOCC) and the corresponding code, WSDCGOCC3DMT, from combining aspects of the OCCAM and DCG methods. As with OCCAM, the DCGOCC method divides the inversion into two phases. In Phase I the misfit is brought down to a desired level. In Phase II unnecessary structures are smoothed out. Because its mathematical basis is of a similar form to that of DCG, its memory requirement is similarly low but more stable. However, DCGOCC is significantly faster than both methods. We demonstrate the computational performances with comparisons of all three methods with both synthetic and EXTECH field data sets.

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In this research, a new numerical method, called the hybrid finite difference-finite element (hybrid FD-FE) method, is developed to solve 2-D magnetotelluric modeling by taking advantage of both the finite difference (FD) and finite element (FE) methods. With the hybrid FD-FE method, the model is first discretized as rectangular blocks and separated into two zones: the FD and FE zones. The FD zone is set for the subregions where topography or bathymetry does not appear. The FD approximation, which is fast, accurate and requires less memory resources, is then applied. For the FE zones where topography or bathymetry exists, the rectangular blocks are transformed into quadrilateral elements to handle the topography or bathymetry appropriately. Then, the FE approximation with quadrilateral elements, which is more accurate for topography or bathymetry zones, is applied. The system of equations for the hybrid FD-FE method is then formed according to the FD and FE schemes. The obtained system is a combination of the FD and FE equations. Three numerical methods are applied to test models with and without topography and bathymetry. The accuracy and efficiency in terms of errors, computational time and memory storage are presented, compared and discussed. The numerical experiments indicate that the FD scheme has a shorter computational time than the other schemes when modeling without topography and retains accuracy equivalent to that of the FE method, whereas FE is more practical when modeling with topography and bathymetry. However, our proposed hybrid FD-FE method is efficient in both situations. Without topography or bathymetry, its efficiency and accuracy approach those of the FD scheme. With topography and bathymetry, the hybrid FD-FE method is as accurate as FE, but its speed is slightly slower than that of FD. In terms of memory storage, the hybrid FD-FE method consumes slightly more storage than the FD method. This hybrid FD-FE method can be further extended and implemented for 3-D magnetotelluric modeling for more efficient computation.

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Digital learning ecosystem plays important roles for transforming teaching classroom into learning community. This article used a mixed method to (1) explore the teaching and learning activities and components of a digital learning ecosystem through eighteen in-depth interviews and six observation classroom teachings of three subjects (English, Mathematics, and Science), and (2) measure the factors influencing the success of the ecosystem from a survey of 350 teachers at high schools in Thailand under the KKU Smart Learning Project. The quantitative data is analyzed by using descriptive statistics, exploratory factor analysis, and confirmatory factor analysis; meanwhile, researchers used manually coding method for qualitative data to categorize data into themes and sub-themes. The findings revealed an ecosystem includes four core components: learning community, learning atmosphere, active learning, and teaching and learning support. However, it is important for junior high schools to clarify and improve seven factors affecting the development of this ecosystem to boost classroom transformation into learning community. This study is useful for policy makers, educators, and teachers to issue educational policies and objectives, invest infrastructure, design curriculum, and organize teaching activities to enhance the effectiveness of learning outcomes.