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Shale rock formed from small clay particles, and shale compaction is an essential factor to estimate shale reserves. The classical Athy’s model has been used to obtain the shale compaction curve to describe the relationship between porosity and depth, an essential input data for basin modelling. But recent studies revealed that burial time, among other factors, should be considered and that geological age is another important factor in some regions. This is because geological and lithological histories are crucially different among geological ages. This study employed the newest data of Thailand shales and confirmed that different geological ages (Cenozoic, Mesozoic, and Paleozoic ages) require different shale compaction curves by estimating numerical geological time with the relationship of velocity and depth in each geological age. We obtained empirical models of the shale compaction curve of each geological age by multi-linear regression. The standard curve of shale compaction with the relationship among porosity, depth, and time, proposed in a previous study, was also re-affirmed with the newly obtained models.

Phuket is well-known around the world as a popular tourist destination. Tourism-related population growth depends more on groundwater as the only available source of potable water in Phuket. The proper precautions must be taken to reduce the risk of spending large sums of money in sinking abortive boreholes, and a groundwater potential map would enhance the success rate of future groundwater exploration and exploitation in the study area. Geoelectrical surveys were carried out in this study to collect electrical properties of the subsurface, and the Dar-Zarrouk parameters (DZP) were calculated using the geoelectrical data. The first thematic groundwater potential map was constructed using the interpretation of DZP, while the second thematic groundwater potential map was created using the basis maps of the depth-of-basement and aquifer thickness parameters. Finally, two thematic maps based on geographic information system (GIS) environments were overlaid on a groundwater potential map of Phuket. However, in order to provide a reliable assessment of groundwater potential, time-lapse electrical resistivity imaging was used to confirm the area of the high-potential zone indicated on the map. The map created by this study is aimed to act as a reference for future groundwater exploration and exploitation, preventing water supplies from becoming unsustainable through botched borehole drilling for groundwater production.

When traveling in a foreign country, road transport is most frequently and conveniently used particularly, but road conditions and manners are stunningly different among countries, which increases potential risks to meet accidents. If tourists feel such risks, it may be that they will not be repeaters and their impressions will also be shared with others, leading to a potential decrease of tourists. Phuket, Thailand, a worldly wellknown beach resort, faces problems in road conditions and manners, with a high rate of traffic accidents and casualties, and governmental actions have to be taken. This paper reports a questionnaire survey result about how international tourists feel and understand road conditions in Phuket during their stay, which is expected to offer good evidence on which governmental actions should be based. The survey results revealed (mostly through the behaviors and recommendations) that the majority of international tourists to Phuket felt risks in terms of road conditions, regardless of whether they drove by themselves or not, and most of them wanted the basic information on road conditions as well as regulations, so that they could prepare themselves. Relations between demographic characteristics and questionnaire replies were also investigated, suggesting that there be a necessity of reducing risks particularly for elderly tourists, and that the driving manner of local residents including professional drivers should be improved.

Heavy metals have been detected in coastal regions, in the vicinity of former mining sites. The level of these heavy metals frequently exceed the safe limits in what represents an important aquatic source of food for human consumption. A thorough and conventional study into the levels of contamination in the suspected area is a costly but necessary exercise in order to inform any appropriate mitigating actions. This study demonstrates the benefits of integrating scientific investigating methods comprising chemical analysis using aqua regia and geo-electrical surveys, i.e., electrical resistivity imaging and induced polarization, to attain the approximate levels of contamination for both lateral and vertical landscapes in an efficient way. The demonstration to present the conformity and reliability of this joint methods is at Saphan Hin, situated on the southeastern coast of Phuket, Thailand. The site is located downstream of Bang-Yai canal; a potential intermediary carrying heavy metals especially Lead and Zinc from a number of mines where the nearest is approximately 4 km away. Saphan Hin is an important place marine fishery to supply to an increasing number of tourists to Phuket. The combined results from using all methods in this study are consistent in showing that the extent of the over-the-limit contamination can be found up to at least 200 m from the estuary, and with the decreasing depths from around 20 m to 50 cm at further distances from mainland, demanding urgent measures for the health of local people as well as tourists. A model to adapt these monitoring methods for implementation in operating mines is proposed as an early warning system to prevent any leakage probably occurring under normal conditions.

Semantic image segmentation is an interesting problem in Computer Vision with many potential applications. The DeepLab model is combined with two other networks: Resnet and Conditional Random Field networks, making the DeepLab model a fairly deep network structure to increase semantic segmentation performance. Many previous studies argued that there are some limits on the deep learning model's depth as the deep structure may lead to vanishing/exploding gradient, which the model's performance. This paper presents an experimental study to compare the effect of several ImageNet pre-trained Resnet variant models with different network layers used as feature extractor in DeepLab model to solve semantic image segmentation task. In this study, three Resnet34, Resnet50, and Resnet101 models as network extractor of DeepLabV3were explored. The experiment found that semantic image segmentation model performance measured by the best accuracy and average accuracies of DeepLabV3- Resnet34, DeepLabV3-Resnet50, and DeepLabV3-Resnet101 are (0.87, 0.86) (0.86, 0.84), and (0.92, 0.88) respectively. Based on the experiment, DeepLabV3-Resnet101 achieved the best semantic segmentation performance than the other models

Currently, the growth of information and technology is rapid. It makes a lot of things in various fields including education becoming more effective and efficient. In education, one of its implementation which is a game is proven to be a useful tool to support conventional teaching methods and bring a more natural understanding of learning materials. Moreover, as a product of the popular culture in modern society, video game mirrors the general culture practice in real life and reflects it via its own culture inside the game. This makes a video game can give a contribution to the social construction of reality as it affects the player’s view towards learning in real life and vice versa. From there, we see that there is an opportunity for learning about religion to be supported by its utilization to provide an interactive and fun learning experience. In this paper, we discuss how a video game is implemented to support religious learning. The game was developed with the scrum method where we surveyed to gather the user requirements before the design step. The game design was made by using the use case diagram and storyboard, and it was built using Unity version 2017.3.0f3.

Many researchers pay attention to training a nonlinear function according to training data because machine learning can represent a more flexible function. One of the approaches is extending a linear function to a nonlinear function with a kernel method. A typical method is Support Vector Machine (SVM), which is a discriminative model. In SVM, a kernel function is defined previously and you need much knowledge on tasks to choose a good kernel function. Hence, the kernel function selection affects the final performance of SVM are many kernel functions are developed according to each task. Another approach is Gaussian process, which is a generative model. Gaussian process estimate describes a distribution over functions and directly search the optimal function in function space. In SVM, a cost function is defined previously and the optimal parameters are searched with respect to training data. Gaussian process and SVM can construct a nonlinear function but their approaches are different. How are Gaussian process and SVM different in practice? In this paper, we evaluate Gaussian process and SVM with respect to review rating prediction, which is one of natural language tasks. Sentiment analysis research denotes some words contribute to sentiment polarity of a sentence strongly. However, deep learning improves the sentiment analysis adding nonlinear feature construction and it is clear that we have to deal with nonlinearity in sentiment analysis. Moreover, review rating prediction is one of multi-class classifications and the prediction needs more flexibility. Finally, when a review is transformed into a numerical vector with a Bag-of-Words model, the review vector has a very high dimension. Hence, the task has to avoid the curse of dimensionality. Kernel trick is the main solution in SVM and a flexible regression function is an additional solution in Gaussian process regression.

This paper proposes new visualization based on distance between persons, which are defined with Normalized Restricted Dynamic Time Warping. In the visualization persons are located based on their distances that are defined by their movement histories with t-SNE. In experiments we visualize academic conference participants with our proposed method and confirm that the proposed method helps us to find participants moving together.

Deep learning is applied to many research topics; Natural Language Processing, Image Processing, and Acoustic Recognition. In deep learning, neural networks have a very complex and deep structure and it is difficult to discuss why they work well or not. So you have to take a trial-and-error to improve their performances. We develop a mechanism to show how neural networks predict final results and help you to design a new neural network architecture based on its prediction criteria. Speaking concrete, we visualize important features to predict the final results with an attentional mechanism. In this paper, we take up sentient analysis, which is one of natural language processing tasks. In image processing visualizing weights of a neural network is a major approach and you can obtain intuitive results; object outlines and object components. However, in natural language processing, the approach is not interpretable because a discriminate function constructed by a neural network is a complex and nonlinear one and it is very difficult to correlate weights and words in a text. We employ Gated Convolutional Neural Network (GCNN) and introduce a self-attention mechanism to understand how GCNN determines sentiment polarities from raw reviews. GCNN can simulate an n-gram model and the self-attention mechanism can make correspondence between weights of a neural network and words clear. In experiments, we used Amazon reviews and evaluated the performance of the proposed method. Especially, the proposed method was able to emphasize some words in the review to determine sentiment polarity. Moreover, when the prediction was wrong, we were able to understand why the proposed method made mistakes because we found what words the proposed method emphasized.

Many locations in the world have been changing their main economic activity from agriculture to tourism pursuing more favorable opportunities for their development. Such a change in economic structure also leads to a significant alteration in the provincial greenhouse gas emissions. In order to investigate these differences, this study compares the greenhouse gases emitted from the major human activities in agriculture and tourism-based provinces. For this purpose, Phuket and Phayao provinces in Thailand were chosen as representatives for tourism and agriculture, respectively. Environmental and economic performances of other tourist and agricultural areas in different countries were also presented to examine commonalities and differences. It was found that in the agricultural provinces, the major part of greenhouse gas emissions came from rice cultivation, while in the tourist provinces, from the energy sector. The comparative results among several locations show that the tourist areas (especially urban-tourism-based areas) mostly had a higher eco-efficiency due to the large income from tourism and other services. They also had higher greenhouse gas indices than agricultural areas, due to energy consumption. The performance of agriculture-and-tourism-based provinces was outstanding with the lowest average carbon footprint per capita while not performing badly on the other indices, partly due to a decrease in biomass burning. At a time when global warming is the major world problem, agri-tourism can be considered to be an optimal choice and direction for the sustainable development of both agricultural and tourist provinces.

User's review on products and services is valuable information for both users and providers. The present paper conducted a polarity estimation of 73,589 reviews on hotels in Europe. Users rated one to five points for seven aspects (Value, Rooms, Location, Cleanliness, Check-in, Service, Business, Overall). In this paper, we predicted the polarity (positive/negative) of each aspect by using a machine learning method, SVM (Support Vector Machine), and feature selection, with more than 4 points being positive and less than 3 being negative. As a result, positive reviews with respect to six aspects, other than Business, were able to achieve 74% prediction performance (F-measure) with only 20 feature words. On the other hand, for negative reviews, optimal prediction performance could not be obtained unless almost all words were used, and on average F-measure was only 27%. The results indicate that positive reviews are simple, meanwhile negative reviews are diverse and hard to predict mechanically