Public private partnership in sustainable tourism development in Trang An landscape complex : Ninh Binh in the context of climate change

The aim was to understand how rapid changes to Trang An through urbanization, tourism, and climate change, are affecting people’s well-being in Truong Yen Commune (Viet Nam). The paper examines the drivers of these changes, and assesses the adaptive capacity of residents, local governments, and the private sector. In an innovative arrangement, the Trang An tourism complex operates through a public-private partnership (PPP) between the Ninh Binh Provincial People’s Committee and the Xuan Truong Corporation, a private company. Local government plays a key role. Proactive comprehensive planning involving government, the private sector, and civil society can reduce environmental and social risks of tourism.The Social Sciences and Humanities Research Council (SSHRC)Thailand Environment Institute (TEI

BRCA1とCtIPは、相同組換えにおいてDNA2重鎖末端にDNA2を呼び込むのに必要である

京都大学0048新制・課程博士博士(医学)甲第19555号医博第4062号新制||医||1012(附属図書館)32591京都大学大学院医学研究科医学専攻(主査)教授 高田 穣, 教授 戸井 雅和, 教授 鈴木 実学位規則第4条第1項該当Doctor of Medical ScienceKyoto UniversityDFA

Using Pictures As Non-Verbal Language Motivating Students With English Speaking Lessons At Vietnam Primary Schools

The study investigates the reality of students' low participation in speaking activities and the possibility of using pictures to increase their participation in classroom speaking activities if necessary. The reason for this research is the lack of using pictures as the visual aids to teach speaking skill for the students in Vietnam’s context. Although several studies have been conducted on these subjects with different approaches, Vietnamese students, especially the primary ones, are not paid much attention as an investigated object. By using methods of questionnaires and tests (pre-tests and post-tests) combined with qualitative analytical analysis, as well as taking 50 students in grade 4 of a primary school in Hanoi as the objects to assess their current situation of learning English with pictures. The research findings indicate that students are more interested in learning English speaking when using pictures. Moreover, the students’ learning strategies and students’ attitude towards learning English are the two main causes that make them have low motivation in learning. In all, motivation and performance of students after the process of applying pictures in learning English presented an improvement. The results of this study are expected to be beneficial for the teachers in conducting the teaching curriculum as they can apply pictures as effective supporters

AN EFFECTIVE REVERSIBLE DATA HIDING METHOD BASED ON PIXEL-VALUE-ORDERING

This paper presents a new effective reversible data hiding method based on pixel-value-ordering (iGePVO-K) which is improvement of a recent GePVO-K method that recently is considered as a PVO-used method having highest embedding capacity. In comparison with GePVO-K method, iGePVO-K has the following advantages. First, the embedding capacity of the new method is higher than that of GePVO-K method by using data embedding formulas reasonably and reducing the location map size. Second, for embedding data, in the new method, each pixel value is modified at most by one, while in GePVO-K method, each pixel value may be modified by two. In fact, in the GePVO-K method, the largest pixels are modified by two for embedding bits 1 and by one for bits 0. This is also true for the smallest pixels. Meanwhile, in the proposed method, the largest pixels are modified by one for embedding bits 1 and are unchanged if embedding bits 0. Therefore, the stego-image quality in proposed method is better than that in GePVO-K method. Theoretical analysis and experiment results show that the proposed method has higher embedding capacity and better stego image quality than GePVO-K method

Model Updating for Large-Scale Railway Bridge Using Grey Wolf Algorithm and Genetic Alghorithms

This paper proposes a novel hybrid algorithm to deal with an inverse problem of a large-scale truss bridge. Grey Wolf Optimization (GWO) Algorithm is a well-known and widely applied metaheuristic algorithm. Nevertheless, GWO has two major drawbacks. First, this algorithm depends crucially on the positions of the leading Wolf. If the position of the leaderis far from the best solution, the obtained results are poor. On the other hand, GWO does not own capacities to improve the quality of new generations if elements are trapped into local minima. To remedy the shortcomings of GWO, we propose a hybrid algorithm combining GWO with Genetic Algorithm (GA), termed HGWO-GA. This proposed method contains two key features (1) based on crossover and mutation capacities, GA is first utilized to generate the high-quality elements (2) after that, the optimization capacity of GWO is employed to seek the optimal solutions. To assess the effectiveness of the proposed approach, a large-scale truss bridge is employed for model updating. The obtained results show that HGWO-GA not only provides a good agreement between numerical and experimental results but also outperforms traditional GWO in terms of accuracy

Model Updating for Large-Scale Railway Bridge Using Grey Wolf Algorithm and Genetic Alghorithms

This paper proposes a novel hybrid algorithm to deal with an inverse problem of a large-scale truss bridge. Grey Wolf Optimization (GWO) Algorithm is a well-known and widely applied metaheuristic algorithm. Nevertheless, GWO has two major drawbacks. First, this algorithm depends crucially on the positions of the leading Wolf. If the position of the leaderis far from the best solution, the obtained results are poor. On the other hand, GWO does not own capacities to improve the quality of new generations if elements are trapped into local minima. To remedy the shortcomings of GWO, we propose a hybrid algorithm combining GWO with Genetic Algorithm (GA), termed HGWO-GA. This proposed method contains two key features (1) based on crossover and mutation capacities, GA is first utilized to generate the high-quality elements (2) after that, the optimization capacity of GWO is employed to seek the optimal solutions. To assess the effectiveness of the proposed approach, a large-scale truss bridge is employed for model updating. The obtained results show that HGWO-GA not only provides a good agreement between numerical and experimental results but also outperforms traditional GWO in terms of accuracy

Dynamic response analysis of truss bridges under the effect of moving vehicles

With the characteristics of heavy and concentrated loads, the influence of moving loads on the dynamic response of the bridges is significant. Therefore, in this paper, the dynamic response of a large-scale truss bridge is studied to consider the effect of the various parameters of moving loads. The considered main parameters consist of moving mass, moving velocity, and type of moving loads. The nonlinear dynamics of the bridge based on time history analysis are obtained using the Wilson-  method. four time history – based dynamic analysis method including modal superposition in frequency domain, modal superposition in time domain; direct time integration, and direct solution in the frequency domain are employed to analysis the obtained results. To compare the effectiveness of the aforementioned method. A large-scale railway truss bridge is employed for dynamic response analysis. The obtained results give more insight into the nature of the problem and help to determine the significant parameters of moving load affecting the bridge response

Damage Detection in Structural Health Monitoring using Hybrid Convolution Neural Network and Recurrent Neural Network

The process of damage identification in Structural Health Monitoring (SHM) gives us a lot of practical information about the current status of the inspected structure. The target of the process is to detect damage status by processing data collected from sensors, followed by identifying the difference between the damaged and the undamaged states. Different machine learning techniques have been applied to attempt to extract features or knowledge from vibration data, however, they need to learn prior knowledge about the factors affecting the structure. In this paper, a novel method of structural damage detection is proposed using convolution neural network and recurrent neural network. A convolution neural network is used to extract deep features while recurrent neural network is trained to learn the long-term historical dependency in time series data. This method with combining two types of features increases discrimination ability when compares with it to deep features only. Finally, the neural network is applied to categorize the time series into two states - undamaged and damaged. The accuracy of the proposed method was tested on a benchmark dataset of Z24-bridge (Switzerland). The result shows that the hybrid method provides a high level of accuracy in damage identification of the tested structure

The influences of the number of concrete dowels to shear resistance based on push out tests

To reduce the depth of floor-beam structures and to save the cost of headed-shear studs, many types of shallow composite beam have been developed during the last few years. Among them, the shallow-hollow steel beam consists of web openings, infilled with in-situ concrete (named concrete dowel) has been increasingly focused recently. In this new kind of structure, this concrete dowel plays an important role as the principal shear connector. This article presents an investigation on the shear transferring mechanism and failure behavior of the trapezoid shape concrete dowel. An experimental campaign of static push-out tests has been conducted with variability in the number of web openings (WOs). The results indicate that the mechanical behavior of concrete dowel could be divided into crushing, compression, and tension zones and exhibits brittle behavior. The longitudinal shear resistance and specimen's stiffness are strongly affected by the number of considered WO