The application of perturbation theory toward the determination of molecular energies and properties

Perturbation theory applied to calculating molecular energies and propertie

Discovering the Technology Adoption of Local OTOP Entrepreneurs in Pattani: Exploratory of the Network Structure

This paper explores the technology adoption of the local entrepreneurs and the intention to use the technology i.e., objectives and technology adoption behaviors. 150 local entrepreneurs located in Pattani province in Thailand voluntarily provided information. They were asked to fill in a set of questionnaires regarding the objectives, type, frequency, and time of adopted the technology. The data analytics techniques, including, hierarchical clustering, Epistemic Network Analysis (ENA) were used to explore the qualitative data. Clustering enabled to categorize the entrepreneurs into groups based on the similarity of technology adoption objectives. ENA illustrated the connections of the studied elements and formed a structure of a network. The results suggested that currently, local entrepreneurs adopt the commonly use social media technology in order to reach out to the targeted customers. The duration of technology adoption was different between the two groups. This exploration suggested that the intention of using the technology and the technology adoption behaviors were different among the local entrepreneurs who sell the different types of products. The main implication derived from this study are two twofold. That is, there are differences in terms of technology adoption between local entrepreneurs who sales necessary goods and unnecessary goods. Government and related sectors may use this insight in order to design the campaign or provide the necessary training on how to use technology effectively

Discovering the Technology Adoption of Local OTOP Entrepreneurs in Pattani: Exploratory of the Network Structure

This paper explores the technology adoption of the local entrepreneurs and the intention to use the technology i.e., objectives and technology adoption behaviors. 150 local entrepreneurs located in Pattani province in Thailand voluntarily provided information. They were asked to fill in a set of questionnaires regarding the objectives, type, frequency, and time of adopted the technology. The data analytics techniques, including, hierarchical clustering, Epistemic Network Analysis (ENA) were used to explore the qualitative data. Clustering enabled to categorize the entrepreneurs into groups based on the similarity of technology adoption objectives. ENA illustrated the connections of the studied elements and formed a structure of a network. The results suggested that currently, local entrepreneurs adopt the commonly use social media technology in order to reach out to the targeted customers. The duration of technology adoption was different between the two groups. This exploration suggested that the intention of using the technology and the technology adoption behaviors were different among the local entrepreneurs who sell the different types of products. The main implication derived from this study are two twofold. That is, there are differences in terms of technology adoption between local entrepreneurs who sales necessary goods and unnecessary goods. Government and related sectors may use this insight in order to design the campaign or provide the necessary training on how to use technology effectively

“Where are our dead?”:Changing views of death and the afterlife in Late Nineteenth and Early Twentieth-Century Scottish Presbyterianism

Various ideas have been borrowed from 1D inter symbol interference (ISI) detectors towards approximation of near maximum likelihood (ML) detection over 2D ISI channels. Generalized belief propagation (GBP) algorithm is a graph based algorithm different from these algorithms and is observed to give the best bit error rate (BER) performance by minimizing KL-distance metric. GBP algorithm passes messages between regions instead of messages between nodes in an iterative fashion. However, GBP algorithm has a very high computational complexity and is not suitable for practical deployment. In this paper, we propose a GBP based signal detection algorithm using a quadratic approximation of the KL-distance metric. This allows us to minimize the cost function by solving a set of linear equations i.e., obtain a one shot solution instead of the iterative message passing in the GBP algorithm. We also provide an intuition into the nature of the hard decisions given by the algorithm. The idea opens up various approximations of the GBP algorithm using different convex approximations of the cost function with the desired nature of obtaining the solution. We show the efficacy of the proposed algorithm by detecting 5�5 pages of binary data over a chosen channel with 3�3 ISI span. The quadratic approximation is observed to give 1.5 dB inferior performance in signal-to-noise ratio (SNR) as compared to the GBP algorithm. © 2018 IEEE

Tracing learning strategies in online learning environments: a learning analytics approach

Learning has expanded beyond formal education; yet, students continue to face the challenge of how to effectively direct their learning. Among the processes of learning, the selection and application of learning tactics and strategies are fundamental steps. Learning tactics and strategies have long been considered as key predictors of learning performance. Theoretical models of self-regulated learning (SRL) assert that the choice and use of learning tactics and strategies are influenced by the internal (cognitive) and external (task) conditions. These conditions are consistently updated when students receive internal/external feedback. However, internal feedback generated based on students’ evaluation of their own performance against the expectation and/or learning goal is not accurate. Guiding students to apply appropriate learning strategies i.e. providing external feedback, hence, could enhance the students’ learning. Recent research literature suggests that learning analytics can be leveraged to support students in the selection and use of effective learning tactics and strategies. However, there has been limited literature on the ways this can be achieved. This thesis aims to fill this gap in the literature. This thesis begins by exploring the state of the art regarding how students receive learning analytics-based support for the selection and application of learning tactics and strategies. The systematic literature review on this topic reveals that students rarely receive feedback on learning tactics and strategies with learning analytics dashboards. One of the barriers to providing feedback on learning tactics and strategies is the difficulty in detecting learning tactics and strategies that students used when interacting with learning activities. Hence, this thesis proposes a novel analyticsbased approach to detect learning tactics and strategies based on digital trace data recorded in learning environments. The proposed analytics-based approach is based on process, sequence mining and clustering techniques. To validate the results of the proposed approach and the credibility of the automatically detected learning tactics and strategies, associations with academic performance and different feedback conditions are explored. To further validate the approach, the efficacy of each proposed approach in the detection of learning tactics and strategies is investigated. In addition, the thesis explores the alignment of the automatically detected learning tactics and strategies with relevant models of SRL. This is done by examining the association between the internal conditions and external conditions. Specifically, internal conditions are represented by the disposition of students based on self-reports of personality traits, whereas external conditions are represented by course instructional designs and delivery modalities. The thesis is concluded with a discussion of the implications of the proposed analytics methodology on research and practice of learning and teaching

Adaptive Relaying of Radial Distribution System with Distributed Generation

In this paper, the effect of DG penetration on the short circuit level has been studied in a distribution system.  The number of DG sources is increased to study the effect that these changes may have on the coordination of protective directional over-current relays (DOCR). The relays in the distribution system have to be coordinated so as to avoid mal-operation and unnecessary outage of healthy part of the system.  Results are compared to that of the normal case to investigate the impact of the DG on the short circuit currents of the network to deduce the effect on protective devices and some conclusions are documented. This paper presents the short circuit analysis of single phase to ground fault applied to simple radial distribution system and the corresponding Overcurrent relay coordination is presented using NEPLAN Software. Results obtained are verified by manual calculation.DOI:http://dx.doi.org/10.11591/ijece.v3i3.257

Real Time Domestic Power Consumption Monitoring using Wireless Sensor Networks

This paper subsumes the implementation of automation in tracking the electrical consumption data of household systems over the network (WEB). This could sub-sequentially cut down the manual work involved in the process of collecting no: of units consumed from each house, thereby avoiding the manual costs and errors by building an automatic network access. The installation of this system is quite an easy task, which do not need much hardware work. The key elements that make this system are Current sensor and Voltage sensor interfaced to an Arduino board (A General Purpose Micro Controller board) with an Ethernet shield and a WIFI Router for transmission of data wirelessly to the server for storing consumption values into the database. Hosting web pages with the database connectivity will make the administrator generate electricity bill automatically that facilitates user’s to view and pay his electricity bill online

AN EXPLORATORY STUDY TO INVESTIGATE THE USE OF AUGMENTED REALITY IN SUPPORTING COLLABORATIVE LEARNING PROCESSES

Collaborative learning mediated by technology was proven as one ofthe efficient learning approaches which benefit in both academic and soft skills. Despite advantages offered by technologies, some obstacles were also created e.g. problem in supporting interaction and communication, disregard of physical and sharing objects' roles. Augmented Reality (AR) offers a unique learning experience by combining the physical and virtual object. The benefits of rich media were enduring and the role of physical material is also considered. However, the literature reveals that there is no design guideline specifically intended for AR based collaborative learning. Hence, this research aims to study and proposed a conceptual framework to guide the development of collaborative AR in learning