With the increasing popularity of e-learning in higher education institutions, there is a need to develop data analytics tools to analyze e-learning data, student learning behavior and student performance. In recent years, there has been growing interest in educational data mining, which can provide useful insights into student learning behavior, providing holistic analysis. This paper presents an online data analytics tool called Studentlyzer, which applies data mining to analyze student data. It can cluster student datasets using K-means clustering, and visualize the graphical results through a web browser. Two real-world student e-learning datasets, the Open University Learning Analytics Dataset (OULAD) and Educational Processing Mining (EPM) dataset, were used to demonstrate Studentlyzer’s usefulness. The results provide valuable insights about students. In general, Studentlyzer can help identify students who are similar (e.g., with similar study behavior) and provide useful information about student performance and student behavior (e.g., their correlation).
With the advent of cloud computing, there is a potential need for the interconnection of clouds (i.e., Intercloud). Intercloud seeks to connect heterogeneous clouds together to form a network of clouds. As an extension to our previous work, this paper gives an overview of an Intercloud system with a focus on the Intercloud Gateways and Intercloud communications protocol. To analyze the performance of the Intercloud systems, we have conducted experiments under different settings and scenarios. Furthermore, we have evaluated the Intercloud system to support a mobile Intercloud application.
In this paper, we present a mobile intercloud system with blockchain by combining cloud computing, mobile computing and blockchain-based computing. Each mobile terminal is associated with a virtual terminal for performing mobile cloud computing and other functions. Virtual terminals, as well as data, programs and applications, can be moved across clouds. Furthermore, blockchains can be set up among virtual terminals for sharing data and files in a distributed manner (i.e., without using common servers). A prototype system focusing on the blockchain component has been developed. Performance analysis for the blockchain component is presented, which provides valuable insights into the development of the mobile intercloud system.
The article presents the necessity to perform reliability evaluation of railway traction system with respect to the current status and plans for further development of high-speed railways in Russia. Basing on the statistical data provided on failures for various units of electric trains, the traction system was selected. A complex analysis of the traction system was carried out, and a model for the interconnection of system components was compiled. The reliability chart of traction system of the high-speed train was constructed based on the FMEA modelling approach using TARAS software. The calculation has resulted in a total failure rate of the system equal to 4,46% or 580 hours of suspended operation of the train. Also, the most vulnerable element of the traction system was identified a traction system motor. Basing on these outcomes a set of recommendations has been made in order to increase the performance efficiency of the high-speed trains.
We introduce a stochastic programming model for scheduling a single operating room using the conditional value-at-risk (CVaR) as a criterion. This criterion expresses the risk-averse attitude of the scheduler to the risk event that the expected end time of a surgery presumed by a surgeon can be considerably delayed. One of the important advantages of the CVaR is that the stochastic programming problem can be treated as a linear programming problem. Owing to this characteristic, the CVaR is more practical than traditional expectation based approaches. In this paper, we evaluate the effectiveness of the proposed model using numerical experiments.
This project establishes a strategy of accurately modeling rotating annular flow of drilling fluid to improve the numerical predication of pressure loss in an annulus. Pressure loss is vital within several engineering applications from HVAC design to oil gas drilling. By being able to accurately predict this through numerical methods it creates the potential for innovation and efficiency. The project will build on previous recommendation of wall y+ by Salim et al. [1] that looked at high Reynolds number turbulent flow for the predication of wall bounded flow. A strategy was established with the aid of the wall y+ value to investigate the most suitable turbulence model in ANSYS FLUENT to create a method that will reduce time and costs in the development of drilling tools. Out of 5 turbulence approaches, the k – ω model was found to be the most accurate for a wall y+ of less than 5. The k – ε model performed least well and its was observed that there was a direct link between the turbulent intensity found in the annulus and the performance of the turbulence model. The k – ε was found to over predict the turbulent kinetic energy for the mesh set-up and thus contributed to inaccurate results regarding the pressure loss in the annulus. This project, therefore, suggests that a structured mesh with a y+ < 5 and the k – ω turbulence model will provide sufficiently accurate data in the investigation of pressure loss in an annulus. This will provide benefit to the industry and to researchers who wish to model this flow situation where experimental data is not available. The strategy can be used by design engineers to create drilling tools and allow them to try more experimental designs, without the need to build expensive and time-consuming prototypes. It may also be used as an investigation tool for researchers wishing to gain a greater understanding of the complex fluid flow that occurs during rotating annular flow.
In the literature, most mathematical models for supply chains assume that transportation links will not fail. However, in reality, transportation links are subject to various sorts of disruptions. Furthermore, in most supply chain models, there is little consideration given to the diminishing value of the product. In this paper, we have designed an integrated supply chain network for perishable products that takes into account random disruptions in transportation links. We also consider several capacitated manufacturing facilities and retail outlets and stochastic demands. This model considers both demand and process uncertainty (which is incorporated through random disruptions in the transportation link between the manufacturers and the retailers), simultaneously. The model also investigates the manufacturer’s facility locations and shipment decisions in the supply chain and minimizes the total cost of the entire supply chain. The paper discusses the model output through a numerical example and we observe that the resilient model (the model considering transportation link disruptions) and the disruption free model yields different designs. Finally, the paper provides an extensive statistical analysis of disruption uncertainties in the supply chain.
