John Brown University disaster shelter competition

John Brown University hosted the 6th annual Disaster Shelter Relief Competition in April 2017 for which the team built a prototype shelter and proposed a camp plan. Both the shelter and the camp plan were designed to house refugees coming into Greece from the Middle East. The shelter would accommodate a family of four and the camp plan was designed to hold 1250 shelters, or 5000 people. The shelter was built on site at John Brown University and was required to take less than two hours to fully construct. This report summarizes the work the team did for the competition, including a review of existing shelter designs currently in use, a description of the method of design of the prototype, validation that the prototype meets the criteria, a discussion of the cultural appropriateness of the shelter to the scenario, suggested modifications and improvements that can be made, photos and drawings of the prototype, and the camp plan

A DevOps approach to integration of software components in an EU research project

We present a description of the development and deployment infrastructure being created to support the integration effort of HARNESS, an EU FP7 project. HARNESS is a multi-partner research project intended to bring the power of heterogeneous resources to the cloud. It consists of a number of different services and technologies that interact with the OpenStack cloud computing platform at various levels. Many of these components are being developed independently by different teams at different locations across Europe, and keeping the work fully integrated is a challenge. We use a combination of Vagrant based virtual machines, Docker containers, and Ansible playbooks to provide a consistent and up-to-date environment to each developer. The same playbooks used to configure local virtual machines are also used to manage a static testbed with heterogeneous compute and storage devices, and to automate ephemeral larger-scale deployments to Grid5000. Access to internal projects is managed by GitLab, and automated testing of services within Docker-based environments and integrated deployments within virtual-machines is provided by Buildbot

MULTIVARIATE AUTOREGRESSIVE MODEL FOR FORECASTING THE DEMAND OF CONTAINER THROUGHPUT IN INDONESIA

Abstract This research was proposed to forecast the demand of container throughput in Indonesia. The analysis was carried out in multivariate autoregressive model. Augmented Dickey-Fuller (ADF) test was used to check the stationarity of data and order of integration. Johansen approach was used to find the existence and the number of cointegration relationship. The number of cointegration relations was established by a sequential likelihood ratio test on the rank of an estimated parameter matrix from vector error correction model (VECM), impulse response function (IRF) was performed to know response to a shock of a variable of other variables. The empirical analysis demonstrated that the estimation model provides indication of goodness-of-fit and the forecasting potential of the model. Most of the model estimation results follow the long-term development of the actual data series closely. The impulse response of a shock of a variable to itself and other variables die out after certain period. These results verified the stability of all the estimated models. The forecast of container throughput in Indonesia generated by VECM indicated the reasonable result. Keywords: container throughput, forecasting, multivariate autoregressive