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DevOps paradigm
(2021)
DevOps, the widely used term in software industry, integrates the Development and IT Operations activities to frequently deliver, deploy, and release quality software features. DevOps approach emphasizes collaboration among Developments and IT operations teams throughout System Development Life Cycle (SDLC). The DevOps process is supported by wide variety of tool chains for various phases of SDLC. There exist many DevOps models. However, in this paper authors use a simple four phase pedagogical models to demonstrate principles of DevOps. In this paper authors attempt to show how DevOps principles can effectively be used to manage and implement business problems in classroom setting. Specifically, DevOps methodology is applied to manage develop and implement a small web application.
This pedagogical approach is specially aimed at students who do not have prior experiences and skillsets in applying DevOps methodology and associated toolsets to every stages of SDLC. At the conclusion of the project, students gained valuable insights on how to apply DevOps principles to business problems and to select and use commonly used state of the arts tools to plan, manage, build, test, monitor, deploy tasks at every stages of DevOps. The authors also discuss the limitations and practical issues related to implementing DevOps within classroom settings.
Evelin – ein Forschungsprojekt zur systematischen Verbesserung des Lernens von Software Engineering
(2012)
Dieses Projekt beschäftigt sich mit der bereichsübergreifenden Personalsteuerung in Form von Leihungen und Verleihungen, einem Teilbereich der monatlichen Personalplanung. Durch diese Thematik entstandene Über-/Unterkapazitäten sollen durch die Einführung eines IT-Tools vermieden werden. Leihungen bzw. Verleihungen dienen dem kurzfristigen Ausgleich von Personalüberdeckungen bzw. Personalunterdeckungen. In der Montage des BMW Werks Regensburg wird üblicherweise auch aus anderen Gründen wie beispielsweise der Hilfe bei dem Anlauf eines neuen Modells geliehen.
A composite material with a porous inorganic-nonmetallic matrix and a second material, characterized in that said porous inorganic-nonmetallic matrix has a bending strength of >=40 MPa as measured according to ISO 6 872; said second material is an organic material which at least partly fills the pores of said porous matrix; and said composite material has a modulus of elasticity, E, of >=25 GPa as measured according to ISO 10 477.
Bag-of-Visual Words (BoVW) and deep learning techniques have been widely used in several domains, which include computer-assisted medical diagnoses. In this work, we are interested in developing tools for the automatic identification of Parkinson’s disease using machine learning and the concept of BoVW. The proposed approach concerns a hierarchical-based learning technique to design visual dictionaries through the Deep Optimum-Path Forest classifier. The proposed method was evaluated in six datasets derived from data collected from individuals when performing handwriting exams. Experimental results showed the potential of the technique, with robust achievements.
Parkinson's disease (PD) is a neurodegenerative disease that affects millions of people worldwide, causing mental and mainly motor dysfunctions. The negative impact on the patient's daily routine has moved the science in search of new techniques that can reduce its negative effects and also identify the disease in individuals. One of the main motor characteristics of PD is the hand tremor faced by patients, which turns out to be a crucial information to be used towards a computer-aided diagnosis. In this context, we make use of handwriting dynamics data acquired from individuals when submitted to some tasks that measure abilities related to writing skills. This work proposes the application of recurrence plots to map the signals onto the image domain, which are further used to feed a Convolutional Neural Network for learning proper information that can help the automatic identification of PD. The proposed approach was assessed in a public dataset under several scenarios that comprise different combinations of deep-based architectures, image resolutions, and training set sizes. Experimental results showed significant accuracy improvement compared to our previous work with an average accuracy of over 87%. Moreover, it was observed an improvement in accuracy concerning the classification of patients (i.e., mean recognition rates above to 90%). The promising results showed the potential of the proposed approach towards the automatic identification of Parkinson's disease.
We present a new method to analyze the strain state of epitaxial hetero structures by high-resolution spot profile analysis low-energy electron diffraction. From the variation of the spot profiles with the vertical scattering vector we determine the hetero film roughness, the change of interlayer spacing due to tetragonal distortion, and the variation of the interlayer distance due to partial relaxation of the strained rough film. The practical implementation of this method is simple and can be used to determine the onset of strain-relieving defects during the growth process.