A Reference Architecture based on Reflection for Self-adaptive Software

Self-adaptive Software (SaS) presents specific characteristics compared to traditional ones, as it makes possible adaptations to be incorporated at runtime. These adaptations, when manually performed, normally become an onerous, error-prone activity. In this scenario, automated approaches have been proposed to support such adaptations; however, the development of SaS is not a trivial task. In parallel, reference architectures are reusable artifacts that aggregate the knowledge of architectures of software systems in specific domains. They have facilitated the development, standardization, and evolution of systems of those domains. In spite of their relevance, in the SaS domain, reference architectures that could support a more systematic development of SaS are not found yet. Considering this context, the main contribution of this paper is to present a reference architecture based on reflection for SaS, named RA4SaS (Reference Architecture for SaS). Its main purpose is to support the development of SaS that presents adaptations at runtime. To show the viability of this reference architecture, a case study is presented. As result, it has been observed that RA4SaS has presented good perspective to efficiently contribute to the area of SaS.

Estudo do desempenho de barreiras de geotêxtil para contenção de sedimentos carreados pelo escoamento superficial

Pós-graduação em Engenharia Civil e Ambiental - FEB

Tool condition monitoring of aluminum oxide grinding wheel in dressing operation using acoustic emission and neural networks

The grinding operation gives workpieces their final finish, minimizing surface roughness through the interaction between the abrasive grains of a tool (grinding wheel) and the workpiece. However, excessive grinding wheel wear due to friction renders the tool unsuitable for further use, thus requiring the dressing operation to remove and/or sharpen the cutting edges of the worn grains to render them reusable. The purpose of this study was to monitor the dressing operation using the acoustic emission (AE) signal and statistics derived from this signal, classifying the grinding wheel as sharp or dull by means of artificial neural networks. An aluminum oxide wheel installed on a surface grinding machine, a signal acquisition system, and a single-point dresser were used in the experiments. Tests were performed varying overlap ratios and dressing depths. The root mean square values and two additional statistics were calculated based on the raw AE data. A multilayer perceptron neural network was used with the Levenberg-Marquardt learning algorithm, whose inputs were the aforementioned statistics. The results indicate that this method was successful in classifying the conditions of the grinding wheel in the dressing process, identifying the tool as "sharp''(with cutting capacity) or "dull''(with loss of cutting capacity), thus reducing the time and cost of the operation and minimizing excessive removal of abrasive material from the grinding wheel.

Greening of the essential activities of an Engineering School: a case study in the Area/Department of Production Engineering

This article analyzes the inclusion of the issue of “environmental management” in the department of Production Engineering of the “Alfa” School of Engineering. For this, a case study was conducted at the School of "Alpha" Engineering, with a focus on the area of Production Engineering. Professors were interviewed; documents were reviewed as well as information collected from direct observations by of one of the authors of this article. It was observed that the department of Production Engineering at the Alpha School of Engineering has been developing activities covering all those aspects proposed by Jabbour [8]. "Environmental management" has been included in the curriculums of: (a) Teaching: in the creation of undergraduate courses (obligatory) and graduate Master's degree (optional), (b) Research: formalization of research groups in environmental management for the creation of master’s post graduation research, formalization of environmental management as one of the subjects that should be chosen by candidates for a Professorship in the area of production engineering; (c) Extension: Course in Environmental Management, Symposium (which in recent years has been focusing on environmental issues), creation of sustainability indicators for universities, (d) University Management: initiatives to raise awareness, distribution of reusable mugs and installation of special bins for selective collection in the Campus.