20,180 research outputs found

    Towards a homogeneous characterization of the model-driven web development methodologies

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    In recent years a large number of Model-Driven Web development approaches have been designed and are being applied with success in real environments. However, as new ones are frequently emerging in this changing time, authors have to change and update them constantly and, consequently; development teams do not know which is the most suitable for them because in many cases it depends on their project scope. Furthermore, approaches are usually appearing with different concepts and terminologies in many cases, although all lack the use of standards and practical experience. Thus, the need of managing quality in this type of approach arises every day. This paper suggests a characterization of these methodologies in order to use this information for the quality management of Model-Driven Web development methodologies for authors and development teams alike. In addition, an experimental study in order to analyse and evaluate a Model-Driven Web development methodology (the NDT methodology) has been carried out within a specific work context.Junta de Andalucía TIC-5789Ministerio de Educación y Ciencia TIN2010-20057-C03-0

    A Framework to Evaluate Software Developer’s Productivity The VALORTIA Project

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    Currently, there is a lack in companies developing software in relation to assessing their staff’s productivity before executing software projects, with the aim of improving effectiveness and efficiency. QuEF (Quality Evaluation Framework) is a framework that allows defining quality management tasks based on a model. The main purpose of this framework is twofold: improve an entity’s continuous quality, and given a context, decide between a set of entity’s instances on the most appropriate one. Thus, the aim of this paper is to make this framework available to evaluate productivity of professionals along software development and select the most appropriate experts to implement the suggested project. For this goal, Valortia platform, capable of carrying out this task by following the QuEF framework guidelines, is designed. Valortia is a platform to certify users' knowledge on a specific area and centralize all certification management in its model by means of providing protocols and methods for a suitable management, improving efficiency and effectiveness, reducing cost and ensuring continuous quality.Ministerio de Ciencia e Innovación TIN2013-46928-C3-3-

    Review of the Synergies Between Computational Modeling and Experimental Characterization of Materials Across Length Scales

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    With the increasing interplay between experimental and computational approaches at multiple length scales, new research directions are emerging in materials science and computational mechanics. Such cooperative interactions find many applications in the development, characterization and design of complex material systems. This manuscript provides a broad and comprehensive overview of recent trends where predictive modeling capabilities are developed in conjunction with experiments and advanced characterization to gain a greater insight into structure-properties relationships and study various physical phenomena and mechanisms. The focus of this review is on the intersections of multiscale materials experiments and modeling relevant to the materials mechanics community. After a general discussion on the perspective from various communities, the article focuses on the latest experimental and theoretical opportunities. Emphasis is given to the role of experiments in multiscale models, including insights into how computations can be used as discovery tools for materials engineering, rather than to "simply" support experimental work. This is illustrated by examples from several application areas on structural materials. This manuscript ends with a discussion on some problems and open scientific questions that are being explored in order to advance this relatively new field of research.Comment: 25 pages, 11 figures, review article accepted for publication in J. Mater. Sc

    Dielectric Breakdown in Chemical Vapor Deposited Hexagonal Boron Nitride

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    Insulating films are essential in multiple electronic devices because they can provide essential functionalities, such as capacitance effects and electrical fields. Two-dimensional (2D) layered materials have superb electronic, physical, chemical, thermal, and optical properties, and they can be effectively used to provide additional performances, such as flexibility and transparency. 2D layered insulators are called to be essential in future electronic devices, but their reliability, degradation kinetics, and dielectric breakdown (BD) process are still not understood. In this work, the dielectric breakdown process of multilayer hexagonal boron nitride (h-BN) is analyzed on the nanoscale and on the device level, and the experimental results are studied via theoretical models. It is found that under electrical stress, local charge accumulation and charge trapping/detrapping are the onset mechanisms for dielectric BD formation. By means of conductive atomic force microscopy, the BD event was triggered at several locations on the surface of different dielectrics (SiO2, HfO2, Al2O3, multilayer h-BN, and monolayer h-BN); BD-induced hillocks rapidly appeared on the surface of all of them when the BD was reached, except in monolayer h-BN. The high thermal conductivity of h-BN combined with the one-atom-thick nature are genuine factors contributing to heat dissipation at the BD spot, which avoids self-accelerated and thermally driven catastrophic BD. These results point to monolayer h-BN as a sublime dielectric in terms of reliability, which may have important implications in future digital electronic devices.Fil: Jiang, Lanlan. Soochow University; ChinaFil: Shi, Yuanyuan. Soochow University; China. University of Stanford; Estados UnidosFil: Hui, Fei. Soochow University; China. Massachusetts Institute of Technology; Estados UnidosFil: Tang, Kechao. University of Stanford; Estados UnidosFil: Wu, Qian. Soochow University; ChinaFil: Pan, Chengbin. Soochow University; ChinaFil: Jing, Xu. Soochow University; China. University of Texas at Austin; Estados UnidosFil: Uppal, Hasan. University of Manchester; Reino UnidoFil: Palumbo, Félix Roberto Mario. Comisión Nacional de Energía Atómica; Argentina. Universidad Tecnológica Nacional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Lu, Guangyuan. Chinese Academy of Sciences; República de ChinaFil: Wu, Tianru. Chinese Academy of Sciences; República de ChinaFil: Wang, Haomin. Chinese Academy of Sciences; República de ChinaFil: Villena, Marco A.. Soochow University; ChinaFil: Xie, Xiaoming. Chinese Academy of Sciences; República de China. ShanghaiTech University; ChinaFil: McIntyre, Paul C.. University of Stanford; Estados UnidosFil: Lanza, Mario. Soochow University; Chin

    Engineering Crowdsourced Stream Processing Systems

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    A crowdsourced stream processing system (CSP) is a system that incorporates crowdsourced tasks in the processing of a data stream. This can be seen as enabling crowdsourcing work to be applied on a sample of large-scale data at high speed, or equivalently, enabling stream processing to employ human intelligence. It also leads to a substantial expansion of the capabilities of data processing systems. Engineering a CSP system requires the combination of human and machine computation elements. From a general systems theory perspective, this means taking into account inherited as well as emerging properties from both these elements. In this paper, we position CSP systems within a broader taxonomy, outline a series of design principles and evaluation metrics, present an extensible framework for their design, and describe several design patterns. We showcase the capabilities of CSP systems by performing a case study that applies our proposed framework to the design and analysis of a real system (AIDR) that classifies social media messages during time-critical crisis events. Results show that compared to a pure stream processing system, AIDR can achieve a higher data classification accuracy, while compared to a pure crowdsourcing solution, the system makes better use of human workers by requiring much less manual work effort
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