Towards Quality-Aware Development of Big Data Applications with DICE

© Springer International Publishing Switzerland 2016.Model-driven engineering (MDE) has been extended in recent years to account for reliability and performance requirements since the early design stages of an application. While this quality-aware MDE exists for both enterprise and cloud applications, it does not exist yet for Big Data systems. DICE is a novel Horizon2020 project that aims at filling this gap by defining the first quality-driven MDE methodology for Big Data applications. Concrete outputs of the project will include a data-aware UML profile capable of describing Big Data technologies and architecture styles, data-aware quality prediction methods, and continuous delivery tools

The effect of a mixture of herbal essential oils or á-tocopheryl acetate on performance parameters and oxidation of body lipid in broilers

No Abstract. South African Journal of Animal Science Vol. 34 (1) 2004: pp.52-6

XR-RF Imaging Enabled by Software-Defined Metasurfaces and Machine Learning: Foundational Vision, Technologies and Challenges

We present a new approach to Extended Reality (XR), denoted as iCOPYWAVES, which seeks to offer naturally low-latency operation and cost-effectiveness, overcoming the critical scalability issues faced by existing solutions. iCOPYWAVES is enabled by emerging PWEs, a recently proposed technology in wireless communications. Empowered by intelligent (meta)surfaces, PWEs transform the wave propagation phenomenon into a software-defined process. We leverage PWEs to i) create, and then ii) selectively copy the scattered RF wavefront of an object from one location in space to another, where a machine learning module, accelerated by FPGAs, translates it to visual input for an XR headset using PWEdriven, RF imaging principles (XR-RF). This makes for an XR system whose operation is bounded in the physical layer and, hence, has the prospects for minimal end-to-end latency. Over large distances, RF-to-fiber/fiber-to-RF is employed to provide intermediate connectivity. The paper provides a tutorial on the iCOPYWAVES system architecture and workflow. A proof-of-concept implementation via simulations is provided, demonstrating the reconstruction of challenging objects in iCOPYWAVES produced computer graphics

Towards quality-aware development of Big Data applications with DICE

Model-driven engineering (MDE) has been extended in recent years to account for reliability and performance requirements since the early design stages of an application. While this quality-aware MDE exists for both enterprise and cloud applications, it does not exist yet for Big Data systems. DICE is a novel Horizon2020 project that aims at filling this gap by defining the first quality-driven MDE methodology for Big Data applications. Concrete outputs of the project will include a data-aware UML profile capable of describing Big Data technologies and architecture styles, data-aware quality prediction methods, and continuous delivery tools

A software architecture framework for quality-aware devops

DevOps is an emerging software engineering strategy entailing the joined efforts of development and operations people, their concerns and best practices with the purpose of realising a coherent working group for increased software development and operations speed. To allow software architecture practitioners to enrich and properly elaborate their architecture specifications in a manner which is consistent with DevOps, we surveyed a number of DevOps stakeholders. We studied concerns and challenges to be tackled with respect to preparing a software architecture which is DevOps-ready, i.e., described in all details needed to enact DevOps scenarios. Subsequently, we introduce SQUID, that stands for Specification Quality In DevOps. SQUID is a software architecture framework that supports the model-based documentation of software architectures and their quality properties in DevOps scenarios with the goal of providing DevOps- ready software architecture descriptions. We illustrate our framework in a case-study in the Big Data domain

Evaluation of toyocerin, a probiotic containing Bacillus toyoi spores, on health status and productivity of weaned, growing and finishing pigs

The aim of the study was to assess the efficacy of Toyocerin, a probiotic containing Bacillus toyoi spores, on the health status and productivity of pigs, during nursery, growing and finishing phases. On a commercial farrow-to-finish farm in Greece, 3 experimental groups were formed, each of 72 weaned piglets. The pigs of the first group (T1 group; negative controls) received normal feed with no antimicrobials or probiotics, the pigs of the second group (T2 group) received the same type of feed but supplemented with 1.0×109, 0.5×109 and 0.2×109 spores per kg of feed at weaning, growing and finishing stage, respectively, and the pigs of the third group (T3 group) were fed with Toyocerin at the dose of 1.0×10 9 spores per kg of feed during the entire fattening period (weaning, growing and finishing stages). The results have shown that, compared to the controls, Toyocerin treated pigs had reduced incidence of post-weaning diarrhoea (p<0.05). Enterotoxigenic strains of Escherichia coli were detected in faecal samples of 0% to 25% of pigs of the treated groups, but in 33.5% to 50% of pigs of the non-treated group (p<0.05). Over the negative controls, a significant improvement of weight gain (4.5% and 8.3% for T2 and T3 groups, respectively), and of feed conversion ratio (6.6% and 13.0% for T2 and T3 groups, respectively) was observed. The 76.5% of the carcasses of the T3 group was classified in the top three categories of the EUROP scale (S, E and U), whilst the respective figures were 47.8% for T2 group and only 10.5% for T1 group (p<0.05)