No abstract

Big data storage and processing are considered as one of the main applications for cloud computing systems. Furthermore, the development of the Internet of Things (IoT) paradigm has advanced the research on Machine to Machine (M2M) communications and enabled novel tele-monitoring architectures for E-Health applications. However, there is a need for converging current decentralized cloud systems, general software for processing big data and IoT systems. The purpose of this paper is to analyze existing components and methods of securely integrating big data processing with cloud M2M systems based on Remote Telemetry Units (RTUs) and to propose a converged E-Health architecture built on Exalead CloudView, a search based application. Finally, we discuss the main findings of the proposed implementation and future directions.

Time-critical applications, such as early warning systems or live event broadcasting, present particular challenges. They have hard limits on Quality of Service constraints that must be maintained, despite network fluctuations and varying peaks of load. Consequently, such applications must adapt elastically on-demand, and so must be capable of reconfiguring themselves, along with the underlying cloud infrastructure, to satisfy their constraints. Software engineering tools and methodologies currently do not support such a paradigm. In this paper, we describe a framework that has been designed to meet these objectives, as part of the EU SWITCH project. SWITCH offers a flexible co-programming architecture that provides an abstraction layer and an underlying infrastructure environment, which can help to both specify and support the life cycle of time-critical cloud native applications. We describe the architecture, design and implementation of the SWITCH components and describe how such tools are applied to three time-critical real-world use cases.

Antimicrobial resistance is one of the most important public health issues. Besides classical multidrug resistance species associated with medical care involved in superficial or invasive infections, there are strains less commonly associated with hospital or outpatient setting’s infections. Non-diphtheria Corynebacterium spp. could produce infections in patients with or without immune-compromised status. The aim of our study was to determine the susceptibility to antimicrobial agents to Corynebacterium spp. from clinical samples collected from Romanian hospitalized individuals and outpatients. Twenty Corynebacterium strains were isolated and identified as Corynebacterium striatum (n = 7), Corynebacterium amycolatum (n = 7), C. urealyticum (n = 3), Corynebacterium afermentans (n = 2), and Corynebacterium pseudodiphtheriticum (n = 1). All isolates have been tested for antibiotic susceptibility by standardized disc diffusion method and minimal inhibitory concentration (MIC) tests. Seventeen isolates demonstrated multidrug resistance phenotypes. The molecular support responsible for high resistance to quinolones for ten of these strains was determined by the detection of point mutation in the gene sequence gyrA.

Hydro-, Aeolian and Solar energy show significant promise in helping to reduce pollutants and greenhouse gases, which is a primary focus in today's sustainable development culture. To enjoy the power of them, the human society needs solutions to transform the energies mentioned above into electric energy, at specific reliability, efficiency and sustainability parameters. Maintaining this kind of parameters implies, among other things, the careful and permanent monitoring of equipment. The monitoring process implies remote monitoring, as we are talking about preserving natural resources. The equipment is mostly situated in the middle of nature, covering large areas mostly outside of populated locations. In their great majority installations for wind and solar energy have been designed and manufactured relatively recently, which makes them contain systems of tele-monitoring that are designed and included by default, as a part of great importance to the entire investment. The situation is different in the case of hydroelectric plants, which have a tradition of over 130 years and have been and are still built to this day. Renewable energy sources are being increasingly used and need to be constantly monitored for optimizing the power grid. Unfortunately, such micro power plants are located in difficult to access remote locations where often only satellite or sparse GSM radio signals are available. In this paper we study the way how to process big data gathered by a & George Suciu decentralized cloud system, based on general systems and remote telemetry units (RTUs), for tele-monitoring of renewable energy objectives. Also, we analyze a proposed cloud M2M system, where each RTU communicates by radio with a telemetry data gateway connected to a cloud computing infrastructure equipped with appropriate software that delivers processed data. Furthermore, we present how we use a search based application built on Exalead CloudView to search for weak signals in big data. In particular, given the telemetry application, we propose to leverage trivial and non-trivial connections between different sensor signals and data from other online environmental wireless sensor networks, in order to find patterns that are likely to provide innovative solutions to existing problems. The aggregation of such weak signals will provide evidence of connections between renewable energies and environmental related issues faster and better than trivial mining of sensor data. As a consequence, the software has a significant potential for matching environmental applications and challenges that are related in non-obvious ways. Finally, we present the measurement results for a hydro-energy case study and discuss the applicability for other renewable sources such as solar or wind energy.