2,946 research outputs found

    RIO Country Report 2015: Czech Republic

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    The 2015 series of RIO Country Reports analyse and assess the policy and the national research and innovation system developments in relation to national policy priorities and the EU policy agenda with special focus on ERA and Innovation Union. The executive summaries of these reports put forward the main challenges of the research and innovation systems.JRC.J.6-Innovation Systems Analysi

    ERAWATCH Country Reports 2013: Czech Republic

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    The Analytical Country Reports analyse and assess in a structured manner the evolution of the national policy research and innovation in the perspective of the wider EU strategy and goals, with a particular focus on the performance of the national research and innovation (R&I) system, their broader policy mix and governance. The 2013 edition of the Country Reports highlight national policy and system developments occurring since late 2012 and assess, through dedicated sections: -National progress in addressing Research and Innovation system challenges; -National progress in addressing the 5 ERA priorities; -The progress at Member State level towards achieving the Innovation Union; -The status and relevant features of Regional and/or National Research and Innovation Strategies on Smart Specialisation (RIS3); -As far relevant, country Specific Research and Innovation (R&I) Recommendations. Detailed annexes in tabular form provide access to country information in a concise and synthetic manner. The reports were originally produced in December 2013, focusing on policy developments occurring over the preceding twelve months.JRC.J.2-Knowledge for Growt

    RIO Country Report 2016: Czech Republic

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    The 2016 series of the RIO Country Report analyses and assesses the development and performance of the national research and innovation system of the EU-28 Member States and related policies with the aim of monitoring and evaluating the EU policy implementation as well as facilitating policy learning in the Member States.JRC.B.7-Knowledge for Finance, Innovation and Growt

    A Case Study on Formal Verification of Self-Adaptive Behaviors in a Decentralized System

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    Self-adaptation is a promising approach to manage the complexity of modern software systems. A self-adaptive system is able to adapt autonomously to internal dynamics and changing conditions in the environment to achieve particular quality goals. Our particular interest is in decentralized self-adaptive systems, in which central control of adaptation is not an option. One important challenge in self-adaptive systems, in particular those with decentralized control of adaptation, is to provide guarantees about the intended runtime qualities. In this paper, we present a case study in which we use model checking to verify behavioral properties of a decentralized self-adaptive system. Concretely, we contribute with a formalized architecture model of a decentralized traffic monitoring system and prove a number of self-adaptation properties for flexibility and robustness. To model the main processes in the system we use timed automata, and for the specification of the required properties we use timed computation tree logic. We use the Uppaal tool to specify the system and verify the flexibility and robustness properties.Comment: In Proceedings FOCLASA 2012, arXiv:1208.432

    Determinants of Business Linkage Between Medium-Small and Large Business Enterprises in Manufacturing Sector: The Case of Kombolcha City

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    The Medium and Small Enterprises (MSE) sector in Ethiopia is faced with many challenges and constraints including business linkages with large enterprises. Business linkages give MSEs the opportunity to participate as suppliers, distributors or retailers in commercial value chains which can help increase local job and wealth creation, enhance skills and capacity, add purchasing power and generally stimulate economic activity and development-contributing in the process, to quality of life. Despite the critical roles played by the business linkages in distributor MSEs in the textile sub-sector, they have been experiencing determinants leading to low- performance or limited linkage with large enterprises. This study was conducted in Amhara region, south wollo zone, Kombolcha city. The main purpose of this study was to explore the basic determinant factors of business linkages between MSEs and large firms in textile sub-sector in Kombolcha town. The population of this study was 71 MSEs and 1 large enterprise. Census method was used to conduct the research throughout enterprises in textile sub-sector and primary data was collected with the use of Self-administered questionnaire and key informant interview. The researcher had used mixed approach that was both qualitative and quantitative methods to identify and analyze determinants. Descriptive statistical methods and logit regression model supported by qualitative analysis had been employed SPSS and STATA software to analyses the collected data. The result showed that the business linkage between small and large business in Kombolcha city particularly in textile sub-sector was found limited. MSEs in Kombolcha city are facing a lot of problems that hinder their business linkage; lack of raw materials is the most series problem. Determinants such as education levels, raw materials, technology, training, and leadership were found significant that affect business linkage positively. Therefore  the study suggest that improving educational levels of MSEs owners, availability of raw materials, training like technical and managerial skills, giving attention to assure capable and committed leadership and for the future intervention strategies aimed at the mutual benefit between MSEs and large enterprises and technological transformation to strengthen business linkage . The government and private sectors are expected to exert strong effort to create business linkage between MSEs and large businesses in textile sub-sector. Keywords: - Business linkage, Determinants, MSEs, Large, Enterprises DOI: 10.7176/JESD/10-3-0

    Market-Related Reforms and Increased Energy Efficiency in Transition Countries: Empirical Evidence

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    Energy efficiency improvement is a desirable response to growing climate change and security of energy supply concerns. This article studies the impacts of a varied set of macro-level market-oriented reforms as well as structural change on economy-wide measure of energy efficiency across a group of the transition countries. These countries experienced a rapid marketization process, which, since the early 1990s, transformed their economies from central planning towards market-driven models. We use a bias-corrected fixed-effect analysis technique to estimate this effect for the period 1990 to 2010. The results suggest that reforms aimed at market liberalization, financial sector and most infrastructure industries drove energy efficiency improvements. We find significant differences in improvements in energy efficiency between transitional Central European and Baltic States, South East Europe ones and the Commonwealth of Independent States. The reasons for these differences are also discussed

    Political economy, political class, and political system in recivilianized Nigeria

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    African Studies Center Working Paper No. 4

    Anomaly detection in the cloud using data density

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    Cloud computing is now extremely popular because of its use of elastic resources to provide optimized, cost-effective and on-demand services. However, clouds may be subject to challenges arising from cyber attacks including DoS and malware, as well as from sheer complexity problems that manifest themselves as anomalies. Anomaly detection techniques are used increasingly to improve the resilience of cloud environments and indirectly reduce the cost of recovery from outages. Most anomaly detection techniques are computation ally expensive in a cloud context, and often require problem-specific parameters to be predefined in advance, impairing their use in real-time detection. Aiming to overcome these problems, we propose a technique for anomaly detection based on data density. The density is computed recursively, so the technique is memory-less and unsupervised, and therefore suitable for real-time cloud environments. We demonstrate the efficacy of the proposed technique using an emulated dataset from a testbed, under various attack types and intensities, and in the face of VM migration. The obtained results, which include precision, recall, accuracy, F-score and G-score, show that network level attacks are detectable with high accuracy

    Single Process Architecture for E-Learning Over Cloud Computing

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    A Cloud is a type of parallel and distributed system consisting of a collection of interconnected and virtualized computers that are dynamically provisioned and presented as one or more unified computing resources based on service-level agreements established through negotiation between the service provider and consumers. C loud Computing refers to both the applications delivered as services over the Internet and the hardware and systems software in the data centres that provide those services (Software as a Service - SaaS). The data center hardware and software is what we will call a Cloud. Fr om the studies of various research papers and works done by various researchers it has been found that the major areas of focus in the field of cloud computing are architecture definitions, security, integration of services on various layers, inclusion of Various network and communication devices being developed rapidly. E -Learning through cloud computing is a promising area for the ease of both faculties and students around the world. The work done in cloud computing based e-Learning is oriented on centralized server and further improvement in this can be done. I n this research, a new distributed architecture is being proposed to provide an opportunity to the learners around the world to use the resources being shared by the faculties and online communication between the faculties and students
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