Narrow Results

The performance of computational grids mainly depends on the resource allocation service of a resource management system. Efficient resource allocation is essential for better resource utilisation which could be for both providers and grid users. Resource allocation includes the scheduling of gridlets to the available resources. However, the biggest challenges for grid users are to select the best resources from the available grid resources and to allocate these resources for scheduling of the gridlets. To address these issues and enhance the resource utilisation process, we propose a semantic and proximity-aware fuzzy rule-based model that improves the resource utilisation in a grid environment. The model uses fuzzy techniques with four parameters such as semantic similarity, proximity, number of total machines and number of total processors of each machine. The experimental results provide promising results. Overall, the proposed semantic and proximity-aware fuzzy rule-based decentralised resource discovery model improves the resource utilisation by 23% as compared to non-fuzzy first come first serve (FCFS) technique in a computational grid environment.

Leading biopharmaceutical firms need to dynamically optimize pipeline portfolios of internally and externally generated product candidates to ensure sustainable growth. This paper provides an empirical analysis of leading global biotechnology firms with respect to technology agglomeration patterns, proximity to alliance partners, and firm performance for the period 1996–2006. Findings suggest that the absolute number of technology and product alliances were approximately twice as important as proximity to partners in terms of firm performance. These results indicate that a strategy of relentless pipeline building, without regard to geographic proximity of alliance partners, may enhance relative and absolute performance of biopharmaceutical industry leaders.

There is a gap in research literature between cross-industry innovation (CII), knowledge transfer, and relational proximities. In this study, we analyze contributions from these three streams of literature and an empirical case of CII, and propose a conceptual model for knowledge transfer in CII, which also incorporates the influences of proximities. The focus of our study is on CII from the petroleum industry to the medical industry. Theoretical and practical implications are discussed in the paper.

Diaspora networks' role in supporting and stimulating entrepreneurial activities in Sub-Saharan African (SSA) economies need hardly be elaborated. For instance, some SSA countries have established government agencies to encourage diasporas to help local communities and provide policy advice. At the 2003 Extra-Ordinary Summit of the Assembly of Heads of State and Governments, the African Union (AU) amended Article Three of its Constitutive Act to invite and encourage African diaspora's active participation. However, institutional changes associated with diaspora networks are a phenomenon that has been noted but poorly understood. This paper addresses a little examined intersection between the diaspora literature and the institutions literature. We examine the contexts, mechanisms and processes associated with diaspora networks' roles as institutional change agents in the context of entrepreneurial behaviors in SSA economies. Our dependent variables are measures of changes in institutions associated with diaspora network. We have related our analysis mainly to the nature of the diaspora networks compared to other networks, characteristics of the environments in which diaspora networks are embedded in and operate, and some activities, mechanisms and modes that serve to transmit institutions from the host country to the homeland.

This study examines the effects of new technology-based firms (NTBFs) internal and external resources on their early business performance. Based on a large unique dataset of 401 newly started NTBFs, we find that founders’ business experience and their firms’ proximity to other firms positively affect early business performance. Growth orientation is found to be negatively related to business performance, which might suggest a certain level of over-optimism among entrepreneurs with aspiration to grow fast.

Proximity between actors within an organisation can be relevant for interactive learning and innovation because it promotes collaboration and knowledge exchange. However, proximity has mainly been studied as a characteristic of the relationships between actors belonging to different organisations. In this paper, a case study of a faculty at a university is used to investigate proximity within this organisation and related to a change process. Based on 23 interviews and a self-ethnographic approach, we offer a detailed micro-perspective on how the geographic, cognitive, and social dimensions of proximity influence interactive learning and innovation between employees within the organisation. We also identify two distinct proximity configurations that have emerged during the change process. Finally, we analyse how the proximity dimensions are balanced within these configurations and offer two propositions explaining their influence on interactive learning and innovation. We thereby contribute to the existing theory on proximity by extending the insight into proximity from an inter-organisational to an intra-organisational level and by adding new knowledge to the understanding of proximity dynamics.

Regularization is a principle, concerning a wide range of science domains. Several methods, using this technique, have been proposed. However, there are some limitations to the functionals used in regularization. To remove these, the idea is to employ nonlocal operators on weighted graphs in regularization process. In images, pixels have a specific organization expressed by their spatial connectivity. Therefore, a typical structure used to represent images is a graph. The problem is to choose the correct one, because the topology of graphs can be arbitrary and each type of graph is proper to different type of problem. In this work, we focus on method based on nonlocal Laplace operator, which has become increasingly popular in image processing. Moreover, we introduce the representation of F-transform based Laplace operator.

This chapter explains how cluster analysis organizes information in applications such as Computer Vision and Pattern Recognition. Information is represented as points in multidimensional feature spaces where each coordinate represents a measurement. Some tools from exploratory data analysis are discussed, with an emphasis on linear projections derived from the covariance matrix. Two types of clustering are reviewed — hierarchical and partitional. Hierarchical clustering leads to nested partitions of the data. SAHN algorithms for hierarchical clustering are defined and some of the common characteristics are explained. Partitional clustering arranges data in separate clusters, as with the K-Means algorithm. The chapter ends with a discussion of validation that centers on external and internal tests of validity and tests for the number of clusters. A bibliography is provided for further reading.

We show that a whole bunch of well known topological constructs have isomorphic descriptions by means of sets structured by some collection of (generalized) metrics of a certain type together with suitable morphisms. The classification of the categories listed in the table at the end of the paper, is based on different saturation conditions acting on certain collections of generalized metrics. These conditions are composed out of different building blocks. For instance on collections of quasi-pre-metrics we encounter saturation conditions that are combinations of the following properties: prime ideal, ideal, locally qualified saturation, uniform qualified saturation, locally quantified saturation and uniform quantified saturation. Combinations of these enable us to characterize the constructs in the first colum of the summarizing table at the end of the paper: pretopological spaces, preclosure spaces, semi-quasi uniform spaces, pre-approach spaces, quasi -pre metric spaces. The more important subconstructs of these examples, such as topological spaces, completely regular spaces, uniform spaces, proximity spaces, approach spaces and metric spaces, are characterized using essentially the same building blocks of saturation properties, restricted to particular subclasses of generalised metrics.

This chapter introduces the system for crisis management in Sweden. Over the last century, Sweden has gone from being a poor European backwater to being among the countries with the highest human development in the world. The droughts and harsh winters that killed thousands and drove numerous Swedes to emigrate in the past are mere distractions today and the contemporary system for crisis management is designed to deal with a broader variety of crises than the ones triggered by natural hazards. The system is based on the principles of responsibility, parity and proximity, and distributes sector and area responsibility for crisis management to numerous actors. It is built to a great extent on collaboration between these actors, which is challenging but working relatively well in the cultural context of consensus-seeking and compliance to official guidelines and accepted rules of engagement. However, the system is in itself ambiguous in the sense of distributing responsibility to all kinds of actors and then focusing almost exclusively on public actors in legislation, guidelines and practice. There is also often a gap between policy and practice concerning how area responsibility is exercised, and a lack of clarity in current sector specific legislation.

This chapter explains how cluster analysis organizes information in applications such as Computer Vision and Pattern Recognition. Information is represented as points in multidimensional feature spaces where each coordinate represents a measurement. Some tools from exploratory data analysis are discussed, with an emphasis on linear projections derived from the covariance matrix. Two types of clustering are reviewed—hierarchical and partitional. Hierarchical clustering leads to nested partitions of the data. SAHN algorithms for hierarchical clustering are defined and some of the common characteristics are explained. Partitional clustering arranges data in separate clusters, as with the K-Means algorithm. The chapter ends with a discussion of validation that centers on external and internal tests of validity and tests for the number of clusters. A bibliography is provided for further reading.