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The purpose of this paper is to study the coupled fixed point problem and the coupled best proximity problem for single-valued and multi-valued contraction type operators defined on cyclic representations of the space. The approach is based on fixed point results for appropriate operators generated by the initial problems.

“It should also be noted that the objective of convergence and equal distribution, including across under-performing areas, can hinder efforts to generate growth. Contrariwise, the objective of competitiveness can exacerbate regional and social inequalities, by targeting efforts on zones of excellence where projects achieve greater returns (dynamic major cities, higher levels of general education, the most advanced projects, infrastructures with the heaviest traffic, and so on). If cohesion policy and the Lisbon Strategy come into conflict, it must be borne in mind that the former, for the moment, is founded on a rather more solid legal foundation than the latter” European Commission (2005, p. 9)Adaptation of Cohesion Policy to the Enlarged Europe and the Lisbon and Gothenburg Objectives.

In this work, a new general framework is proposed to guide navigation over a collaborative concept network, in order to discover paths between concepts. Finding semantic chains between concepts over a semantic network is an issue of great interest for many applications, such as explanation generation and query expansion. Collaborative concept networks over the web tend to have features such as large dimensions, high connectivity degree, dynamically evolution over the time, which represent special challenges for efficient graph search methods, since they result in huge memory requirements, high branching factors, unknown dimensions and high cost for accessing nodes. The paper aims to discuss these issues.

The proposed framework is based on the novel notion of heuristic semantic walk (HSW). In the HSW framework, a semantic proximity measure among concepts, reflecting the collective knowledge embedded in search engines or other statistical sources, is used as a heuristic in order to guide the search in the collaborative network. Different search strategies, information sources and proximity measures, can be used to adapt HSW to the collaborative semantic network under consideration.

Experiments held on the Wikipedia network and Bing search engine on a range of different semantic measures show that the proposed HSW approach with weighted randomized walk strategy outperforms state-of-the-art search methods.

To the best of the authors' knowledge, the proposed HSW model is the first approach which uses search engine-based proximity measures as heuristic for semantic search.

Sizing functions are crucial inputs for unstructured mesh generation since they determine the element distributions of resulting meshes to a large extent. Meanwhile, automating the procedure of creating a sizing function is a prerequisite to set up a fully automatic mesh generation pipeline. In this paper, an automatic algorithm is proposed to create a high-quality sizing function for an unstructured surface and volume mesh generation by using a triangular mesh as the background mesh.

A practically efficient and effective solution is developed by using local operators carefully to re-mesh the tessellation of the input Computer Aided Design (CAD) models. A nonlinear programming (NLP) problem has been formulated to limit the gradient of the sizing function, while in this study, the object function of this NLP is replaced by an analytical equation that predicts the number of elements. For the query of the sizing value, an improved algorithm is developed by using the axis-aligned bounding box (AABB) tree structure.

The local operations of re-meshing could effectively and efficiently resolve the banding issue caused by using the default tessellation of the model to define a sizing function. Experiments show that the solution of the revised NLP, in most cases, could provide a better solution at the lower cost of computational time. With the help of the AABB tree, the sizing function defined at a surface background mesh can be also used as the input of volume mesh generation.

Theoretical analysis reveals that the construction of the initial sizing function could be reduced to the solution of an optimization problem. The definitions of the banding elements and surface proximity are also given. Under the guidance of this theoretical analysis, re-meshing and ray-casting technologies are well-designed to initial the sizing function. Smoothing with the revised NLP and querying by the AABB tree, the paper provides an automatic method to get a high-quality sizing function for both surface and volume mesh generation.

Until recently there were very few publications dealing with comparison of different CDROM information retrieval software packages (Richards & Robinson 1993). The few exceptions (e.g. Harry & Oppenheim 1993; Jacsó 1991; McFaul 1992; Tian‐Zhu 1991; Zink 1991) mostly deal only with the principles of such comparisons, not with actual comparisons, and mostly only with user interfaces, not with the software as a whole. They usually give collections of rules — how to rank the CDROM publications, how to give ‘award marks’ to them — but this usually characterises CDROM publications and databases, not the software itself. Moreover, the performance factors, the properties most important for professional searchers, are usually completely missing from these collections of rules.

The spatial and non-spatial attributes are the two important characteristics of a spatial point, which belong to the two different attribute domains in many Geographic Information Systems applications. The dual clustering algorithms take into account both spatial and non-spatial attributes, where a cluster has not only high proximity in spatial domain but also high similarity in non-spatial domain. In a geographical dataset, traditional dual spatial clustering algorithms discover homogeneous spatially adjacent clusters suffering from the between-cluster inhomogeneity where those spatial points are described in non-spatial domain. To overcome this limitation, a novel dual-domain clustering algorithm (DDCA) is proposed by considering both spatial proximity and attribute similarity with the presence of inhomogeneity.

In this algorithm, Delaunay triangulation with edge length constraints is first employed to construct spatial proximity relationships amongst objects. Then, a clustering strategy based on statistical change detection is designed to obtain clusters with similar attributes.

The effectiveness and practicability of the proposed algorithm are illustrated by experiments on both simulated datasets and real spatial events. It is found that the proposed algorithm can adaptively and accurately detect clusters with spatial proximity and similar non-spatial attributes under the consideration of inhomogeneity.

Traditional dual spatial clustering algorithms discover homogeneous spatially adjacent clusters suffering from the between-cluster inhomogeneity where those spatial points are described in non-spatial domain. The research here is a contribution to developing a dual spatial clustering method considering both spatial proximity and attribute similarity with the presence of inhomogeneity. The detection of these clusters is useful to understand the local patterns of geographical phenomena, such as land use classification, spatial patterns research and big geo-data analysis.

Aim of the present monograph is the economic analysis of the role of MNEs regarding globalisation and digital economy and in parallel there is a reference and examination of some legal aspects concerning MNEs, cyberspace and e‐commerce as the means of expression of the digital economy. The whole effort of the author is focused on the examination of various aspects of MNEs and their impact upon globalisation and vice versa and how and if we are moving towards a global digital economy.

The aim of the research work presented in this paper is to investigate a mechanism that can recognise high level activities (for example, going for a walk, travelling on the bus, doing evening activity, etc.) and behaviour of low entropy people (people with regular daily life routines, e.g. elderly people with dementia, patients with regular routines) in order to help them improve their health related daily life activities by using wireless proximity data (e.g. Bluetooth, Wi‐Fi).

The paper adopted a tiered approach to recognise activities and behaviour. Higher level activities are divided into sub‐activities and tasks. Separating the tasks from the raw wireless proximity data is achieved by designing task separator (TASE) algorithm. TASE takes wireless proximity data as an input and separates it into different tasks. These detected tasks and the high level daily activity plans that are made in a planning language Asbru, are then fed into the activity recogniser that compares the detected tasks with the plans and recognises the high level activities that the user is performing.

The paper provides an insight to how only wireless proximity data can be utilised to recognise high level activities and behaviour of individuals. A number of scenarios and experiments are designed to prove the validity of the proposed methodology.

This paper focussed on relatively low entropy individuals with regular routines and behavioural patterns which can be improved by increasing the level of entropies in behavioural routines.

The paper includes implications for the utilisation in health care environments for elderly people and physically impaired individuals.

This paper provides a detailed and original study of algorithms and techniques that can be used to recognise high level activities and behaviour of individuals by using only wireless proximity data.

The purpose of this paper is to study the impact of spatial proximity on supplier selection in the French public sector. While French public procurement legislation forbids consideration of supplier location in the procurement process, public contractors may still rely on spatial proximity for complex transactions necessitating mutual adjustments with suppliers.

Using French Official Journals (BOAMP), the authors compiled 565,557 transactions completed on three public procurement markets between 6,182 contractors and 26,570 suppliers, over a period of six years (between 2006 and 2011). The authors conducted a two-level hierarchical linear auto-regression analysis and a feature evaluation analysis for all transactions.

The paper finds significant variation between the transactions on different markets: a negative effect of spatial proximity on the number of contract notices in the public market and a positive effect of spatial proximity on the number of notices in the services and supplies markets. The difference lies in the levels of mutual adjustment required to optimally manage the relationship between public contractor and supplier.

The research is based on an econometric analysis conducted uniquely in the French context, which calls into question the external validity of the results obtained. The study also rests on segmentation into three aggregate markets, which might be considered too general.

Rather than analyze public contractors’ perceptions of the importance of the criterion of spatial proximity, the paper examines 565,557 actual transactions. The results point to the emergence of a new type of relationship with certain suppliers, which should lead public contractors to integrate relationship management competencies, in addition to legal and economic competencies, in the organization of calls for tenders.

Local networks of small- and medium-sized enterprises (SMEs) are founded both on collaborative and competitive inter-organizational relationships within the same network characterised by spatial proximity between firms. This paper aims to propose an original analytical framework to understand how spatial proximity between firms within local networks influences strategy of SMEs.

Theories and the existing knowledge were used to develop a preliminary conceptual framework. This preliminary framework was then developed as data collection and analysis were carried out. That is, in the more advanced stages of the research, both the literature and data allowed the design of a more specific analytical framework with the development of theoretical propositions. The data supporting the findings was derived from extensive content analysis of secondary documents from manufacturers, channel agents, industry organisations, public agencies and statistical sources. These data were also used as background briefing for 52 in-depth interviews with 36 senior managers within the sector.

An extensive qualitative research carried out in two major local networks of footwear production in Portugal shows that spatial proximity influences the following major variables of SME strategy: offer specialization, market specialization, competitive behaviour and collaborative behaviour. This paper examines the strong subcontracting relationships among manufacturers within each local network, discusses the competitive and collaborative behaviour of firms within their local networks of subcontracting and explains the nature of interaction between them contributing for a better understanding on the impact of spatial proximity on strategy.

Some management implications can be drawn from this research at the level of the network and of the firm, although both levels are profoundly intertwined. The paper discusses both these implications.

This research clarifies the relationship between spatial proximity and strategy of SMEs immersed in local networks. In particular, the findings show that, in the context of local networks of footwear production, SME strategy is influenced by spatial proximity between firms in the following characteristics of strategy: offer specialization, market specialization, competitive behaviour and collaborative behaviour. As it was discussed and empirically supported, spatial proximity between firms favour resource mobility, firms' interaction and reduction of transaction costs, which, in turn, impact strategy.