TRANSVERSALITY, TECHNOLOGICAL TRANSFER NETWORKS AND POLICY IMPLICATIONS: THE CASE OF REGIONAL INNOVATION POLICIES IN TUSCANY REGION (SDP 2000-2006)

Recently at the European level the theme of innovation has been further fostered with the Smart Specialization Strategy underlined within the COM(2010) 553 “Regional policy contributing to smart growth in Europe 2020â€. The aim of this study is to investigate the co-evolutive dynamics of the technological transfer processes at regional level, and in particular the issue of transversality and bases of knowledge between networks according to an evolutionary perspective. Transversality is analysed considering networks’ differences and proximities in terms of industry of application, applied technology, and local dimensions of relationships. In order to analyze these phenomena, we apply the Social Network Analysis to investigate the structural features of the space of relations and relational flows, and to roles and attributes of the universe of the co-funded actors. The structural analysis of the relations’ system (centrality, closeness, betweenness, local dimension) has been analyzed across five regional initiatives, studying over 150 networks and over 1300 co-funded actors. Relations between and within networks have been normalized and the role of specific agents has been underlined with regards to transversality dynamics. As conclusion, policy implications can be drawn, in particular as far as supply-led and demand-led innovation policy. The study is structured as follows. After the introduction describing the context of regional innovation policies over the last Regional Planning period (SPD 2000-2006), the first paragraph describes the main characteristics of the concept of transversality, with connections to RIS model and innovation networks. The second paragraph describes the Social Networks Analysis methodology used to study the evolutionary process of agglomeration with regards to bases of knowledge and transversality. The third paragraph deals with the results of the analysis and the fourth paragraph presents conclusive remarks on policy implication in terms of industrial policies.

Network structural properties for cluster long run dynamics. Evidence from collaborative R&D networks in the European mobile phone industry

In a recent literature, the structural properties of knowledge networks have been pointed out as a critical factor for cluster structural changes and long run dynamics. Mixing evolutionary economic geography and network-based approach of clusters, this contribution aims at capturing and discussing the particular influence of hierarchy (degree distribution) and assortativity (degree correlation) in the innovative capabilities of clusters along the industry life cycle. We test our propositions in the field of the mobile phone industry in Europe from 1988 to 2008. We use EPO PATSTAT and OECD REGPAT to capture cluster trends, and R&D relations from European Framework Programs to capture knowledge networks and their evolving structural properties. Our findings provide new insights to understand the organization of clusters over time in order to perform along the industry life cycl

Analysis of Neighbourhoods in Multi-layered Dynamic Social Networks

Social networks existing among employees, customers or users of various IT systems have become one of the research areas of growing importance. A social network consists of nodes - social entities and edges linking pairs of nodes. In regular, one-layered social networks, two nodes - i.e. people are connected with a single edge whereas in the multi-layered social networks, there may be many links of different types for a pair of nodes. Nowadays data about people and their interactions, which exists in all social media, provides information about many different types of relationships within one network. Analysing this data one can obtain knowledge not only about the structure and characteristics of the network but also gain understanding about semantic of human relations. Are they direct or not? Do people tend to sustain single or multiple relations with a given person? What types of communication is the most important for them? Answers to these and more questions enable us to draw conclusions about semantic of human interactions. Unfortunately, most of the methods used for social network analysis (SNA) may be applied only to one-layered social networks. Thus, some new structural measures for multi-layered social networks are proposed in the paper, in particular: cross-layer clustering coefficient, cross-layer degree centrality and various versions of multi-layered degree centralities. Authors also investigated the dynamics of multi-layered neighbourhood for five different layers within the social network. The evaluation of the presented concepts on the real-world dataset is presented. The measures proposed in the paper may directly be used to various methods for collective classification, in which nodes are assigned to labels according to their structural input features.Comment: 16 pages, International Journal of Computational Intelligence System

Structural holes, knowledge intermediaries and evolution of the triple helix system with reference to the hard disk drive industry in Thailand

This article explores the evolutionary process underlying the development of the triple helix innovation system and the role of knowledge intermediaries in the process. It draws on the experience of knowledge network development in an SME cluster in the Thai hard disk drive industry. Conceptually, the evolutionary process starts with inter-firm networks, which occur in the form of supply chain-based vertical links and trade association or cluster-based horizontal links. These evolve into triple helix networks and culminate into the triple helix innovation system through the agency of network dynamics. Intermediaries enhance network development as sponsors, providing funds; as brokers, closing and bridging structural holes that disconnect network players; and as boundary spanners, facilitating knowledge circulation. The case study suggests that knowledge network development in Thailand has a long way to go before morphing into the triple helix innovation system. Some evidence of network dynamics was nonetheless detected; but for lack of trust in the triple helix culture the fledgling network dynamics fizzled out when the government prop, which initiated the process, was withdrawn. The article concludes by highlighting the need for policy to promote the culture of trust among network players and for knowledge intermediaries to be robustly systemic in their organization and operation

Complex Reaction Network Thermodynamic and Kinetic Autoconstruction Based on Ab Initio Statistical Mechanics: A Case Study of O₂ Activation on Ag₄ Clusters

An approach based on ab initio statistical mechanics is demonstrated for autoconstructing complex reaction networks. Ab initio molecular dynamics combined with Markov state models are employed to study relevant transitions and corresponding thermodynamic and kinetic properties of a reaction. To explore the capability and flexibility of this approach, we present a study of oxygen activation on Ag4 as a model reaction. Specifically, with the same sampled trajectories, it is possible to study the structural effects and the reaction rate of the cited reaction. The results show that this approach is suitable for automatized construction of reaction networks, especially for non-well-studied reactions, which can benefit from this ab initio molecular dynamics based approach to construct comprehensive reaction networks with Markov state models without prior knowledge about the potential energy landscape

The Small Worlds of Wikipedia: Implications for Growth, Quality and Sustainability of Collaborative Knowledge Networks

This work is a longitudinal network analysis of the interaction networks of Wikipedia, a free, user-led collaborativelygenerated online encyclopedia. Making a case for representing Wikipedia as a knowledge network, and using the lens of contemporary graph theory, we attempt to unravel its knowledge creation process and growth dynamics over time. Typical small-world characteristics of short path-length and high clustering have important theoretical implications for knowledge networks. We show Wikipedia’s small-world nature to be increasing over time, while also uncovering power laws and assortative mixing. Investigating the process by which an apparently un-coordinated, diversely motivated swarm of assorted contributors, create and maintain remarkably high quality content, we find an association between Quality and Structural Holes. We find that a few key high degree, cluster spanning nodes - ‘hubs’ - hold the growing network together, and discuss implications for the networks’ growth and emergent quality

Reality Mining with Mobile Data: Understanding the Impact of Network Structure on Propagation Dynamics

Recent studies have increasingly turned to graph theory to model Realistic Contact Networks (RCNs) for characterizing propagation dynamics. Several of these studies have demonstrated that RCNs are best described as having exponential degree distributions. In this article, based on the mobile data gathered from in-vehicle wireless devices, we show that RCNs do not always have exponential degree distributions, especially in dynamic environments. On this basis, a model is designed to recognize the structure of networks. Based on the model, we investigate the impacts of network structure on disease dynamics that is an important empirical study to the propagation dynamics. The time varying infected number R is the important parameter that is used to quantify the disease dynamics. In this study, the prediction accuracy for R is improved by utilizing realistic structural knowledge mined by our recognition model

Neural Modeling and Control of Diesel Engine with Pollution Constraints

The paper describes a neural approach for modelling and control of a turbocharged Diesel engine. A neural model, whose structure is mainly based on some physical equations describing the engine behaviour, is built for the rotation speed and the exhaust gas opacity. The model is composed of three interconnected neural submodels, each of them constituting a nonlinear multi-input single-output error model. The structural identification and the parameter estimation from data gathered on a real engine are described. The neural direct model is then used to determine a neural controller of the engine, in a specialized training scheme minimising a multivariable criterion. Simulations show the effect of the pollution constraint weighting on a trajectory tracking of the engine speed. Neural networks, which are flexible and parsimonious nonlinear black-box models, with universal approximation capabilities, can accurately describe or control complex nonlinear systems, with little a priori theoretical knowledge. The presented work extends optimal neuro-control to the multivariable case and shows the flexibility of neural optimisers. Considering the preliminary results, it appears that neural networks can be used as embedded models for engine control, to satisfy the more and more restricting pollutant emission legislation. Particularly, they are able to model nonlinear dynamics and outperform during transients the control schemes based on static mappings.Comment: 15 page

Fundamental activity constraints lead to specific interpretations of the connectome

The continuous integration of experimental data into coherent models of the brain is an increasing challenge of modern neuroscience. Such models provide a bridge between structure and activity, and identify the mechanisms giving rise to experimental observations. Nevertheless, structurally realistic network models of spiking neurons are necessarily underconstrained even if experimental data on brain connectivity are incorporated to the best of our knowledge. Guided by physiological observations, any model must therefore explore the parameter ranges within the uncertainty of the data. Based on simulation results alone, however, the mechanisms underlying stable and physiologically realistic activity often remain obscure. We here employ a mean-field reduction of the dynamics, which allows us to include activity constraints into the process of model construction. We shape the phase space of a multi-scale network model of the vision-related areas of macaque cortex by systematically refining its connectivity. Fundamental constraints on the activity, i.e., prohibiting quiescence and requiring global stability, prove sufficient to obtain realistic layer- and area-specific activity. Only small adaptations of the structure are required, showing that the network operates close to an instability. The procedure identifies components of the network critical to its collective dynamics and creates hypotheses for structural data and future experiments. The method can be applied to networks involving any neuron model with a known gain function.Comment: J. Schuecker and M. Schmidt contributed equally to this wor