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Clusters seem to not only contribute directly to productivity of the nation as a whole but seem to have a positive effect on other clusters. Yet there has been no research made by researchers that look into the development of business models or joint value propositions made by a cluster as a whole. The authors have failed to identify any common view on the research field today of just how a business model for an industrial logistic cluster should look like as well as what components are essential and must be included. Therefore, the purpose of our research was to explore the contextual conditions for developing BM for industrial cluster in the logistic area.  In order to reach the goal of our research, inductive based approach was used when trying to investigate a cluster in Halmstad called “Innovative Logistic in Halmstad”. The cluster is in a early stage at this moment. Data was collected through interviews and secondary data was collected to complement our findings as well.  The main findings of our study are as follows. The authors create a conceptual business model for industrial cluster in the logistic area. We also offer a value package proposal where the cluster offer a client a joint value proposition as the main majority of companies buy their logistic transports. This could result in e.g. more eco-driven means of transportation.

This thesis addresses a central topic in auroral physics, namely particle accelerationproducing intense aurora as well as energetic plasma outflow. Cluster satellitemeasurements of electric and magnetic fields, electrons and ions, collected across auroralfield lines, are used to study various aspects of the quasi-static auroral accelerationregion (AAR), its relation to the auroral density cavity, and the relative role of quasistaticand Alfvénic acceleration for producing aurora.The acceleration potential structures and electro-dynamical features of a large-scaleauroral surge is studied based on data from the Cluster satellites, crossing differentmagnetic local time (MLT) sectors of a surge-horn system. This allows snapshots of theacceleration potential structure and of the current systems to be provided, including thefield-aligned current closure for the different segments of the surge-horn aurora.The relative role of quasi-static and Alfvénic acceleration for producing auroral arcs isaddressed for the case of a large-scale substorm surge, crossed by the Cluster C2 satellite. Thetwo contributions to the downward electron energy flux is estimated for each of the smallerscalearc structures crossed by C2 within and adjacent to the large-scale surge. For these, thequasi-static acceleration typically dominates, except for the polar cap boundary arc, and in thesurge head, where the Alfvénic contribution is significant.The occurrence of intense electric fields and associated plasma densities versus altitude andMLT is the subject of a statistical study based on 9.5 years of Cluster data, collected ataltitudes between 2 and 4 RE. Intense arc-associated electric fields occur in two altituderegions, separated by a gap around 2.8 RE. The low-altitude fields are interpreted as mainlyquasi-static and the high-altitude fields as mainly Alfvénic. The results which are supportedby estimates of the (ΔE/ΔB)/VA ratio, indicate that, on the average, the quasi-static fieldsextend up to 2.6 RE, above which a transition to Alfvénic fields occur.The auroral density cavity, intimately associated with the auroral acceleration process, wasthe subject of a statistical study based on Cluster data, collected between 2002 and 2007, ataltitudes between 2.0 RE and 5.5 RE. Decreasing electron densities are observed between 2 and 3.3 RE, and between 4.6 and 5.5 RE, corresponding to climbing the parallel potential hillof the AAR. Furthermore, the density is found to decrease while ascending above the AAR,indicating that the cavities are not necessarily confined by it. / <p>QC 20141110</p>

Cluster analysis is the study of how to partition data into homogeneous subsets so that the partitioned data share some common characteristic. In one to three dimensions, the human eye can distinguish well between clusters of data if clearly separated. However, when there are more than three dimensions and/or the data is not clearly separated, an algorithm is required which needs a metric of similarity that quantitatively measures the characteristic of interest. Linear Grouping Analysis (LGA, Van Aelst et al. 2006) is an algorithm for clustering data around hyperplanes, and is most appropriate when: 1) the variables are related/correlated, which results in clusters with an approximately linear structure; and 2) it is not natural to assume that one variable is a “response”, and the remainder the “explanatories”. LGA measures the compactness within each cluster via the sum of squared orthogonal distances to hyperplanes formed from the data. In this dissertation, we extend the scope of problems to which LGA can be applied. The first extension relates to the linearity requirement inherent within LGA, and proposes a new method of non-linearly transforming the data into a Feature Space, using the Kernel Trick, such that in this space the data might then form linear clusters. A possible side effect of this transformation is that the dimension of the transformed space is significantly larger than the number of observations in a given cluster, which causes problems with orthogonal regression. Therefore, we also introduce a new method for calculating the distance of an observation to a cluster when its covariance matrix is rank deficient. The second extension concerns the combinatorial problem for optimizing a LGA objective function, and adapts an existing algorithm, called BIRCH, for use in providing fast, approximate solutions, particularly for the case when data does not fit in memory. We also provide solutions based on BIRCH for two other challenging optimization problems in the field of robust statistics, and demonstrate, via simulation study as well as application on actual data sets, that the BIRCH solution compares favourably to the existing state-of-the-art alternatives, and in many cases finds a more optimal solution.

Gas phase anion clusters, consisting of solvent molecules hydrogen bonded to halide anions, are characterised by vibrational predissociation spectroscopy using a combination of mass spectrometry and laser spectroscopy. Monitoring changes in the vibrational properties of neutral solvent molecules when they are attached to a halide anion allows one to infer cluster structures. In some cases, the spectra contain rotationally resolved features, so that quantitative structural parameters can be obtained. (For complete abstract open document)

Zugl.: Atlanta, Georgia, USA, Georgia Inst. of Technology, Diss., 2007

Zugl.: Tübingen, University, Diss., 2006.

Universiẗat, Diss., 2006--Karlsruhe.

Thesis (M.A.)--University of Toronto, 2005. / Includes bibliographical references (leaves 48-51).

Zugl.: Braunschweig, Techn. Univ., Diss., 2007