Development of research networks : the case of social capital

The aim of this study is to explore how a new concept appears inscientific discussion and research, how it diffuses to other fields and out of the scientific communities, and how the networks are formed around the concept. Text and terminology take the interest of a reader in the digital environment. Texts create networks where the terminology used is dependent on the ideas, viewsand paradigms of the field. This study is based mainly on bibliographic data. Materials for bibliometric studies have been collected from different databases. The databases are also evaluated and their quality and coverage are discussed. The thesauri of those databases that have been selected for a more in depth study have also been evaluated. The material selected has been used to study how long and in which ways an innovative publication, which can be seen as a milestone in a specific field, influences the research. The concept that has been chosen as a topic for this research is Social Capital, because it has been a popular concept in different scientific fields as well as in everyday speech and the media. It seemed to be a `fashion concept´ that appeared in different situations at the Millennium. The growth and diffusion of social capital publications has been studied. The terms connected with social capital in different fields and different stages of the development have also been analyzed. The methods that have been used in this study are growth and diffusion analysis, content analysis, citation analysis, coword analysis and cocitation analysis. One method that can be used tounderstand and to interpret results of these bibliometric studies is to interview some key persons, who are known to have a gatekeeper position in the diffusion of the concept. Thematic interviews with some Finnish researchers and specialists that have influenced the diffusion of social capital into Finnish scientificand social discussions provide background information. iv The Milestone Publications on social capital have been chosen and studied. They give answers to the question "What is Social Capital?" By comparing citations to Milestone Publications with the growth of all social capital publications in a database, we can drawconclusions about the point at which social capital became generally approved `tacit knowledge´. The contribution of the present study lies foremost in understanding the development of network structures around a new concept that has diffused in scientific communities and also outside them. The network means both networks of researchers, networks of publications and networks of concepts that describe the research field. The emphasis has been on the digital environment and onthe socalled information society that we are now living in, but in this transitional stage, the printed publications are still important and widely used in social sciences and humanities. The network formation is affected by social relations and informal contacts that push new ideas. This study also gives new information about using different research methods, like bibliometric methods supported by interviews and content analyses. It is evident that interpretation of bibliometric maps presupposes qualitative information and understanding of the phenomena under study.

Evaluation of follicular lymphoid depletion in the Bursa of Fabricius: an alternative methodology using digital image analysis and artificial neural networks

Fifty Bursa of Fabricius (BF) were examined by conventional optical microscopy and digital images were acquired and processed using Matlab® 6.5 software. The Artificial Neuronal Network (ANN) was generated using Neuroshell® Classifier software and the optical and digital data were compared. The ANN was able to make a comparable classification of digital and optical scores. The use of ANN was able to classify correctly the majority of the follicles, reaching sensibility and specificity of 89% and 96%, respectively. When the follicles were scored and grouped in a binary fashion the sensibility increased to 90% and obtained the maximum value for the specificity of 92%. These results demonstrate that the use of digital image analysis and ANN is a useful tool for the pathological classification of the BF lymphoid depletion. In addition it provides objective results that allow measuring the dimension of the error in the diagnosis and classification therefore making comparison between databases feasible.

Modelling flexible asset management in industrial maintenance companies and networks

The tightening competition and increasing dynamism have created an emerging need for flexible asset management. This means that the changes of market demand should be responded to with adjustments in the amount of assets tied to the balance sheets of companies. On the other hand, industrial maintenance has recently experienced drastic changes, which have led to an increase in the number of maintenance networks (consisting of customer companies that buy maintenance services, as well as various supplier companies) and inter-organizational partnerships. However, the research on maintenance networks has not followed the changes in the industry. Instead, there is a growing need for new ways of collaboration between partnering companies to enhance the competitiveness of the whole maintenance network. In addition, it is more and more common for companies to pursue lean operations in their businesses. This thesis shows how flexible asset management can increase the profitability of maintenance companies and networks under dynamic operating conditions, and how the additional value can then be shared between the network partners. Firstly, I have conducted a systematic literature review to identify what kind of requirements for asset management models are set by the increasing dynamism. Then I have responded to these requirements by constructing an analytical model for flexible asset management, linking asset management to the profitability and financial state of a company. The thesis uses the model to show how flexible asset management can increase profitability in maintenance companies and networks, and how the created value can be shared in the networks to reach a win-win situation. The research indicates that the existing models for asset management are heterogeneous by nature due to the various definitions of ‘asset management’. I conclude that there is a need for practical asset management models which address assets comprehensively with an inter-organizational, strategic view. The comprehensive perspective, taking all kinds of asset types into account, is needed to integrate the research on asset management with the strategic management of companies and networks. I will show that maintenance companies can improve their profitability by increasing the flexibility of their assets. In maintenance networks, reorganizing the ownership of the assets among the different network partners can create additional value. Finally, I will introduce flexible asset management contracts for maintenance networks. These contracts address the value sharing related to reorganizing the ownership of assets according to the principles of win-win situations.

Communication Solutions for LVDC Island Networks

Production and generation of electrical power is evolving to more environmental friendly technologies and schemes. Pushed by the increasing cost of fossil fuels, the operational costs of producing electrical power with fossil fuels and the effect in the environment, like pollution and global warming, renewable energy sources gain con-stant impulse into the global energy economy. In consequence, the introduction of distributed energy sources has brought a new complexity to the electrical networks. In the new concept of smart grids and decen-tralized power generation; control, protection and measurement are also distributed and requiring, among other things, a new scheme of communication to operate with each other in balance and improve performance. In this research, an analysis of different communication technologies (power line communication, Ethernet over unshielded twisted pair (UTP), optic fiber, Wi-Fi, Wi-MAX, and Long Term Evolution) and their respective characteristics will be carried out. With the objective of pointing out strengths and weaknesses from different points of view (technical, economical, deployment, etc.) to establish a richer context on which a decision for communication approach can be done depending on the specific application scenario of a new smart grid deployment. As a result, a description of possible optimal deployment solutions for communication will be shown considering different options for technologies, and a mention of different important considerations to be taken into account will be made for some of the possible network implementation scenarios.

Forecasting of wind speeds and directions with artificial neural networks

In this master’s thesis, wind speeds and directions were modeled with the aim of developing suitable models for hourly, daily, weekly and monthly forecasting. Artificial Neural Networks implemented in MATLAB software were used to perform the forecasts. Three main types of artificial neural network were built, namely: Feed forward neural networks, Jordan Elman neural networks and Cascade forward neural networks. Four sub models of each of these neural networks were also built, corresponding to the four forecast horizons, for both wind speeds and directions. A single neural network topology was used for each of the forecast horizons, regardless of the model type. All the models were then trained with real data of wind speeds and directions collected over a period of two years in the municipal region of Puumala in Finland. Only 70% of the data was used for training, validation and testing of the models, while the second last 15% of the data was presented to the trained models for verification. The model outputs were then compared to the last 15% of the original data, by measuring the mean square errors and sum square errors between them. Based on the results, the feed forward networks returned the lowest generalization errors for hourly, weekly and monthly forecasts of wind speeds; Jordan Elman networks returned the lowest errors when used for forecasting of daily wind speeds. Cascade forward networks gave the lowest errors when used for forecasting daily, weekly and monthly wind directions; Jordan Elman networks returned the lowest errors when used for hourly forecasting. The errors were relatively low during training of the models, but shot up upon simulation with new inputs. In addition, a combination of hyperbolic tangent transfer functions for both hidden and output layers returned better results compared to other combinations of transfer functions. In general, wind speeds were more predictable as compared to wind directions, opening up opportunities for further research into building better models for wind direction forecasting.

From emerging opportunities to successful business networks – evidence from bioenergy

Increasing renewable energy utilization is a challenge that is tried to be solved in different ways. One of the most promising options for renewable energy is different biomasses, and the bioenergy field offers numerous emerging business opportunities. The actors in the field have rarely all the needed know-how and resources for exploiting these opportunities, and thus it is reasonable to seize them in cooperation. Networking is not an easy task to carry out, however, and in addition to its advantages for the firms engaged, it sets numerous challenges as well. The development of a network is a result of several steps firms need to take. In order to gain optimal advantage of their networks, firms need to weigh out with whom, why and how they should cooperate. In addition, everything does not depend on the firms themselves, as several factors in the external environment set their own enablers and barriers for cooperation. The formation of a network around a business opportunity is thus a multiphase process. The objective of this thesis is to depict this process via a step-by-step analysis and thus increase understanding on the whole development path from an entrepreneurial opportunity to a successful business network. The empirical evidence has been gathered by discussing the opportunities of animal manure refinement to biogas and forest biomass utilization for heating in Finland. The thesis comprises two parts. The first part provides an overview of the study, and the second part includes five research publications. The results reveal that it is essential to identify and analyze all the steps in the development process of a network, and several frameworks are used in the thesis to analyze these steps. The frameworks combine the views of theory and practical experiences of empirical study, and thus give new multifaceted views for the discussion on SME networking. The results indicate that the ground for cooperation should be investigated adequately by taking account of the preconditions in all the three contexts in which the actors operate: the social context, the region and the institutional environment. In case the project advances to exploitation, the assets and objectives of the actors should be paired off, which sets a need for relationships and sub-networks differing in breadth and depth. Different relationships and networks require different kinds of maintenance and management. Moreover, the actors should have the capability to change the formality or strategy of the relationships if needed. The drivers for these changes come along with the changing environment, which causes changes in the objectives of the actors and this way in the whole network. Bioenergy as the empirical field of the study represents well an industrial field with many emerging opportunities, a motley group of actors, and sensitivity for fast changes.

Strategic management of value networks: how to create value in cross-sector collaboration and partnerships

Cross-sector collaboration and partnerships have become an emerging and desired strategy in addressing huge social and environmental challenges. Despite its popularity, cross-sector collaboration management has proven to be very challenging. Even though cross-sector collaboration and partnership management have been widely studied and discussed in recent years, their effectiveness as well as their ability to create value with respect to the problems they address has remained very challenging. There is little or no evidence of their ability to create value. Regarding all these challenges, this study aims to explore how to manage cross-sector collaborations and partnerships to be able to improve their effectiveness and to create more value for all partners involved in collaboration as well as for customers. The thesis is divided into two parts. The first part comprises an overview of relevant literature (including strategic management, value networks and value creation theories), followed by presenting the results of the whole thesis and the contribution made by the study. The second part consists of six research publications, including both quantitative and qualitative studies. The chosen research strategy is triangulation, as the study includes four types of triangulation: (1) theoretical triangulation, (2) methodological triangulation, (3) data triangulation and (4) researcher triangulation. Two publications represent conceptual development, which are based on secondary data research. One publication is a quantitative study, carried out through a survey. The other three publications represent qualitative studies, based on case studies, where data was collected through interviews and workshops, with participation of managers from all three sectors: public, private and the third (nonprofit). The study consolidates the field of “strategic management of value networks,” which is proposed to be applied in the context of cross-sector collaboration and partnerships, with the aim of increasing their effectiveness and the process of value creation. Furthermore, the study proposes a first definition for the strategic management of value networks. The study also proposes and develops two strategy tools that are recommended to be used for the strategic management of value networks in cross-sector collaboration and partnerships. Taking a step forward, the study implements the strategy tools in practice, aiming to show and to demonstrate how new value can be created by using the developed strategy tools for the strategic management of value networks. This study makes four main contributions. (1) First, it brings a theoretical contribution by providing new insights and consolidating the field of strategic management of value networks, also proposing a first definition for the strategic management of value networks. (2) Second, the study makes a methodical contribution by proposing and developing two strategy tools for value networks of cross-sector collaboration: (a) value network mapping, a method that allows us to assess the current and the potential value network and (b) the Value Network Scorecard, a method of performance measurement and performance prediction in cross-sector collaboration. (3) Third, the study has managerial implications, offering new solutions and empirical evidence on how to increase the effectiveness of cross-sector collaboration and also allow managers to understand how new value can be created in cross-sector partnerships and how to get the full potential of collaboration. (4) And fourth, the study also has practical implications, allowing managers to understand how to use in practice the strategy tools developed in this study, providing discussions on the limitations regarding the proposed tools as well as general limitations involved in the study.

Synergistic control of forearm based on accelerometer data and artificial neural networks

In the present study, we modeled a reaching task as a two-link mechanism. The upper arm and forearm motion trajectories during vertical arm movements were estimated from the measured angular accelerations with dual-axis accelerometers. A data set of reaching synergies from able-bodied individuals was used to train a radial basis function artificial neural network with upper arm/forearm tangential angular accelerations. The trained radial basis function artificial neural network for the specific movements predicted forearm motion from new upper arm trajectories with high correlation (mean, 0.9149-0.941). For all other movements, prediction was low (range, 0.0316-0.8302). Results suggest that the proposed algorithm is successful in generalization over similar motions and subjects. Such networks may be used as a high-level controller that could predict forearm kinematics from voluntary movements of the upper arm. This methodology is suitable for restoring the upper limb functions of individuals with motor disabilities of the forearm, but not of the upper arm. The developed control paradigm is applicable to upper-limb orthotic systems employing functional electrical stimulation. The proposed approach is of great significance particularly for humans with spinal cord injuries in a free-living environment. The implication of a measurement system with dual-axis accelerometers, developed for this study, is further seen in the evaluation of movement during the course of rehabilitation. For this purpose, training-related changes in synergies apparent from movement kinematics during rehabilitation would characterize the extent and the course of recovery. As such, a simple system using this methodology is of particular importance for stroke patients. The results underlie the important issue of upper-limb coordination.

Energy Management System for LVDC Island Networks

Recent developments in power electronics technology have made it possible to develop competitive and reliable low-voltage DC (LVDC) distribution networks. Further, islanded microgrids—isolated small-scale localized distribution networks— have been proposed to reliably supply power using distributed generations. However, islanded operations face many issues such as power quality, voltage regulation, network stability, and protection. In this thesis, an energy management system (EMS) that ensures efficient energy and power balancing and voltage regulation has been proposed for an LVDC island network utilizing solar panels for electricity production and lead-acid batteries for energy storage. The EMS uses the master/slave method with robust communication infrastructure to control the production, storage, and loads. The logical basis for the EMS operations has been established by proposing functionalities of the network components as well as by defining appropriate operation modes that encompass all situations. During loss-of-powersupply periods, load prioritizations and disconnections are employed to maintain the power supply to at least some loads. The proposed EMS ensures optimal energy balance in the network. A sizing method based on discrete-event simulations has also been proposed to obtain reliable capacities of the photovoltaic array and battery. In addition, an algorithm to determine the number of hours of electric power supply that can be guaranteed to the customers at any given location has been developed. The successful performances of all the proposed algorithms have been demonstrated by simulations.

The role of diaspora networks in opportunity exploitation by ethnic entrepreneurs in Finland

The thesis explores the role of diaspora network in opportunity exploitation by ethnic entrepreneurs in Finland. The purpose of this study is to how ethnic entrepreneurs in Finland can exploit diaspora networks and how significant support of these networks in comparison with governmental and other official institutional support. Increased mobility of the global population and number of immigrants all around the world contribute into interest of academics to this topic. At the same time, in comparison with other European countries for Finland this phenomenon is new. However, increasing number of immigrants add to academic interest to this topic. The theoretical background of this thesis consists of the collection of studies done on diaspora networks and ethnic entrepreneurs as internationally as in Finland. The concept of cooperation between diaspora and ethnic entrepreneurs describes how diaspora can support ethnic entrepreneur and emphasize importance of diaspora ties for business. At the end of the theoretical section theoretical a special grid was proposed as summary of the academic literature review. The empirical research for this study is based on the interviews with five ethnic entrepreneurs in Finland. Empirical data was analyzed and compared with the similar studies done internationally. During the interviews analyses were stressed aspects of the significance of governmental and other official institutional support. During the interviews analyses were stressed aspects of the significance of the governmental and other official institutional support. According to the main findings diaspora can play positive and in some cases crucial role in the business of ethnic entrepreneurs in Finland. Moreover, diaspora can be a facilitator of internationalization of ethnic entrepreneurs. At the same time, there are several factors which might increase or decrease role of diaspora. The ways of the utilization of diaspora network were combined in the empirical grid. The study enhance understanding of the challenges of ethnic entrepreneurs and provides certain recommendations for the policy makers, ethnic entrepreneurs and managers of official institutions involved in cooperation with ethnic entrepreneurs

The infared optical properties of Sr2RuO4 and SmTiO3 including an object-oriented resistivity interface /

The perovskite crystal structure is host to many different materials from insulating to superconducting providing a diverse range of intrinsic character and complexity. A better fundamental description of these materials in terms of their electronic, optical and magnetic properties undoubtedly precedes an effective realization of their application potential. SmTiOa, a distorted perovskite has a strongly localized electronic structure and undergoes an antiferromagnetic transition at 50 K in its nominally stoichiometric form. Sr2Ru04 is a layered perovskite superconductor (ie. Tc % 1 K) bearing the same structure as the high-tem|>erature superconductor La2_xSrrCu04. Polarized reflectance measurements were carried out on both of these materials revealing several interesting features in the far-infrared range of the spectrum. In the case of SmTiOa, although insulating, evidence indicates the presence of a finite background optical conductivity. As the temperature is lowered through the ordering temperature a resonance feature appears to narrow and strengthen near 120 cm~^ A nearby phonon mode appears to also couple to this magnetic transition as revealed by a growing asymmetry in the optica] conductivity. Experiments on a doped sample with a greater itinerant character and lower Neel temperature = 40 K also indicate the presence of this strongly temperature dependent mode even at twice the ordering temperature. Although the mode appears to be sensitive to the magnetic transition it is unclear whether a magnon assignment is appropriate. At very least, evidence suggests an interesting interaction between magnetic and electronic excitations. Although Sr2Ru04 is highly anisotropic it is metallic in three-dimensions at low temperatures and reveals its coherent transport in an inter-plane Drude-like component to the highest temperatures measured (ie. 90 K). An extended Drude analysis is used to probe the frequency dependent scattering character revealing a peak in both the mass enhancement and scattering rate near 80 cm~* and 100 cm~* respectively. All of these experimental observations appear relatively consistent with a Fermi-liquid picture of charge transport. To supplement the optical measurements a resistivity station was set up with an event driven object oriented user interface. The program controls a Keithley Current Source, HP Nano-Voltmeter and Switching Unit as well as a LakeShore Temperature Controller in order to obtain a plot of the Resistivity as a function of temperature. The system allows for resistivity measurements ranging from 4 K to 290 K using an external probe or between 0.4 K to 295 K using a Helium - 3 Cryostat. Several materials of known resistivity have confirmed the system to be robust and capable of measuring metallic samples distinguishing features of several fiQ-cm.

Crystal growth, Raman scattering and optical properties of the superconductor Cd2Re2O7(16O, 18O)/

Using the Physical Vapor Transport method, single crystals of Cd2Re207 have been grown, and crystals of dimensions up to 8x6x2 mm have been achieved. X-ray diffraction from a single crystal of Cd2Re207 has showed the crystal growth in the (111) plane. Powder X-ray diffraction measurements were performed on ^^O and ^^O samples, however no difference was observed. Assigning the space group Fd3m to Cd2Re207 at room temperature and using structure factor analysis, the powder X-ray diffraction pattern of the sample was explained through systematic reflection absences. The temperatiure dependence of the resistivity measurement of ^^O has revealed two structural phase transitions at 120 and 200 K, and the superconducting transition at 1.0 K. Using Factor Group Analysis on three different structiures of Cd2Re207, the number of IR and Raman active phonon modes close to the Brillouin zone centre have been determined and the results have been compared to the temperature-dependence of the Raman shifts of ^^O and ^*0 samples. After scaling (via removing Bose-Einstein and Rayleigh scattering factors from the scattered light) all spectra, each spectrum was fitted with a number of Lorentzian peaks. The temperature-dependence of the FWHM and Raman shift of mode Eg, shows the effects of the two structurjil phase transitions above Tc. The absolute reflectance of Cd2Re207 - '^O single crystals in the far-infrared spectral region (7-700 cm~^) has been measured in the superconducting state (0.5 K), right above the superconducting state (1.5 K), and in the normal state (4.2 K). Thermal reflectance of the sample at 0.5 K and 1.5 K indicates a strong absorption feature close to 10 cm~^ in the superconducting state with a reference temperature of 4.2 K. By means of Kramers-Kronig analysis, the absolute reflectance was used to calculate the optical conductivity and dielectric function. The real part of optical conductivity shows five distinct active phonon modes at 44, 200, 300, 375, and 575 cm~' at all temperatures including a Drude-like behavior at low frequencies. The imaginary part of the calculated dielectric function indicates a mode softening of the mode 44 cm~' below Tc.

Optical Properties of Sb2Te3, and Dilute Magnetic Semiconductors Sb1.97 VO.03 Te3 and Sb1.94 CrO.06 Te3

This thesis reports on the optical properties of the dilute magnetic semiconductors, Sb1.97 V 0.03 Te3 and Sb1.94Cr0.06Te3, along with the parent compound Sb2Te3' These materials develop a ferromagnetic state at low temperature with Curie temperatures of 22 K and 16 K respectively. All three samples were oriented such that the electric field vector of the light was perpendicular to the c-axis. The reflectance profile of these samples in the mid-infrared (500 to 3000 cm-1) shows a pronounced plasma edge which retracts with decreasing temperature. The far-infrared region of these samples exhibits a phonon at ~ 60 cm-1 which softens as temperature decreases. Kramers-Kronig analysis and a Drude-Lorentz model were employed to determine the optical constants of the bulk samples. The real part of the optical conductivity is shown to consist of intraband contributions at frequencies below the energy gap (~0.26 eV) and interband contributions at frequencies above the energy gap. The temperature dependence of the scattering rate show that a mix of phonon and impurity scattering are present, while the signature of traditional spin disorder (magnetic) scattering was difficult to confirm.

Automatic Inference of Graph Models for Complex Networks with Genetic Programming

Complex networks can arise naturally and spontaneously from all things that act as a part of a larger system. From the patterns of socialization between people to the way biological systems organize themselves, complex networks are ubiquitous, but are currently poorly understood. A number of algorithms, designed by humans, have been proposed to describe the organizational behaviour of real-world networks. Consequently, breakthroughs in genetics, medicine, epidemiology, neuroscience, telecommunications and the social sciences have recently resulted. The algorithms, called graph models, represent significant human effort. Deriving accurate graph models is non-trivial, time-intensive, challenging and may only yield useful results for very specific phenomena. An automated approach can greatly reduce the human effort required and if effective, provide a valuable tool for understanding the large decentralized systems of interrelated things around us. To the best of the author's knowledge this thesis proposes the first method for the automatic inference of graph models for complex networks with varied properties, with and without community structure. Furthermore, to the best of the author's knowledge it is the first application of genetic programming for the automatic inference of graph models. The system and methodology was tested against benchmark data, and was shown to be capable of reproducing close approximations to well-known algorithms designed by humans. Furthermore, when used to infer a model for real biological data the resulting model was more representative than models currently used in the literature.

Heuristics for the Critical Node Detection Problem in Large Complex Networks

Complex networks have recently attracted a significant amount of research attention due to their ability to model real world phenomena. One important problem often encountered is to limit diffusive processes spread over the network, for example mitigating pandemic disease or computer virus spread. A number of problem formulations have been proposed that aim to solve such problems based on desired network characteristics, such as maintaining the largest network component after node removal. The recently formulated critical node detection problem aims to remove a small subset of vertices from the network such that the residual network has minimum pairwise connectivity. Unfortunately, the problem is NP-hard and also the number of constraints is cubic in number of vertices, making very large scale problems impossible to solve with traditional mathematical programming techniques. Even many approximation algorithm strategies such as dynamic programming, evolutionary algorithms, etc. all are unusable for networks that contain thousands to millions of vertices. A computationally efficient and simple approach is required in such circumstances, but none currently exist. In this thesis, such an algorithm is proposed. The methodology is based on a depth-first search traversal of the network, and a specially designed ranking function that considers information local to each vertex. Due to the variety of network structures, a number of characteristics must be taken into consideration and combined into a single rank that measures the utility of removing each vertex. Since removing a vertex in sequential fashion impacts the network structure, an efficient post-processing algorithm is also proposed to quickly re-rank vertices. Experiments on a range of common complex network models with varying number of vertices are considered, in addition to real world networks. The proposed algorithm, DFSH, is shown to be highly competitive and often outperforms existing strategies such as Google PageRank for minimizing pairwise connectivity.