Given the increasing complexity of global ecological and social problems, innovation plays a key role in solutions for sustainable development. Within innovation ecosystems, intermediaries such as innovation hubs play an important part in influencing other actors like startups, investors and policymakers to create solutions for change. Therefore, it is essential that innovation hubs incorporate sustainability into their practices, if they are to contribute to addressing the Global Sustainability Challenge (GSC).   To see how this could be done, this study examines the case of the Venture Café Global Network (VCGN) a type of sector agnostic innovation hub. The study used a multi-method qualitative approach. Data was gathered through semi-structured interviews with different players in the innovation ecosystem, as well as with actors from within VCGN, and through a documentary analysis.   The results and discussion are presented according to certain overarching themes that emerged from the interviews and answer the research questions. Along with a longer list of recommendations, this study concludes that the adoption of a shared organisational definition of sustainability is the bedrock for any sustainability integration and vital for innovation hubs to impact other actors in their innovation ecosystems, to address the GSC.

Purpose – The purpose of this study is to understand How Corporate Innovation Hubs (CIHs) can Mitigate NIH and NSH Problems in Knowledge Transfer. To fulfill this purpose, the following research questions were derived: RQ1: What are the causes of NIH & NSH problems among actors collaborating through a CIH? RQ2: What are the consequences of NIH & NSH problems among actors collaborating through a CIH? RQ3: What mechanisms can a CIH use to mitigate NIH & NSH problems among collaborating actors? Method – The study is an explorative inductive multiple case-study, investigating five CIHs situated in either Silicon Valley, US or Gothenburg, Sweden. In total, 39 interviews were conducted in three waves, and results were derived using a Gioia analysis. Findings – This study resulted in a framework illustrating connections of causes and consequences of NIH and NSH problems with corresponding mitigating mechanisms. The most critical causes are Obsessive control (NIH), Internal antagonism (NIH) and Low confidential awareness (NSH). The most severe consequences are Use of irrelevant knowledge (NIH), Suffocation of external ideas (NIH) and Restrained problem-solving (NSH). The most important mitigating mechanisms are Translate relevance of ideas (NIH) and Create mutual confidential understanding (NSH). Theoretical and Practical Implications – This study contributes to the scarce literature on NIH and NSH problems among multiple actors collaborating through CIHs. By identifying causes, consequences and mitigating mechanisms of NIH and NSH problems, CIHs will be able to detect NIH and NSH tendencies among its collaborating actors, to mitigate its causes and prevent its consequences. Limitations and Future Research – The study is limited by the investigated CIHs focus on exploring future transportation solutions, indicating that future studies can investigate CIHs in other industry settings and among other actors collaborating through CIHs. Keywords: Corporate Innovation Hubs; NIH; NSH; Knowledge Transfer

In this thesis, we introduce the Partitioning-Hub Location-Routing problem (PHLRP), which can be classied as a variant of the hub location problem. PHLRP consists of partitioning a network into sub-networks, locating at least one hub in each subnetwork and routing the traffic within the network such that all inter-subnetwork traffic is routed through the hubs and all intra-subnetwork traffic stays within the sub-networks all the way from the source to the destination. Obviously, besides the hub location component, PHLRP also involves a graph partitioning component and a routing component. PHLRP finds applications in the strategic planning or deployment of the Intermediate System-Intermediate System (ISIS) Internet Protocol networks and the Less-than-truck load freight distribution systems. First, we introduce three IP formulations for solving PHLRP. The hub location component and the graph partitioning components of PHLRP are modeled in the same way in all three formulations. More precisely, the hub location component is represented by the p-median variables and constraints; and the graph partitioning component is represented by the size-constrained graph partitioning variables and constraints. The formulations differ from each other in the way the peculiar routing requirements of PHLRP are modeled. We then carry out analytical and empirical comparisons of the three IP formulations. Our thorough analysis reveals that one of the formulations is provably the tightest of the three formulations. We also show analytically that the LP relaxations of the other two formulations do not dominate each other. On the other hand, our empirical comparison in a standard branch-and-cut framework that is provided by CPLEX shows that not the tightest but the most compact of the three formulations yield the best performance in terms of solution time. From this point on, based on the insight gained from detailed analysis of the formulations, we focus our attention on a common sub-problem of the three formulations: the so-called size-constrained graph partitioning problem. We carry out a detailed polyhedral analysis of this problem. The main benet from this polyhedral analysis is that the facets we identify for the size-constrained graph partitioning problem constitute strong valid inequalities for PHLRP. And finally, we wrap up our efforts for solving PHLRP. Namely, we present the results of our computational experiments, in which we employ some facets of the size-constrained graph partitioning polytope in a branch-and-cut algorithm for solving PHLRP. Our experiments show that our approach brings signicant improvements to the solution time of PHLRP when compared with the default branch-and-cut solver of XPress. / Dans cette thèse, nous introduisons le problème Partitionnement-Location des Hubs et Acheminement (PLHA), une variante du problème de location de hubs. Le problème PLHA partitionne un réseau afin d'obtenir des sous-réseaux, localise au moins un hub dans chaque sous-réseau et achemine le traffic dans le réseau de la maniére suivante : le traffic entre deux sous-réseaux distincts doit être éxpedié au travers des hubs tandis que le traffic entre deux noeuds d'un même sous-réseau ne doit pas sortir de celui-ci. PLHA possède des applications dans le planning stratégique, ou déploiement, d'un certain protocole de communication utilisé dans l'Internet, Intermediate System - Intermediate System, ainsi que dans la distribution des frets. Premièrement, nous préesentons trois formulations linéaires en variables entières pour résoudre PLHA. Le partitionnement du graphe et la localisation des hubs sont modélisées de la même maniére dans les trois formulations. Ces formulations diffèrent les unes des autres dans la maniére dont l'acheminement du traffic est traité. Deuxièmement, nous présentons des comparaisons analytiques et empiriques des trois formulations. Notre comparaison analytique démontre que l'une des formulations est plus forte que les autres. Néanmoins, la comparaison empirique des formulations, via le solveur CPLEX, montre que la formulation la plus compacte (mais pas la plus forte) obtient les meilleures performances en termes de temps de résolution du problème. Ensuite, nous nous concentrons sur un sous-problème, à savoir, le partitionnement des graphes sous contrainte de taille. Nous étudions le polytope des solutions réalisables de ce sous-problème. Les facettes de ce polytope constituent des inégalités valides fortes pour PLHA et peuvent être utilisées dans un algorithme de branch-and-cut pour résoudre PLHA. Finalement, nous présentons les résultats d'un algorithme de branch-and-cut que nous avons développé pour résoudre PLHA. Les résultats démontrent que la performance de notre méthode est meilleure que celle de l'algorithme branch-and-cut d'Xpress.

Individual proteins, and small collections of proteins, have been extensively studied for at least two hundred years. Today, more than 350 genomes have been completely sequenced and the proteomes of these genomes have been at least partially mapped. The inventory of protein coding genes is the first step toward understanding the cellular machinery. Recent studies have generated a comprehensive data set for the physical interactions between the proteins of Saccharomyces cerevisiae, in addition to some less extensive proteome interaction maps of higher eukaryotes. Hence, it is now becoming feasible to investigate important questions regarding the evolution of protein-protein networks. For instance, what is the evolutionary relationship between proteins that interact, directly or indirectly? Do interacting proteins co-evolve? Are they often derived from each other? In order to perform such proteome-wide investigations, a top-down view is necessary. This is provided by network (or graph) theory. The proteins of the cell may be viewed as a community of individual molecules which together form a society of proteins (nodes), a network, where the proteins have various kinds of relationships (edges) to each other. There are several different types of protein networks, for instance the two networks studied here, namely metabolic networks and protein-protein interaction networks. The metabolic network is a representation of metabolism, which is defined as the sum of the reactions that take place inside the cell. These reactions often occur through the catalytic activity of enzymes, representing the nodes, connected to each other through substrate/product edges. The indirect interactions of metabolic enzymes are clearly different in nature from the direct physical interactions, which are fundamental to most biological processes, which constitute the edges in protein-protein interaction networks. This thesis describes three investigations into the evolution of metabolic and protein-protein interaction networks. We present a comparative study of the importance of retrograde evolution, the scenario that pathways assemble backward compared to the direction of the pathway, and patchwork evolution, where enzymes evolve from a broad to narrow substrate specificity. Shifting focus toward network topology, a suggested mechanism for the evolution of biological networks, preferential attachment, is investigated in the context of metabolism. Early in the investigation of biological networks it seemed clear that the networks often display a particular, 'scale-free', topology. This topology is characterized by many nodes with few interaction partners and a few nodes (hubs) with a large number of interaction partners. While the second paper describes the evidence for preferential attachment in metabolic networks, the final paper describes the characteristics of the hubs in the physical interaction network of S. cerevisiae.

With the rapid growth of energy demand and consequently growth in supply, increasing energy costs, and environmental concerns, there is a critical need to find new ways to make better use of existing energy systems and resources and decelerate the demand growth towards a sustainable energy system. All of these facts are leading to the proposal of novel approaches to optimize the utilization of energy in different sectors to reduce the customer's total energy costs, demand and greenhouse gas (GHG) emissions while taking into account the end-user preferences. Utilities have implemented Demand Side Management (DSM) and Demand Response (DR) programs to better manage their network, offer better services to their customers, handle the increase in electricity demand, and at the same time increase system reliability and reduce environmental impacts. Smart Grid developments such as information technology, communication infrastructure and smart meters improve the effectiveness and capability of Energy Management Systems (EMSs) and facilitate the development of automated operational decision-making structures for energy systems, thus assisting DSM and DR programs to reach their full potential. The literature review indicates that whereas significant work has been done in DSM and DR in utilities, these works have mostly focused on direct load control of particular loads, and there is a lack of a general framework to consider all types of energy hubs in an integrated Energy Hub Management System (EHMS). In this context, mathematical modeling of energy systems for EMSs, which is the main concern of the present work, plays a critical role. This research proposes mathematical optimization models of energy hubs which can be readily incorporated into EHMS in the context of Smart Grids. The energy hub could be a single or multi-carrier energy system in residential, commercial, agricultural and/or industrial sectors. Therefore, mathematical models for energy hubs in residential, commercial, and agricultural sectors have been developed and are presented and discussed in this thesis. In the residential sector, this research presents mathematical optimization models of residential energy hubs which can be readily incorporated into automated decision making technologies in Smart Grids, and can be solved efficiently in a real-time frame to optimally control all major residential energy loads, storage and production components while properly considering the customer preferences and comfort levels. Mathematical models for major household demand, i.e., fridge, freezer, dishwasher, washer and dryer, stove, water heater, hot tub, and pool pumps, are formulated. Also, mathematical models of other components of a residential energy system including lighting, heating, and air-conditioning are developed, and generic models for solar PV panels and energy storage/generation devices are proposed. The developed mathematical models result in a Mixed Integer Linear Programming (MILP) optimization problem, whose objective is to minimize demand, total costs of electricity and gas, emissions and peak load over the scheduling horizon while considering end-user preferences. The application of this model to a real household are shown to result in savings of up to 20% on energy costs and 50% on peak demand, while maintaining the household owner's desired comfort levels. In the commercial sector, mathematical optimization models of produce storage facilities to optimize the operation of their energy systems are proposed. In the storage facilities, climate control of the storage rooms consumes considerable energy; thus, a mathematical model of storage facilities appropriate for their optimal operation is developed, so that it can be implemented as a supervisory control in existing climate controllers. The proposed model incorporates weather forecasts, electricity price information, and the end-user preferences to optimally operate existing climate control systems in storage facilities. The objective is to minimize total energy costs and demand charges while considering important parameters of storage facilities; in particular, inside temperature and humidity should be kept within acceptable ranges. Effects of uncertainty in electricity price and weather forecast on optimal operation of the storage facilities are studied via Monte-Carlo simulations. The presented simulation results show the effectiveness of the proposed model to reduce total energy costs while maintaining required operational constraints. In the agricultural sector, this work presents mathematical optimization models of greenhouses to optimize the operation of their energy systems. In greenhouses, artificial lighting, CO2 production, and climate control consume considerable energy; thus, a mathematical model of greenhouses appropriate for their optimal operation is developed, so that it can be implemented as a supervisory control in existing greenhouse controllers. The proposed model incorporates weather forecasts, electricity price information, and the end-user preferences to optimally operate existing control systems in greenhouses. The objective is to minimize total energy costs and demand charges while considering important parameters of greenhouses; in particular, inside temperature and humidity, CO2 concentration, and lighting levels should be kept within acceptable ranges. Effects of uncertainty in electricity price and weather forecast on optimal operation of the storage facilities are studied via Monte-Carlo simulations and robust optimization approach. The presented simulation results show the effectiveness of the proposed model to reduce total energy costs while maintaining required operational constraints.

The increasing price of fuel and energy, combined with environmental laws and regulations, have led many different energy producers to integrate renewable, clean energy sources with non-renewable ones, forming the idea of energy hubs. Energy hubs are systems of technologies where different energy forms are conditioned and transformed. These energy hubs offer many advantages compared to traditional single-energy sources, including increased reliability and security of meeting energy demand, maximizing use of energy and materials resulting in increasing the overall system efficiency. In this thesis, we consider an energy hub consisting of natural gas (NG) turbines for the main source of energy— electricity and heat— combined with two renewable energy sources—wind turbines and PV solar cells. The hub designed capacity is meant to simulate and replace the coal-fired Nanticoke Generating Station with NG-fired power plant. The generating station is integrated with renewable energy sources, including wind and solar. The hub will also include water electrolysers for hydrogen production. The hydrogen serves as an energy storage vector that can be used in transportation applications, or the hydrogen can be mixed into the NG feed stream to the gas turbines to improve their emission profile. Alkaline electrolysers’ technology is fully mature to be applied in large industrial applications. Hydrogen, as an energy carrier, is becoming more and more important in industrial and transportation sectors, so a significant part of the thesis will focus on hydrogen production and cost. In order to achieve the goal of replacing the Nanticoke Coal-fired Power Plant by introducing the energy hub concept, the study investigates the modeling of the combined system of the different technologies used in terms of the total energy produced, cost per kWh, and emissions. This modeling is done using GAMS® in order to make use of the optimization routines in the software. The system is modeled so that a minimum cost of energy is achieved taking into account technical and thermodynamic constrains. Excess energy produced during off-peak demand by wind turbines and PV solar cells is used to feed the electrolyser to produce H2 and O2. Through this method, a significant reduction in energy cost and greenhouse gas (GHG) emissions are achieved, in addition to an increased overall efficiency.

The planetary gear train are applied in multi-speed drive hubs for bicycles. Since a multi-speed drive hub has the advantages of the small volume and stable gear shifting, it is used widely in folding bicycles and electric bicycles. The distributed-flow-type multi-speed hubs could provide more gears, the related design theory is not well development. Thus, the purpose of this research is to develop a systematical methodology for the design of the distributed-flow-type multi-speed hub for bicycles. First, an existing patent is analyzed to identify the basic characteristics and the requirements of the multi-speed hubs. Based on the basic characteristics and the requirements, a systematical procedure is proposed to synthesize the feasible concepts of the planetary gear trains. Second, another procedure is proposed to determine the feasible clutching sequence tables. Third, the difference in value of the angular velocity is assigned to calculate the gear ratio and to determine the numbers of the teeth of all gears. Finally, the shifting-gear system in the multi-speed drive hubs is designed and arranged. The evaluation of the multi-speed drive hubs is proceeded to select the better alternatives. The result of this work obtains twenty-senven types of the distributed-flow multi-speed hubs for bicycles, three of them could reach sixteen speeds.

ICT4D, here defined as the use of Information and Communication Technologies (ICT) in developing regions, can be seen as one of the most powerful and cost efficient ways to improve the standard of living in the developing world. Many regions in Asia have shown a rapid but heterogeneous development where information technology had a drastic impact on development but often with the problems related to ICT4D 1.0: lack of sustainability and lack of scalability. This study analysed the Sri Lankan infrastructure for region-wide dissemination of blended learning in the 21st century based on the exploration of some selected ICT4D hubs and educational initiatives. The overall aim of the research was to observe, describe and analyse how the selected ICT4D initiatives and the creation of ICT4D hubs in Sri Lanka might support region-wide dissemination of blended learning and local development. A longitudinal case study has been the overall approach where a number of embedded thematic units were explored in long-term fieldwork conducted between 2006 and 2012. Data has been collected from a combination of observations, interviews, group discussions, surveys and document analysis. Findings showed that several of the studied ICT4D hubs have contributed to the general development but the country’s internal digital divide has in fact grown, as urban growth has been so much faster than the growth in rural areas, leaving the country with geographic as well as socio-economic gaps. Some of the former war zones have definitely been left behind and there is a need for further support of the Eastern and Northern regions of the island. Sri Lanka has had an outcome that must be classified as better than average compared to other developing regions with increased opportunities for education and with some ICT4D hubs as multipurpose meeting points. Contributing factors to the successful development are the high literacy rate, the chain of ICT4D projects rolled out in the right order and a committed implementation of educational eServices. On the other hand there were other, more negative findings indicating that sustainability, knowledge sharing and inter-project cooperation and coordination have often failed. The identified strength in the Sri Lankan model, which can be recommended for other parts of the world as well, is the way top-down management of infrastructure sometimes is combined with bottom-up grass-root activities. Other recommendations, that also are global, are to extend existing ICT4D hubs and upgrade them to more intelligent, autonomous and multi-service ICT4D routers that could also handle the future need for eServices in the fields of eHealth, eFarming and eGovernance.

With the rapid growth of energy demand and consequently growth in supply, increasing energy costs, and environmental concerns, there is a critical need to find new ways to make better use of existing energy systems and resources and decelerate the demand growth towards a sustainable energy system. All of these facts are leading to the proposal of novel approaches to optimize the utilization of energy in different sectors to reduce the customer's total energy costs, demand and greenhouse gas (GHG) emissions while taking into account the end-user preferences. Utilities have implemented Demand Side Management (DSM) and Demand Response (DR) programs to better manage their network, offer better services to their customers, handle the increase in electricity demand, and at the same time increase system reliability and reduce environmental impacts. Smart Grid developments such as information technology, communication infrastructure and smart meters improve the effectiveness and capability of Energy Management Systems (EMSs) and facilitate the development of automated operational decision-making structures for energy systems, thus assisting DSM and DR programs to reach their full potential. The literature review indicates that whereas significant work has been done in DSM and DR in utilities, these works have mostly focused on direct load control of particular loads, and there is a lack of a general framework to consider all types of energy hubs in an integrated Energy Hub Management System (EHMS). In this context, mathematical modeling of energy systems for EMSs, which is the main concern of the present work, plays a critical role. This research proposes mathematical optimization models of energy hubs which can be readily incorporated into EHMS in the context of Smart Grids. The energy hub could be a single or multi-carrier energy system in residential, commercial, agricultural and/or industrial sectors. Therefore, mathematical models for energy hubs in residential, commercial, and agricultural sectors have been developed and are presented and discussed in this thesis. In the residential sector, this research presents mathematical optimization models of residential energy hubs which can be readily incorporated into automated decision making technologies in Smart Grids, and can be solved efficiently in a real-time frame to optimally control all major residential energy loads, storage and production components while properly considering the customer preferences and comfort levels. Mathematical models for major household demand, i.e., fridge, freezer, dishwasher, washer and dryer, stove, water heater, hot tub, and pool pumps, are formulated. Also, mathematical models of other components of a residential energy system including lighting, heating, and air-conditioning are developed, and generic models for solar PV panels and energy storage/generation devices are proposed. The developed mathematical models result in a Mixed Integer Linear Programming (MILP) optimization problem, whose objective is to minimize demand, total costs of electricity and gas, emissions and peak load over the scheduling horizon while considering end-user preferences. The application of this model to a real household are shown to result in savings of up to 20% on energy costs and 50% on peak demand, while maintaining the household owner's desired comfort levels. In the commercial sector, mathematical optimization models of produce storage facilities to optimize the operation of their energy systems are proposed. In the storage facilities, climate control of the storage rooms consumes considerable energy; thus, a mathematical model of storage facilities appropriate for their optimal operation is developed, so that it can be implemented as a supervisory control in existing climate controllers. The proposed model incorporates weather forecasts, electricity price information, and the end-user preferences to optimally operate existing climate control systems in storage facilities. The objective is to minimize total energy costs and demand charges while considering important parameters of storage facilities; in particular, inside temperature and humidity should be kept within acceptable ranges. Effects of uncertainty in electricity price and weather forecast on optimal operation of the storage facilities are studied via Monte-Carlo simulations. The presented simulation results show the effectiveness of the proposed model to reduce total energy costs while maintaining required operational constraints. In the agricultural sector, this work presents mathematical optimization models of greenhouses to optimize the operation of their energy systems. In greenhouses, artificial lighting, CO2 production, and climate control consume considerable energy; thus, a mathematical model of greenhouses appropriate for their optimal operation is developed, so that it can be implemented as a supervisory control in existing greenhouse controllers. The proposed model incorporates weather forecasts, electricity price information, and the end-user preferences to optimally operate existing control systems in greenhouses. The objective is to minimize total energy costs and demand charges while considering important parameters of greenhouses; in particular, inside temperature and humidity, CO2 concentration, and lighting levels should be kept within acceptable ranges. Effects of uncertainty in electricity price and weather forecast on optimal operation of the storage facilities are studied via Monte-Carlo simulations and robust optimization approach. The presented simulation results show the effectiveness of the proposed model to reduce total energy costs while maintaining required operational constraints.

The increasing price of fuel and energy, combined with environmental laws and regulations, have led many different energy producers to integrate renewable, clean energy sources with non-renewable ones, forming the idea of energy hubs. Energy hubs are systems of technologies where different energy forms are conditioned and transformed. These energy hubs offer many advantages compared to traditional single-energy sources, including increased reliability and security of meeting energy demand, maximizing use of energy and materials resulting in increasing the overall system efficiency. In this thesis, we consider an energy hub consisting of natural gas (NG) turbines for the main source of energy— electricity and heat— combined with two renewable energy sources—wind turbines and PV solar cells. The hub designed capacity is meant to simulate and replace the coal-fired Nanticoke Generating Station with NG-fired power plant. The generating station is integrated with renewable energy sources, including wind and solar. The hub will also include water electrolysers for hydrogen production. The hydrogen serves as an energy storage vector that can be used in transportation applications, or the hydrogen can be mixed into the NG feed stream to the gas turbines to improve their emission profile. Alkaline electrolysers’ technology is fully mature to be applied in large industrial applications. Hydrogen, as an energy carrier, is becoming more and more important in industrial and transportation sectors, so a significant part of the thesis will focus on hydrogen production and cost. In order to achieve the goal of replacing the Nanticoke Coal-fired Power Plant by introducing the energy hub concept, the study investigates the modeling of the combined system of the different technologies used in terms of the total energy produced, cost per kWh, and emissions. This modeling is done using GAMS® in order to make use of the optimization routines in the software. The system is modeled so that a minimum cost of energy is achieved taking into account technical and thermodynamic constrains. Excess energy produced during off-peak demand by wind turbines and PV solar cells is used to feed the electrolyser to produce H2 and O2. Through this method, a significant reduction in energy cost and greenhouse gas (GHG) emissions are achieved, in addition to an increased overall efficiency.