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AbstractThis paper investigates the impact of social network on the labor market outcomes. This is a case study of highly-educated Iranian immigrant women who have been living in Malmö for more than five years. It is assumed that the high level of education, language skill and work experience are the major characteristics of human capital that affect their social integration. The role of social network in social and economic life of Iranian immigrant women is discussed. The data is comprised of six semi-structured interviews with highly educated Iranian immigrant women in Malmö. The results of the study indicate that Iranian immigrant women are highly motivated to participate in the social and economic sectors of Sweden. Regarding the social network, having weak ties is associated with better labor market achievement for Iranian immigrant women and the power of social network is immense in the process of social integration. The level of education has both positive and negative effects on the degree of social integration. The results of the study demonstrate that re-education is a strategy and investment for a better employment opportunity in future.

This thesis is divided into two distinct chapters. In the first chapter, we apply network representation learning to the field of materials science in order to predict aluminum grain boundaries' properties and locate the most influential atoms and subgraphs within each grain boundary. We create fixed-length representations of the aluminum grain boundaries that successfully capture grain boundary structure and allow us to accurately predict grain boundary energy. We do this through two distinct methods. The first method we use is a graph convolutional neural network, a semi-supervised deep learning algorithm, and the second method is graph2vec, an unsupervised representation learning algorithm. The second chapter presents our dynamic global value chain network, the combination of the dynamic global supply chain network and the dynamic global strategic alliance network. Our global value chain network provides a level of scope and accessibility not found in any other global value chain network, commercial or academic. Through applications of network theory, we discover business applications that would increase the robustness and resilience of the global value chain. We accomplish this through an analysis of the static, dynamic, and community structure of our global value chain network.

With a focus of always being connected, it's become typical for laptops and mobile devices to include multiple wireless network devices. Though the additional network devices have created mobility and versatility of how a user is connected, it is common for only one to be active at any given time. While likely that new mesh protocols will help maximize connectivity and power consumption by utilizing lower-power multi-hop techniques, it is still difficult to visualize these protocols due to the complexity created by each node's simple choices. Further challenges are presented by the variety of network devices which share frequency ranges with different output power, sensitivities, and antenna radiation patterns. Due to the complexity of these configurations and environments, it becomes clear that reproducible simulations are required. While several network simulators have been thoroughly tested over their many years of use, they often lack realistic handling of key factors that affect wireless networks. A few examples include cross-channel interference, propagation delays, interference caused by nodes beyond communication range, channel switching delays, and non-uniform radiation patterns. Another key limitation of these past tools is their limited methods for clearly displaying characteristics of multi-channel communication. Furthermore, these past utilities lack the graphical and interactive functions which promote the discovery of edge cases through the use of human intuition and pattern recognition. Even with their other limitations, many of these simulators are also extendable with new components and simulation abilities. As a result, a large set of protocols and other useful discoveries have been developed. While the concepts are well tested and verified, a new challenge is found when moving code from prototype to production due to code portability problems. Due to the sophistication of these creations, even small changes in code during a protocols release can have dramatic effects on its functionality. Both to encourage quicker development cycles and maintain code validation, it would be advantageous to provide simulation interfaces which directly match that of production systems. To overcome the various challenges presented and encourage the use of innate human abilities, this paper presents a novel simulation framework, Network Channel Visualizing Simulator (NCVS), with a real-time, interactive, 3D environment with clear representation and simulation of multi-channel RF communication through multiple network device types.

Moore and Shannon's reliability polynomial can be used as a global statistic to explore the behavior of diffusive processes on a graph dynamical system representing a finite sized interacting system. It depends on both the network topology and the dynamics of the process and gives the probability that the system has a particular desired property. Due to the complexity involved in evaluating the exact network reliability, the problem has been classified as a NP-hard problem. The estimation of the reliability polynomials for large graphs is feasible using Monte Carlo simulations. However, the number of samples required for an accurate estimate increases with system size. Instead, an adaptive method using Bernstein polynomials as kernel density estimators proves useful. Network reliability has a wide range of applications ranging from epidemiology to statistical physics, depending on the description of the functionality. For example, it serves as a measure to study the sensitivity of the outbreak of an infectious disease on a network to the structure of the network. It can also be used to identify important dynamics-induced contagion clusters in international food trade networks. Further, it is analogous to the partition function of the Ising model which provides insights to the interpolation between the low and high temperature limits. / Ph. D. / The research presented here explores the effects of the structural properties of an interacting system on the outcomes of a diffusive process using Moore-Shannon network reliability. The network reliability is a finite degree polynomial which provides the probability of observing a certain configuration for a diffusive process on networks. Examples of such processes analyzed here are outbreak of an epidemic in a population, spread of an invasive species through international trade of commodities and spread of a perturbation in a physical system with discrete magnetic spins. Network reliability is a novel tool which can be used to compare the efficiency of network models with the observed data, to find important components of the system as well as to estimate the functions of thermodynamic state variables.

This thesis deals with the potential application of neural network technology to aviation fuel consumption estimation. This is achieved by developing neural networks representative jet aircraft. Fuel consumption information obtained directly from the pilot's flight manual was trained by the neural network. The trained network was able to accurately and efficiently estimate fuel consumption of an aircraft for a given mission. Statistical analysis was conducted to test the reliability of this model for all segments of flight. Since the neural network model does not require any wind tunnel testing nor extensive aircraft analysis, compared to existing models used in aviation simulation programs, this model shows good potential. The design of the model is described in depth, and the MATLAB source code are included in appendices. / Master of Science

This thesis consists of two related studies presented as three separate manuscripts (all three have been published in peer-reviewed journals) and a study protocol that has been submitted for peer-reviewed funding. The over-arching theme of this thesis was to better characterize the efficacy of different intravenous fluids used for the resuscitation of intensive care unit (ICU) patients with severe sepsis or septic shock. We performed an extensive search including multiple databases which found 20 randomized controlled trials (RCTs) that examined the effects of different intravenous fluids used in septic patients and met our a priori inclusion and exclusion criteria. In the first manuscript, we described in detail the composition of the 19 unique fluid products that were used in the various studies. This description included the fluid type, trade name, osmolality, tonicity, electrolyte content, molecular composition, pH, and manufacturer. We reviewed manufacturer’s websites, product monographs, and emailed industry representatives or study authors for more information regarding the fluids as required. The results of this study and systematic review led us to the second and third manuscripts which reported on a Bayesian network meta-analysis (NMA) of all fluid type comparisons. Despite multiple well-done RCTs, comparative data regarding the clinical effect of different resuscitative fluids when used for sepsis was incomplete. Most RCTs used 0.9% saline (normal saline) as control fluid and very few studies compared colloids directly. The advantage of using an NMA model in this setting was the ability to include indirect data into the overall point estimates. Data was abstracted from the 14 studies which focused on adult ICU patients and analyzed examining the outcomes of mortality (manuscript #2) and the use of renal replacement therapy (RRT) (manuscript #3). Certainty of evidence was evaluated for both outcomes using the GRADE approach. Results of the analysis clearly document the harm of starch-based fluids when used in septic patients. Albumin containing fluids and crystalloids (such as normal saline and Ringer’s Lactate) are better options. Lower chloride solutions, such as Ringer’s Lactate, showed a signal towards decreased mortality and a decreased use of renal replacement therapy when compared to higher chloride fluids, such as normal saline, however this was based on indirect data, not statistically significant, and warrants direct comparison trials. The final component of this thesis is a pilot study protocol for a study assessing the feasibility of a larger RCT examining the effect of low chloride versus high chloride fluids for resuscitation in patients with sepsis and septic shock. This protocol has been submitted as part of a peer-reviewed grant with the hopes of addressing this clinically important and timely question. / Thesis / Master of Science (MSc) / This thesis examines the ideal intravenous fluid to be given to patients with severe infection causing low blood pressure. A review of the current literature is presented with a protocol for future work.

This dissertation devotes itself to algorithmic approaches to the problem of scalable multicast with network coding. Several original contributions can be concluded as follows. We have proved that the scalable multicast problem is NP-hard, even with the ability to perform network coding at the network nodes. Several approximations are derived based on different heuristics, and systematic approaches have been devised to solve those problems. We showed that those traditional routing methods reduce to a special case in the new network coding context. Two important frameworks usually found in traditional scalable multicast solutions, i.e. layered multicast and rainbow multicast, are studied and extended to the network coding scenario. Solutions based on these two frameworks are also presented and compared. Suprisingly, these two distinctive approaches in the traditional sense become connected and share a similar essence of data mixing in the light of network coding. Cases are presented where these two approaches become equivalent and achieve the same Performance. We have made significant advances in constructing good solutions to the scalable multicast problem by solving various optimization problems formulated in our approaches. In the layered multicast framework, we started with a straight-forward extension of the traditional layered multicast to the network coding context. The proposed method features an intra-layer network coding technique which is applied on different optimized multicast graphs. Later on, we further improved this method by introducing the inter-layer network coding concept. By allowing network coding among data from different data layers, more leverage is gained when optimizing the network flow, thus higher performance is achieved. In the rainbow multicast framework, we choose uneven erasure protection (UEP) technique as the practical way of constructing balanced MDC, and optimize this MDC design using the max-flow information of receivers. After the MDC design is finalized, a single linear network broadcast code is employed to deliver MDC encoded data to receivers while satisfying the individual max-flow of all the receivers. Although this rainbow multicast based solution may sacrifice the performance in some cases, it greatly simplifies the rate allocation problem raised in the layered multicast framework. The use of one single network code also makes the network codes construction process a lot clearer. Extensive amount of simulation is performed and the results show that network coding based scalable multicast solutions can significantly outperform those traditional routing based solutions. In addition to the imaginary linear objective function used in the simulation, the practical convex objective function and real video data are also used to verify the effectiveness of the proposed solutions. The role of different parameters in the proposed approaches are analyzed, which gives us more guidelines on how to fine-tune the system. / Thesis / Doctor of Philosophy (PhD)