The agent-based micro-simulation modelling technique for transportation planning is rapidly developing and is being applied to practice in recent years. In contrast to conventional four-step modelling with static assignment theory, this emerging technique employs a dynamic assignment principle. Based on summary of various types of traffic assignment models and algorithms, the thesis elucidates in detail the theories of two models, MATSim and EMME/2, which represent two genres of traffic assignment, i.e., dynamic stochastic stationary state assignment and static deterministic user equilibrium assignment. In the study, the two models are compared and validated to reflect both spatial and temporal variation of the traffic flow pattern. The comparison results indicate that numerical outputs produced by MATSim are not only compatible to those by EMME/2 but more realistic from a temporal point of view. Therefore, agent-based micro-simulation models reflect a promising direction of next generation of transportation planning models.

This thesis is concerned with the channel assignment (CA) problem in multi-channel multi-interface wireless mesh networks (M2WNs). First, for M2WNs with general topologies, we rigorously demonstrate using the combinatorial principle of inclusion/exclusion that the CA solution space can be quantified, indicating that its cardinality is greatly influenced by the number of radio interfaces installed on each router. Based on this analysis, a novel scheme is developed to construct a new reduced search space, represented by a lattice structure, that is searched more efficiently for a CA solution. The elements in the reduced lattice-based space, labeled Solution Structures (SS), represent groupings of feasible CA solutions satisfying the radio constraints at each node. Two algorithms are presented for searching the lattice structure. The first is a greedy algorithm that finds a good SS in polynomial time, while the second provides a user-controlled depthfirst search for the optimal SS. The obtained SS is used to construct an unconstrained weighted graph coloring problem which is then solved to satisfy the soft interference constraints. For the special class of full M2WNs (fM2WNs), we show that an optimal CA solution can only be achieved with a certain number of channels; we denote this number as the characteristic channel number and derive upper and lower bounds for that number as a function of the number of radios per router. Furthermore, exact values for the required channels for minimum interference are obtained when certain relations between the number of routers and the radio interfaces in a given fM2WN are satisfied. These bounds are then employed to develop closed-form expressions for the minimum channel interference that achieves the maximum throughput for uniform traffic on all communication links. Accordingly, a polynomial-time algorithm to find a near-optimal solution for the channel assignment problem in fM2WN is developed. Experimental results confirm the obtained theoretical results and demonstrate the performance of the proposed schemes.

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Pollen contamination is a severe problem in production breeding programs since it reduces the expected gain. In an attempt to solve this problem Skogforsk created an isolation experiment in the advanced Scots pine (Pinus sylvestris) seed orchard Västerhus in Västerbotten, Sweden. This experiment involves covering blocks of trees with a tent during the pollination period so that they only can mate with each other inside. To evaluate the effects of this tent treatment one tree from inside a tent with supplementary mass pollination (SMP) and one tree from the open control were chosen for this study. 48 seeds from each tree were sampled and genotypes at 9 microsatellite (SSR) loci. The likelihood and exclusion methods for paternity assignment were used to establish the fathers to these seeds. The results showed 0% contamination inside the tent and 4-8% outside in the control. The number of fathers contributed to the fertilization of the 48 seeds was 9 inside and 15 outside. The selfing rate was unexpectedly high, 10% inside the tent and 19% outside. The mating system inside the tent need to be further evaluated to fully understand what other effect the treatment has on the future progeny.

Nuclear magnetic resonance (NMR) spectroscopy technique is becoming exceedingly significant due to its capability of studying protein structures in solution. However, NMR protein structure determination has remained a laborious and costly process until now, even with the help of currently available computer programs. After the NMR spectra are collected, the main road blocks to the fully automated NMR protein structure determination are peak picking from noisy spectra, resonance assignment from imperfect peak lists, and structure calculation from incomplete assignment and ambiguous nuclear Overhauser enhancements (NOE) constraints. The goal of this dissertation is to propose error-tolerant and highly-efficient methods that work well on real and noisy data sets of NMR protein structure determination and the closely related protein structure prediction problems. One major contribution of this dissertation is to propose a fully automated NMR protein structure determination system, AMR, with emphasis on the parts that I contributed. AMR only requires an input set with six NMR spectra. We develop a novel peak picking method, PICKY, to solve the crucial but tricky peak picking problem. PICKY consists of a noise level estimation step, a component forming step, a singular value decomposition-based initial peak picking step, and a peak refinement step. The first systematic study on peak picking problem is conducted to test the performance of PICKY. An integer linear programming (ILP)-based resonance assignment method, IPASS, is then developed to handle the imperfect peak lists generated by PICKY. IPASS contains an error-tolerant spin system forming method and an ILP-based assignment method. The assignment generated by IPASS is fed into the structure calculation step, FALCON-NMR. FALCON-NMR has a threading module, an ab initio module, an all-atom refinement module, and an NOE constraints-based decoy selection module. The entire system, AMR, is successfully tested on four out of five real proteins with practical NMR spectra, and generates 1.25A, 1.49A, 0.67A, and 0.88A to the native reference structures, respectively. Another contribution of this dissertation is to propose novel ideas and methods to solve three protein structure prediction problems which are closely related to NMR protein structure determination. We develop a novel consensus contact prediction method, which is able to eliminate server correlations, to solve the protein inter-residue contact prediction problem. We also propose an ultra-fast side chain packing method, which only uses local backbone information, to solve the protein side chain packing problem. Finally, two complementary local quality assessment methods are proposed to solve the local quality prediction problem for comparative modeling-based protein structure prediction methods.

Nuclear magnetic resonance (NMR) spectroscopy technique is becoming exceedingly significant due to its capability of studying protein structures in solution. However, NMR protein structure determination has remained a laborious and costly process until now, even with the help of currently available computer programs. After the NMR spectra are collected, the main road blocks to the fully automated NMR protein structure determination are peak picking from noisy spectra, resonance assignment from imperfect peak lists, and structure calculation from incomplete assignment and ambiguous nuclear Overhauser enhancements (NOE) constraints. The goal of this dissertation is to propose error-tolerant and highly-efficient methods that work well on real and noisy data sets of NMR protein structure determination and the closely related protein structure prediction problems. One major contribution of this dissertation is to propose a fully automated NMR protein structure determination system, AMR, with emphasis on the parts that I contributed. AMR only requires an input set with six NMR spectra. We develop a novel peak picking method, PICKY, to solve the crucial but tricky peak picking problem. PICKY consists of a noise level estimation step, a component forming step, a singular value decomposition-based initial peak picking step, and a peak refinement step. The first systematic study on peak picking problem is conducted to test the performance of PICKY. An integer linear programming (ILP)-based resonance assignment method, IPASS, is then developed to handle the imperfect peak lists generated by PICKY. IPASS contains an error-tolerant spin system forming method and an ILP-based assignment method. The assignment generated by IPASS is fed into the structure calculation step, FALCON-NMR. FALCON-NMR has a threading module, an ab initio module, an all-atom refinement module, and an NOE constraints-based decoy selection module. The entire system, AMR, is successfully tested on four out of five real proteins with practical NMR spectra, and generates 1.25A, 1.49A, 0.67A, and 0.88A to the native reference structures, respectively. Another contribution of this dissertation is to propose novel ideas and methods to solve three protein structure prediction problems which are closely related to NMR protein structure determination. We develop a novel consensus contact prediction method, which is able to eliminate server correlations, to solve the protein inter-residue contact prediction problem. We also propose an ultra-fast side chain packing method, which only uses local backbone information, to solve the protein side chain packing problem. Finally, two complementary local quality assessment methods are proposed to solve the local quality prediction problem for comparative modeling-based protein structure prediction methods.

NMR spectroscopy is not only for protein structure determination, but also for drug screening and studies of dynamics and interactions. In both cases, one of the main bottleneck steps is backbone assignment. When a homologous structure is available, it can accelerate assignment. Such structure-based methods are the focus of this thesis. This thesis aims for fast and robust methods for NMR assignment problems; in particular, structure-based backbone assignment and chemical shift mapping. For speed, we identified situations where the number of 15N-labeled experiments for structure-based assignment can be reduced; in particular, when a homologous assignment or chemical shift mapping information is available. For robustness, we modeled and directly addressed the errors. Binary integer linear programming, a well-studied method in operations research, was used to model the problems and provide practically efficient solutions with optimality guarantees. Our approach improved on the most robust method for structure-based backbone assignment on 15N-labeled data by improving the accuracy by 10% on average on 9 proteins, and then by handling typing errors, which had previously been ignored. We show that such errors can have a large impact on the accuracy; decreasing the accuracy from 95% or greater to between 40% and 75%. On automatically picked peaks, which is much noisier than manually picked peaks, we achieved an accuracy of 97% on ubiquitin. In chemical shift mapping, the peak tracking is often done manually because the problem is inherently visual. We developed a computer vision approach for tracking the peak movements with average accuracy of over 95% on three proteins with less than 1.5 residues predicted per peak. One of the proteins tested is larger than any tested by existing automated methods, and it has more titration peak lists. We then combined peak tracking with backbone assignment to take into account contact information, which resulted in an average accuracy of 94% on one-to-one assignments for these three proteins. Finally, we applied peak tracking and backbone assignment to protein-ligand docking to illustrate the potential for fast 3D complex determination.

This thesis proposes a potential solution for future ubiquitous broadband wireless access networks, called a cellular wireless mesh network (CMESH), and investigates a number of its performance issues. A CMESH is organized in multi-radio, multi-channel, multi-rate and multi-hop radio cells. It can operate on abundant high radio frequencies, such as 5-50 GHz, and thus may satisfy the bandwidth requirements of future ubiquitous wireless applications.<p> Each CMESH cell has a single Internet-connected gateway and serves up to hundreds of mesh nodes within its coverage area. This thesis studies performance issues in a CMESH, focusing on cell capacity, expressed in terms of the max-min throughput. In addition to introducing the concept of a CMESH, this thesis makes the following contributions.<p> The first contribution is a new method for analyzing theoretical cell capacity. This new method is based on a new concept called Channel Transport Capacity (CTC), and derives new analytic expressions for capacity bounds for carrier-sense-based CMESH cells.<p> The second contribution is a new algorithm called the Maximum Channel Collision Time (MCCT) algorithm and an expression for the nominal capacity of CMESH cells. This thesis proves that the nominal cell capacity is achievable and is the exact cell capacity for small cells within the abstract models.<p> Finally, based on the MCCT algorithm, this thesis proposes a series of greedy algorithms for channel assignment and routing in CMESH cells. Simulation results show that these greedy algorithms can significantly improve the capacity of CMESH cells, compared with algorithms proposed by other researchers.

The purpose of this study was to examine how Swedish-based MNCs handle their repatriation process with a focus on knowledge transfer upon return. Moreover, the study reviewed whether a global mindset is present or not in the companies and if it is associated with how repatriates’ knowledge is utilized. The study was conducted through interviews with seven repatriates and six HR managers in six different MNCs. The findings show a lack of awareness about what the repatriates have learnt during their assignments, and that there are no routines in place for capturing the knowledge upon return. Also, it identifies a gap between the companies’ and the repatriates’ views about the goals of the assignment and the value of the overall international experience. Finally, the study suggests various improvements for companies on how to better utilize their resources and enhance their global mindset in order to create a sustainable competitive advantage.