The cell is governed by the complex interactions among various types of biomolecules. Coupled with environmental factors, variations in DNA can cause alterations in normal gene function and lead to a disease condition. Often, such disease phenotypes involve coordinated dysregulation of multiple genes that implicate inter-connected pathways. Towards a better understanding and characterization of mechanisms underlying human diseases, here, I present GUILD, a network-based disease-gene prioritization framework. GUILD associates genes with diseases using the global topology of the protein-protein interaction network and an initial set of genes known to be implicated in the disease. Furthermore, I investigate the mechanistic relationships between disease-genes and explain the robustness emerging from these relationships. I also introduce GUILDify, an online and user-friendly tool which prioritizes genes for their association to any user-provided phenotype. Finally, I describe current state-of-the-art systems-biology approaches where network modeling has helped extending our view on diseases such as cancer. / La cèl•lula es regeix per interaccions complexes entre diferents tipus de biomolècules. Juntament amb factors ambientals, variacions en el DNA poden causar alteracions en la funció normal dels gens i provocar malalties. Sovint, aquests fenotips de malaltia involucren una desregulació coordinada de múltiples gens implicats en vies interconnectades. Per tal de comprendre i caracteritzar millor els mecanismes subjacents en malalties humanes, en aquesta tesis presento el programa GUILD, una plataforma que prioritza gens relacionats amb una malaltia en concret fent us de la topologia de xarxe. A partir d’un conjunt conegut de gens implicats en una malaltia, GUILD associa altres gens amb la malaltia mitjancant la topologia global de la xarxa d’interaccions de proteïnes. A més a més, analitzo les relacions mecanístiques entre gens associats a malalties i explico la robustesa es desprèn d’aquesta anàlisi. També presento GUILDify, un servidor web de fácil ús per la priorització de gens i la seva associació a un determinat fenotip. Finalment, descric els mètodes més recents en què el model•latge de xarxes ha ajudat extendre el coneixement sobre malalties complexes, com per exemple a càncer.

Software maintenance reportedly accounts for much of the total cost associated with developing software. These costs occur because modifying software is a highly error-prone task. Changing software to correct faults or add new functionality can cause existing functionality to regress, introducing new faults. To avoid such defects, one can re-test software after modifications, a task commonly known as regression testing. Regression testing typically involves the re-execution of test cases developed for previous versions. Re-running all existing test cases, however, is often costly and sometimes even infeasible due to time and resource constraints. Re-running test cases that do not exercise changed or change-impacted parts of the program carries extra cost and gives no benefit. The research community has thus sought ways to optimize regression testing by lowering the cost of test re-execution while preserving its effectiveness. To this end, researchers have proposed selecting a subset of test cases according to a variety of criteria (test case selection) and reordering test cases for execution to maximize a score function (test case prioritization). This dissertation presents a novel framework for optimizing regression testing activities, based on a probabilistic view of regression testing. The proposed framework is built around predicting the probability that each test case finds faults in the regression testing phase, and optimizing the test suites accordingly. To predict such probabilities, we model regression testing using a Bayesian Network (BN), a powerful probabilistic tool for modeling uncertainty in systems. We build this model using information measured directly from the software system. Our proposed framework builds upon the existing research in this area in many ways. First, our framework incorporates different information extracted from software into one model, which helps reduce uncertainty by using more of the available information, and enables better modeling of the system. Moreover, our framework provides flexibility by enabling a choice of which sources of information to use. Research in software measurement has proven that dealing with different systems requires different techniques and hence requires such flexibility. Using the proposed framework, engineers can customize their regression testing techniques to fit the characteristics of their systems using measurements most appropriate to their environment. We evaluate the performance of our proposed BN-based framework empirically. Although the framework can help both test case selection and prioritization, we propose using it primarily as a prioritization technique. We therefore compare our technique against other prioritization techniques from the literature. Our empirical evaluation examines a variety of objects and fault types. The results show that the proposed framework can outperform other techniques on some cases and performs comparably on the others. In sum, this thesis introduces a novel Bayesian framework for optimizing regression testing and shows that the proposed framework can help testers improve the cost effectiveness of their regression testing tasks.

Software maintenance reportedly accounts for much of the total cost associated with developing software. These costs occur because modifying software is a highly error-prone task. Changing software to correct faults or add new functionality can cause existing functionality to regress, introducing new faults. To avoid such defects, one can re-test software after modifications, a task commonly known as regression testing. Regression testing typically involves the re-execution of test cases developed for previous versions. Re-running all existing test cases, however, is often costly and sometimes even infeasible due to time and resource constraints. Re-running test cases that do not exercise changed or change-impacted parts of the program carries extra cost and gives no benefit. The research community has thus sought ways to optimize regression testing by lowering the cost of test re-execution while preserving its effectiveness. To this end, researchers have proposed selecting a subset of test cases according to a variety of criteria (test case selection) and reordering test cases for execution to maximize a score function (test case prioritization). This dissertation presents a novel framework for optimizing regression testing activities, based on a probabilistic view of regression testing. The proposed framework is built around predicting the probability that each test case finds faults in the regression testing phase, and optimizing the test suites accordingly. To predict such probabilities, we model regression testing using a Bayesian Network (BN), a powerful probabilistic tool for modeling uncertainty in systems. We build this model using information measured directly from the software system. Our proposed framework builds upon the existing research in this area in many ways. First, our framework incorporates different information extracted from software into one model, which helps reduce uncertainty by using more of the available information, and enables better modeling of the system. Moreover, our framework provides flexibility by enabling a choice of which sources of information to use. Research in software measurement has proven that dealing with different systems requires different techniques and hence requires such flexibility. Using the proposed framework, engineers can customize their regression testing techniques to fit the characteristics of their systems using measurements most appropriate to their environment. We evaluate the performance of our proposed BN-based framework empirically. Although the framework can help both test case selection and prioritization, we propose using it primarily as a prioritization technique. We therefore compare our technique against other prioritization techniques from the literature. Our empirical evaluation examines a variety of objects and fault types. The results show that the proposed framework can outperform other techniques on some cases and performs comparably on the others. In sum, this thesis introduces a novel Bayesian framework for optimizing regression testing and shows that the proposed framework can help testers improve the cost effectiveness of their regression testing tasks.

Transit agencies seeking to improve transit service delivery are increasingly considering the deployment of transit signal priority (TSP). However, the impact of TSP on transit service and on the general traffic stream is a function of many factors, including intersection geometry, signal timings, traffic demands, TSP strategies and parameters, transit vehicle headways, timing when transit vehicles arrive at the intersection, etc. Previous studies have shown that depending on these factors, the net impact of TSP in terms of vehicle or person delay can be positive or negative. Furthermore, due to financial constraints, transit agencies are often able to deploy TSP at only a portion of all of the candidate intersections. Consequently, there is a need to estimate the impact of TSP prior to implementation in order to assist in determining at which intersections TSP should be deployed. Currently, the impacts of TSP are often estimated using microscopic simulation models. However, the application of these models is resource intensive and requires specialized expertise that is often not available in-house to transit agencies. In this thesis, an analytical model was proposed for estimating the delay impacts of green extension and early green (red truncation) TSP strategies. The proposed model is validated with analytical model reported in the literature and microscopic simulation model. This is followed by model sensitivity analysis. A software module is developed using the proposed model. The usefulness of the model is illustrated through its application to estimate the TSP performance. Finally, a prioritization is conducted on sixteen intersections with different geometric and operational traffic strategies. The overall results indicate that the proposed model is suitable for both estimating the pre-deployment and post-deployment TSP performance. The proposed model is suitable for implementation within a spreadsheet and requires considerably less effort, and less technical expertise, to apply than a typical micro-simulation model and therefore is a more suitable tool for transit agencies to use for prioritising TSP deployment.

A challenge related to the expansion of wind power concerns how wind power developers can foster a good relationship with local communities. Building on research on social acceptance for wind power, this thesis addresses two identified gaps. The main focus is a theoretical gap, where previous research is criticized for assuming perfectly flexible organizations when suggesting how social acceptance can be enhanced. Also, an empirical gap is addressed by studying India, a different socio-economic and socio-cultural context compared to western contexts, which previous research has focused on. The aim is to study how management’s stakeholder prioritization affects community acceptance through a qualitative case study of a large Indian wind power developer with data from semi-structured interviews with senior management and internal company reports. The results show that managers’ stakeholder prioritizations and organizational constraints affect community acceptance and that the factors that enhance community acceptance has to be adapted to the context.

The Texas Department of Transportation has been experiencing maintenance budget fluctuations recently. The shortage of budget has a negative impact on the agency’s maintenance strategies and results in the undesirable deterioration of highway conditions increasing the risk towards both road users and the agency. This paper aims at developing a methodology to minimize the impact of budget fluctuation by quantifying the risk of not performing a maintenance activity and identifying the priority of maintenance activities based on the quantified risk. With the help of maintenance experts from TxDOT, four maintenance objectives and 16 maintenance function groups were identified and a hierarchy structure was developed based on the objectives and function groups. Four pilot districts were selected to represent the different demographic and climatic regions in Texas and maintenance experts were selected from the four districts to participate in the workshop. The Overall Relative Weights of 16 maintenance function groups were determined based on the individual evaluator’s judgments using the Analytical Hierarchy Process. To determine if the four pilot districts give different relative importance to the four defined objectives and different priority to the 16 maintenance groups, statistical analyses were conducted with the four sets of values, one for each of the four pilot districts, using Kruskal-Wallis test. At last, a web-based prototype system was developed to assist users in generating the list of maintenance projects under budget constraints. Exposure factors, ADT and truck volume, were applied in the system to factor in the impact of traffic to the maintenance strategy. Users of this system can choose to use the weights and parameter values from one of the pilot districts which they think is most comparable to their own district or the state average values that has been proved to be applicable to all the districts in Texas. / text

We take a novel perspective on real-life decision making problems involving binary activity-selection decisions that compete for scarce resources. The current literature in operations research approaches these problems by forming an optimal portfolio of activities that meets the specified resource constraints. However, often practitioners in industry and government do not take the optimal-portfolio approach. Instead, they form a rank-ordered list of activities and select those that have the highest priority. The academic literature tends to discredit such ranking schemes because they ignore dependencies among the activities. Practitioners, on the other hand, sometimes discredit the optimal-portfolio approach because if the problem parameters change, the set of activities that was once optimal no longer remains optimal. Even worse, the new optimal set of activities may exclude some of the previously optimal activities, which they may have already selected. Our approach takes both viewpoints into account. We rank activities considering both the uncertainty in the problem parameters and the optimal portfolio that will be obtained once the uncertainty is revealed. We use stochastic integer programming as a modeling framework. We develop several mathematical formulations and discuss their relative merits, comparing them theoretically and computationally. We also develop cutting planes for these formulations to improve computation times. To be able to handle larger real-life problem instances, we develop parallel branch-and-price algorithms for a capital budgeting application. Specifically, we construct a column-based reformulation, develop two branching strategies and a tabu search-based primal heuristic, propose two parallelization schemes, and compare these schemes on parallel computing environments using commercial and open-source software. We give applications of prioritization in facility location and capital budgeting problems. In the latter application, we rank maintenance and capital-improvement projects at the South Texas Project Nuclear Operating Company, a two-unit nuclear power plant in Wadsworth, Texas. We compare our approach with several ad hoc ranking schemes similar to those used in practice. / text

Syfte: Att undersöka hur sjuksköterskor på medicinska vårdavdelningar värderar sina arbetsuppgifter och hur de upplever sin psykosociala arbetsmiljö. Metod: En kvantitativ, komparativ tvärsnittsstudie. Enkäter delades ut till 57 sjuksköterskor på två medicinska vårdavdelningar på Akademiska sjukhuset. Resultat: Trettiofyra sjuksköterskor (60%) besvarade enkäten. Få sjuksköterskor upplevde sitt arbete som lugnt och rofyllt och flera sjuksköterskor uppgav att de sällan eller aldrig hinner med det som ska utföras under ett arbetspass. Sjuksköterskorna uppgav överlag att det är mycket viktigt att känna till sina patienters vitalparametrar. Den arbetsuppgift som påverkades mest av hög arbetsbelastning var dock att känna till sina patienters urinproduktion, vilket endast 17 sjuksköterskor (50%) uppgav att de hinner med. Den arbetsuppgift som flest antal sjuksköterskor (n=28, 82%) uppgav att de hinner med är att känna till sina patienters medvetandegrad, följt av att ronda med läkare (n=27, 79%). Den arbetsuppgift som flest sjuksköterskor uppgav att de inte hinner med var att ge sina patienter munvård (n=20, 59%), följt av att känna till hur mycket de har ätit (n=16, 47%). Sjuksköterskor som hade arbetat kortare än 2,5 år värderade vissa administrativa och patientnära arbetsuppgifter högre än sjuksköterskor som hade arbetat längre än 2,5 år. / Aim: To explore how nurses in medical wards evaluate their work and how they experience their psychosocial work environment. Method: A quantitative, comparative cross-sectional study. Questionnaires were distributed to 57 nurses on two medical wards at Akademiska hospital. Results: Thirtyfour nurses (60%) participated. Few nurses considered their work as calm and peaceful, and several nurses said they rarely or never had time to do the tasks that should be performed during a shift. The nurses generally stated that it’s very important to know patients' vital signs. The task that was most affected by high workload was to know patients' urine output, which only 17 nurses (50%) said they had time to check. The task that most nurses (n=28, 82%) said they had time for is to know patients' level of consciousness, followed by the physicians round  (n=27, 79%). The task that most nurses said that they did not have time for was to give patients oral care (n=20, 59%), followed by knowing how much patients have eaten (n=16, 47%). Nurses who had worked under 2,5 years valued certain administrative and patient-related duties higher than nurses who had worked over 2,5 years.

Transit agencies seeking to improve transit service delivery are increasingly considering the deployment of transit signal priority (TSP). However, the impact of TSP on transit service and on the general traffic stream is a function of many factors, including intersection geometry, signal timings, traffic demands, TSP strategies and parameters, transit vehicle headways, timing when transit vehicles arrive at the intersection, etc. Previous studies have shown that depending on these factors, the net impact of TSP in terms of vehicle or person delay can be positive or negative. Furthermore, due to financial constraints, transit agencies are often able to deploy TSP at only a portion of all of the candidate intersections. Consequently, there is a need to estimate the impact of TSP prior to implementation in order to assist in determining at which intersections TSP should be deployed. Currently, the impacts of TSP are often estimated using microscopic simulation models. However, the application of these models is resource intensive and requires specialized expertise that is often not available in-house to transit agencies. In this thesis, an analytical model was proposed for estimating the delay impacts of green extension and early green (red truncation) TSP strategies. The proposed model is validated with analytical model reported in the literature and microscopic simulation model. This is followed by model sensitivity analysis. A software module is developed using the proposed model. The usefulness of the model is illustrated through its application to estimate the TSP performance. Finally, a prioritization is conducted on sixteen intersections with different geometric and operational traffic strategies. The overall results indicate that the proposed model is suitable for both estimating the pre-deployment and post-deployment TSP performance. The proposed model is suitable for implementation within a spreadsheet and requires considerably less effort, and less technical expertise, to apply than a typical micro-simulation model and therefore is a more suitable tool for transit agencies to use for prioritising TSP deployment.

Requirements management is an important part of the software development process. The success of a project may depend on how this is handled. Even though several research studies indicates that more attention should be paid on non-functional requirements, the primary focus in practical projects still regards identifying functional requirements. Especially the prioritization of the non-functional requirements has been proven to be of great importance for the success of a project. This report investigates basics in agile requirements management involving opinions from experts from a software development company. This is done with help of existing literature and interviews with key actors involved in prioritization at the company. I investigate prioritization of non-functional requirements and possibilities for agile project development. The results contribute to developing an overall understanding of the agile way of working. The methodology of this report follows a qualitative approach. It is based on secondary data from literature and documents, but also on data collected via interviews. The results are acknowledging earlier findings from the literature and illustrate with examples actual prioritization of non-functional requirements, and how and why prioritization is a complex activity at a company. However, according to one of the most important findings of this study, the strict use of prioritization techniques is not the most urgent necessity for the success of a project. / Kravhanteringen är en viktig del av systemutvecklingsprocessen. Ett projekts framgång kan kopplas till hur detta genomförs. Även om flera studier pekar på att mer uppmärksamhet bör läggas på icke-funktionella krav är den primära fokusen i flera projekt fortfarande att identifiera funktionella krav. Speciellt prioriteringen av de icke-funktionella kraven har visat sig vara av stor betydelse för ett lyckat projekt.  Den här rapporten undersöker grunderna i den agila kravhanteringen som involverar åsikter från experter i ett företag inom mjukvaruutveckling. Detta görs med hjälp av befintlig litteratur samt intervjuer med nyckelaktörer involverade i prioriteringen hos företaget. Jag undersöker prioriteringen av icke-funktionella krav och möjligheter för agil projektutveckling hos företaget. Följaktligen kommer resultatet bidra till att ge läsaren en allmän förståelse om det agila arbetssättet. Metodologin för den här rapporten följer ett kvalitativt tillvägagångssätt. Den baseras på sekundär data från litteratur och dokument, men även data insamlat via intervjuer. Resultaten medger tidigare upptäckter från litteraturen och visar med exempel verklig prioritering av icke-funktionella krav samt hur och varför prioriteringen är en komplex aktivitet hos ett företag. Dock är, enligt en av de viktigaste upptäckterna i den här rapporten, ett strikt användande av prioriteringstekniker inte den viktigaste nödvändigheten för ett lyckat projekt.