To provide a more in-depth analysis of potential roadway projects, the Utah Department of Transportation (UDOT) desired a method of evaluating projects according to their economic potential without using potentially costly computer models or excessive data collection. Brigham Young University (BYU) was retained to research and recommend criteria for the economic development criteria in the project-prioritization process. A literature review was first undertaken to better understand the transportation-economic development relationship. Using the literature review, combined with the information from the Economic Development Corporation of Utah, the Governor's Office of Planning and Budget, the Governor's Office of Economic Development, and a Technical Advisory Committee (TAC), criteria were established to evaluate the economic potential of a roadway project. The criteria were finalized using a Policy Delphi method that included the Research Team and TAC. The four aggregate criteria and one bonus criterion recommended are: 1) population and education; 2) existing infrastructure; 3) economic attractiveness; 4) tourism; and 5) the bonus: economic choke-points, which allows UDOT regions to specify a prioritized list of projects that could help increase the economic development potential of an area if those projects are built. An evaluation framework was also developed for the economic development criteria. Any project that passes the Tier I analysis is recommended to be subjected to the economic analysis of the Tier II process. The researchers recommend that once a list of passing Tier I projects is received, the list should be sent out to any participating in the expert feedback portion of the economic attractiveness scoring as well as to the UDOT regions and districts for choke-point prioritization analysis. All of the databases will be updated to provide the most up-to-date scoring possible. When all of the scores have been assigned, the projects will then be listed by highest to lowest scores. The list will then be compiled by UDOT who will present the information to the Transportation Commission in a manner that will best assist in the decision-making process. The research created a scoring evaluation for each recommended criterion. Each criterion also received a weighting. The scoring and the framework are recommended to UDOT as the economic analysis of the Tier II evaluation. The criteria are recommended to be automated in a geographic information systems (GIS) database to aid in the scoring process.

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Yes / In wireless visual sensor networks (WVSNs), streaming all imaging data is impractical due to resource constraints. Moreover, the sheer volume of surveillance videos inhibits the ability of analysts to extract actionable intelligence. In this work, an energy-efficient image prioritization framework is presented to cope with the fragility of traditional WVSNs. The proposed framework selects semantically relevant information before it is transmitted to a sink node. This is based on salient motion detection, which works on the principle of human cognitive processes. Each camera node estimates the background by a bootstrapping procedure, thus increasing the efficiency of salient motion detection. Based on the salient motion, each sensor node is classified as being high or low priority. This classification is dynamic, such that camera nodes toggle between high-priority and low-priority status depending on the coverage of the region of interest. High-priority camera nodes are allowed to access reliable radio channels to ensure the timely and reliable transmission of data. We compare the performance of this framework with other state-of-the-art methods for both single and multi-camera monitoring. The results demonstrate the usefulness of the proposed method in terms of salient event coverage and reduced computational and transmission costs, as well as in helping analysts find semantically relevant visual information. / Supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2013R1A1A2012904).

Background and Motivation. Requirements engineering is decision intensiveand involves many roles and stakeholders. As humans are often subjective in theirdecision-making and biased by subjective criteria, we are interested in exploring howthis impacts requirements prioritization. Each requirements prioritization techniquehas its advantages and limitations to use on software products for single/multiplepurposes in the software field. Understanding how human aspects affect requirementsprioritization remains greatly unexplored. Objectives. This thesis aims to understand how human factors impact requirementsprioritization. The primary goal is to address and understand the various human as-pects that affect people when they make decisions. The secondary goal is to identifyvarious human aspects that receive more attention while prioritizing requirements. Methods. Systematic Literature Review (SLR) and survey were chosen as the re-search methods for this thesis. A snowballing method was used to extract empiricalresearch papers that were used for implementing the survey questionnaire. Each em-pirical paper from snowballing method has identified some human aspects throughone or more prioritization techniques and prioritization criteria. Using these humanaspects as input a survey questionnaire is designed for gaining insights on occur-rences/experiences of these human aspects in a large organization of Agile practi-tioners. Results. From the literature review, we identified 21 papers through the snow-balling method. And we identified more than two human aspects from each SLRpaper that impact requirements prioritization that were grouped into 11 categories.We also discovered many requirements prioritization techniques and their criteriawhere we included the top 15 RP techniques, 11 human aspects, and 17 RP cri-teria in the web-based survey questionnaire that were extracted through the SLRapproach. Our survey respondents considered the human aspects as very importantare Domain Knowledge of Individuals/ Stakeholders/ Analysts; Ability to consid-er/understand multiple perspectives; Ability to build/reach Consensus; Cognitiveskills and Limitations; Group Cohesion/ Team Maturity; and Accept Diversity as-pects as having the largest impact when prioritizing requirements. We have alsodiscovered that Emotions/ Emotional Cohesion which is also rated by the surveyrespondents as very important and is having the least impact as a human aspectwhen prioritizing requirements. Conclusions. Our study focus on the human aspects in requirements prioritizationmethod, the actual human aspects are least graded and human behavior that is con-sidered as an human aspect is highly graded by the practitioners in the survey. So aclear map is needed to identify the human aspect bias for requirements prioritizationand the results of this study can be helpful to all the researchers who want to carryour research on requirements prioritization in relation with human aspects.

Background: Maintaining system reliability and customer satisfaction in a DevOps environment requires effective incident management. In the modern day, due to increasing system complexity, several incidents occur daily. Incident prioritization and resolution are essential to manage these incidents and lessen their impact on business continuity. Prioritization of incidents, estimation of recovery time objective (RTO), and resolution times are traditionally subjective processes that rely more on the DevOps team’s competence. However, as the volume of incidents rises, it becomes increasingly challenging to handle them effectively.  Objectives: This thesis aims to develop an approach that prioritizes incidents and estimates the corresponding resolution times and RTO values leveraging machine learning. The objective is to provide an effective solution to streamline DevOps activities. To verify the performance of our solution, an evaluation is later carried out by the users in a large organization (Ericsson).  Methods: The methodology used for this thesis is design science methodology. It starts with the problem identification phase, where a rapid literature review is done to lay the groundwork for the development of the solution. Cross-Industry Standard Process for Data Mining (CRISP-DM) is carried out later in the development phase. In the evaluation phase, a static validation is carried out in a DevOps environment to collect user feedback on the tool’s usability and feasibility.  Results:  According to the results, the tool helps the DevOps team prioritize incidents and determine the resolution time and RTO. Based on the team’s feedback, 84% of participants agree that the tool is helpful, and 76% agree that the tool is easy to use and understand. The tool’s performance evaluation of the three metrics chosen for estimating the priority was accuracy 93%, Recall 78%, F1 score 87% on average for all four priority levels, and the BERT accuracy for estimating the resolution time range was 88%. Hence, we can expect the tool to help speed up the incident response’s efficiency and decrease the resolution time.  Conclusions: The tool’s validation and implementation indicate that it has the potential to increase the reliability of the system and the effectiveness of incident management in a DevOps setting. Prioritizing incidents and predicting resolution time ranges based on impact and urgency can enable the DevOps team to make well-informed decisions. Some of the future progression for the tool can be to investigate how to integrate it with other third-party DevOps tools and explore areas to provide guidelines to handle sensitive incident data. Another work could be to analyze the tool in a live project and obtain feedback.

Bakgrund: Inom det kirurgiska specialistområdet vårdas både elektiva, akuta och palliativa patienter. Det finns forskning som beskriver sjuksköterskors upplevelser av att vårda akuta och palliativa patienter var för sig. Det finns dock begränsad forskning kring sjuksköterskors upplevelser av att vårda akuta, elektiva och palliativa patienter samtidigt, på samma avdelning. Syfte: Beskriva sjuksköterskors upplevelser av att vårda elektiva, akuta och palliativa patienter samtidigt på en kirurgavdelning. Metod: Kvalitativ intervjustudie med sju sjuksköterskor. Data analyserades genom kvalitativ innehållsanalys. Resultat: I resultatet framkom två huvudkategorier, med tre subkategorier till respektive huvudkategori. Under huvudkategorin; Patientvariationer skapar stimulans och ställer krav beskrivs patienternas varierade omvårdnadsbehov, betydelsen av anpassning samt arbetstillfredsställelse. Under huvudkategorin; Arbetsmiljöns påverkan på vård och hälsa beskrivs den pressade arbetsmiljön, behov av prioritering samt känsla av otillräcklighet.  Konklusion: Att vårda akuta, elektiva och palliativa patienter samtidigt beskrivs som varierade och lärorikt. Samtidigt ställer det krav på att anpassning. Vidare skapar den höga arbetsbelastningen behov av att prioritera. Prioriteringen leder till att vissa delar av omvårdanden och patientgrupper blir lidande. Resultaten visar också att det är särskilt svårt att vårda palliativa patienter på en botande avdelning. / Background: Within the surgical specialist care, both elective, acute, and palliative patients are cared for. There is research that describes nurses' experiences of caring for these patients separately. However, there is limited research on nurses' experiences of caring for these groups of patients simultaneously. Aim: Describe nurses' experiences of caring for elective, acute and palliative patients simultaneously in a surgical ward. Method: Qualitative interview study with seven nurses. The data was analyzed through qualitativecontent analysis. Findings: The results revealed two main categories, with three subcategories belonging to each main category. The main category: Patient variations create stimulation and set requirements gives a description of the patients' varied nursing needs, the importance of adaptation and job satisfaction. The main category: The impact of the work environment on care and health, describes the pressured work environment, the need for prioritization and the feeling of inadequacy. Conclusion: Caring for acute, elective and palliative patients simultaneously is described as varied and instructive. At the same time, it requires adaptation. Furthermore, the high workload creates the need to prioritize, which leads to the suffering of certain parts of the care and patient groups. The results also suggest that it’s particularly difficult to care for palliative patients in a curative ward.

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In Sweden, three principles prescribed by law compose the ethical platform aiming to ensure a fair distribution of healthcare resources. The goals of each of the three principles are to 1: ensure equal care, regardless of personal characteristics or social function; 2: give priority to patients of bad health; 3: base priority decisions on cost-effectiveness. The weights given to the last two principles yield different implications on which pharmaceuticals and medical procedures to subsidize and can be seen as an equity-efficiency trade-off. Knowledge of the Swedish public's views on this balance has been stated to be of large value to decision-makers, to be used as a basis for priority decisions. However, no such information exists to date. A large share of studies from other countries has moreover provided counter-intuitive estimates, possibly indicating a need for methodological development. In this thesis, I provide a suggestion on a discrete choice approach to quantify opinions on how to weight disease severity and cost-effectiveness in healthcare prioritization. In addition, I present a novel method to use the estimates to rank treatments. The design is furthermore tested in a pilot study, being the first to investigate this question in a Swedish setting. The results indicate that the population values both the amount and distribution of health created, favoring individuals suffering from severe conditions, which is seen as in line with a priori expectations. The thesis contributes to the literature aiming to quantify opinions on healthcare prioritization.