Around ten percent of patients admitted to National Health Service (NHS) hospitals have experienced a patient safety incident, and an important reason for the high rate of patient safety incidents is medical errors. Research shows that appropriate increase in the use of clinical decision support systems (CDSSs) could help to reduce medical errors and result in substantial improvement in patient safety. However several barriers continue to impede the effective implementation of CDSSs in clinical settings, among which representation of and reasoning about medical knowledge particularly under uncertainty are areas that require refined methodologies and techniques. Particularly, the knowledge base in a CDSS needs to be updated automatically based on accumulated clinical cases to provide evidence-based clinical decision support. In the research, we employed the recently developed belief Rule-base Inference Methodology using the Evidential Reasoning approach (RIMER) for design and development of an online belief rule-based group CDSS prototype. In the system, belief rule base (BRB) was used to model uncertain clinical domain knowledge, the evidential reasoning (ER) approach was employed to build inference engine, a BRB training module was developed for learning the BRB through accumulated clinical cases, and an online discussion forum together with an ER-based group preferences aggregation tool were developed for providing online clinical group decision support.We used a set of simulated patients in cardiac chest pain provided by our research collaborators in Manchester Royal Infirmary to validate the developed online belief rule-based CDSS prototype. The results show that the prototype can provide reliable diagnosis recommendations and the diagnostic performance of the system can be improved significantly after training BRB using accumulated clinical cases.

The current guideline for culverts design account for hydrologic, hydraulic, and geomorphological conditions at the construction site. Less attention is given to the assessment of the potential for sedimentation within and in the culvert vicinity. Sedimentation becomes an increasing concern for regions where the soil erodibility is historically high (such as the US Midwest) as the recent changes in the land use and climate exacerbate the problem. Culvert design is based on a wide variety of data sources typically stored in various formats in multiple data provider repositories. Assembly of the data needed for design as currently conducted is time consuming and inefficient while the wealth of information garnered through post-construction monitoring is not used to inform the construction of new culverts in the same geographical area. This thesis presents a solution to the issues above using Hydroinformatics approaches. The end product of the thesis is a geo-platform designed to achieve automated culvert design, sediment mitigation design, and digital culvert inspection. The platform has two primary workflows: culvert design and culvert monitoring. All the functions and tools inside the platform are developed using information and GIS technologies. The platform is web assessable, light weighted, and user friendly.

The purpose of this study is to investigate the use of decisions support systems (DSS) by critical care physicians and to address the following questions: Does the use of a decision support system during diagnosis reduce diagnostic error and how are decision support systems used by critical care physicians? There are no studies that address these research questions in a clinical setting. The information assessment method (IAM) was used to guide the development of the survey questions. Critical care physicians from the University of Oklahoma Health Sciences Center were surveyed. Chi squared test for independence was used to determine the relationship between DSS use and diagnostic error rates. There were three main findings of the study: (1) use of a DSS by a critical care physician can decrease diagnostic error by up to 60%; (2) 56% of critical care physicians are using a DSS during diagnosis to learn something new, confirm something they already knew, and/or to reassure themselves; and (3) the increased use of a DSS by critical care physicians can lead to a decrease in the belief of the ability of a DSS to reduce diagnostic error.

Denna fallstudie syftar till att undersöka effekterna av ett intelligent beslutsstöd på beslutsprocessen i en konsultverksamhet. Vi har utvecklat ett intelligent beslutsstöd och sedan undersökt beslutsprocessen vid kompetensmatchning av konsulter och kundförfrågningar. Vår forskningsfråga är således: Hur kan beslutsprocessen vid kompetensmatchning påverkas av ett intelligent beslutsstöd i en konsultverksamhet? Det finns studier som visar effekten av ett vanligt beslutsstöd och beslutsforskaretror att mer utvecklade beslutsstöd har potential. Dock studeras inte denna typ av intelligent beslutsstöd vanligtvis i kombination med beslutsprocessen. Syftet med studien är att bidra till forskningsläget, på grund av bristen på forskning inom detta område. För att undersöka detta fall behövde vi samla in kvalitativa data, som gjordes med semistrukturerade intervjuer och observationer, före och efter införandet av ett intelligent beslutsstöd. När vi analyserade den insamlade datan användes en kvalitativ innehållsanalys för att hitta teman och kategorier, som vi sedan jämförde resultaten före och efter meden teoretisk referensram. Vi fann att det intelligenta beslutsstödet kunde tillgängliggöra informationen i beslutsprocessen som kunde frigöra tid. Utifrån det kunde tiden istället fokusera på utvärdering. Detta ledde till att beslutsprocessen blev mer rationell i tre av fyra steg. Avslutningsvis fann vi att det intelligenta beslutsstödet hjälpte beslutsprocessen att bli mer rationell. / This case study aims to investigate the impact of an intelligent decision support system on the decisionmaking in a consulting firm. We have developed an intelligent decision support system and then investigated the decision-making when competence matching consultants and customer requests. Thus, our research question: How can the decision-making process when competence matching be affected by an intelligent decision support system in a consulting business? Studies shows the effect of an ordinary decision support system, and decision scientists thinks that more evolved decision support system has potential. However, this type of intelligent decision support system is not usually researched in combination with the decision-making process. The purpose of this study is to contribute to the gap of missing research in this area. To investigate this case, we needed to collect qualitative data, which was done with semi structured interviews and observations, before and after the introduction of the intelligent decision support system. When analyzing our data, we used a qualitative content analysis to find themes and categories, which we then compared the before and after results with theoretical input. We found that the intelligent decision support system could enable the information in the decisionmaking process which could make more time available. With that extra time, the focus of the time could shift to evaluation. This led the decision-making process to become more rational in three of four steps. In conclusion we found that the intelligent decision support system helped the decision-making process to become more rational.

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Decentralized digital currencies are emerging in today’s society. Bitcoin was the first to surface in late 2009, and today there are thousands of digital currencies with various properties. The vast amount of transaction data being generated from decentralized systems can be difficult to comprehend. Information visualization can be used to simplify this apprehension difficulty. It is a tool that allows us to effectively understand complex data by projecting it to a visual medium. This is a research study on multiple stakeholders for a web-based real-time decision support system for visualizing transaction data in a global decentralized financial system and set out to explore the stakeholders requirements in order to provide them support. To achieve this, an exploratory interview was conducted in order to gain knowledge of the requirements for each type of stakeholders. After which, a prototype of the decision support system was made, built upon the requirements and fundamental principles in information visualization. The prototype is web compliant with room for configurations to personalize. It is best suited for an overview. Complementary research could be additional studies on the stakeholders to improve the usability or develop additional supportive features to make it a smart system. / Decentraliserade digitala valutor växer fram i dagens samhälle. Bitcoin var den första som skapades i slutet av 2009 och idag finns det tusentals digitala valutor med olika egenskaper. Den stora mängd transaktionsdata som genereras från decentraliserade system kan vara svår att förstå. Informationsvisualisering kan användas för att förenkla denna förståelsen. Det är ett verktyg som gör det möjligt för oss att effektivt förstå komplex data genom att projicera det till ett visuellt medium. Detta är en undersökning på multipla användare för ett webbaserat realtid beslutstödssystem för att visualisera transaktionsdata i ett globalt decentraliserat finansiellt system med mål att utarbeta användarnas krav för att kunna ge dom stöd. För att uppnå detta genomfördes en förberedande intervju för att få kunskap om kraven för varje typ av användare. Därefter gjordes en prototyp av beslutsstödsystemet, byggt på kraven och grundläggande principer för informationsvisualisering. Prototypen är webkompatibel med utrymme för konfigurationer för att personifiera. Den passar bäst för att ge en översikt. Kompletterande forskning kan vara ytterligare studier på intressenterna för att förbättra användbarheten eller utveckla ytterligare stödfunktioner för att göra det till ett smart system.

The success of construction projects depends on proper use of construction equipment and machinery to a great extent. Thus, appropriate planning and control of the activities that rely on construction equipment could have significant effects on improving the efficiency of project operations. Cranes are the largest and most conspicuous construction equipment, widely used in typical construction sites. They play a major role in relocation of materials in horizontal and vertical directions on construction sites. Given the nature of activities relying on construction cranes in various stages of a project, cranes normally have control over the critical path of the project with the potential to create schedule bottlenecks and delaying the completion of the project. This dissertation intends to improve crane operations efficiency by developing a new framework for optimizing crane service sequence schedule. The crane service sequence problem is mathematically formulated as an NP-complete optimization problem based on the well-known Travel Salesman Problem (TSP) and is solved using different optimization techniques depending on the problem’s size and complexity. The proposed framework sets the basis for developing near-real time decision support tools for on-site optimization of crane operations sequence. To underline the value of the proposed crane sequence optimization methods, these methods are employed to solve several numerical examples. Results show that the proposed method can create a travel time saving of 28% on average in comparison with conventional scheduling methods such as First in First out (FIFO), Shortest Job First (SJF), and Earliest Deadline First (EDF).

Many claim that, with regard to transportation infrastructure, only partnerships between public and private entities, which draw on the strengths of both, can achieve the goals of enhancing operational efficiency, increasing service delivery, improving asset maintenance, and stretching scarce federal, state, and local tax dollars. While some completed public-private partnership (P3) agreements on existing toll roads in the United States have seen a measure of success, others have raised critical questions pertaining to the true costs and benefits associated with these agreements for all stakeholders. Of particular concern is an apparent reliance on monetary calculations alone to determine toll road lump sum value. This primary focus on monetary considerations appears to neglect a number of non-monetary variables associated with potential costs and benefits. Four distinct groups of variables to consider in the decision process are presented in the dissertation: Monetary, Monetizable, Quantitative, and Qualitative. The last two groups represent variables of a non-monetary nature, which can reflect the much larger stewardship role that government plays in our society. The objectives of this research are twofold: to formulate a conceptual framework for a decision support system (DSS); and to propose an approach, including a set of analytical methods, that assesses the costs, benefits and other impacts associated with alternative P3 strategies. The primary user of the conceptual framework is identified as the public sector decision maker who has been asked to make recommendations regarding different strategies of toll road operation. Two analytical methods are presented. The first uses cash flow diagrams to calculate the net present value (NPV) for each of three core P3 strategies. The second, weighs the relative importance of quantitative and qualitative (non-monetizable) variables. When used as part of a sevenstep process, these two analytical methods help create a decision support system framework that provides stakeholders with a more complete analysis of the costs and benefits associated with the P3 toll road decision process.

Several sustainable development indicators have been used to monitor and measure the progress of various countries. Similarly, reports and data available about countries progress prove that development has not been equal in all regions.  On the brighter side, the data can be used to inform decision making in areas that are experiencing deficiencies. In this research, a decision support system(DSS) is built to help governments and NGOs to properly choose projects that align with the needs of the people. We approached this research by utilizing Abraham Maslow’s proven psychological framework on the hierarchy of needs as the main criteria for choosing projects for sustainable development. The system ranks development projects based on the needs priority and how much it has been fulfilled. It ranks projects that meet an urgent need that is also lacking fulfillment higher than other project alternatives. The social progress index (SPI), a comprehensive open data that measures the social progress of counties were correlated to the needs indicated by Maslow’s Hierarchy. The needs were then used as criteria in the AHP decision analysis model to build a classic DSS to aid in selecting the most appropriate development project.

This thesis deals with the energy saving in smart building with focus on the impact of the user behaviour on the energy consumption. The problem of human behaviour modelling has been widely studied in the state of the art, but it is still an open problem in the field of smart building since the stochastic nature of the behaviour is difficult to be accurately represented by numerical tools. An interdisciplinary approach is proposed in order to identify the suitable user features from the psychological and social point of view and to integrate such a representation into a DSS for appliance scheduling and energy cost reduction. The proposed method has exploited location-based features of the users in order to represent their habits and needs and to compute the schedules that maximize the user acceptance toward an “energy-aware” behaviour. The obtained results point out a reduction of the peak-to-average ratio higher than 40% also considering the user constraints imposed by their presence into the building.