The aim of this thesis is to carry out research into the application of experimental design in an activity-based environment; specifically into the methodology and potential application of experimental design and analysis as an activity-based management decision support tool in the general manufacturing, systems engineering and logistics engineering fields. This thesis will detail the author's research into the decision support framework that will be introduced in Chapter 1.0 and developed during the thesis. Chapter 2.0 will introduce activity-based management and develop a systems dynamics model of a US industrial enterprise. Experimental design and analysis will be introduced in Chapter 3.0. The development of a knowledge-based expert system (KBES) trainer called DESIGNS.ART will be covered in Chapter 4.0, and' a copy of the program has been included in Appendix A This KBES trainer has been designed to train engineers in US industry in the application of experimental design and analysis. Chapter 5.0 discusses the potential application of experimental design and analysis in each of the three fields. Chapter 6.0 combines experimental design and analysis with activity-based management by fully developing the activity-based management (ABM) framework introduced in Chapter 1.0. Chapter 6.0 includes a hypothetical case study of the ABM framework in operation. Chapter 7.0 proposes that this ABM framework be integrated into a US firm which has implemented activity-based costing. This research into the integration of experimental design and analysis with activity-based management in systems engineering, logistics engineering and general manufacturing is original, and it results in a contribution to both the systems engineering, logistics engineering and the general manufacturing fields. / Master of Science

Yes / Most people living with dementia want to remain in their own homes, supported by family and paid carers. Care at home often breaks down, necessitating transition to a care home and existing interventions are limited. To inform the development of psychosocial interventions to enable people with dementia to live well for longer at home, we qualitatively explored the views of people living with dementia, family carers and health and social care professionals, on how to achieve and maintain independence at home and what impedes this. METHODS: We conducted an inductive thematic analysis of qualitative interviews with 11 people living with dementia, 19 professionals and 22 family carers in England. RESULTS: We identified four overarching themes: being in a safe and familiar environment, enabling not disabling care, maintaining relationships and community connectedness, and getting the right support. For people living with dementia, the realities of staying active were complex: there was a tension between accepting support that enabled independence and a feeling that in doing so they were accepting dependency. Their and professionals' accounts prioritised autonomy and 'living well with dementia', while family carers prioritised avoiding harm. Professionals promoted positive risk-taking and facilitating independence, whereas family carers often felt they were left holding this risk. DISCUSSION: Psychosocial interventions must accommodate tensions between positive risk-taking and avoiding harm, facilitating autonomy and providing support. They should be adaptive and collaborative, combining self-management with flexible support. Compassionate implementation of rights-based dementia care must consider the emotional burden for family carers of supporting someone to live positively with risk. / This work was supported by the Alzheimer’s Society (UK) and was carried out within the UCL Alzheimer’s Society Centre of Excellence for Independence at home, NIDUS (New Interventions in Dementia Study) programme (Alzheimer’s Society Centre of Excellence grant 330). This project is also part-funded funded by The National Institute for Health Research Applied Research Collaboration North West Coast (ARC NWC).

The difficulty of projecting ecological impacts, as well as the increasing familiarity of planners with Geographical Information Systems and other software technology has led to an increase in the use of Planning Support Systems (PSS) by city and regional planners. Due to their newness and rapid development, there is, of yet, a lack of a comprehensive peer-reviewed literature on the design and implementation of these systems. This thesis proposes and applies a rating framework for PSS in order to facilitate accessibility to and critical investigation of PSS. The rating framework’s criteria are based on the “seven sins” of comprehensive land use models identified by Douglass Lee’s 1973 article “Requiem for Large-Scale Models." / text

In order to cope with the turbulent environments in digital age, an enterprise should response to the changes quickly. Therefore, an enterprise must improve her ability of real-time decision-making. One way to increase the competence of real-time decision-making is to use Real-Time Decision Support Systems (RTDSS). A key feature for a Decision Support Systems (DSS) to successfully support real-time decision-making is to help decision-makers selecting the best models within deadline. This study focuses on developing methods to support the mechanism of model selection in DSS. There are five results in this study. Firstly, we have developed a time-based framework to evaluate models. This framework can help decision-makers to evaluate the quality and cost of model solutions. Secondly, based on the framework of models evaluation, we also developed three models selection strategies. These strategies can help decision-makers to select the best model within deadline. Thirdly, according the definitions of parameter value precision and model solution precision in this study, we conduct a simulation analysis to understand the impacts of the precision of parameter values to the precision of a model solution. Fourthly, in order to understand the interaction among the model selection variables, we also simulate the application of model selection strategies. The results of simulation indicate our study can support models selection well. Finally, we developed a structure-based model retrieval method to help decision-makers find alternative models from model base efficiently and effectively. In conclusion, the results of this research have drawn a basic skeleton for the development of models selection. This research also reveals much insight into the development of real-time decision support systems.

The paradigm of Decision Support Systems (DSS) is to support decision-making, while an Expert System’s (ES) major objective is to provide expert advice in specialised situations. Knowledge-Based DSS (KB-DSS), also called Intelligent Decision Support Systems (IDSS), integrate traditional DSS with the advances of ES. A KB-DSS’ knowledge base usually contains knowledge expressed by an expert and captured by a knowledge engineer. The indirect transfer between the domain expert and the knowledge base through a knowledge engineer may lead to a long and inefficient knowledge acquisition process. This thesis compares 11 DSS packages in search of a (KB-) DSS generator where domain experts can specify and maintain a Specific Decision Support System (SDSS) to assist users in making decisions. The proposed (KB-) DSS-generator is tested with a university and study-program prototype. Since course and study plan programs change intermittently, the (KB-) DSS’ knowledge base enables domain experts to set and maintain their course and study plan rules without the assistance of a knowledge engineer. Criteria are set to govern the (KB-) DSS generator search process. Example knowledge base rules are inspected to determine if domain experts will be able to maintain a set of production rules used in a student registration advice system. By developing a prototype and inspecting knowledge base rules, it was found that domain experts would be able to maintain their knowledge in the decentralised knowledge base, on condition that the objects and attributes used in the rule base were first specified by a builder/programmer. / Dissertation (MSc Computer Science)--University of Pretoria, 2005. / Computer Science / unrestricted

A data warehouse is a single data store, where data from multiple data sources is integrated for online business analytical processing (OLAP) of an entire organisation. The rationale being single and integrated is to ensure a consistent view of the organisational business performance independent from different angels of business perspectives. Due to its wide coverage of subjects, data warehouse design is a highly complex, lengthy and error-prone process. Furthermore, the business analytical tasks change over time, which results in changes in the requirements for the OLAP systems. Thus, data warehouse and OLAP systems are rather dynamic and the design process is continuous. In this thesis, we propose a method that is integrated, formal and application-tailored to overcome the complexity problem, deal with the system dynamics, improve the quality of the system and the chance of success. Our method comprises three important parts: the general ASMs method with types, the application tailored design framework for data warehouse and OLAP, and the schema integration method with a set of provably correct refinement rules. By using the ASM method, we are able to model both data and operations in a uniform conceptual framework, which enables us to design an integrated approach for data warehouse and OLAP design. The freedom given by the ASM method allows us to model the system at an abstract level that is easy to understand for both users and designers. More specifically, the language allows us to use the terms from the user domain not biased by the terms used in computer systems. The pseudo-code like transition rules, which gives the simplest form of operational semantics in ASMs, give the closeness to programming languages for designers to understand. Furthermore, these rules are rooted in mathematics to assist in improving the quality of the system design. By extending the ASMs with types, the modelling language is tailored for data warehouse with the terms that are well developed for data-intensive applications, which makes it easy to model the schema evolution as refinements in the dynamic data warehouse design. By providing the application-tailored design framework, we break down the design complexity by business processes (also called subjects in data warehousing) and design concerns. By designing the data warehouse by subjects, our method resembles Kimball's "bottom-up" approach. However, with the schema integration method, our method resolves the stovepipe issue of the approach. By building up a data warehouse iteratively in an integrated framework, our method not only results in an integrated data warehouse, but also resolves the issues of complexity and delayed ROI (Return On Investment) in Inmon's "top-down" approach. By dealing with the user change requests in the same way as new subjects, and modelling data and operations explicitly in a three-tier architecture, namely the data sources, the data warehouse and the OLAP (online Analytical Processing), our method facilitates dynamic design with system integrity. By introducing a notion of refinement specific to schema evolution, namely schema refinement, for capturing the notion of schema dominance in schema integration, we are able to build a set of correctness-proven refinement rules. By providing the set of refinement rules, we simplify the designers's work in correctness design verification. Nevertheless, we do not aim for a complete set due to the fact that there are many different ways for schema integration, and neither a prescribed way of integration to allow designer favored design. Furthermore, given its °exibility in the process, our method can be extended for new emerging design issues easily.

Syftet med denna studie är att utreda begreppen big data och analytics. Utifrån vetenskapliga teorier om begreppen undersöks hur konsultföretag uppfattar och använder sig av big data och analytics. För att skapa en nyanserad bild har även en organisation inom vården undersökts för att få kunskap om hur de kan dra nytta av big data och analytics. Ett antal viktiga svårigheter och framgångsfaktorer kopplade till båda begreppen presenteras. De svårigheterna kopplas sedan ihop med en framgångsfaktor som anses kunna bidra till att lösa det problemet. De mest relevanta framgångsfaktorer som identifierats är att högkvalitativ data finns tillgänglig men även kunskap och kompetens kring hur man hanterar data. Slutligen tydliggörs begreppens innebörd där man kan se att big data oftast beskrivs ur dimensionerna volym, variation och hastighet och att analytics i de flesta fall syftar till att deskriptiv och preventiv analys genomförs. / The purpose of this study is to investigate the concepts of big data and analytics. The concepts are explored based on scientific theories and interviews with consulting firms. A healthcare organization has also been interviewed to get a richer understanding of how big data and analytics can be used to gain insights and how an organisation can benefit from them. A number of important difficulties and sucess facors connected to the concepts are presented. These difficulties are then linked to a sucess factor that is considered to solve the problem. The most relevant success factors identified are the avaliability of high quality data and knowledge and expertise on how to handle the data. Finally the concepts are clarified and one can see that big data is usually described from the dimensions volume, variety and velocity and analytics is usually described as descriptive and preventive analysis.

The objective of the proposed research is to study the integration of humans and automation for the operation of regenerative life support systems (RLSS). RLSS combine physico-chemical and biological processes with the purpose of increasing the autonomy of space habitats and the life quality of their living organisms by properly reusing byproducts and regenerating consumable resources. However, these processes require energy and time to transform chemical compounds and organic wastes into nutrients, consumables, and edible products. Consequently, the maintenance of RLSS imposes a considerable workload on human operators. In addition, the uncertainties introduced by unintended chemical reactions promoted by material loop closure may create unexpected situations that, if unattended, could translate into performance deterioration, human errors, and failures. The availability of novel chemical and biological sensors together with computational resources enable the development of monitoring and automation systems to alleviate human workload, help avoid human error, and increase the overall reliability of these systems. This research aggregates sensor data and human-expert situation assessments to create a representation of their situation knowledge base (\gloss{skb}). The representation is used in a switched control approach to the automation of RLSS, for decision support, and human-automation coordination. The aggregation method consists of an optimization process based on particle swarms. The purpose of this work is to contribute to the methodological development of situation-oriented and user-centered design approaches to human-automation systems. Experiments and simulations are supported on the process of respiration in an aquatic habitat acting as a RLSS.

A strong trend of returning to nuclear power is evident in different places in the world. Forty-five countries are planning to add nuclear power to their grids and more than 66 nuclear power plants are under construction. Nuclear power plants that generate electricity and steam need to improve safety to become more acceptable to governments and the public. One novel practical solution to increase nuclear power plants' safety factor is to build them away from urban areas, such as offshore or underground. To date, Land-Based siting is the dominant option for siting all commercial operational nuclear power plants. However, the literature reveals several options for building nuclear power plants in safer sitings than Land-Based sitings. The alternatives are several and each has advantages and disadvantages, and it is difficult to distinguish among them and choose the best for a specific project. In this research, we recall the old idea of using the alternatives of offshore and underground sitings for new nuclear power plants and propose a tool to help in choosing the best siting technology. This research involved the development of a decision model for evaluating several potential nuclear power plant siting technologies, both those that are currently available and future ones. The decision model was developed based on the Hierarchical Decision Modeling (HDM) methodology. The model considers five major dimensions, social, technical, economic, environmental, and political (STEEP), and their related criteria and sub-criteria. The model was designed and developed by the author, and its elements' validation and evaluation were done by a large number of experts in the field of nuclear energy. The decision model was applied in evaluating five potential siting technologies and ranked the Natural Island as the best in comparison to Land-Based, Floating Plant, Artificial Island, and Semi-Embedded plant.

ITC/USA 2006 Conference Proceedings / The Forty-Second Annual International Telemetering Conference and Technical Exhibition / October 23-26, 2006 / Town and Country Resort & Convention Center, San Diego, California / This paper presents the description of the updated Data Display Markup Language (DDML), a neutral format for data display configurations. The development of DDML is motivated by the fact that in joint service program systems, there is a critical need for common data displays to support distributed T&E missions, irrespective of the test location, data acquisition system, and display system. DDML enables standard data displays to be specified for any given system under test, irrespective of the display vendor or system in which they will be implemented. The version 3.0 of DDML represents a more mature language than the version 1.0 presented at the 2003 ITC. The updated version has been validated for completeness and robustness by developing translators between DDML and numerous vendor formats. The DDML schema has been presented to the Range Commander’s Council (RCC) Data Multiplex Committee for consideration for inclusion in the IRIG 106 standard. The DDML model will be described in terms of both the XML schema and the UML model, and various examples of DDML models will be presented. The intent of this paper is to solicit specific input from the community on this potential RCC standard.