Scholars widely accept that the well-documented benefits of biophilia–the human being's strong urge to connect with nature–are genuine to improved health. Then why, with the global acceptance and scientific validity of wellness design concepts, do architects not use this beneficial concept regularly–especially when designing isolated workspaces? This qualitative research explores architecture's current design decision process to better understand this design phenomenon, and to identify where architectural biophilic knowledge domains may be deficient. This study explores questions such as: Does the architect's lack of biophilic knowledge and/or structured wellness design decision support framework affect the decision? Would the existence of a wellness design tool better support the design decision? An explanatory case study using a purposeful study sample of architects, biophilia design experts, and associated specialists is used to develop design decision support frameworks. Level 1 establishes a propositional theory derived from the literature and professional experience, level 2 from architect interviews and observational meetings, and level 3 from a Delphi workgroup session. Framework evolutions help identify design-phase-specific knowledge gaps. This study finds that a deficiency in early exposure to a priori, explicit and tacit biophilic knowledge is creating a critical gap, thus diminishing a posteriori biophilic knowledge and research in the architecture profession. This study asserts that early exposure to biophilic theories and principles can enhance the profession and provide a knowledge bridge using an informed biophilia design support framework with a proposed biophilia project management tool. / Doctor of Philosophy / Few will dispute that the well-documented benefits of biophilia–the human being's strong urge to connect with nature–are genuine to improved health. Then why, with the global acceptance and scientific validity of wellness design concepts, do architects not use this beneficial concept regularly–especially when designing windowless workspaces? A qualitative explanatory case study using a purposeful study sample of architects, biophilia design experts, and associated specialists was used to develop a design decision support framework that evolved from level 1-3. Framework progressions helped identify specific knowledge gaps in each design phase. This study found that a deficiency in early exposure to a priori, explicit and tacit biophilic knowledge is creating a critical gap, thus diminishing a posteriori biophilic knowledge and research in the architecture profession. This study asserts that early exposure to biophilic theories and principles can enhance the profession and provide a knowledge bridge using an informed biophilia design decision support framework with a proposed biophilia project management tool.

Creating a sense of place through a comfortable indoor condition is a goal of the architectural design process. Thermal comfort is an important component of this condition. To achieve thermally comfortable environments mechanical systems such as Radiant Cooling (RC) could be used. RC systems have potential benefit of lower energy consumption when compared to other common cooling, ventilating and air-conditioning systems. Decisions related to the use of mechanical systems such as these should be considered in the early stages of design to maximize the building performance through systems integration and minimize redesign as part of the design process. RC systems have several special demands and related variables. Architects, HVAC system engineers, and decision-makers have to understand these issues and variables and their impact on the other building performance mandates. Through this understanding, these professionals can better evaluate tradeoffs to reach the desired solution of the design problem. Unfortunately, in the United States few architects and engineers have experience with RC systems which in turn limits the application of these systems. Through systematic literature review, a series of case studies, and interviews with experienced professionals, this research captures and structures knowledge related to how decisions are made concerning RC systems. Through this knowledge capturing procedure, the relevant design performance mandates, barriers and constraints, and potential advantages and benefits of radiant cooling systems are determined and mapped to a decision-support framework. This framework is graphically presented which may later be translated to a decision-support software package which could then be developed as a radiant cooling system design assistance tool for architects and HVAC engineers. / Ph. D.

Clinical risk assessment of chronic illnesses is a challenging and complex task which requires the utilisation of standardised clinical practice guidelines and documentation procedures in order to ensure consistent and efficient patient care. Conventional cardiovascular decision support systems have significant limitations, which include the inflexibility to deal with complex clinical processes, hard-wired rigid architectures based on branching logic and the inability to deal with legacy patient data without significant software engineering work. In light of these challenges, we are proposing a novel ontology and machine learning-driven hybrid clinical decision support framework for cardiovascular preventative care. An ontology-inspired approach provides a foundation for information collection, knowledge acquisition and decision support capabilities and aims to develop context sensitive decision support solutions based on ontology engineering principles. The proposed framework incorporates an ontology-driven clinical risk assessment and recommendation system (ODCRARS) and a Machine Learning Driven Prognostic System (MLDPS), integrated as a complete system to provide a cardiovascular preventative care solution. The proposed clinical decision support framework has been developed under the close supervision of clinical domain experts from both UK and US hospitals and is capable of handling multiple cardiovascular diseases. The proposed framework comprises of two novel key components: (1) ODCRARS (2) MLDPS. The ODCRARS is developed under the close supervision of consultant cardiologists Professor Calum MacRae from Harvard Medical School and Professor Stephen Leslie from Raigmore Hospital in Inverness, UK. The ODCRARS comprises of various components, which include: (a) Ontology-driven intelligent context-aware information collection for conducting patient interviews which are driven through a novel clinical questionnaire ontology. (b) A patient semantic profile, is generated using patient medical records which are collated during patient interviews (conducted through an ontology-driven context aware adaptive information collection component). The semantic transformation of patients’ medical data is carried out through a novel patient semantic profile ontology in order to give patient data an intrinsic meaning and alleviate interoperability issues with third party healthcare systems. (c) Ontology driven clinical decision support comprises of a recommendation ontology and a NICE/Expert driven clinical rules engine. The recommendation ontology is developed using clinical rules provided by the consultant cardiologist from the US hospital. The recommendation ontology utilises the patient semantic profile for lab tests and medication recommendation. A clinical rules engine is developed to implement a cardiac risk assessment mechanism for various cardiovascular conditions. The clinical rules engine is also utilised to control the patient flow within the integrated cardiovascular preventative care solution. The machine learning-driven prognostic system is developed in an iterative manner using state of the art feature selection and machine learning techniques. A prognostic model development process is exploited for the development of MLDPS based on clinical case studies in the cardiovascular domain. An additional clinical case study in the breast cancer domain is also carried out for the development and validation purposes. The prognostic model development process is general enough to handle a variety of healthcare datasets which will enable researchers to develop cost effective and evidence based clinical decision support systems. The proposed clinical decision support framework also provides a learning mechanism based on machine learning techniques. Learning mechanism is provided through exchange of patient data amongst the MLDPS and the ODCRARS. The machine learning-driven prognostic system is validated using Raigmore Hospital's RACPC, heart disease and breast cancer clinical case studies.

In this study, four real-life problem situations were used to explore the challenges of developing and implementing decision support tools for planning and management within an urban water utility. The study sought to explore how the degree of adoption of formal decision support tools in practice, generally perceived to be low, could be improved. In the study, an Action Research (AR) approach was used. AR is an inquiry process that involves partnership between researchers and practitioners for the purpose of addressing a real-life problem issue, while simultaneously generating scientific knowledge. Unlike other research methods where the researcher seeks to study organizational phenomena but not to change them, the action researcher attempts to create organizational change and simultaneously to study the process. During the study, a number of prototype data management tools were developed. GIS-based spatial analysis and visualisation tools were extensively used to inform and enhance the processes of participatory problem identification and structuring, while a number of modelling tools were applied in the generation and evaluation of alternative solutions. As an outcome of the study, a prototype framework for the application of decision support tools within an urban water supply planning and management context was proposed. The study highlighted the challenges of embedding formal decision support processes within existing work systems in organizations, and recommendations were made on how best to achieve this. The AR approach was found to be useful in bridging the gap between academic research and technological practice, supporting the development of computerised planning and decision support tools of practical benefit to organizations. / QC 20100723

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Identity management is a critical concept for enterprises, and it has turned to more challenging issue since businesses are significantly moving towards service oriented architecture (SOA) with the aim to provide seamless service delivery to their customers, partners and employees. The organizational domains are expanded to blur the virtual borders, simplify the business collaboration and maximize opportunities in the competitive market place, which explicitly shows the essentiality for federating the identities. Real-world identity comprises of different dimensions such as Law, Business, Policy, Technology and Society, therefore reliable digital identity management and successful federation are required to take these dimensions and complexity into consideration. Considering variety of academic and industrial researches that report on remarkable demands for identity federation adoption by enterprises, this study has approached federated Identity Management from technological point of view. Technologies provide tools and mechanisms to satisfy the business requirements and enable single sign-on capability in reliable federated platform. Different authentication technologies and standards have emerged to enable federated single sign-on (FSSO) implementation as a core service of the FIdM, each with different features and capabilities. This brings more complexity and confusion for experts and decision makers for FIdM adoption and development. To overcome this obstacle and accelerate the data collection and analysis process for decision makers, this research contributes to the filed by providing a conceptual framework to simplify the analysis of underlying technology for decision making process. In this framework 1) a list of state-of-the-art requirements and mechanisms for successful identity federation and reliable SSO is elaborated, 2) Six most prevalent standard authentication technologies along with latest specifications are analysed, explained and assessed against the defined criteria, and 3) several security and privacy consideration are gathered. The usage of framework is monitored and the efficiency of it is evaluated in 2 real business case scenarios by five IT experts and the result is reported.

This thesis explores the imperative of intelligent data and potential analysis in the realm of mechatronic product development. The persistent challenges of synchronization and efficiency underscore the need for advanced methodologies. Leveraging the substantial advancements in Artificial Intelligence (AI), particularly in generative AI, presents unprecedented opportunities. However, significant challenges, especially regarding robustness and trustworthiness, remain unaddressed. In response to this critical need, a comprehensive methodology is introduced, examining the entire development process through the illustrative V-Model and striving to establish a robust AI landscape. The methodology explores acquiring suitable and efficient knowledge, along with methodical implementation, addressing diverse requirements for accuracy at various stages of development.  As the landscape of mechatronic product development evolves, integrating intelligent data and harnessing the power of AI not only addresses current challenges but also positions organizations for greater innovation and competitiveness in the dynamic market landscape.

<p>The agricultural data landscape is largely dysfunctional because of the industry’s highvariability  in  scale,  scope,  technological  adoption,  and  relationships.   Integrated  data  andmodels of agricultural sub-systems could be used to advance decision-making, but interoperability  challenges  prevent  successful  innovation.   In  this  work,  temporal  and  geospatial indexing  strategies  and  aggregation  were  explored  toward  the  development  of  functional data  structures  for  soils,  weather,  solar,  and  machinery-collected  yield  data  that  enhance data context, scalability, and sharability.</p> <p>The data structures were then employed in the creation of decision support tools including web-based  applications  and  visualizations.   One  such  tool  leveraged  a  geospatial  indexing technique called geohashing to visualize dense yield data and measure the outcomes of on-farm yield trials.  Additionally, the proposed scalable, open-standard data structures were used to drive a soil water balance model that can provide insights into soil moisture conditions critical to farm planning, logistics, and irrigation.  The model integrates SSURGO soil data,weather data from the Applied Climate Information System, and solar data from the National Solar Radiation Database in order to compute a soil water balance, returning values including runoff, evaporation, and soil moisture in an automated, continuous, and incremental manner.</p> <p>The approach leveraged the Open Ag Data Alliance framework to demonstrate how the data structures can be delivered through sharable Representational State Transfer Application Programming Interfaces and to run the model in a service-oriented manner such that it can be operated continuously and incrementally, which is essential for driving real-time decision support tools.  The implementations rely heavily on the Javascript Object Notation data schemas leveraged by Javascript/Typescript front-end web applications and back-end services delivered through Docker containers.  The approach embraces modular coding concepts and several levels of open source utility packages were published for interacting with data sources and supporting the service-based operations.</p> <p>By making use of the strategies laid out by this framework, industry and research canenhance data-based decision making through models and tools.  Developers and researchers will  be  better  equipped  to  take  on  the  data  wrangling  tasks  involved  in  retrieving  and parsing unfamiliar datasets, moving them throughout information technology systems, and understanding those datasets down to a semantic level.</p>

Inland recreational fisheries has grown philosophically and scientifically to consider economic and sociopolitical aspects (non-biological) in addition to the biological. However, integrating biological and non-biological aspects of inland fisheries has been challenging. Thus, an opportunity exists to develop approaches and tools which operationalize planning and decision-making processes which include biological and non-biological aspects of a fishery. This dissertation expands the idea that a core set of goals and objectives is shared among and within inland fisheries agencies; that many routine operations of inland fisheries managers can be regimented or standardized; and the novel concept that current information and operations can be used to improve decision making through structured decision making and adaptive management approaches at the agency scale. In CHAPTER II, my results show that the goals of inland fisheries agencies tend to be more similar than different but have expanded and diversified since the 1970s. I suggest that changes in perspectives and communication technology, as well as provisions within nationwide funding mechanisms, have led to goals becoming more homogenous across the USA and more diverse within each bureau. In CHAPTER III, I found that standardized collection and careful curation of data has allowed one inland fisheries bureau to acquire a large fish and fisheries database and that managers use this database to summarize common fish population parameters and indices, craft objectives, and set targets. The regimentation of data management and analysis has helped managers within the inland fisheries bureau to assess fish populations and fisheries efficiently and effectively across waterbodies within their districts and state. In CHAPTER IV, I extend CHAPTERS II and III to show that biological and non-biological management objectives and their associated measurable attributes and management actions can be synthesized into a common set of decision elements. I demonstrate how common decision elements enable managers to easily structure decisions and help to address common problems at the agency scale. Using a subset of common decision elements, I demonstrate how existing agency operations (e.g., monitoring) can be used to expedite learning and improve decision making for a common problem faced by managers in multiple, similar systems.