Crop development models are commonly used in research. However, their use as crop management tools for growers is rare. Decision support systems (DSS), which combine crop models with expert systems, are being developed to provide management assistance to growers. Researchers at Oregon State University are in the process of developing a DSS. Research was conducted to develop a computer program to provide current and generated weather data for use by the DSS. The objectives of this research were to obtain a weather station, develop a set of quality control procedures to check data from the station, obtain a weather generator program, and create a weather data manager program to implement the above objectives. A weather station was obtained and was placed near two existing weather stations for ten months. Data from the weather station was compared with the other two stations for values of monthly average maximum temperature, minimum temperature, and daily total solar radiation and monthly total precipitation. The weather station performed well. Only measurements of total daily solar radiation were consistently different from the other stations. Based on a comparison of the weather station with an Eppley pyranometer, a factor was calculated to correct the solar radiation readings. The quality control procedures used on the weather data were adapted from automated procedures given in the literature. When tested, the procedures performed as desired. When used on actual data from the weather station, values that failed the procedures were apparently legitimate values. Options were added to the data manager program that allow the user to quickly decide what to do with failed values. For a weather data generator, WGEN was chosen from the generators presented in the literature. An input parameter file was created for the Corvallis, Oregon area and thirty years of data were generated. Monthly means from this data were compared with thirty-year historical monthly means for Corvallis. Precipitation data from WGEN compared well with the historical data. The generated data for maximum and minimum temperature and daily total solar radiation had great differences from the historical data. It is believed that the input parameters for the Corvallis area suggested by the authors of WGEN are not appropriate. The weather data manager program was written in the C programming language, and occupies approximately 98 kilobytes of disk space, not including the eleven files created directly and indirectly by the program. The main functions of the program are: 1) retrieving data from the weather station and performing quality control procedures on the data (allowing the user to decide what to do with values that failed QC); 2) viewing and editing of files by the user; 3) weather data generation (creating a file of only generated data or appending generated data to the file of current data from the weather station to create a file containing a full year of weather data); and 4) miscellaneous functions (monitoring the weather station, setting the calendar in the station's datalogger, and changing information used by the data manager program). It is hoped that this program will be a significant contribution towards the development of a decision support system. / Graduation date: 1992

Finding a product with high quality and reasonable price online is a difficult task due to the fuzzy nature of data and queries. In order to handle the fuzzy problem, a new type-2 fuzzy reasoning based decision support system, the Web Shopping Expert for online users is proposed. In the Web Shopping Expert, an interval type-2 fuzzy logic system is used and a fuzzy output can be obtained using the up-low limit technique, which offers an opportunity to directly employ all the rules and methods of the type-1 fuzzy sets onto the type-2 fuzzy sets. To achieve the best performance the fuzzy inference system is optimized by the least square and numerical method. The key advantages of the least square method are the efficient use of samples and the simplicity of the implementation. The Web Shopping Expert based on the interval type-2 fuzzy inference system provides more reasonable conclusions for online users.

Heart failure is a common disease which is difficult to diagnose. To aid physicians in diagnosing heart failure, a decision support system has been proposed. Parameters useful to the system are suggested. Some of these, such as age and gender, should be provided by the physician, and some should be derived from electro- and phonocardiographic signals. Various methods of signal processing, such as wavelet theory and principal components analysis, are described. Heart failure should be diagnosed based on the parameters, and so various forms of decision support systems, such as neural networks and support vector machines, are described. The methods of signal processing and classification are discussed and suggestions on how to develop the system are made.

Computer decision aids can help integrate and apply diverse sources of information and expertise to problems of integrated pest management (IPM) in agriculture and forestry. AppleMgr combines a rule-based expert system with databases and spreadsheets in a prototype decision aid intended to be expanded and modified for use by extension workers in the Northwest U.S. The program requires an IBMcompatible microcomputer with hard disk. AppleMgr concentrates on the two most important insect pests on apple in the Northwest--codling moth, Cydia, pomonella (L.), and San Jose scale, Quadraspidiotus perniciosus Comstock, and on phytophagous mites, whose control largely depends on predators. The primary goal of AppleMgr is to demonstrate an improved process of decision making in apple IPM. AppleMgr is composed of modules for diagnosis of pest injury, identification of pest and natural enemy specimens, and management. The first two modules arrive at conclusions through backward-chaining inference from user observations. The management module uses backward chaining supplemented with external calculation programs to find the net benefit of a pesticide application. A method is included to predict yield and fruit size from crop samples. Cullage from codling moth and San Jose scale, mite effect on fruit size, probability of biological mite control and pesticide efficacy are predicted from researchers' data and estimates. Selected relative beneficial and adverse side effects of pesticides are presented in spreadsheets. An analysis of packing house records for apple crops from eight orchards at three yields using 1987 and 1988 prices and packing charges showed that net crop value varied by up to $8000 per acre. The variability in crop value and the importance of adverse side effects of pesticides suggest that the commonly-used "action thresholds" for treatment are seriously inadequate. AppleMgr may point the way toward more dynamic and realistic methods of IPM decision making. / Graduation date: 1990

Osteoporosis is a major public health concern, affecting over 200 million people worldwide. There is valid evidence outlining how osteoporosis can be diagnosed and managed, but gaps exist between evidence and practice. Graham’s “Knowledge to Action” (KTA) process for knowledge translation and the Medical Research Council (MRC) framework for complex interventions were used to address these gaps. The first 4 KTA steps were collapsed into 3 phases of the PhD research plan. In PhD Phase 1, a systematic review was conducted to identify tools that facilitate decision making in osteoporosis disease management (DM). Results showed that few DM tools exist, but promising strategies were those that incorporated reminders and education and targeted physicians and patients. PhD Phase 2 used the findings from the systematic review and consultation with clinical and human factors engineering experts to develop a conceptual design of the tool. Multiple components targeted to both physicians and patients at the point of care, and which could be used as a standalone system or modifiable for integration with electronic health record systems were outlined. PhD Phases 3a and 3b were devoted to the assessment of the barriers to knowledge. In Phase 3a, a qualitative study of focus groups was conducted with physicians to identify attitudes and perceived barriers to implementing decision support tools in practice, and to identify the features that should be included in the design. Findings from 4 focus groups combined with aging research, and input from design and information experts were used to transform the conceptual design into a functional prototype. In Phase 3b, each component of the prototype was tested in 3 usability evaluation studies using an iterative, participant-centered approach to assess how well the prototype met end users’ needs. Findings from the usability study informed the final prototype, which is ready for implementation as part of the post PhD plan to fulfill the requirements of the remaining steps of the KTA and MRC frameworks.

Visualising uncertainty information has been a research area for the past decade or so, and this thesis contains the results of an experiment that examines whether prior research on icon degradation for showing uncertainty can be used in a simulated aircraft cockpit environment. Using icon degradation has been suggested as being effective to combat overconfidence bias, as well as to accurately convey information about uncertainty. Two icon sets using icon degradation were taken from prior research, and one new icon set using shape change and colour change was created for comparison. Subjects flew a flight simulator while reading icons to evaluate the uncertainty displayed, and also evaluating their own confidence in their reading. The results show that shape change leads to much higher accuracy in reading icons, and slightly higher levels of confidence. Furthermore, icon degradation results in a higher variance in reading icons and an increase in errors when no time-pressure or distraction is present. This suggests that the suitability of icon degradation for showing uncertainty is questionable in all situations, and that other design approaches such as shape change should be considered. Furthermore, problems were uncovered in the prior research that the old icons were taken from, and these problems call into question the general approach used in that research. Keywords: Uncertainty visualisation, Naturalistic decision-making, NDM, Aviation, Aircraft cockpit, Decision support, Situation assessment, Threat assessment.

The Nursing Coordinators (NCs) at the University of Ottawa Heart Institute (UOHI) fields phone calls from patients who have been discharged and are undergoing home care procedures at a daily basis. The project described in this thesis aims to provide tools for the Personal Digital Assistant (PDA) that the NCs can use during the phone calls. The Cognitive Work Analysis (CWA) and Ecological Interface Design (EID) approach are used to identify the information requirements to design the system. Major challenges of the telephone consultation process that are additionally identified by literature review and interviewing the NCs included visibility of patients, individual differences, and lack of standardized procedures. A combination of decision trees and visualization techniques is proposed to aid the process. Implementation of decision trees would help unload mental workload especially accesses to "knowledge in the head" as well as facilitate expert knowledge transfer to less experienced nurses. Visualization tools display integration of multiple-cues from patients in an abstract nature and can be accessed by users at any point of decision process. <br /><br /> Preliminary experiment with static images showed that visualization tools helped the decision makers more when the judgement tasks were more complex. The effects of different types of decision support on the cardiac nurses in simulated telephone consultation processes were examined. The system improved the performance of the decision makers and induced different types of strategic behaviours: a standardized checklist, OLDCAR, induced more through assessment, the decision algorithms induced efficient and more detailed recommendation, and the semantic network symptom map induced information gathering more relevant to diagnosis. <br /><br /> The research also explored methodologies to examine multi-layered decision process, where many decision makers with varying expertise are involved in modeling the strategic behaviours. This type of process can be applicable when the primary decision makers do not monitor the work domain, but can be alerted when something goes wrong.

Objectives: Based on the analysis of electromyographic (EMG) data muscles are often characterized as normal or affected by a neuromuscular disease process. A clinical decision support system (CDSS) for the electrophysiological characterization of muscles by analyzing motor unit potentials (MUPs) was developed to assist physicians and researchers with the diagnosis, treatment & management of neuromuscular disorders and analyzed against criteria for use in a clinical setting. Methods: Quantitative MUP data extracted from various muscles from control subjects and patients from a number of clinics was used to compare the sensitivity, specificity, and accuracy of a number of different clinical decision support methods. The CDSS developed in this work known as AMC-PD has three components: MUP characterization using Pattern Discovery (PD), muscle characterization by taking the average of MUP characterizations and calibrated muscle characterizations. Results: The results demonstrated that AMC-PD achieved higher accuracy than conventional means and outlier analysis. Duration, thickness and number of turns were the most discriminative MUP features for characterizing the muscles studied in this work. Conclusions: AMC-PD achieved higher accuracy than conventional means and outlier analysis. Muscle characterization performed using AMC-PD can facilitate the determination of “possible”, “probable”, or “definite” levels of disease whereas the conventional means and outlier methods can only provide a dichotomous “normal” or “abnormal” decision. Therefore, AMC-PD can be directly used to support clinical decisions related to initial diagnosis as well as treatment and management over time. Decisions are based on facts and not impressions giving electromyography a more reliable role in the diagnosis, management, and treatment of neuromuscular disorders. AMC-PD based calibrated muscle characterization can help make electrophysiological examinations more accurate and objective.

This thesis studies the use of a collaborative spatial Multi-Criteria Analysis tool in site evaluation with multiple participants. The approach is situated within the context of three concepts of space, choice and participation, and is informed by fields as diverse as Decision-Making, Participatory Planning, Geographical Information Systems, Decision Support Systems, Voting, and Group Collaboration. A collaborative spatial Multi-Criteria Analysis software tool called MapChoice was designed for this thesis, built upon open source components and featuring easy-to-use decision support functionality in both single-user and collaborative modes. MapChoice was then evaluated in a real-world site selection situation with a case study on the location of much-needed affordable housing in the Town of Collingwood, Ontario. Based on previous discussions and workshops on the project, a workshop was held with a group of community housing advocates to compare a set of possible sites for an affordable housing project according to a set of spatial and aspatial criteria. The study indicates that a collaborative spatial MCA approach can be used in dealing with complex planning problems, and that it has the potential to contribute to improved consensus between participants.

In industry, energy efficiency reduces system cost and emissions to the environment. Energy audits are carried out in industry to identify measures that would increase energy efficiency. However, the usual case is that low-cost measures are implemented while capital intensive measures receive less attention possibly due to, example, inadequate information available to study risks involved. Decisions support tools have been identified as a means of supporting complex production related investment decision. The aim of this paper is to investigate profitability and potential global CO2 emission reduction of energy conversion investments in a small energy intensive industry by using an optimisation method as a decision support tool. The investments are evaluated using consistent future energy market scenarios with interdependent parameters. An optimisation model is developed with reMIND optimisation tool which is used to optimise the system cost of each scenario. The reduction in system cost and global CO2 emissions of the new investments and results from sensitivity analysis are evaluated to determine the optimal investment solution. In the report, it is established that optimisation methods provide a structured means of studying the risk involved in capital intensive investments. The optimisation results show that investment in a small-scale steam turbine combined heat and power production is a profitable and robust investment. The net reduction of global CO2 emission is substantial compared with the reference system. Furthermore, it is shown that biofuel policies alone may not make cost intensive biofuel investments attractive, further reduction in investment cost is required. The energy savings and global CO2 emission reductions achieved in this study can play an important role in achieving the aims of the European Union to reduce greenhouse gas emissions by 20% and save 20 % of energy by the year 2020.