The single biggest challenge facing municipalities today is a shortage of funds and labor for upgrading and expanding aging infrastructure. This continued lack of funding impairs the municipalities ability to maintain desired levels of service. Over the last decade, many Canadian municipalities have faced pressures of increasing complexity in infrastructure asset management decision-making which can be partly attributed to cost escalation, increasing service demand and interdependencies between networks. The goal of this research is to develop the framework for Asset Levels of Service (ALOS)-based decision support systems for municipal infrastructure network investment. The proposed framework is based on the fact that ALOS should be one of the main criteria for municipal infrastructure maintenance, repair and rehabilitation (MR&R). Since ALOS is based on qualitative and quantitative parameters, the use of ALOS in municipal infrastructure MR&R decisions will result in improved funding allocation. Secondary parameters used for municipal infrastructure investment decision making in the proposed framework are the physical deterioration of assets, future growth and the impact on the dependent infrastructure network. The proposed framework focuses on funding allocation for the MR&R of municipal networks. The framework is applicable to municipal infrastructure networks, excluding the other assets such as buildings, parks, etc. Application of the proposed framework is demonstrated by its implementation in the case of urban roads. Implementation is carried out in four phases. Phase I involves the quantification of ALOS for urban roads. Quantification of ALOS for urban roads has various challenges such as multiple users and interdependencies of levels of services between various users. An Analytical Hierarchy Process (AHP) has been used to quantify ALOS. Phase II involves the determination of a multiattribute utility function for investment decision. Calculated multiattribute utility of investment decision is used in the multiobjective optimization model in Phase III. In Phase IV, the proposed methodology is incorporated into a computer application called OPTIsys (OPTImum Infrastructure SYStems). OPTIsys will facilitate MR&R decision making based on fully integrated considerations of ALOS, future demand and network interdependencies. Stakeholders benefiting from OPTIsys include the general public, asset-managers, infrastructure departments and municipal councils. OPTIsys will enable infrastructure departments to maintain the operational capability of the network in compliance with the targeted levels of service. Overall, municipalities will be able to reduce the infrastructure deficit while maximizing economic returns. / Construction Engineering and Management

Complex decision-making is a prominent aspect of requirements engineering (RE) and the need for improved decision support for RE decision-makers has been identified by a number of authors in the research literature. The fundamental viewpoint that permeates this thesis is that RE decision-making can be substantially improved by RE decision support systems (REDSS) based on the actual needs of RE decision-makers as well as the actual generic human decision-making activities that take place in the RE decision processes. Thus, a first step toward better decision support in requirements engineering is to understand complex decision situations of decision-makers. In order to gain a holistic view of the decision situation from a decision-maker’s perspective, a decision situation framework has been created. The framework evolved through an analysis of decision support systems literature and decision-making theories. The decision situation of RE decision-makers has been studied at a systems engineering company and is depicted in this thesis. These situations are described in terms of, for example, RE decision matters, RE decision-making activities, and RE decision processes. Factors that affect RE decision-makers are also identified. Each factor consists of problems and difficulties. Based on the empirical findings, a number of desirable characteristics of a visionary REDSS are suggested. Examples of characteristics are to reduce the cognitive load, to support creativity and idea generation, and to support decision communication. One or more guiding principles are proposed for each characteristic and available techniques are described. The purpose of the principles and techniques is to direct further efforts concerning how to find a solution that can fulfil the characteristic. Our contributions are intended to serve as a road map that can direct the efforts of researchers addressing RE decision-making and RE decision support problems. Our intention is to widen the scope and provide new lines of thought about how decision-making in RE can be supported and improved.

Patient data in health care have traditionally been used to support direct patient care. Although there is great potential in combining such data with genetic information from patients to improve diagnosis and therapy decisions (i.e. personalized medicine) and in secondary uses such as data mining, this is complex to realize due to technical, commercial and legal issues related with combining and refining patient data. Clinical decision support systems (CDSS) are great catalysts for enabling evidence-based medicine in clinical practice. Although patient data can be the base for CDSS logic, it is often scattered among heterogenous data sources (even in different health care centers). Data integration and subsequent data mining must consider codification of patient data with terminology systems in addition to legal and ethical aspects of using such data. Although computerization of the patient record systems has been underway for a long time, some data is still unstructured. Investigation regarding the feasibility of using electronic patient records (EPR) as data sources for data mining is therefore important. Association rules can be used as a base for CDSS development. Logic representation affect the usability of the systems and the possibility of providing explanations of the generated advice. Several properties of these rules are relatively easy to explain (such as support and confidence), which in itself can improve end-user confidence in advice from CDSS. Information from information sources other than the EPR can also be important for diagnosis and/or treatment decisions. Drug prescription is a process that is particularly dependent on reliable information regarding, among other things, drug-drug interactions which can have serious effects. CDSS and other information systems are not useful unless they are available at the time and location of patient care. This motivates using mobile devices for CDSS. Information structures of interactions affect representation in informatics systems. These structures can be represented using a category theory based implementation of rough sets (rough monads). Development of guidelines and CDSS can be based on existing guidelines with connections to external information systems that validate advice given the particular patient situation (for example, previously prescribed drugs may interact with recommended drugs by CDSS). Rules for CDSS can also be generated directly from patient data but this assumes that such data is structured and representative. Although there is great potential in CDSS to improve the quality and efficiency of health care, these systems must be properly integrated with existing processes in health care (workflows) and with other information systems. Health care workflows manage physical resources such as patients and doctors and can help to standardize care processes and support management decisions through workflow simulation. Such simulations allow information bottle-necks or insufficient resources (equipment, personnel) to be identified. As personalized medicine using genetic information of patients become economically feasible, computational requirements increase. In this sense, distributing computations through web services and system-oriented workflows can complement human-oriented workflows. Issues related to dynamic service discovery, semantic annotations of data, service inputs/outputs affect the feasibility of system-oriented workflow construction and sharing. Additionally, sharing of system-oriented workflows increase the possibilities of peer-review and workflow re-usage.

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.

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

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.

The purpose of this research was to study agricultural crop production 'decision support systems' as a means of transferring agricultural technology from research labs and plots to producers, extension specialists, agriculture service agencies, and scientists, on the Western Canadian Prairies. A 'decision support system' is a computer program that analyses problems spanning several knowledge or problem areas producing results that aid the management decision-making process. The primary objective was to develop a computer application program that would fulfill the farm manager's decision support needs and be "open" to future enhancements. This interdisciplinary study has a strong agricultural presence in the application context of the resultant computerized agricultural decision support system, with agronomics being the foundation on which the system was built, and computer science being the toolbox used to build it. Farm Smart 2000 is the resultant decision support system, providing "single-window" access to three different tiers of decision support utilizing the Internet, ' expert systems' and integrated multiple heterogeneous 'reusable agents' in a cooperative problem-solving environment. An ' expert system' is a computer program that solves complicated problems, within a specific knowledge or problem area, that would otherwise require human expertise. Expert systems integrated with each other within a decision support system are called 'agents. Reusable agents' are modular computer programs (e.g. expert systems) which can be used in more than one computer application with little or no modification. Farm Smart 2000 provides support for most management aspects of crop production including variety selection, crop rotations, weed management, disease management, residue management, harvesting, soil conservation, and economics, for the crops of wheat, canola, barley, peas, and flax. Tier-3, the most sophisticated level of Farm Smart 2000, is the focus of this dissertation and utilizes multiple reusable agents, integrating them such that they cooperate together to solve complex interrelated crop production problems. A Global Control Expert achieves the required communication and coordination among the agents resulting in an "open system", enabling Farm Smart 2000 to extend its problem-solving capabilities by integrating additional agents and knowledge, without system re-engineering, thereby remaining an ongoing technology transfer vehicle.

Gait analysis is the process of collecting and analyzing quantitative information about walking patterns of the people. Gait analysis enables the clinicians to differentiate gait deviations objectively. Diagnostic decision making from gait data only requires high level of medical expertise of neuromusculoskeletal system trained for the purpose. An automated system is expected to decrease this requirement by a &lsquo / transformed knowledge&rsquo / of these experts. This study presents a clinical decision support system for the detecting and scoring of a knee disorder, namely, Osteoarthritis (OA). Data used for training and recognition is mainly obtained through Computerized Gait Analysis software. Sociodemographic and disease characteristics such as age, body mass index and pain level are also included in decision making. Subjects are allocated into four OA-severity categories, formed in accordance with the Kellgren-Lawrence scale: &ldquo / Normal&rdquo / , &ldquo / Mild&rdquo / , &ldquo / Moderate&rdquo / , and &ldquo / Severe&rdquo / . Different types of classifiers are combined to incorporate the different types of data and to make the best advantages of different classifiers for better accuracy. A decision tree is developed with Multilayer Perceptrons (MLP) at the leaves. This gives an opportunity to use neural networks to extract hidden (i.e., implicit) knowledge in gait measurements and use it back into the explicit form of the decision trees for reasoning. Individual feature selection is applied using the Mahalanobis Distance measure and most discriminatory features are used for each expert MLP. Significant knowledge about clinical recognition of the OA is derived by feature selection process. The final system is tested with test set and a success rate of about 80% is achieved on the average.