Weeds typically grow in patches across agricultural landscapes. Because of this characteristic growth pattern, it seems logical to apply herbicides site-specifically to control them. To do this effectively, methods must be identified to accurately map weed presence and make cost effective herbicide application decisions to control them. The primary objective of this research was to develop a site-specific herbicide decision support system. Additional objectives include evaluating the effects of sampling patterns and interpolation techniques for weed mapping accuracy and evaluating texture analysis for weed patch detection in row-crops. A geographic information system (GIS) extension was developed to work in conjunction with a commercial software component for calculating site-specific herbicide applications based on user input weed maps. Results of the extension were compared to that of the commercial software. The GIS extension was able to accurately develop herbicide options based on the given weed densities and potential net return for treatment of the weeds in any specific area of the field. Sampling techniques and interpolation methods were compared to assess the accuracy of each pattern/method combination. The patterns used in this study were the W- and Z-shaped pattern, and the interpolation methods used were kriging and inverse distance weighted. Neither the pattern nor method impacted the results of the predicted average values for a given weed species. The last objective addressed was texture analysis? ability to distinguish weed patches in row-crops. Texture analysis was also tested to determine its ability to distinguish between areas requiring a herbicide application and areas not requiring a herbicide application. The analysis was performed on 3 vegetative indices and the NIR band of multispectral imagery at three different spatial resolutions (0.14 m, 0.5 m, and 1 m), and for two dates in the growing season. Texture analysis performed better on late season for both scenarios, with the highest classification accuracies (45 to 75%) coming from distinguishing areas that were below a given weed threshold from those that were above.

With increased degradation of natural resources due to land use decisions and the subsequent loss of biodiversity across large spatial scales, there is a need for a Spatial Decision Support System (SDSS) which showcases the impacts of developments on terrestrial and aquatic ecosystems. The Gap Analysis Program (GAP) and a Bayesian Belief Network (BBN) were used to assess the impacts of an impoundment in the Bienville National Forest, Smith County, Mississippi on landcovers, threatened and endangered species, species richness and fish populations. A test impoundment site was chosen on Ichusa Creek and using GAP data, landcovers, species and species richness were compared with those of Bienville National Forest, Smith County, Mississippi. For the aquatic analysis, a BBN model was developed for each fish so that population probabilities could be calculated using a given configuration of available habitats and compared to current fish population.

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The realization that the U.S. infrastructure is deteriorating and that there is a need to establish a strategy to prevent an infrastructure catastrophe have propelled the development of various infrastructure management systems. Often, the expansion of transportation facilities is regarded as a means to the improvement of the condition of transportation infrastructure. However, building more infrastructure than can be properly maintained causes serious deterioration of the existing infrastructure. Sustainable development from a highway management perspective can be equated with qualitative development, which improves the current condition of the highway system, rather than expanding its physical resources. The objective of this research is to develop a highway management strategy to help achieve sustainable development for the Commonwealth of Virginia. This research is performed by developing a transportation planning model for state highway management (TPMSHM) within the framework of a decision support system (DSS). The planning model consists of ten subsystems, including pavement and bridge management subsystems. These subsystems encompass various socioeconomic parameters that influence the physical status of highways. In the dynamic simulation model, these parameters are expressed in causal relationships using a system dynamics methodology. The types of trajectories for highway conditions that lead to sustainable development are provided. This research proposes a state-dependent prioritization strategy for calculating efficient budget shares by hierarchical levels of highway conditions. In this strategy, the proportions of the highway budget allocated to each level of management activity are determined by the physical conditions of the highways. Highways in the worst condition are given the first priority to receive the budget allocations. The model also addresses the policy of raising fuels tax to increase the state's transportation revenue. The adverse impact of a fuels tax increase is discussed in terms of revenue, the physical sufficiency of highways, and user benefits. The TPMSHM constitutes a leading component of the DSS and governs the building processes of other two components, which include a Data Base and a Display Base. A Data Base is constructed by listing all the parameters needed by the TPMSHM within a frame designed in terms of the records and fields of the parameters. A Display Base is demonstrated in a possible form using system dynamics' Powersim software. The graphical capability of representing the simulation results and the interactive user interface inherent in the software are examined. The emphasis of this research is placed on the development of the TPMSHM, which strives to manage the physical condition of the state highway system at an acceptable level through a state-dependent prioritization strategy to achieve sustainable development. / Ph. D.

This study presents a multi-attribute decision making (MADM) framework in support of strategic resource planning of electric utilities. Study efforts have focused on four technical issues identified to be essentially important to the process of strategic resource development, i.e., decision data expansion, MADM analysis with imprecise information, MADM analysis under uncertainty and screening applications. Main contributions from this study are summarized as follows. First, an automatic learning method is introduced for decision data expansion aiming at reducing the amount of computations involved in the creation of decision database. Test results have shown that the proposed method is feasible, easy to implement, and more accurate than the techniques available in the existing literature. Second, an interval-based MADM methodology is developed, which extends the traditional utility function model with the measure of composite utility variance, accounting for individual errors from inaccurate attribute measurements and inconsistent priority judgments. This enhanced decision approach would help the decision-maker (DM) gain insight into how the imprecise data may affect the choice toward the best solution and how a range of acceptable alternatives may be identified with certain confidence. Third, an integrated MADM framework is developed for multi-attribute planning under uncertainty which combines attractive features of utility function, tradeoff/risk analysis and analytical hierarchy process and thus provides a structured decision analysis platform accommodating both probabilistic evaluation approach and risk evaluation approach. Fourth, the application of screening models is investigated in the context of integrated resource planning of electric utilities as to identify cost effective demand-side options and robust generation expansion planning schemes. / Ph. D.

This research involves the development of a comprehensive decision support system for energy planning through the increased use of renewable energy sources, while still considering the role of existing electricity generating facilities. This dissertation focuses on energy planning at the regional level, with the Greater Southern Appalachian Mountain region chosen for analysis due to the dependence on coal as the largest source of generation and the availability of wind and solar resources within the region. The first stage of this planning utilizes a geographic information system (GIS) for the discovery of renewable energy sources. This GIS model analyzes not just the availability of wind and solar power based on resource strength, but also considers the geographic, topographic, regulatory, and other constraints that limit the use of these resources. The model determines potential wind and solar sites within the region based on these input constraints, and finally the model calculates the cost and generation characteristics for each site. The results of the GIS model are then input into the second section of the model framework which utilizes a multi-objective linear programming (MOLP) model to determine the optimal mix of new renewable energy sources and existing fossil fuel facilities. In addition to the potential wind and solar resources discovered in the GIS, the MOLP model considers the implementation of solid wood waste biomass for co-fire at coal plants. The model consists of two competing objectives, the minimization of annual generation cost and the minimization of annual greenhouse gas emissions, subject to constraints on electricity demand and capital investment, amongst others. The model uses the MiniMax function in order to find solutions that consider both of the objective functions. The third major section of this dissertation analyzes three potential public policies — renewable portfolio standard, carbon tax, and renewable energy production tax credit — that have been used to foster increased renewable energy usage. These policies require minor modifications to the MOLP model for implementation. The results of these policy cases are then analyzed to determine the impact that these policies have on generation cost and pollution emissions within the region. / Ph. D.

Yes / To identify a subset of software issues occurring in daily Swedish healthcare practice and devise a set of local solutions to overcome the challenges. Methods: A sample of 46 incident reports was collected from one of Sweden's national incident reporting repositories, ranging from June 2019 to December 2021. The reports were first subjected to an algorithm to identify if they were health information technology-related incidents and were analysed using an existing framework, i.e., the Health Information Technology Classification System, to identify the software-related incidents. The incidents associated with software issues were then subjected to thematic analysis, in which themes were extracted and presented under the category assigned by the existing framework used. Results: Of 46 reports, 45 (with one exception) were included using the algorithm. Of 45 incidents, 31 software-related incidents were identified using the classification system. Six types of software issues were identified, including software functionality (n = 10), interface with other software systems or components (n = 10), system configuration (n = 7), interface with devices (n = 2), record migration (n = 1) and increased volume of transactions (n = 1). Each issue was further categorised into different themes; for example, software interface-related problems were grouped into ‘two patients being active in the system simultaneously’ (n = 6) and ‘transfer of patient information’ (n = 4). Conclusions: The study provided some insights into software issues and relevant consequences. A set of local solutions were devised to overcome the present challenges encountered in Swedish healthcare in their daily clinical practice. Systematic identification and characterisation of such software challenges should be a routine part of clinical practice for all major health information technology implementations. / This work has been part of being an Affiliated Researcher at the Department of Medicine and Optometry, Linnaeus University, Sweden. A publishing grant was received from Linnaeus University as a part of the University Library's research support.

Coal is a fossil fuel mineral, which is presently a major source of electricity and energy to industries. From past to present, there are many coal reserves around the world and large scale coal mining operates in various areas such as the USA, Russia, China, Australia, India, and Germany, etc. Thailand’s coal resources can be found in many areas; there are lignite mining in the north of Thailand, the currently operational Mae Moh Lignite Mine, and also coal reserves in the south of Thailand, such as Krabi and Songkhla, where mines are not yet operating. The main consumption of coal is in electricity production, which increases annually. In 2019, the Thai Government and Electricity Generating Authority of Thailand (EGAT) plans to run a 800 MW coal power plant at Krabi, which may run on imported coal, despite there being reserves of lignite at Krabi; the use of domestic coal is a last option because of social and environmental concerns about the effects of coal mining. There is a modern trend in mining projects, the responsibility of mining should cover not only the mining activity, but the social and environmental protection and mine closure activities which follow. Thus, the costs and decisions taken on by the mining company are increasingly complicated. To reach a decision on investment in a mining project is not easy; it is a complex process in which all variables are connected. Particularly, the responsibility of coal mining companies to society and the environment is a new topic. Thus, a tool to help to recognize and generate information for decision making is in demand and very important. In this thesis, the system dynamics model of coal mine planning is made by using Vensim Software and specifically designed to encompass many variables during the period of mining activity until the mine closure period. The decisions use economic criteria such as Net Present Value (NPV), Net Cash Flow (NCF), Payback Period (PP), and Internal Rate of Return (IRR), etc. Consequently, the development of the decision support system of coal mine planning as a tool is proposed. The model structure covers the coal mining area from mine reserves to mine closure. It is a fast and flexible tool to perform sensitivity analysis, and to determine an optimum solution. The model results are clear and easily understandable on whether to accept or reject the coal mine project, which helps coal mining companies make the right decisions on their policies, economics, and the planning of new coal mining projects. Furthermore, the model is used to analyse the case study of the Krabi coal-fired power plant in Thailand, which may possibly use the domestic lignite at Krabi. The scenario simulations clearly show some potential for the use of the domestic lignite. However, the detailed analysis of the Krabi Lignite Mine Project case shows the high possible risks of this project, and that this project is currently not feasible. Thus, the model helps to understand and confirm that the use of domestic lignite in Krabi for the Krabi Coal Power Plant Project is not suitable at this time. Therefore, the best choice is imported coal from other countries for supporting the Krabi Coal Power Plant Project. Finally, this tool successfully is a portable application software, which does not need to be installed on a computer, but can run directly in a folder of the existing application. Furthermore, it supports all versions of Windows OS.