A Decision Support System for Automated Adjustment of Insulin Parameters in Type 1 Diabetes During Ramadan: A Randomized Controlled Trial

Introduction.The article deals with adjusting the parameter settings of a combine harvester working bodies. For adjustment of complex hierarchical multilevel systems, the intellectual methods based on fuzzy expert information are used. The incoming quantitative, qualitative and evaluation information is analyzed when adjusting the combine harvester. The different types of uncertainty in considering semantic spaces of external environment factors and regulated parameters of the machine cause the application of logical and linguistic approach and mathematical apparatus of fuzzy logic for determining the optimal initial settings. The complex system of interrelations between parameters, indicators of quality of harvest, and factors of external environment causes the necessity to adjust the parameters of combine working elements in the process of harvesting. This function is performed by the correction unit in the intelligent decision support system. In the present article, the questions of creating a knowledge base for correcting adjustment parameters in cases when there are deviations of values of harvesting quality indicators from normative values are considered in detail. Materials and Methods. Interrelations between performance indicators and regulated parameters are established by empirical rules obtained through the collection and analysis of expert information. To optimize the mechanism of intellectual information system output and reduce the time of decision making, there is a necessity to establish the relevance of used knowledge base rules. To solve this problem, theoretical and game approaches are used, concepts of the matrix of performance indicators and the matrix of risks of making an inefficient decision are used. Results. An example of choosing a strategy of searching for an adequate response to the fault of the harvesting indices in the form of “losses of feeble grain with chaff” has been given. The choice of fault response strategies on the basis of Laplace criterion, expectedvalue criterion, and Savage test used for decision-making in “games with nature” has been considered. The method of the decision-making process in the problem under consideration with the application of the mentioned criteria were illustrated, the analysis of the obtained results was carried out. Discussion and Conclusion. The suggested approach substantially increases performance of the unit of intelligent system updating. It allows structuring the expert knowledge base and establishing an optimal sequence of application of production rules; this provides efficiency of the updating process of the adjustable harvester parameters and also reduces the time for decision-making. This approach can be used while solving the problems of updating technological adjustments in different technical systems and devices.

The problems of creating intelligent systems for information support in making decisions on preliminary technological adjustment of complex harvesting machines functioning in the field are considered. The solution of the problem for a combine harvester being a universal machine for harvesting grain, leguminous and other cultivated crops is presented. A combine harvester is considered as a complex mechatronic system that functions in a changing environment. Different types of uncertainty in the consideration of the semantic spaces of environmental factors and adjustable machine parameters cause the application of the logical-linguistic approach and the mathematical apparatus of fuzzy logic to find the optimal initial values of the adjustable parameters. The models of studied semantic spaces have been built. An expert knowledge base has been created, quantitative assessments of the consistency of expert information have been obtained. On the basis of the system of production rules, further fuzzy inference of solutions in the task of preliminary technological adjustment has been carried out. The proposed formal logical scheme of the decision-making process is applied to the selection of the values of the most important adjustable parameters of the combine, such as the speed, the rotational speed of the threshing drum, rotor speed of a separator fan.

Bases for pesticide dose expression and adjustment in 3D crops and comparison of decision support systems

Finding appropriate decision support systems (DSS) development processes and methodologies is a topic that has kept researchers in the decision support community busy for the past three decades at least. Inspired by Gibson and Nolan’s curve (Gibson & Nolan 1974; Nolan, 1979), it is fair to contend that the field of DSS development is reaching the end of its expansion (or contagion) stage, which is characterized by the proliferation of processes and methodologies in all areas of decision support. Studies on DSS development conducted during the last 15 years (e.g., Arinze, 1991; Saxena, 1992) have identified more than 30 different approaches to the design and construction of decision support methods and systems (Marakas, 2003). Interestingly enough, none of these approaches predominate and the various DSS development processes usually remain very distinct and project-specific. This situation can be interpreted as a sign that the field of DSS development should soon enter in its formalization (or control) stage. Therefore, we propose a unifying perspective of DSS development based on the notion of context. In this article, we argue that the context of the target DSS (whether organizational, technological, or developmental) is not properly considered in the literature on DSS development. Researchers propose processes (e.g., Courbon, Drageof, & Tomasi, 1979; Stabell 1983), methodologies (e.g., Blanning, 1979; Martin, 1982; Saxena, 1991; Sprague & Carlson, 1982), cycles (e.g., Keen & Scott Morton, 1978; Sage, 1991), guidelines (e.g., for end-user computer), and frameworks, but often fail to explicitly describe the context in which the solution can be applied.

Finding appropriate decision support systems (DSS) development processes and methodologies is a topic that has kept researchers in the decision support community busy for the past three decades at least. Inspired by Gibson and Nolan’s curve (Gibson & Nolan 1974; Nolan, 1979), it is fair to contend that the field of DSS development is reaching the end of its expansion (or contagion) stage, which is characterized by the proliferation of processes and methodologies in all areas of decision support. Studies on DSS development conducted during the last 15 years (e.g., Arinze, 1991; Saxena, 1992) have identified more than 30 different approaches to the design and construction of decision support methods and systems (Marakas, 2003). Interestingly enough, none of these approaches predominate and the various DSS development processes usually remain very distinct and project-specific. This situation can be interpreted as a sign that the field of DSS development should soon enter in its formalization (or control) stage. Therefore, we propose a unifying perspective of DSS development based on the notion of context. In this article, we argue that the context of the target DSS (whether organizational, technological, or developmental) is not properly considered in the literature on DSS development. Researchers propose processes (e.g., Courbon, Drageof, & Tomasi, 1979; Stabell 1983), methodologies (e.g., Blanning, 1979; Martin, 1982; Saxena, 1991; Sprague & Carlson, 1982), cycles (e.g., Keen & Scott Morton, 1978; Sage, 1991), guidelines (e.g., for end-user computer), and frameworks, but often fail to explicitly describe the context in which the solution can be applied.

Crop protection and pest management are major economic and environmental concerns throughout Europe. The consultation of decision support systems (DSS) to guide decisions relating to Integrated Pest Management (IPM) is one of the key principles of IPM, reducing the ambiguity around potential risks to crop health. 'Pests' in this context include invertebrate pests, weeds and pathogens. The impact of DSS can be limited by a lack of awareness of DSS availability, inconsistencies in the user functions of different DSS, regional fragmentation of access, and a lack of transparency of the origin, validity, and benefits of DSS. Failure to address these limitations undermines trust in IPM DSS and leads to a reluctance of farmers and advisors to invest time in consulting multiple DSS sources as part of their agronomic decision toolbox. The EU-funded IPM Decisions project (Grant agreement ID: 817617) addressed these limitations by creating a Europe-wide free-access online platform. The IPM Decisions platform was designed in consultation with farmers, advisors and wider stakeholders to increase access to and uptake of IPM DSS integrated within it. It offers an end-point for IPM researchers and DSS developers to make adapted and novel DSS available to users, and provides a 'one-stop shop' for farmers and advisors looking to consult free access or paid IPM DSS. Dedicated dashboards within the platform facilitate farm set up, consultation of DSS, comparison of DSS outputs, and adjustment of model parameters for adaption to different pests/regions. The IPM Decisions digital infrastructure enables easy integration of models and data with external platforms, providing a framework for accessing and sharing models and data between researchers and developers. The platform therefore provides both a ready to go user interface for new DSS, as well as the infrastructure to support and connect existing and future user interfaces.

Crop protection and pest management are major economic and environmental concerns throughout Europe. The consultation of decision support systems (DSS) to guide decisions relating to Integrated Pest Management (IPM) is one of the key principles of IPM, reducing the ambiguity around potential risks to crop health. ‘Pests’ in this context include invertebrate pests, weeds and pathogens. The impact of DSS can be limited by a lack of awareness of DSS availability, inconsistencies in the user functions of different DSS, regional fragmentation of access, and a lack of transparency of the origin, validity, and benefits of DSS. Failure to address these limitations undermines trust in IPM DSS and leads to a reluctance of farmers and advisors to invest time in consulting multiple DSS sources as part of their agronomic decision toolbox. The EU-funded IPM Decisions project (Grant agreement ID: 817617) addressed these limitations by creating a Europe-wide free-access online platform. The IPM Decisions platform was designed in consultation with farmers, advisors and wider stakeholders to increase access to and uptake of IPM DSS integrated within it. It offers an end-point for IPM researchers and DSS developers to make adapted and novel DSS available to users, and provides a ‘one-stop shop' for farmers and advisors looking to consult free access or paid IPM DSS. Dedicated dashboards within the platform facilitate farm set up, consultation of DSS, comparison of DSS outputs, and adjustment of model parameters for adaption to different pests/regions. The IPM Decisions digital infrastructure enables easy integration of models and data with external platforms, providing a framework for accessing and sharing models and data between researchers and developers. The platform therefore provides both a ready to go user interface for new DSS, as well as the infrastructure to support and connect existing and future user interfaces.

The expanding quantity of health data and the complexity of its applications are pointing to the need for greater application of computer resources to provide support for decision-making in public health and clinical practice. Decision support and expert systems, as illustrated by the immunization-forecasting program IMM/Serve, offer such support, both now and in the future. Would-be developers of such systems, however, must recognize that the systems are both inherently complex and work-intensive in development. Successful decision support and expert systems require incorporation of comprehensive knowledge and sound logic, extensive testing by use of a variety of methods, and consideration of the nature of the decision-making to be supported and the appropriateness of the environment in which such systems will be placed, including the willingness of users to participate in the development process. Clearly, decision support systems can be appropriate for a number of potential applications in public health practice, including analysis of surveillance data, resource management, and the dissemination of practice guidelines.

I: General Issues.- Modeling Knowledge: Model-based Decision Support and Soft Computations.- Benefits of Decision Support Using Soft Computing.- Evolving Connectionist-based Decision Support Systems.- An Agent-based Soft Computing Society with Application Applications in Financial Investment Planning.- A Rough Sets/Neoral Networks Approach to Knowledge Discovery for the Development of Decision Support Systems.- II: Applications and Implementations.- Decision Support Systems in Healthcare: Emerging Trends and Success Factors.- A View of Public and Private Sectors for Taiwan's BOT Project Financing Using Fuzzy Multi-Criteria Methods.- Relational Structures for the Analysis of Decision Information in an Electronic Market.- A Fuzzy Evaluation Model: A Case for Intermodal Terminals in Europe.- Application of Kemeny's Median for Group Decision Support.- An Internet-based Group Decision and Consensus Reaching Support System.- Limpio: A DSS for the Urban Waste Collection Problem.- A Decision Support System for Air Quality Control Based on Soft Computing Methods.- Multicriteria Genetic Tuning for the Optimization and Control of HVAC Systems.- Intelligent Information Systems for Crime Analysis.- Application of Fuzzy Decision Trees to Reservoir Recognition.- Introducing SACRA: A Decision Support System for the Construction of Cattle Diets.- Prediction of Parthenium Weed Dispersal Using Fuzzy Logic on GIS Spatial Image.

The Multicriteria Assessment Methodology of the Decision Support System Implementation EffectivenessThe multi-criteria assessment methodology of implementation effectiveness of information systems illustrated by an example of decision support system (DSS) realized in w information technologies is presented in the article. The assessment of DSS under consideration takes place using the knowledge recorded in the form of fuzzy neural network, collected in an enterprise, on the basis of earlier realized implementations of other information systems. A model of retrieved DSS is expressed by means of a set of functionalities serving business processes of the enterprise under consideration. A model of implementation undertaking determined by means of a set of preparatory actions for the implementation and a set of directly implementation and exploitation actions is built for the retrieved DSS as well. Furthermore, a vector determining a current and planned implementation state of a set of DSS functionalities in the enterprise at time moments, before and after the commencement of planned implementation of the retrieved DSS is built. A concept of trapezoidal fuzzy numbers is used in building DSS models. An adjustment of fuzzy parameters of DSS models takes place by means of geometrical method of maximum absolute error points. A presented methodology enables to execute a multi-criteria effectiveness assessment of planned undertaking in relation to subjective criteria established by the enterprise (preferred time, cost and values of priority indexes). Additionally, the knowledge collected on the basis of earlier realized implementations of information systems and applied imprecise description of parameters taking into account errors made in their estimation in the past is used.

This paper critically analyses the nature and state of decision support systems (DSS) research. To provide context for the analysis, a history of DSS is presented which focuses on the evolution of a number of sub-groupings of research and practice: personal DSS, group support systems, negotiation support systems, intelligent DSS, knowledge management-based DSS, executive information systems/business intelligence, and data warehousing. To understand the state of DSS research an empirical investigation of published DSS research is presented. This investigation is based on the detailed analysis of 1,020 DSS articles published in 14 major journals from 1990 to 2003. The analysis found that DSS publication has been falling steadily since its peak in 1994 and the current publication rate is at early 1990s levels. Other findings include that personal DSS and group support systems dominate research activity and data warehousing is the least published type of DSS. The journal DSS is the major publishing outlet; US ‘Other’ journals dominate DSS publishing and there is very low exposure of DSS in European journals. Around two-thirds of DSS research is empirical, a much higher proportion than general IS research. DSS empirical research is overwhelming positivist, and is more dominated by positivism than IS research in general. Design science is a major DSS research category. The decision support focus of the sample shows a well-balanced mix of development, technology, process, and outcome studies. Almost half of DSS papers did not use judgement and decision-making reference research in the design and analysis of their projects and most cited reference works are relatively old. A major omission in DSS scholarship is the poor identification of the clients and users of the various DSS applications that are the focus of investigation. The analysis of the professional or practical contribution of DSS research shows a field that is facing a crisis of relevance. Using the history and empirical study as a foundation, a number of strategies for improving DSS research are suggested.

The paper considers the problem of determining optimal technological decisions on a harvester control which includes the choice of the harvester adjustable parameters at presetting and also their updating in the process of operation. To solve this problem, an information system of decision making support based on fuzzy expert knowledge is used. The expedience of this approach is stipulated by the data fuzziness about the factors of the environment in which the harvester is operating and also by complex and system of uncertain interrelations among external factors, adjustable parameters and harvesting quality indices. For the case of harvesting grain crops an expert knowledge base has been made which makes it possible to formalize empirical knowledge about the dependencies of the harvester adjustable parameters on the environment factors such as crop yield, stand of grain humidity, stand of grain dockage rough straw. The most essential adjustable parameters have been considered: speed of the harvester motion, rotational speed of a threshing drum and rotor speed of a separator fan. Fuzzy logic inference has been performed with the help of Fuzzy Logic Toolbox application package (Matlab). The examples of technological decisions of the harvester preliminary adjustment at different environmental conditions have been presented, accurate values of the adjustable parameters were calculated by the «barycentre» method.

The degree with which to parameterize a computer model that is to be used for risk-based resource management decision support has been a topic of much discussion in the environmental modeling industry, and remains a difficult choice facing practitioners. High-dimensional parameterization schemes allow for a more robust expression of model input uncertainty over traditional lower-dimensional schemes, but often incur a higher computational burden and require greater understanding of inverse problem theory to implement effectively. However, a number of significant questions remain, such as: “What level of parameterization is needed to adequately express uncertainty for a given decision-relevant simulated output?”; and “To what extent can a simplified parameterization be adopted while maintaining the ability of the model to serve as a decision-support tool?”. This study addresses these questions, among others, by using empirical paired complex-simple model analyses to investigate the consequences of reduced parameterization on decision-relevant simulated outputs in terms of bias incursion and underestimation of uncertainty. A Bayesian decision analysis approach is adopted to facilitate evaluation of parameterization reduction outcomes, not only in terms of the prior and posterior probability density functions of decision-relevant simulated outputs, but also in terms of the management decisions that would be made on their basis. Two integrated surface water/groundwater model case study examples are presented; the first is a complex synthetic model used to forecast groundwater abstraction-induced changes in ecologically-sensitive streamflow characteristics, and the second is a real-world regional-scale model (Hauraki Plains, New Zealand) used to simulate nitrate-loading impacts on water quality. It is shown empirically that, for some decision-relevant simulated outputs, even relatively high-dimensional parameterization schemes ( > 2,000 adjustable parameters) display significant bias in simulated outputs as a result of improper parameter compensation induced through history matching, relative to complex parameterization schemes ( > 100,000 adjustable parameters)—ultimately leading to incorrect decisions and resource management action. For other decision-relevant simulated outputs, however, reduced parameterization schemes may be appropriate for resource management decision making, especially when considering a prior uncertainty stance (i.e., without undertaking history matching) and when considering differences between simulated outputs that do not depend on local-scale heterogeneity.

Despite evidence that optimal care for diabetes can result in reduced complications and improved economic outcomes, such care is often not achieved. The Vermont Diabetes Information System (VDIS) is a registry-based decision support and reminder system based on the Chronic Care Model and targeted to primary care physicians and their patients with diabetes. To develop and evaluate a regional decision support system for patients with diabetes. Randomized trial of an information system with clustering at the practice level. Ten percent random subsample of patients selected for a home interview. and setting includes 10 hospitals, 121 primary care providers, and 7348 patients in 55 Vermont and New York primary care practices. We report on the study design and baseline characteristics of the population. Patients have a mean age of 63 years and a mean glycosolated hemoglobin A1C of 7.1 %. Sixty percent of the population has excellent glycemic control (A1 C < 7%); 45% have excellent lipid control (serum LDL-cholesterol <100 mg /dL and serum triglycerides <400 mg/dL). Twenty-five percent have excellent blood pressure control (<130/80mmHg). These results compare favorably to recent national reports. However, only 8% are in optimal control for all three of hyperglycemia, lipids and blood pressure. Our experience to date indicates that a low cost decision support and information system based on the Chronic Care Model is feasible in primary care practices that lack sophisticated electronic information systems. VDIS is well accepted by patients, providers and laboratory staff. If proven beneficial in a rigorous, randomized, controlled evaluation, the intervention could be widely disseminated to practices across America and the world with a substantial impact on the outcomes and costs of diabetes. It could also be adapted to other chronic conditions. We anticipate the results of the study will be available in 2006.

In order to integrate web with crop growth simulation and decision-making support system, the field experiment of different basal levels was carried out in experiment area of Jiangsu Academy of Agricultural Sciences in 2005 adopting 4 cultivars such as “Wuyungeng 7”, “Yangdao 6”, “Yueyou 948” and “Nangeng 41”, which mainly were used in collecting cultivar parameters and updating database of them. The database of rice cultivars, soil and weather data were developed using SQL Server 2000. The pages of digital design system of rice cultivation based on web and simulation model (DDSRCBWSM) were designed using Visual Studio.Net, which included register, the main page, cultivar parameter management, site data management, parameter adjustment, decision making for rice cultivation, and so on. The DDSRCBWSM accorded with TCP/IP agreements, which could be installed and run in server (IIS5.0), and be browsed on internet, it inherited mechanism, universal adaptability and utility of rice cultivation simulationoptimization-decision making system(RCSODS), combined web techniques with research of rice growth models, set up web system of RCSODS, made agricultural technicians of main rice production area of china gain pre-sowing optimization cases and rice management suggestion of the current year with dynamic, goal and digital characteristic in accordance with soil, cultivar and weather conditions online, and to fulfill technical direction through many kind manner such as paper, internet, email, television, wall newspaper, and so on, eventually. Researches of Digital Design System of Rice Cultivation Based on Web and Simulation Models 1087