890 resultados para Planning Decision Support System
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Aquesta tesi presenta un projecte de gestió integral d'infraestructures hidràuliques de sanejament a la Conca del riu Besòs. S'han considerat dos sistemes de sanejament (La Garriga i Granollers) amb les seves respectives xarxes de clavegueram i Estacions Depuradores d'Aigües Residuals (EDAR), i un tram del riu Congost, afluent del Besòs, com a medi receptor de les seves aigües residuals. Amb aquesta finalitat es construeix i s'utilitza un Sistema de Suport a la Decisió Ambiental (SSDA). Aquesta eina incorpora l'ús de models de simulació de qualitat de l'aigua pels sistemes de clavegueram, EDAR i riu, com a forma d'extracció de coneixement sobre la gestió integrada d'aquests elements. Aquest coneixement es conceptualitza, posteriorment, en forma d'arbres de decisió, que proporcionaran a l'usuari les actuacions a realitzar davant de les diferents situacions reals de gestió diària.
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Intensive cultivation of fen peat soils (Eutric Histosols) for agricultural purposes, started in Europe about 250 years ago, resulting in decreased soil fertility, increased oxidation of peat and corresponding CO2-emissions to the atmosphere, nutrient transfer to aquatic ecosystems and losses in the total area of the former native wetlands. To prevent these negative environmental effects set-aside programs and rewetting measures were promoted in recent years. Literature results and practical experiences showed that large scale rewetting of intensively used agricultural Histosols may result in the mobilisation of phosphorus (P), its transport to adjacent surface waters and an accelerated eutrophication risk. The paper summarises results from an international European Community sponsored research project and demonstrates how results obtained at different scales and from different scientific disciplines were compiled to derive a strategy to carry out rewetting measures. A decision support system (DSS) for a hydrologically sensitive area in the Droemling catchment in north-eastern Germany was developed and is presented as a tool to regulate rewetting in order to control P release. It is demonstrated that additional laboratory experiments to identify essential processes of P release during rewetting and the site-specific management of the water table, the involvement of specific knowledge and experience of the stakeholders are necessary to develop an applicable DSS. The presented DSS is practically used to prevent freshwater resources from diffuse P pollution.
Drought, pod yield, pre-harvest Aspergillus infection and aflatoxin contamination on peanut in Niger
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Soil moisture and soil temperature affect pre-harvest infection with Aspergillus flavus and production of aflatoxin. The objectives of our field research in Niger, West Africa, were to: (i) examine the effects of sowing date and irrigation treatments on pod yield, infection with A. flavus and aflatoxin concentration; and (ii) to quantify relations between infection, aflatoxin concentration and soil moisture stress. Seed of an aflatoxin susceptible peanut cv. JL24 was sown at two to four different sowing dates under four irrigation treatments (rainfed and irrigation at 7, 14 and 21 days intervals) between 1991 and 1994, giving 40 different 'environments'. Average air and soil temperatures of 28-34 degrees C were favourable for aflatoxin contamination. CROPGRO-peanut model was used to simulate the occurrence of moisture stress. The model was able to simulate yields of peanut well over the 40 environments (r(2) = 0.67). In general, early sowing produced greater pod yields, as well as less infection and lower aflatoxin concentration. There were negative linear relations between infection (r(2) = 0.62) and the average simulated fraction of extractable soil water (FESW) between flowering and harvest, and between aflatoxin concentration (r(2) = 0.54) and FESW in the last 25 days of pod-filling. This field study confirms that infection and aflatoxin concentration in peanut can be related to the occurrence of soil moisture stress during pod-filling when soil temperatures are near optimal for A. flavus. These relations could form the basis of a decision-support system to predict the risk of aflatoxin contamination in peanuts in similar environments. (c) 2005 Elsevier B.V. All rights reserved.
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In the past decade, a number of mechanistic, dynamic simulation models of several components of the dairy production system have become available. However their use has been limited due to the detailed technical knowledge and special software required to run them, and the lack of compatibility between models in predicting various metabolic processes in the animal. The first objective of the current study was to integrate the dynamic models of [Brit. J. Nutr. 72 (1994) 679] on rumen function, [J. Anim. Sci. 79 (2001) 1584] on methane production, [J. Anim. Sci. 80 (2002) 2481 on N partition, and a new model of P partition. The second objective was to construct a decision support system to analyse nutrient partition between animal and environment. The integrated model combines key environmental pollutants such as N, P and methane within a nutrient-based feed evaluation system. The model was run under different scenarios and the sensitivity of various parameters analysed. A comparison of predictions from the integrated model with the original simulation models showed an improvement in N excretion since the integrated model uses the dynamic model of [Brit. J. Nutr. 72 (1994) 6791 to predict microbial N, which was not represented in detail in the original model. The integrated model can be used to investigate the degree to which production and environmental objectives are antagonistic, and it may help to explain and understand the complex mechanisms involved at the ruminal and metabolic levels. A part of the integrated model outputs were the forms of N and P in excreta and methane, which can be used as indices of environmental pollution. (C) 2004 Elsevier B.V All rights reserved.
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Expert systems have been increasingly popular for commercial importance. A rule based system is a special type of an expert system, which consists of a set of ‘if-then‘ rules and can be applied as a decision support system in many areas such as healthcare, transportation and security. Rule based systems can be constructed based on both expert knowledge and data. This paper aims to introduce the theory of rule based systems especially on categorization and construction of such systems from a conceptual point of view. This paper also introduces rule based systems for classification tasks in detail.
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Mirids (Sahlbergella singularis and Distantiella theobroma) are the most important insect pests affecting cocoa production across West Africa. Understanding the population dynamics of mirids is key to their management, however, the current recommended hand-height assessment method is labour intensive. The objective of the study was to compare recently developed mirid sex pheromone trapping and visual hand-height assessment methods as monitoring tools on cocoa farms and to consider implications for a decision support system. Ten farms from the Eastern and Ashanti regions of Ghana were used for the study. Mirid numbers and damage were assessed fortnightly on twenty trees per farm, using both methods, from January 2012 to April 2013. The mirid population increased rapidly in June, reached a peak in September and began to decline in October. There was a significant linear relationship between numbers of mirids sampled to hand-height and mirid damage. High numbers of male mirids were recorded in pheromone traps between January and April 2012 after which there was a gradual decline. There was a significant inverse relationship between numbers of trapped adult mirids and mirids sampled to hand-height (predominantly nymphs). Higher temperatures and lower relative humidities in the first half of the year were associated with fewer mirids at hand-height but larger numbers of adult males were caught in pheromone traps. The study showed that relying solely on one method is not sufficient to provide accurate information on mirid population dynamics and a combination of the two methods is necessary.
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A crucial concern in the evaluation of evidence related to a major crime is the formulation of sufficient alternative plausible scenarios that can explain the available evidence. However, software aimed at assisting human crime investigators by automatically constructing crime scenarios from evidence is difficult to develop because of the almost infinite variation of plausible crime scenarios. This paper introduces a novel knowledge driven methodology for crime scenario construction and it presents a decision support system based on it. The approach works by storing the component events of the scenarios instead of entire scenarios and by providing an algorithm that can instantiate and compose these component events into useful scenarios. The scenario composition approach is highly adaptable to unanticipated cases because it allows component events to match the case under investigation in many different ways. Given a description of the available evidence, it generates a network of plausible scenarios that can then be analysed to devise effective evidence collection strategies. The applicability of the ideas presented here are demonstrated by means of a realistic example and prototype decision support software.
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In the past, the focus of drainage design was on sizing pipes and storages in order to provide sufficient network capacity. This traditional approach, together with computer software and technical guidance, had been successful for many years. However, due to rapid population growth and urbanisation, the requirements of a “good” drainage design have also changed significantly. In addition to water management, other aspects such as environmental impacts, amenity values and carbon footprint have to be considered during the design process. Going forward, we need to address the key sustainability issues carefully and practically. The key challenge of moving from simple objectives (e.g. capacity and costs) to complicated objectives (e.g. capacity, flood risk, environment, amenity etc) is the difficulty to strike a balance between various objectives and to justify potential benefits and compromises. In order to assist decision makers, we developed a new decision support system for drainage design. The system consists of two main components – a multi-criteria evaluation framework for drainage systems and a multi-objective optimisation tool. The evaluation framework is used for the quantification of performance, life-cycle costs and benefits of different drainage systems. The optimisation tool can search for feasible combinations of design parameters such as the sizes, order and type of drainage components that maximise multiple benefits. In this paper, we will discuss real-world application of the decision support system. A number of case studies have been developed based on recent drainage projects in China. We will use the case studies to illustrate how the evaluation framework highlights and compares the pros and cons of various design options. We will also discuss how the design parameters can be optimised based on the preferences of decision makers. The work described here is the output of an EngD project funded by EPSRC and XP Solutions.
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The main goal of this dissertation is to develop a Multi Criteria Decision Aid Model to be used in Oils and Gas perforation rigs contracts choices. The developed model should permit the utilization of multiples criterions, covering problems that exist with models that mainly use the price of the contracts as its decision criterion. The AHP has been chosen because its large utilization, not only academic, but in many other areas, its simplicity of use and flexibility, and also fill all the requirements necessary to complete the task. The development of the model was conducted by interviews and surveys with one specialist in this specific area, who also acts as the main actor on the decision process. The final model consists in six criterions: Costs, mobility, automation, technical support, how fast the service could be concluded and availability to start the operations. Three rigs were chosen as possible solutions for the problem. The results reached by the utilizations of the model suggests that the utilization of AHP as a decision support system in this kind of situation is possible, allowing a simplifications of the problem, and also it s a useful tool to improve every one involved on the process s knowledge about the problem subject, and its possible solutions
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The area of the hospital automation has been the subject a lot of research, addressing relevant issues which can be automated, such as: management and control (electronic medical records, scheduling appointments, hospitalization, among others); communication (tracking patients, staff and materials), development of medical, hospital and laboratory equipment; monitoring (patients, staff and materials); and aid to medical diagnosis (according to each speciality). This thesis presents an architecture for a patient monitoring and alert systems. This architecture is based on intelligent systems techniques and is applied in hospital automation, specifically in the Intensive Care Unit (ICU) for the patient monitoring in hospital environment. The main goal of this architecture is to transform the multiparameter monitor data into useful information, through the knowledge of specialists and normal parameters of vital signs based on fuzzy logic that allows to extract information about the clinical condition of ICU patients and give a pre-diagnosis. Finally, alerts are dispatched to medical professionals in case any abnormality is found during monitoring. After the validation of the architecture, the fuzzy logic inferences were applied to the trainning and validation of an Artificial Neural Network for classification of the cases that were validated a priori with the fuzzy system
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Breast cancer, despite being one of the leading causes of death among women worldwide is a disease that can be cured if diagnosed early. One of the main techniques used in the detection of breast cancer is the Fine Needle Aspirate FNA (aspiration puncture by thin needle) which, depending on the clinical case, requires the analysis of several medical specialists for the diagnosis development. However, such diagnosis and second opinions have been hampered by geographical dispersion of physicians and/or the difficulty in reconciling time to undertake work together. Within this reality, this PhD thesis uses computational intelligence in medical decision-making support for remote diagnosis. For that purpose, it presents a fuzzy method to assist the diagnosis of breast cancer, able to process and sort data extracted from breast tissue obtained by FNA. This method is integrated into a virtual environment for collaborative remote diagnosis, whose model was developed providing for the incorporation of prerequisite Modules for Pre Diagnosis to support medical decision. On the fuzzy Method Development, the process of knowledge acquisition was carried out by extraction and analysis of numerical data in gold standard data base and by interviews and discussions with medical experts. The method has been tested and validated with real cases and, according to the sensitivity and specificity achieved (correct diagnosis of tumors, malignant and benign respectively), the results obtained were satisfactory, considering the opinions of doctors and the quality standards for diagnosis of breast cancer and comparing them with other studies involving breast cancer diagnosis by FNA.
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Operating industrial processes is becoming more complex each day, and one of the factors that contribute to this growth in complexity is the integration of new technologies and smart solutions employed in the industry, such as the decision support systems. In this regard, this dissertation aims to develop a decision support system based on an computational tool called expert system. The main goal is to turn operation more reliable and secure while maximizing the amount of relevant information to each situation by using an expert system based on rules designed for a particular area of expertise. For the modeling of such rules has been proposed a high-level environment, which allows the creation and manipulation of rules in an easier way through visual programming. Despite its wide range of possible applications, this dissertation focuses only in the context of real-time filtering of alarms during the operation, properly validated in a case study based on a real scenario occurred in an industrial plant of an oil and gas refinery
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The northern coast of Rio Grande do Norte State (RN) shows areas of Potiguar basin with high activity in petroleum industry. With the goal of avoiding and reducing the accident risks with oil it is necessary to understand the natural vulnerability, mapping natural resources and monitoring the oil spill. The use of computational tools for environmental monitoring makes possible better analyses and decisions in political management of environmental preservation. This work shows a methodology for monitoring of environment impacts, with purpose of avoiding and preserving the sensible areas in oil contact. That methodology consists in developing and embedding an integrated computational system. Such system is composed by a Spatial Decision Support System (SDSS). The SDSS shows a computational infrastructure composed by Web System of Geo-Environmental and Geographic Information - SWIGG , the System of Environmental Sensibility Maps for Oil Spill AutoMSA , and the Basic System of Environmental Hydrodynamic ( SisBAHIA a System of Modeling and Numerical Simulating SMNS). In a scenario of oil spill occurred coastwise of Rio Grande do Norte State s northern coast, the integration of such systems will give support to decision agents for managing of environmental impacts. Such support is supplied through a system of supporting to spatial decisions
Resumo:
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)