466 resultados para Maximization


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Dissertação de mest. em Aquacultura, Unidade de Ciências e Tecnologia dos Recursos Aquáticos, Univ. do Algarve, 1997

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Os sistemas de informação têm cada vez mais importância nos serviços de saúde. Sistemas de informação eficientes, permitem a maximização da gestão dos serviços promovendo a melhoria da qualidade dos cuidados. A ordem dos enfermeiros sugere a implementação de sistemas de registos que englobem necessidades de cuidados, intervenções de enfermagem e resultados dessas mesmas intervenções. A associação portuguesa de enfermeiros faz referência à importância de uma linguagem comum entre os enfermeiros. A linguagem CIPE® “é uma classificação de fenómenos, intervenções e resultados a nível da enfermagem. Pretende ser uma matriz unificadora em que as taxonomias e classificações de enfermagem já existentes se possam entrecruzar, de modo a permitir comparar dados em enfermagem” (CIE,2000:11) A adaptação da linguagem CIPE à gravidez, trabalho de parto e parto, é uma mais-valia no que concerne á realização de registos de qualidade que permitam uma linguagem comum, abrangente e facilitadora da comunicação entre os enfermeiros, e entre estes e outros profissionais de saúde. Através da consulta de bibliografia, dos manuais CIPE® disponíveis, de documentos existentes no serviço, dos processos clínicos e pelos conhecimentos adquiridos durante a especialização de enfermagem de saúde materna e obstetrícia, foi feito o levantamento dos fenómenos, diagnósticos e intervenções de enfermagem em relação aos focos gravidez, trabalho de parto e parto, para futura introdução no sistema informático, tendo em conta as actividades mais frequentes na maternidade para estes focos. A definição de uma Linguagem uniforme a ser utilizada por todos os enfermeiros do serviço, levou a um esforço conjunto da equipa de enfermagem e chefia. Este trabalho continua em curso, uma vez que as actividades que se realizam na maternidade têm que ser todas parametrizadas, o que torna o trabalho de parametrização um pouco mais moroso. Os dados só serão introduzidos no sistema depois da parametrização de todas actividades do serviço. Após a introdução dos dados no sistema informático e da linguagem CIPE perfeitamente adaptada à realidade da maternidade, é nossa convicção que a economia de tempo e a desburocratização dos registos libertará os profissionais para uma relação enfermeiro/utente que se pretende mais humana.

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The literature on preferences for redistribution has paid little attention to the effect of social mobility on the demand for redistribution, which is in contrast with the literature on class-voting, where studies on the effect of social mobility has been very common. Some works have addressed this issue but no systematic test of the hypotheses connecting social mobility and preferences has been done. In this paper we use the diagonal reference models to estimate the effect of origin and destination class on preferences for redistribution in a sample of European countries using data from the European Social Survey. Our findings indicate that social origin matters to a little extent to explain preferences, as newcomers tend to adopt the preferences of the destination class. Moreover, we have found only limited evidence supporting the acculturation hypothesis and not support for the status maximization hypothesis. Furthermore, the effect of social origin varies largely between countries. In a second step of the analysis we investigate what are the national factors explaining this variation. The empirical evidence we present leads to conclude that high rates of upward social mobility sharply reduce the effect of social origin on preferences for redistribution

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Due to lack of information on the use of non-protein energy sources in diets for pacu (Piaractus mesopotamicus), a 2 x 2 x 3 factorial experiment was conducted to evaluate the performance and digestibility of 12 diets containing approximately two crude protein (CP; 220 and 250 g kg(-1)), two lipid (40 and 80 g kg(-1)) and three carbohydrate levels (410, 460 and 500 g kg(-1)). The pacu juveniles-fed diets containing 220 g kg(-1) CP did not respond (P > 0.05) to increased dietary lipid and carbohydrate levels, but the fish-fed diets containing 250 g kg(-1) CP showed a better feed conversion ratio. There were interactions in weight gain (WG), specific growth rate (SGR), crude protein intake (CPI) and feed conversion rate (FCR) dependent on dietary carbohydrate and lipid levels, showing positive effects of increasing carbohydrate levels only for fish-fed diets containing 80 g kg(-1) lipid level. However, when the diets contained 40 g kg(-1) lipid, the best energy productive value (EPV) results were obtained at 460 g kg(-1) carbohydrate. A higher usage of lipids (80 g kg(-1)) reduced CPI and was detrimental to protein [apparent digestibility coefficient (ADC)(CP)] and energy (ADC(GE)), but did not affect growth. The ADC(GE) improved proportionally as dietary carbohydrate levels increased (P < 0.05), increasing the concentration of digestible energy. In addition, the WG, CPI, ADC(GE) results showed best use of the energy from carbohydrates when dietary protein level was 250 g kg(-1) CP. The utilization of 250 g kg(-1) CP in feeds for juvenile pacu for optimal growth is suggested. Therefore, the optimum dietary lipid and carbohydrate levels depend on their combinations. It can be stated that pacu uses carbohydrates as effectively as lipids in the maximization of protein usage, as long as it is not lower than 250 g kg(-1) CP or approximately 230 g kg(-1) digestible protein.

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In this study, interactions between potential hierarchical value chains existing in the production structure and industry-wise productivity growths are sought. We applied generalized Chenery-Watanabe heuristics for matrix linearity maximization to triangulate the input-output incidence matrix for both Japan and the Republic of Korea, finding the potential directed flow of values spanning the industrial sectors of the basic (disaggregated) industry classifications for both countries. Sector specific productivity growths were measured by way of the Trönquvist index, using the 2000-2005 linked input-output tables for both Japan and Korea.

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Background: Adolescents with chronic disease (CD) can be more vulnerable to adverse psychosocial outcomes. This study aims: 1) to identify differences in psychosocial variables (health-related quality of life, psychosomatic complaints, resilience, self-regulation and social support) among adolescents who feel that CD affects or does not affect school/peers connectedness (measured by self-reported participation in school and social activities); and 2) to assess the extent to which psychosocial variables are associated with connectedness in school and peer domains. Methods: A cross-sectional study was conducted in 135 adolescents with CD (51.9% boys), average age of 14 ± 1. 5 years old (SD = 1.5). Socio-demographic, clinical, and psychosocial variables were assessed, using a self-reported questionnaire, which included the Chronic Conditions Short Questionnaire, KIDSCREEN-10 Index, Symptoms Check-List, Healthy Kids Resilience Assessment Module Scale, Adolescent Self-Regulatory Inventory, and Satisfaction with Social Support Scale. Descriptive statistics, GLM-Univariate ANCOVA and Logistic Regression were performed using the IBM Statistical Package for Social Sciences (SPSS), version 22.0. The significance level was set at p < 0.05. Results: Thirteen to eighteen percent of the adolescents felt that CD affected participation at school (PSCH) and participation in leisure time with friends (PLTF). These adolescents presented lower results for all psychosocial study variables, when compared with adolescents who did not feel affected in both areas of participation. From the studied psychosocial variables, the most important ones associated with PSCH (after controlling for age, gender, diagnosis, and education level of father/mother) were self-regulation and psychosomatic health. Concerning the PLTF, social support was the sole variable explaining such association. Conclusions: The present study pointed out the association between psychosocial variables; and living with a CD and school/peers connectedness. The need to focus on the assessment of the effects of a CD on adolescents’ lives and contexts is suggested, as well as on the identification of vulnerable adolescents. Such identification could help to facilitate the maximization of social participation of adolescents with CD, and to plan interventions centered on providing support and opportunities for a healthy youth development. For that purpose, a complex and multifactorial approach that includes clinicians, schools, family, and peers may be proposed.

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Pitch Estimation, also known as Fundamental Frequency (F0) estimation, has been a popular research topic for many years, and is still investigated nowadays. The goal of Pitch Estimation is to find the pitch or fundamental frequency of a digital recording of a speech or musical notes. It plays an important role, because it is the key to identify which notes are being played and at what time. Pitch Estimation of real instruments is a very hard task to address. Each instrument has its own physical characteristics, which reflects in different spectral characteristics. Furthermore, the recording conditions can vary from studio to studio and background noises must be considered. This dissertation presents a novel approach to the problem of Pitch Estimation, using Cartesian Genetic Programming (CGP).We take advantage of evolutionary algorithms, in particular CGP, to explore and evolve complex mathematical functions that act as classifiers. These classifiers are used to identify piano notes pitches in an audio signal. To help us with the codification of the problem, we built a highly flexible CGP Toolbox, generic enough to encode different kind of programs. The encoded evolutionary algorithm is the one known as 1 + , and we can choose the value for . The toolbox is very simple to use. Settings such as the mutation probability, number of runs and generations are configurable. The cartesian representation of CGP can take multiple forms and it is able to encode function parameters. It is prepared to handle with different type of fitness functions: minimization of f(x) and maximization of f(x) and has a useful system of callbacks. We trained 61 classifiers corresponding to 61 piano notes. A training set of audio signals was used for each of the classifiers: half were signals with the same pitch as the classifier (true positive signals) and the other half were signals with different pitches (true negative signals). F-measure was used for the fitness function. Signals with the same pitch of the classifier that were correctly identified by the classifier, count as a true positives. Signals with the same pitch of the classifier that were not correctly identified by the classifier, count as a false negatives. Signals with different pitch of the classifier that were not identified by the classifier, count as a true negatives. Signals with different pitch of the classifier that were identified by the classifier, count as a false positives. Our first approach was to evolve classifiers for identifying artifical signals, created by mathematical functions: sine, sawtooth and square waves. Our function set is basically composed by filtering operations on vectors and by arithmetic operations with constants and vectors. All the classifiers correctly identified true positive signals and did not identify true negative signals. We then moved to real audio recordings. For testing the classifiers, we picked different audio signals from the ones used during the training phase. For a first approach, the obtained results were very promising, but could be improved. We have made slight changes to our approach and the number of false positives reduced 33%, compared to the first approach. We then applied the evolved classifiers to polyphonic audio signals, and the results indicate that our approach is a good starting point for addressing the problem of Pitch Estimation.

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This paper presents a stochastic mixed-integer linear programming approach for solving the self-scheduling problem of a price-taker thermal and wind power producer taking part in a pool-based electricity market. Uncertainty on electricity price and wind power is considered through a set of scenarios. Thermal units are modelled by variable costs, start-up costs and technical operating constraints, such as: forbidden operating zones, ramp up/down limits and minimum up/down time limits. An efficient mixed-integer linear program is presented to develop the offering strategies of the coordinated production of thermal and wind energy generation, having as a goal the maximization of profit. A case study with data from the Iberian Electricity Market is presented and results are discussed to show the effectiveness of the proposed approach.

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In a recent paper [1] Reis showed that both the principles of extremum of entropy production rate, which are often used in the study of complex systems, are corollaries of the Constructal Law. In fact, both follow from the maximization of overall system conductivities, under appropriate constraints. In this way, the maximum rate of entropy production (MEP) occurs when all the forces in the system are kept constant. On the other hand, the minimum rate of entropy production (mEP) occurs when all the currents that cross the system are kept constant. In this paper it is shown how the so-called principle of "minimum energy expenditure" which is often used as the basis for explaining many morphologic features in biologic systems, and also in inanimate systems, is also a corollary of Bejan's Constructal Law [2]. Following the general proof some cases namely, the scaling laws of human vascular systems and river basins are discussed as illustrations from the side of life, and inanimate systems, respectively.

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As descrições de produtos turísticos na área da hotelaria, aviação, rent-a-car e pacotes de férias baseiam-se sobretudo em descrições textuais em língua natural muito heterogénea com estilos, apresentações e conteúdos muito diferentes entre si. Uma vez que o sector do turismo é bastante dinâmico e que os seus produtos e ofertas estão constantemente em alteração, o tratamento manual de normalização de toda essa informação não é possível. Neste trabalho construiu-se um protótipo que permite a classificação e extracção automática de informação a partir de descrições de produtos de turismo. Inicialmente a informação é classificada quanto ao tipo. Seguidamente são extraídos os elementos relevantes de cada tipo e gerados objectos facilmente computáveis. Sobre os objectos extraídos, o protótipo com recurso a modelos de textos e imagens gera automaticamente descrições normalizadas e orientadas a um determinado mercado. Esta versatilidade permite um novo conjunto de serviços na promoção e venda dos produtos que seria impossível implementar com a informação original. Este protótipo, embora possa ser aplicado a outros domínios, foi avaliado na normalização da descrição de hotéis. As frases descritivas do hotel são classificadas consoante o seu tipo (Local, Serviços e/ou Equipamento) através de um algoritmo de aprendizagem automática que obtém valores médios de cobertura de 96% e precisão de 72%. A cobertura foi considerada a medida mais importante uma vez que a sua maximização permite que não se percam frases para processamentos posteriores. Este trabalho permitiu também a construção e população de uma base de dados de hotéis que possibilita a pesquisa de hotéis pelas suas características. Esta funcionalidade não seria possível utilizando os conteúdos originais. ABSTRACT: The description of tourism products, like hotel, aviation, rent-a-car and holiday packages, is strongly supported on natural language expressions. Due to the extent of tourism offers and considering the high dynamics in the tourism sector, manual data management is not a reliable or scalable solution. Offer descriptions - in the order of thousands - are structured in different ways, possibly comprising different languages, complementing and/or overlap one another. This work aims at creating a prototype for the automatic classification and extraction of relevant knowledge from tourism-related text expressions. Captured knowledge is represented in a normalized/standard format to enable new services based on this information in order to promote and sale tourism products that would be impossible to implement with the raw information. Although it could be applied to other areas, this prototype was evaluated in the normalization of hotel descriptions. Hotels descriptive sentences are classified according their type (Location, Services and/or Equipment) using a machine learning algorithm. The built setting obtained an average recall of 96% and precision of 72%. Recall considered the most important measure of performance since its maximization allows that sentences were not lost in further processes. As a side product a database of hotels was built and populated with search facilities on its characteristics. This ability would not be possible using the original contents.

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We point out in this article centered on Education the main principles that shape the educational model of the industrial age. Those principles were explained by Alvin Toffler in his book The Third Wave. They are: standardization, specialization, synchronization, concentration, maximization and centralization. After emphasizing them and showing how each one of them leaves its mark in the formal education in the industrial age, we defend the thesis that the new education is originated and configures itself in a process of dismantling and replacement of the principles of the industrialism by the new principles of the raising post-industrial society, also called Society of the Knowledge.

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In this project an optimal pose selection method for the calibration of an overconstrained Cable-Driven Parallel robot is presented. This manipulator belongs to a subcategory of parallel robots, where the classic rigid "legs" are replaced by cables. Cables are flexible elements that bring advantages and disadvantages to the robot modeling. For this reason, there are many open research issues, and the calibration of geometric parameters is one of them. The identification of the geometry of a robot, in particular, is usually called Kinematic Calibration. Many methods have been proposed in the past years for the solution of the latter problem. Although these methods are based on calibration using different kinematic models, when the robot’s geometry becomes more complex, their robustness and reliability decrease. This fact makes the selection of the calibration poses more complicated. The position and the orientation of the endeffector in the workspace become important in terms of selection. Thus, in general, it is necessary to evaluate the robustness of the chosen calibration method, by means, for example, of a parameter such as the observability index. In fact, it is known from the theory, that the maximization of the above mentioned index identifies the best choice of calibration poses, and consequently, using this pose set may improve the calibration process. The objective of this thesis is to analyze optimization algorithms which aim to calculate an optimal choice of poses both in quantitative and qualitative terms. Quantitatively, because it is of fundamental importance to understand how many poses are needed. Not necessarily a greater number of poses leads to a better result. Qualitatively, because it is useful to understand if the selected combination of poses actually gives additional information in the process of the identification of the parameters.

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In the last decade, manufacturing companies have been facing two significant challenges. First, digitalization imposes adopting Industry 4.0 technologies and allows creating smart, connected, self-aware, and self-predictive factories. Second, the attention on sustainability imposes to evaluate and reduce the impact of the implemented solutions from economic and social points of view. In manufacturing companies, the maintenance of physical assets assumes a critical role. Increasing the reliability and the availability of production systems leads to the minimization of systems’ downtimes; In addition, the proper system functioning avoids production wastes and potentially catastrophic accidents. Digitalization and new ICT technologies have assumed a relevant role in maintenance strategies. They allow assessing the health condition of machinery at any point in time. Moreover, they allow predicting the future behavior of machinery so that maintenance interventions can be planned, and the useful life of components can be exploited until the time instant before their fault. This dissertation provides insights on Predictive Maintenance goals and tools in Industry 4.0 and proposes a novel data acquisition, processing, sharing, and storage framework that addresses typical issues machine producers and users encounter. The research elaborates on two research questions that narrow down the potential approaches to data acquisition, processing, and analysis for fault diagnostics in evolving environments. The research activity is developed according to a research framework, where the research questions are addressed by research levers that are explored according to research topics. Each topic requires a specific set of methods and approaches; however, the overarching methodological approach presented in this dissertation includes three fundamental aspects: the maximization of the quality level of input data, the use of Machine Learning methods for data analysis, and the use of case studies deriving from both controlled environments (laboratory) and real-world instances.

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Over the past years, ray tracing (RT) models popularity has been increasing. From the nineties, RT has been used for field prediction in environment such as indoor and urban environments. Nevertheless, with the advent of new technologies, the channel model has become decidedly more dynamic and to perform RT simulations at each discrete time instant become computationally expensive. In this thesis, a new dynamic ray tracing (DRT) approach is presented in which from a single ray tracing simulation at an initial time t0, through analytical formulas we are able to track the motion of the interaction points. The benefits that this approach bring are that Doppler frequencies and channel prediction can be derived at every time instant, without recurring to multiple RT runs and therefore shortening the computation time. DRT performance was studied on two case studies and the results shows the accuracy and the computational gain that derives from this approach. Another issue that has been addressed in this thesis is the licensed band exhaustion of some frequency bands. To deal with this problem, a novel unselfish spectrum leasing scheme in cognitive radio networks (CRNs) is proposed that offers an energy-efficient solution minimizing the environmental impact of the network. In addition, a network management architecture is introduced and resource allocation is proposed as a constrained sum energy efficiency maximization problem. System simulations demonstrate an increment in the energy efficiency of the primary users’ network compared with previously proposed algorithms.