996 resultados para LEARNED PATTERNS
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A Work Project, presented as part of the requirements for the Award of a Masters Degree in Finance from the NOVA – School of Business and Economics
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A Work Project, presented as part of the requirements for the Award of a Masters Degree in Economics from the NOVA – School of Business and Economics
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Thesis submitted in partial fulfillment of the requirements for the Degree of Doctor of Statistics and Information Management
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Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Gestão de Informação
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A Work Project, presented as part of the requirements for the Award of a Masters Degree in Economics from the NOVA – School of Business and Economics
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Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.
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The aim of this study was to evaluate the temperature and relative humidity influence in the life cycle, mortality and fecundity patterns of Triatoma rubrovaria. Four cohorts with 60 recently laid eggs each were conformed. The cohorts were divided into two groups. In the controlled conditions group insects were maintained in a dark climatic chamber under constant temperature and humidity, whereas triatomines of the ambiental temperature group were maintained at room temperature. Average incubation time was 15.6 days in the controlled conditions group and 19.1 days in the ambiental temperature. In group controlled conditions the time from egg to adult development lasted 10 months while group ambiental temperature took four months longer. Egg eclosion rate was 99.1% and 98.3% in controlled conditions and ambiental temperature, respectively. Total nymphal mortality in controlled conditions was 52.6% whereas in ambiental temperature was 51.8%. Mean number of eggs/female was 817.6 controlled conditions and 837.1 ambiental temperature. Fluctuating temperature and humidity promoted changes in the life cycle duration and in the reproductive performance of this species, although not in the species mortality.
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Among the vectors of Chagas disease, Triatoma patagonica is a species in the process of adaptation to the human environment. However, its vector competence is not well known. This study had the aim of evaluating and comparing feeding and defecation patterns among fifth-instar nymphs of Triatoma patagonica and Triatoma infestans that were fed ad libitum. The results showed that nymphs of Triatoma patagonica had a feeding pattern similar to that of Triatoma infestans. Sixty nine percent and 58% of nymphs of Triatoma patagonica and Triatoma infestans, respectively, produced their first defecation within five minutes after being fed. Triatoma patagonica defecated during feeding, with an average time until first defecation that was shorter than that of Triatoma infestans (3.4 and 6.2 min, respectively). The nymphs of Triatoma patagonica were capable of defecating during or immediately after feeding.
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The goal of this thesis is the study of a tool that can help analysts in finding sequential patterns. This tool will have a focus on financial markets. A study will be made on how new and relevant knowledge can be mined from real life information, potentially giving investors, market analysts, and economists new basis to make informed decisions. The Ramex Forum algorithm will be used as a basis for the tool, due to its ability to find sequential patterns in financial data. So that it further adapts to the needs of the thesis, a study of relevant improvements to the algorithm will be made. Another important aspect of this algorithm is the way that it displays the patterns found, even with good results it is difficult to find relevant patterns among all the studied samples without a proper result visualization component. As such, different combinations of parameterizations and ways to visualize data will be evaluated and their influence in the analysis of those patterns will be discussed. In order to properly evaluate the utility of this tool, case studies will be performed as a final test. Real information will be used to produce results and those will be evaluated in regards to their accuracy, interest, and relevance.
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Crisis-affected communities and global organizations for international aid are becoming increasingly digital as consequence geotechnology popularity. Humanitarian sector changed in profound ways by adopting new technical approach to obtain information from area with difficult geographical or political access. Since 2011, turkey is hosting a growing number of Syrian refugees along southeastern region. Turkish policy of hosting them in camps and the difficulty created by governors to international aid group expeditions to get information, made such international organizations to investigate and adopt other approach in order to obtain information needed. They intensified its remote sensing approach. However, the majority of studies used very high-resolution satellite imagery (VHRSI). The study area is extensive and the temporal resolution of VHRSI is low, besides it is infeasible only using these sensors as unique approach for the whole area. The focus of this research, aims to investigate the potentialities of mid-resolution imagery (here only Landsat) to obtain information from region in crisis (here, southeastern Turkey) through a new web-based platform called Google Earth Engine (GEE). Hereby it is also intended to verify GEE currently reliability once the Application Programming Interface (API) is still in beta version. The finds here shows that the basic functions are trustworthy. Results pointed out that Landsat can recognize change in the spectral resolution clearly only for the first settlement. The ongoing modifications vary for each case. Overall, Landsat demonstrated high limitations, but need more investigations and may be used, with restriction, as a support of VHRSI.
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INTRODUCTION: Metabolic syndrome (MetS), a risk factor for atherosclerosis and coronary heart disease, is related to an inadequate food intake pattern. Its incidence is increasing among Brazilian adults, including those living in rural areas. Our aim was not only to describe the frequency of MetS in adults with or without MetS but also to compare their food intake pattern as assessed by the healthy eating index (HEI) and serum albumin and C reactive protein (CRP) levels. METHODS: Men and women (n = 246) living in a small village in Brazil were included. MetS was characterized according to the adult treatment panel (ATP III) criteria. Groups were compared by chi-square, student t or Mann-Whitney tests. RESULTS: MetS was diagnosed in 15.4% of the cases. The MetS group showed higher CRP (1.8±1.2 vs. 1.0±0.9 mg/dl) and lower albumin (4.3±0.3 vs. 4.4±0.3 g/dl) serum levels compared to the control group. Additionally, the MetS group showed lower scores (median[range]) in the HEI compared to the control group (53.5[31.2-78.1] vs 58[29.7-89.5], respectively). The MetS group also had decreased scores for total fat and daily variety of food intake. CONCLUSIONS: The results suggest that adults with MetS displayed chronic mild inflammation and a poorer food intake pattern than the control group.
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Generating personalized movie recommendations to users is a problem that most commonly relies on user-movie ratings. These ratings are generally used either to understand the user preferences or to recommend movies that users with similar rating patterns have rated highly. However, movie recommenders are often subject to the Cold-Start problem: new movies have not been rated by anyone, so, they will not be recommended to anyone; likewise, the preferences of new users who have not rated any movie cannot be learned. In parallel, Social-Media platforms, such as Twitter, collect great amounts of user feedback on movies, as these are very popular nowadays. This thesis proposes to explore feedback shared on Twitter to predict the popularity of new movies and show how it can be used to tackle the Cold-Start problem. It also proposes, at a finer grain, to explore the reputation of directors and actors on IMDb to tackle the Cold-Start problem. To assess these aspects, a Reputation-enhanced Recommendation Algorithm is implemented and evaluated on a crawled IMDb dataset with previous user ratings of old movies,together with Twitter data crawled from January 2014 to March 2014, to recommend 60 movies affected by the Cold-Start problem. Twitter revealed to be a strong reputation predictor, and the Reputation-enhanced Recommendation Algorithm improved over several baseline methods. Additionally, the algorithm also proved to be useful when recommending movies in an extreme Cold-Start scenario, where both new movies and users are affected by the Cold-Start problem.
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Learning novel actions and skills is a prevalent ability across multiple species and a critical feature for survival and competence in a constantly changing world. Novel actions are generated and learned through a process of trial and error, where an animal explores the environment around itself, generates multiple patterns of behavior and selects the ones that increase the likelihood of positive outcomes. Proper adaptation and execution of the selected behavior requires the coordination of several biomechanical features by the animal. Cortico-basal ganglia circuits and loops are critically involved in the acquisition, learning and consolidation of motor skills.(...)
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INTRODUCTION: The purpose of this ecological study was to evaluate the urban spatial and temporal distribution of tuberculosis (TB) in Ribeirão Preto, State of São Paulo, southeast Brazil, between 2006 and 2009 and to evaluate its relationship with factors of social vulnerability such as income and education level. METHODS: We evaluated data from TBWeb, an electronic notification system for TB cases. Measures of social vulnerability were obtained from the SEADE Foundation, and information about the number of inhabitants, education and income of the households were obtained from Brazilian Institute of Geography and Statistics. Statistical analyses were conducted by a Bayesian regression model assuming a Poisson distribution for the observed new cases of TB in each area. A conditional autoregressive structure was used for the spatial covariance structure. RESULTS: The Bayesian model confirmed the spatial heterogeneity of TB distribution in Ribeirão Preto, identifying areas with elevated risk and the effects of social vulnerability on the disease. We demonstrated that the rate of TB was correlated with the measures of income, education and social vulnerability. However, we observed areas with low vulnerability and high education and income, but with high estimated TB rates. CONCLUSIONS: The study identified areas with different risks for TB, given that the public health system deals with the characteristics of each region individually and prioritizes those that present a higher propensity to risk of TB. Complex relationships may exist between TB incidence and a wide range of environmental and intrinsic factors, which need to be studied in future research.