Matemática e Estatística | Comunicações em congressos, conferências e seminários / Communications in congresses, conferences
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- Sidewalks of Lisbon and AzoresPublication . Carvalho, Alda; Santos, Carlos; Silva, Jorge; Teixeira, Ricardo; Fong, ChamberlainPart of the beauty of Lisbon and Azores lies under the feet of anyone who walks in the streets. Given their highly symmetrical nature, Portuguese sidewalk drawings are ideal to be analyzed geometrically. Furthermore, these drawings provide opportunities for promoting mathematical concepts through exhibitions, card decks, and other forms of dissemination.
- An application of time series clustering using a combined distancePublication . Martins, Ana; Vaz, Daniel; Silva, Tiago; Cardoso, Margarida; Carvalho, Alda; Loja, AméliaClustering time series aims at uncovering diverse longitudinal patterns. In this study we analyse time series of various parameters collected atop wind turbines in a wind farm in Portugal. Considering wind data in clustering may reveal differences in operation between neighbouring turbines, due to their position relative to one another and to terrain features. In this work, we use an approach to wind speed time series clustering based on a convex combination of distance measures between time series. For visualizing the resulting groups, we propose a graphical representation, Distance Matrix, which is quite more informative than the classical Multidimensional Scaling (MDS) map. This representation allows for quick comparisons between pairs of turbines for (dis)similarities. Our approach provides distinct insights regarding the differences between time series, emphasizing differences in values (Euclidean distance), trends (Pearson-based distance), and cyclical behaviours (Euclidean distance between periodograms and/or autocorrelation structures). In most cases, we found two groups, which were not always coincident with the geographical groups, but the proposed approach could also find the rationale behind the clusters that were formed. The results obtained may help identifying undesirable aerodynamic loads that the blades of a particular wind turbine may be subjected to, thereby shortening its time in-service.
- Analysis of thoracic aortic aneurysm CTA scans using spatial statisticsPublication . Rodriguez, Katalina; Carvalho, Alda; Valente, Rodrigo; Xavier, José; Tomás, AntónioThis study leverages spatial statistics to analyze the spatial distribution of the aorta, aiming to better understand the biomechanical behavior of Ascending Thoracic Aortic Aneurysms (ATAA) and its impact on clinical outcomes. CTA angiography was performed on 87 ATAA patients. Experimental variograms were computed for various variables, such as maximum diameter, from which key parameters of interest were extracted. These parameters were then analyzed over time to assess temporal patterns. The goal of this analysis was to identify whether similar patterns or behaviors emerge in features from CTA scans of patients with aneurysms of similar sizes, ultimately aiming to statistically validate the quality of the CTA scans.
- C.R. Rao: a beacon of excellence in statistical research and practicePublication . Oliveira, Teresa A.This talk shines a spotlight on the remarkable achievements of C.R. Rao, an eminent statistician and mathematician, whose contributions have illuminated the field of statistics and related areas. Rao's transformative ideas in estimation theory, sufficiency and completeness, experimental design, biometry and data science, have revolutionized the way researchers approach data analysis and interpretation. Furthermore, his mentorship and educational efforts have fostered the growth of countless statisticians, ensuring his legacy will continue for generations to come. The article underscores Rao's exceptional impact and highlights his numerous prestigious awards, solidifying his place as a beacon of excellence in the field. Some of the Rao's pivotal role in shaping the future of statistical research and practice will be presented.
- Modeling water level fluctuation in river basins using singular spectrum analysisPublication . Oliveira, Amilcar; Sarmento, CarlaWater scarcity affected 29% of the EU territory during at least one season in 2019. In the face of climate change, it is very important to understand the risk of water scarcity. Water scarcity is becoming a growing problem in southern European countries, such as Portugal. In 2019, Portugal, faced one of the most significant water scarcity conditions in the EU-27 on the seasonal scale (seasonal WEI 66%).The main objective of this work is to study the water level fluctuation in river basins, in order to predict the risks of lack of water. The study area is located in 29 reservoirs from different river basins in Portugal. The collected data refer to the period from November 1993 to August 2022, with a total number of records of 9686. We started by improving the quality of the data and built a monthly time series of the volume of water stored. Next, we analyzed the time series using Singular Spectrum Analysis (SSA), which is a nonparametric technique for analyzing time series.
- Analysis of the inequality into distributions: an alternative approach to the Gini index applied to the spending environmental in EUPublication . Seijas-Macias, Antonio; Oliveira, Amilcar; Oliveira, Teresa A.The Gini index is the most common tool to measure inequality into two distributions. Traditionally, the Gini index and the curve of Lorenz are focused on inequalities measures in the income distribution between countries or regions. But, in the last years, several authors have shown some limitations of the Gini index. In particular, it’s less sensitive to inequality at the tail of income distribution. This type of problem in the Gini index could produce two types of reactions: a new reinterpretation of the Gini index and the proposal of some alternative measures to it. In this paper, we follow the previous work using the Csiszar f-divergence to propose using the α-divergences approach to analyze the differences between the Gini index approach and these alternatives. The Gini index has been applied to the measure of resource inequalities. The AR-Gini is an area-based measure of resource inequality that estimates inequalities between neighbourhoods regarding the consumption of specific consumer goods (Druckman and Jackson, 2008). The AR-Gini could be a useful tool to monitor the distributional impacts of resource-related interventions, but this indicator presents the same overcomes as the Gini index. We can use the Gini concentration coefficient as a measure of the concentration of distribution of a random variable, especially applied to time series of data. In recent years, several studies have studied environmental spending in the European Union (EU). We focus our analysis on the distribution of this type of spending between the countries of the EU. The objective of this study is to show the differences in indexes applied to the study of the distribution of the distribution of monetary resources to environmental conservation and the extension of environmental protected areas into the countries of the European Union (EU). In our comparative study, we use the Gini index and the α-divergence measure and compare the results to get the most accurate measure of the equity of the distribution.
- Big data sets in environmental studiesPublication . Oliveira, AmilcarBig Data datasets for environmental studies play a crucial role in understanding, monitoring and addressing large-scale environmental issues. Big Data datasets for environmental studies deal with huge volumes of data coming from various sources such as satellites, remote sensors, weather stations, sensor networks and mobile devices. These datasets include detailed information on climate change, biodiversity, air quality, water resources and other environmental parameters. Integrating and analyzing data from different sources allows for a more comprehensive understanding of environmental standards and helps in making informed decisions. The generation of environmental data occurs in real time, especially with the increased use of sensors and continuous monitoring technologies. The ability to handle the velocity of data is essential for detecting rapid changes in the environment and responding to critical events such as natural disasters. Predictive models help predict climate patterns, identify areas of environmental risk and assess the impacts of human activities on the ecosystem. This data is crucial for developing mitigation strategies, adapting to climate change and conserving biodiversity. In summary, Big Data datasets play a fundamental role in environmental studies, providing a comprehensive and real-time understanding of environmental challenges, enabling the implementation of effective strategies for conservation and sustainability.
- Avaliação digital na matemática: exames onlinePublication . Nunes, Catarina S.
- An application of Box-Jenkins methodology to model the series of currency in circulation in MozambiquePublication . Arone, Samuel G.; Nunes, Catarina S.; Grilo, Luís M.
- The Holt-Winters method to estimate currency in circulation in MozambiquePublication . Arone, Samuel G.; Nunes, Catarina S.; Grilo, Luís M.