Taxas Finitas de Decomposição: uma Nova Abordagem na Estimativa dos Parâmetros de Arrhenius Valendo-se dos Algoritmos de Partículas Sir e Asir

A conversão termoquímica desempenha um papel importante na geração de energia. Os combustíveis possuem uma diversidade de propriedades termoquímicas oriundas da sua origem que impõe uma complexidade na determinação de padrões na estrutura térmica e química dos processos de conversão. Entender a termoquímica e, consequentemente, a cinética química envolvida na formação de espécies químicas no processo de conversão de sólidos, é um fator importante para na modelagem numérica de processos de pirólise e combustão. Pois, pode-se, em seguida, quantificar os produtos que causam impacto ambiental nesses processos. Este trabalho tem como objetivo aplicar os filtros de partículas Sampling Importance Resampling (SIR) e Auxiliar Sampling Importance Resampling (ASIR) à estimativa de parâmetros cinéticos do modelo de Arrhenius. Para tanto, são discutidos os vários métodos de estimativa de parâmetros cinéticos de Arrhenius. Em seguida, os algoritmos dos filtros de partículas são adaptados para estimativa de parâmetros cinéticos, que a posteriori são implementados em um código computacional que recebe os dados termogravimétricos e o sistema de EDO. Um primeiro teste foi realizado num caso simples da celulose submetido a pirólise em um único passo de reação. Os resultados foram satisfatórios. Finalmente, uma nova abordagem de estimativa de parâmetros cinéticos foi proposta e chamada de Taxa Finita de Decomposição. Os melhores resultados para essa abordagem foram usando a verossimilhança pela TG que ajusta bem a curva da perda de massa. A imprecisão do método é atenuada com o aumento gradativo do número de partículas ou a diminuição do tempo de integração para cada passo de tempo. Palavras-chaves: Filtro Partículas SIR e ASIR. Cinética química. Taxa finita de decomposição. Modelo de Arrhenius

Special issue introduction: Approaching spatial uncertainty visualization to support reasoning and decision making

While research on uncertainty and decision-making has a long history across several disciplines, recent technological developments compel researchers to rethink how to best address and advance the understanding of how humans reason and make decisions under spatial uncertainty. This introduction presents a visual summary graphic to provide an overview of each article in this special issue. Upon viewing these visual summaries, the reader will find that each of these articles covers different topics in the uncertainty visualization domain, offering complementary research in this field. Extending this body of research and finding new ways to explore how these visualizations may help or hinder the analytical and reasoning process of humans continues to be a necessary step towards designing more effective uncertainty visualizations to support reasoning and decision-making

Visual Similarity Perception of Directed Acyclic Graphs: A Study on Influencing Factors

While visual comparison of directed acyclic graphs (DAGs) is commonly encountered in various disciplines (e.g., finance, biology), knowledge about humans' perception of graph similarity is currently quite limited. By graph similarity perception we mean how humans perceive commonalities and differences in graphs and herewith come to a similarity judgment. As a step toward filling this gap the study reported in this paper strives to identify factors which influence the similarity perception of DAGs. In particular, we conducted a card-sorting study employing a qualitative and quantitative analysis approach to identify 1) groups of DAGs that are perceived as similar by the participants and 2) the reasons behind their choice of groups. Our results suggest that similarity is mainly influenced by the number of levels, the number of nodes on a level, and the overall shape of the graph.Comment: Graph Drawing 2017 - arXiv Version; Keywords: Graphs, Perception, Similarity, Comparison, Visualizatio

Cognitive Invariants of Geographic Event Conceptualization: What Matters and What Refines?

Behavioral experiments addressing the conceptualization of geographic events are few and far between. Our research seeks to address this deficiency by developing an experimental framework on the conceptualization of movement patterns. In this paper, we report on a critical experiment that is designed to shed light on the question of cognitively salient invariants in such conceptualization. Invariants have been identified as being critical to human information processing, particularly for the processing of dynamic information. In our experiment, we systematically address cognitive invariants of one class of geographic events: single entity movement patterns. To this end, we designed 72 animated icons that depict the movement patterns of hurricanes around two invariants: size difference and topological equivalence class movement patterns endpoints. While the endpoint hypothesis, put forth by Regier (2007), claims a particular focus of human cognition to ending relations of events, other research suggests that simplicity principles guide categorization and, additionally, that static information is easier to process than dynamic information. Our experiments show a clear picture: Size matters. Nonetheless, we also find categorization behaviors consistent with experiments in both the spatial and temporal domain, namely that topology refines these behaviors and that topological equivalence classes are categorized consistently. These results are critical steppingstones in validating spatial formalism from a cognitive perspective and cognitively grounding work on ontologies

A Neutralizing Human Monoclonal Antibody Protects against Lethal Disease in a New Ferret Model of Acute Nipah Virus Infection

Nipah virus is a broadly tropic and highly pathogenic zoonotic paramyxovirus in the genus Henipavirus whose natural reservoirs are several species of Pteropus fruit bats. Nipah virus has repeatedly caused outbreaks over the past decade associated with a severe and often fatal disease in humans and animals. Here, a new ferret model of Nipah virus pathogenesis is described where both respiratory and neurological disease are present in infected animals. Severe disease occurs with viral doses as low as 500 TCID50 within 6 to 10 days following infection. The underlying pathology seen in the ferret closely resembles that seen in Nipah virus infected humans, characterized as a widespread multisystemic vasculitis, with virus replicating in highly vascular tissues including lung, spleen and brain, with recoverable virus from a variety of tissues. Using this ferret model a cross-reactive neutralizing human monoclonal antibody, m102.4, targeting the henipavirus G glycoprotein was evaluated in vivo as a potential therapeutic agent. All ferrets that received m102.4 ten hours following a high dose oral-nasal Nipah virus challenge were protected from disease while all controls died. This study is the first successful post-exposure passive antibody therapy for Nipah virus using a human monoclonal antibody

Takayasu's arteritis associated with Crohn's disease: a case report

<p>Abstract</p> <p>Introduction</p> <p>The simultaneous presence of Takayasu's arteritis and Crohn's disease in a patient seems to be rare. To our knowledge, no patient with the combination of Crohn's disease and Takayasu's arteritis has been reported from our region.</p> <p>Case presentation</p> <p>Herein we present the case of a 22-year-old Iranian woman previously diagnosed as Crohn's disease and who had subsequently developed Takayasu's arteritis.</p> <p>Conclusion</p> <p>Clinical suspicion, proper imaging, and consideration of the differential diagnosis are important for the correct diagnosis and management of patients with this coincidence.</p

Artificial intelligence and visual analytics in geographical space and cyberspace: Research opportunities and challenges

In recent decades, we have witnessed great advances on the Internet of Things, mobile devices, sensor-based systems, and resulting big data infrastructures, which have gradually, yet fundamentally influenced the way people interact with and in the digital and physical world. Many human activities now not only operate in geographical (physical) space but also in cyberspace. Such changes have triggered a paradigm shift in geographic information science (GIScience), as cyberspace brings new perspectives for the roles played by spatial and temporal dimensions, e.g., the dilemma of placelessness and possible timelessness. As a discipline at the brink of even bigger changes made possible by machine learning and artificial intelligence, this paper highlights the challenges and opportunities associated with geographical space in relation to cyberspace, with a particular focus on data analytics and visualization, including extended AI capabilities and virtual reality representations. Consequently, we encourage the creation of synergies between the processing and analysis of geographical and cyber data to improve sustainability and solve complex problems with geospatial applications and other digital advancements in urban and environmental sciences

Discovery of 74 new bright ZZ Ceti stars in the first three years of TESS

We report the discovery of 74 new pulsating DA white dwarf stars, or ZZ Cetis, from the data obtained by the Transiting Exoplanet Survey Satellite mission, from Sectors 1 to 39, corresponding to the first 3 cycles. This includes objects from the Southern hemisphere (Sectors 1–13 and 27–39) and the Northern hemisphere (Sectors 14–26), observed with 120 s- and 20 s-cadence. Our sample likely includes 13 low-mass and one extremely low-mass white dwarf candidate, considering the mass determinations from fitting Gaia magnitudes and parallax. In addition, we present follow-up time series photometry from ground-based telescopes for 11 objects, which allowed us to detect a larger number of periods. For each object, we analysed the period spectra and performed an asteroseismological analysis, and we estimate the structure parameters of the sample, i.e. stellar mass, effective temperature, and hydrogen envelope mass. We estimate a mean asteroseismological mass of 〈Msis〉 = 0.635 ± 0.015 M⊙, excluding the candidate low or extremely low-mass objects. This value is in agreement with the mean mass using estimates from Gaia data, which is 〈Mphot〉 = 0.631 ± 0.040 M⊙, and with the mean mass of previously known ZZ Cetis of 〈M*〉 = 0.644 ± 0.034 M⊙. Our sample of 74 new bright ZZ Cetis increases the number of known ZZ Cetis by ∼20 per cent