http://hdl.handle.net/11422/96 Wed, 08 Apr 2026 01:33:25 GMT 2026-04-08T01:33:25Z http://hdl.handle.net/11422/28505 Title: Performance indicators for emissions reporting based on artificial intelligence Author(s)/Inventor(s): Xavier, Victor de Almeida Advisor: Lima, Priscila Machado Vieira Abstract: Climate change and global warming have been a trending topic worldwide since the Eco-92 conference. However, little progress has been made in reducing greenhouse gases (GHGs). The problems and challenges related to emissions are complex and require a concerted and comprehensive effort to address them. Emissions reporting is a key component of GHG reduction policy and is therefore the focus of this work. This work presents a method for examining, clustering, and analysing data from emissions reporting initiatives. Using artificial intelligence clustering technologies, performance indicator concepts and qualitative analysis approaches, the proposed method is implemented through a performance indicator development process (PIDP), which aims to search for performance indicators (PIs) among data selected from emissions databases. During the implementation of the PIDP, the results showed that a new model is essential to deal with emission reporting information. Therefore, this study proposes a new model to evaluate emissions reporting processes implemented by cities, which is based on concepts inherited from the capability maturity model (CMM). The main objective of this model is to help cities address the challenges of emission reduction by leveraging the areas and processes associated with emission reporting. This model and how it can be used in the context of emissions reporting is described in detail in the methodology, as are the experiments and other results obtained during the development of this study. Publisher: Universidade Federal do Rio de Janeiro Type: Dissertação Sat, 01 Jan 2022 00:00:00 GMT http://hdl.handle.net/11422/28505 2022-01-01T00:00:00Z http://hdl.handle.net/11422/28504 Title: Fullerene nanodiscs: from chemistry to combinatorics Author(s)/Inventor(s): Cruz, Mariana Martins Ferreira da Advisor: Figueiredo, Celina Miraglia Herrera de Abstract: A graph is a mathematical model used to represent relationships between objects. The general characteristics that objects and their relationships can assume allowed the construction of the (so-called) Graph Theory, which has been applied to model large scale problems in several areas, such as Mathematics, Physics, Computer Science, Engineering, Chemistry and Psychology. Fullerene graphs are mathematical models for carbon-based molecules experimentally discovered in the early 1980. Many parameters associated with these graphs have been discussed to describe the stability of fullerene molecules. By definition, fullerene graphs are cubic, planar, 3-connected with pentagonal and hexagonal faces. A total coloring of a graph G assigns colors to the vertices and edges of G such that adjacent or incident elements have different colors. The famous Total Coloring Conjecture open for 50 years is settled for cubic graphs, but not to arbitrary regular graphs nor to arbitrary planar graphs. The length of the shortest cycle in a graph is called girth. Our goal is to study the total coloring of an infinite subfamily of fullerene graphs, the fullerene nanodiscs Dr, with distance between the inner (outer) layer and the central layer given by the radius parameter r ≥ 2, motivated by a conjecture that the girth of a graph is a relevant parameter in the study of total coloring. To highlight the choice of the studied graph class, we present a historical scenario of the carbon molecule discovery that can be modeled through a special cubic planar graph of girth 5. We contribute by giving the first combinatorial description for fullerene nanodiscs, aiming to improve the understanding of this class and then a conformable coloring for infinite families of fullerene nanodiscs, so that we are able to tackle the challenging total coloring of these graphs. Publisher: Universidade Federal do Rio de Janeiro Type: Dissertação Wed, 01 Jun 2022 00:00:00 GMT http://hdl.handle.net/11422/28504 2022-06-01T00:00:00Z http://hdl.handle.net/11422/26760 Title: CATS# : a testing technique to support the specification of test cases for context-aware software systems Author(s)/Inventor(s): Doreste, Andrea Cristina de Souza Advisor: Travassos, Guilherme Horta Abstract: Contemporary Software Systems (CSS) bring distinctive characteristics compared to conventional systems to the table. One of them is context-awareness when the context and its variation affect the software system's behavior in unthinkable (sometimes unpredictable) ways. Therefore, it is essential to ensure the correct functioning of this type of system. However, as far as it could be investigated, there is a lack of software technologies to support these systems' testing. This work presents CATS#, a testing technique to support the specification of test cases for Context-Aware Software Systems (CASS). CATS# evolves the CATS (Context-Aware Test Suite) Design technique by adapting the test case concept to include the context and offers a test template capable of capturing (and representing) the variation of context that can influence the system's behavior during test execution. CATS# was applied in a project by undergraduate students. The results indicate its initial feasibility to support the specification of CASS test cases for situations not covered by conventional testing techniques. Publisher: Universidade Federal do Rio de Janeiro Type: Dissertação Fri, 01 Oct 2021 00:00:00 GMT http://hdl.handle.net/11422/26760 2021-10-01T00:00:00Z http://hdl.handle.net/11422/26759 Title: Um modelo de verificação de fatos baseado em crowdsourcing Author(s)/Inventor(s): Pinto, Marcos Rodrigues Advisor: Souza, Jano Moreira de Abstract: The process of creating content on the Internet was immensely promoted by the emergence of Social Networks, and with this, the volume of information kept on the web grows enormously. If, on the one hand, the dissemination of information has become facilitated by these technologies, on the other hand, ensuring the veracity of this information has become more difficult, since social networks often allow the publication of content without moderation. Thus, social networks today represent a great potential for disseminating false news. This fact prompted the emergence of factchecking organizations, usually represented by news organizations. News organizations, in turn, have their action limited to their productive capacity, which may not meet the great demand that arises from the immense volume of fake news, to which we are submitted in this era of misinformation. This dissertation then proposes a crowdsourcing-supported fact-checking model, which is capable to filter, analyze and classify news, proposing an alternative, proportional to the pace of fake news creation. In addition to the model proposal, the work comprises the development of a tool to support the process operation, involving the crowd work management. To evaluate the model, experiments were performed using Amazon Mechanical Turk, and at the end, the results were discussed, where the potential of the crowds use in fact-checking tasks was observed in the context of the proposed model. Publisher: Universidade Federal do Rio de Janeiro Type: Dissertação Thu, 01 Jul 2021 00:00:00 GMT http://hdl.handle.net/11422/26759 2021-07-01T00:00:00Z