BDTD - Biblioteca Digital de Teses e Dissertações
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Master Thesis A degradação de geotêxtil tecido de polipropileno sob exposição natural de curta duração em ambiente costeiro(Universidade Federal do Rio Grande do Norte, 2025-08-28) Opolski, Wagner José; França, Fagner Alexandre Nunes de; https://orcid.org/0000-0002-8113-622X; http://lattes.cnpq.br/6854485206744906; https://orcid.org/0009-0006-1634-0722; http://lattes.cnpq.br/0847860777713083; Silva, Jonathan Mota da; https://orcid.org/0000-0001-9995-8451; http://lattes.cnpq.br/3379521354576211; Medeiros, José Ivan de; https://orcid.org/0000-0002-8888-4765; http://lattes.cnpq.br/0875521131771417; Dias Filho, José Luiz Ernandes; https://orcid.org/0000-0001-6315-8049; http://lattes.cnpq.br/6015049259519646; Fonseca, Viviane Muniz; https://orcid.org/0000-0002-2153-0858; http://lattes.cnpq.br/1977121411791179Woven geotextiles are widely used in engineering works, but their durability under coastal weathering, especially in short-term applications, still lacks detailed experimental data. Design reduction factors, often conservative, can lead to the oversizing of temporary structures. The main objective of this study was to evaluate the mechanical properties of a polypropylene (PP) woven geotextile subjected to short-term coastal weathering. For this purpose, an experimental program was conducted in a coastal environment, exposing geotextile samples under a factorial design that included three exposure periods (42, 84, and 126 days), three support types (on a black surface, on a white surface, and suspended), and two inclination angles (6° and 45°). The material’s degradation was quantified through strip tensile tests to determine strength and stiffness, complemented by Thermogravimetric Analysis (TGA) to investigate changes in thermal stability. The microclimate on the sample surfaces was monitored, and the influence of the factors was statistically validated by Analysis of Variance (ANOVA). The results indicated that degradation does not follow a linear pattern but rather a two-phase behavior: an initial phase of material stiffening (up to 84 days), followed by an abrupt and widespread drop in mechanical properties in the third period. The contact surface proved to be a determining factor, with the black substrate inducing the most severe degradation (strength losses up to 31.5% and stiffness losses exceeding 90%) due to intense thermal stress. In contrast, the white surface acted as a degradation mitigator. The suspended samples showed degradation almost as severe as those on the black substrate, suggesting that the combination of photo-oxidation on both faces and mechanical stress from wind is equally critical. The experimentally determined Durability Reduction Factors (F RD), which reached a maximum of 1.46, were significantly lower than the values recommended in the literature for permanent works. It is concluded that design criteria for temporary applications can be optimized based on experimental data, avoiding unnecessary oversizing and promoting a more efficient use of geosynthetics.Master Thesis Arquitetando o futuro eco-tecnológico da indústria de moda, inspirado pelas tecnologias emergentes de inteligência artificial(Universidade Federal do Rio Grande do Norte, 2025-10-29) Aladim, Mariana Dias de Brito Alves; Nascimento, José Heriberto Oliveira do; https://orcid.org/0000-0001-6804-2854; http://lattes.cnpq.br/7033735079037677; http://lattes.cnpq.br/3424663032684104; Limão, Ilmara Pinheiro; https://orcid.org/0000-0002-0072-0978; http://lattes.cnpq.br/3940734079084103; Medeiros, Isadora Santos; http://lattes.cnpq.br/2985378908031485; Silva, Kesia Karina de Oliveira Souto; Barros, Luciani Paola Rocha CruzFashion, a multi-trillion-dollar industry at the center of global commerce, is under immense scrutiny for its unsustainable processes and operational challenges brought on by trends changing faster than one can blink an eye. The following work unravels the promising prospects of Artificial Intelligence (AI) within a sustainable eco-tech future for the textile and clothing sectors. Using AI technologies, including machine learning, deep learning and generative models, the industry can streamline processes and transparency more seamlessly aligning with circular economy principles. The report offers an exhaustive assessment of AI use in the fashion value chain; from raw materials research to supply chain, with a focus on sustainability and productivity. Bibliometrics show an annual-growth of 484% in the number of published studies on AI and fashion sustainability between 2018 and 2024, including information from PRISMA-guided research done Scopus that led to a total of 2.548 papers displaying China as well as the USA/UK at peak-contributions. This work discusses the future of predictive analytics, quality assurance, automated assembly and traceability. There have been challenges such as technological disparities, ethical concerns, and data privacy following strides given by AI in recent years highlighting the importance of inclusion a sustainable adoption into our everyday lives. Exercise like this illustrates that AI has the promise to transform the fashion industry into one which is circular, transparent and innovative and aligns with global goals around sustainability like SDG 12.Master Thesis Análise comparativa de desempenho mecânico e resistência à abrasão de tecidos para proteção de motociclistas(Universidade Federal do Rio Grande do Norte, 2025-12-09) Melo Filho, Sérgio Barros de; Silva, Iris Oliveira da; https://orcid.org/0000-0002-5018-7059; http://lattes.cnpq.br/3890585112132451; http://lattes.cnpq.br/8125188723615481; Lima, Iran Marques de; http://lattes.cnpq.br/0710893285288108; Barros, Luciani Paola Rocha Cruz; Araújo, Rubens Capistrano de; http://lattes.cnpq.br/2544026059947892The accelerated growth in the use of motorcycles as a means of work, especially in the urban delivery sector, has significantly increased workers’ exposure to accidents, many of which are associated with falls and skidding events that lead to severe abrasive injuries. In this context, the mechanical and abrasive resistance of the clothing worn plays a decisive role in mitigating physical damage, creating an urgent need for studies that objectively assess the real performance of different materials. Thus, this research is justified as it seeks to address technical-scientific and regulatory gaps by comparing technical fabrics used in protective motorcycle jackets with conventional fabrics widely worn by delivery workers, contributing to safety recommendations, the development of affordable PPE, and the formulation of public policies.Therefore, this dissertation aimed to comparatively analyze the mechanical performance and abrasion resistance of two fabrics: a technical fabric used in protective jackets and a common knit fabric, correlating laboratory results with real motorcycle accident scenarios. This dissertation carried out a comparative analysis of the mechanical performance and abrasion resistance of two representative fabrics: a technical polyester 300D jacket fabric and a common knit shirt (100% polyester). Tests for grammage, tensile strength, elongation, thickness, and abrasion using the Martindale method were conducted following ABNT, ISO, and ASTM standards, complemented by structural and chemical analyses using SEM and FTIR.The results demonstrated consistent superiority of the technical fabric across all evaluated parameters, with tensile strength ranging from 110,65 to 110,85 kgf, an average thickness of 0.32 mm, and abrasive performance reaching up to 83,000 cycles, whereas the common knit fabric ruptured after approximately 28,000 cycles. It was shown that density, structure, and grammage directly influence energy dissipation and delay rupture, making technical fabrics more suitable for motorcyclist protection.It is concluded that the use of technical clothing has significant potential to reduce the severity of abrasion injuries, generating positive social impacts by lowering hospital costs, reducing work absences, and mitigating occupational risks. Future studies are recommended to incorporate dynamic fall simulations to further approximate laboratory results to real accident conditions.Master Thesis Análise de vibrações mecânicas sutis utilizando técnicas de visão robótica potencializadas por aprendizado de máquina(Universidade Federal do Rio Grande do Norte, 2025-11-24) Freitas, Danilson Kaio de Macêdo; Nagem, Danilo Alves Pinto; Carvalho, Bruno Motta de; https://orcid.org/0000-0002-9122-0257; http://lattes.cnpq.br/0330924133337698; https://orcid.org/0000-0003-4828-1107; http://lattes.cnpq.br/5934458385325202; http://lattes.cnpq.br/3628158236619471; Fernandes, Felipe Ricardo dos Santos; https://orcid.org/0000-0003-0805-1796; http://lattes.cnpq.br/9594127311197032; Salsa Júnior, Rubens Gonçalves; https://orcid.org/0000-0003-3067-1705; http://lattes.cnpq.br/8721885810953331This work connects Robotic Vision techniques with fundamentals of vibration mechanics to detect subtle moves from the equipment operation. In relation to the state-of-the-art, this is an innovative proposal whose object of study is small movements that are impossible for humans to observe with the naked eye. In this way, only a video camera used in the data acquisition, works like a set of around millions of vibration sensors spread over part of an industrial plant. Such a tool, when combined with Machine Learning algorithms, is capable of capturing specific frequencies with satisfactory levels of accuracy, not just at one point, but throughout the entire machine or system, from variations in the neighborhood of unitary figure elements, called pixels. The bibliography consulted in this research line shows that for many years, researchers have tried to solve problems associated with this process, such as defects, blurring, noise amplification and amplification of unwanted movements, achieving processed videos with relevant qualities. An evident extrapolation of such research lies in comparing the current state-of-the-art with conventional techniques for analyzing vibrations, in order to verify the veracity of the measurements carried out and compatibility with real applications. To achieve this, we used a set-up composed of a wheel balancing machine and unbalanced wheel-tire set. The machine-camera-algorithms setup was put on trial, and the experimental results endorse the significant degree of similarity between the current state-of-the-art and conventional technology, pointing to a trend towards supplementation or even replacement with the new methodology in some cases.Doctoral Thesis Trans-desidrocrotonina livre e encapsulada em sistema coloidal SNEDDS e avaliação farmacológica em modelos experimentais in vivo de diabetes mellitus tipo 1 e inflamação pulmonar(Universidade Federal do Rio Grande do Norte, 2025-01-30) Lima, Laís Rocha; Maciel, Maria Aparecida Medeiros; Arcanjo, Daniel Dias Rufino; https://orcid.org/0000-0001-7021-2744; http://lattes.cnpq.br/0537823822525075; http://lattes.cnpq.br/5360188002708095; http://lattes.cnpq.br/2665364140542291; Rizzo, Márcia dos Santos; https://orcid.org/0000-0003-4276-3113; http://lattes.cnpq.br/9049791992923270; Lima, Aurea Echevarria Aznar Neves; http://lattes.cnpq.br/1879077396134052; Medeiros, Caroline Addison Carvalho Xavier de; https://orcid.org/0000-0001-9224-2434; http://lattes.cnpq.br/2982271986555450; Lima, Waldenice de Alencar Morais; http://lattes.cnpq.br/2575667613995470Croton cajucara Benth is used in folk medicine in the Amazon region of Brazil to treat diabetes, diarrhea, fever, jaundice, hepatitis and malaria, among other indications. In this study, the diterpene 19-nor-clerodane trans-dehydrocrotonin (t-DCTN) isolated from the stem bark of C. cajucara was coencapsulated with the oil-resin from Copaifera reticulata Ducke (OCPR), into a self-nanoemulsifying drug delivery system (SNEDDS) to evaluate its efficacy in an experimental model of type 1 diabetes mellitus (T1D) as well in the pulmonary inflammation by lipopolysaccharide (LPS) from Escherichia coli O55:B5, on biochemical and oxidative stress parameters. For this purpose, hematological and biochemical parameters, oxidative stress markers and histopathological analysis were determined. Rattus norvegicus Wistar animals were kept under temperature conditions (24±1 ºC), light/dark cycle (12 h), with free access to food. After fasting (12 h), they received 45 mg kg-1 (i.p.) of Streptozotocin (STZ) diluted in citrate buffer pH 4.5. For the lung inflammation (PI) model, the animals received LPS instillation (4 mg kg-1) (i.t.). The tested samples were: solution of t-DCTN (15 mg kg-1) solubilized in DMSO, which is free t-DCTN (unencapsulated compound, t-DCTN-L), and the nanoproducts SNEDDS-OCPR-DCTN resulting from the co-encapsulation of oil OCPR (0,5%) and t-DCTN (1 mg). Diabetic animals were treated (v.o.) with SNEDDS-OCPR-DCTN administered at doses of 0.1 mL 100 g-1 and 0.05 mL 100 g-1, as well as with solution of t-DCTN-L administered in a single dose (0.1 mL 100 g-1), and also with the SNEDDS-OCPR carrier system (containing 0.5% OCPR) administered in a single dose (0.1 mL 100 g-1). In the T1DM model, the groups treated with SNEDDS-OCPR-DCTN, tDCTN-L, and SNEDDS-OCPR showed weight variation, increased water and food intake, as well as increased relative weight of the liver and kidneys. A reduction in glycemic indices was observed in the groups treated with SNEDDS-OCPR-DCTN (0.1 mL 100 g-1) and t-DCTN-L. Increased AST, ALT, CPK, CK-MB and urea were observed in all groups. The group treated with SNEDDS-OCPR-DCTN (0.1 mL 100 g-1) showed elevated albumin and reduced LDH. Leukocytosis and neutrophilia were observed in the group treated with t-DCTN-L, and lymphocytosis in the group treated with SNEDDSOCPR. Histopathological changes were evidenced in the liver tissue of the group treated with SNEDDSOCPR-DCTN (0.05 mL 100 g-1), with Kupffer cell hypertrophy, and for the group treated with t-DCTNL, slight microvacuolization was observed in some hepatocytes, mild passive congestion and moderate Kupffer cell hyperplasia. Regarding the renal tissue, changes were observed in the group treated with SNEDDS-OCPR-DCTN (0.1 mL 100 g-1) with moderate passive congestion, and mixed inflammatory infiltrate moderate. For SNEDDS-OCPR group it was observed mild nephrosis and moderate passive congestion. In the IP model, there was no variation in the weight of the animals, however, a lower leukocyte index was observed in the group treated with t-DCTN-L. Differential leukocyte analysis showed neutrophilia, as well as macrophages, more evident in the SNEDDS-OCPR-DCTN group (0.05 mL 100 g-1) and less evident in t-DCTN-L. Eosinophils were more evident in the goup treated with SNEDDS-OCPR-DCTN (0.1 mL 100 g-1) and lymphocytes were less evident in the goup treated with SNEDDS-OCPR-DCTN (0.05 mL 100 g-1). There was an elevation of cardiac markers in the goup treated with SNEDDS-OCPR-DCTN (0.1 mL 100 g-1), but it was less evident in the goup treated with t-DCTN-L. The group treated with SNEDDS-OCPR-DCTN (0.1 mL 100 g-1) showed reduced stress oxidative stress in cardiac (MDA) and pulmonary (CAT) tissue. Pulmonary histopathological analysis confirmed the occurrence of acute bronchiolitis, presence of neutrophilic and macrophagic inflammatory infiltrate, and a picture of serous interstitial pneumonia ranging from acute to moderate. The colloidal nanoproduct SNEDDS-OCPR-DCTN is suitable for oral ingestion, showed hypoglycemic results and pulmonary antioxidant activity. Based on these findings, it is being considered as an advanced colloidal system (SNEDDS) to optimize the bioavailability of t-DCTN co-encapsulated with copaiba oil (OCPR).Master Thesis Incorporação de algodão pré-consumo em compósitos poliméricos para aplicações têxteis(Universidade Federal do Rio Grande do Norte, 2025-05-21) Oliveira, Briseis Gonçalves de; Leite, Amanda Melissa Damião; Alves, Salete Martins; https://orcid.org/0000-0002-2659-4746; http://lattes.cnpq.br/8550161853747323; https://orcid.org/0000-0003-1597-4230; http://lattes.cnpq.br/3077817092155432; http://lattes.cnpq.br/6989063367552803; Libório, Maxwell Santana; https://orcid.org/0000-0003-1579-8775; http://lattes.cnpq.br/1840848773429843; Carvalho, Nayara Bezerra; http://lattes.cnpq.br/3746038535591614Considered one of the oldest and most profitable industries in the world, the textile industry is also one of the main generators of solid waste due to the environmentally unsustainable practices of fast fashion, which produce large volumes of textile waste. Among these wastes, cotton stands out as a widely used natural fiber. This research proposes the reuse of preconsumer cotton waste (RTCO) in the development of reinforced composites for fashion applications. Although there are studies on composites reinforced with lignocellulosic waste in other sectors, there is a lack of research on the use of RTCO in structural materials applied directly to clothing. Furthermore, the phase inversion technique, commonly used in membrane production, is rarely explored in composite manufacturing. This work seeks to fill this gap by proposing an innovative and sustainable reverse logistics and circular economy approach for the reuse of this waste in fashion. The composites are developed using a polysulfone (PSU) polymer matrix reinforced with RTCO through phase inversion techniques: precipitation by solvent evaporation and precipitation by immersion, to make structural trimmings, which are non-woven fabrics. The preparation began with 10 % PSU dissolved in 90 % N,ndimethylformamide DMF at room temperature (25°C) under stirring for 8 h. After this time, the processed textile waste was added in proportions of 6 %, 12 % and 18 % under continuous stirring for another hour. With the polymer solution spread in the mold, the formation of the PSU/RTCO-I materials occurred after its immersion in a non-solvent bath (distilled water). While for PSU/RTCO-E, the solvent was allowed to evaporate completely in a controlled manner. The SEM results indicated that the PSU/RTCO-I and PSU/RTCO-E composites presented good interfacial interaction between RTCO and PSU and good distribution of the fibers throughout. The PSU/RTCO-E composites presented greater porosity and hydrophilicity, with emphasis on the PSU/RTCO-E 12%, which presented greater elongation. The PSU/RTCOI composites demonstrated more homogeneous surfaces, lower wettability and lower water absorption. XRD analyses confirmed the preservation of type I cellulose and the intensification of peaks with increasing RTCO concentration. The best mechanical results were observed in PSU/RTCO-I 12 % and 18 % of load, therefore, this technique became the most suitable for application as structural trimmings. This research demonstrates the technical feasibility of composites with textile waste via phase inversion and expands the possibilities for waste reuse in the sector.Master Thesis Localização relativa de um andador robótico inteligente baseada em fusão de sinais por filtro de informação estendido(Universidade Federal do Rio Grande do Norte, 2025-10-02) Oliveira, Alberto Tavares de; Alsina, Pablo Javier; Silva, Bruno Marques Ferreira da; http://lattes.cnpq.br/7878437620254155; http://lattes.cnpq.br/3653597363789712; http://lattes.cnpq.br/3590924317362509; Medeiros, Adelardo Adelino Dantas de; http://lattes.cnpq.br/6787525856497063; Frizera Neto, Anselmo; https://orcid.org/0000-0002-0687-3967; http://lattes.cnpq.br/8928890008799265; Nogueira, Marcelo Borges; https://orcid.org/0000-0003-4747-0811; http://lattes.cnpq.br/5756014037071299; Laura, Tania Luna; http://lattes.cnpq.br/8142774545918274This dissertation presents the development and improvement of a prototype smart walker designed to assist people with reduced mobility during physical therapy rehabilitation. The device is based on the adaptation of a conventional walker, into which geared motors, an Arduino Mega microcontroller, a BeagleBone Blue microcomputer, and sensors such as incremental wheel encoders, an inertial measurement unit (IMU) composed of an accelerometer, gyroscope, and magnetometer, and an RGB-D Kinect camera were integrated. The main focus of the research is to obtain reliable estimates of the walker’s position and orientation through odometry, its calibration, and sensor fusion, using in this study a gyroscope and a magnetometer, although the approach is applicable to other complementary sensors. Based on motion and observation models, an Extended Information Filter (EIF) was implemented to efficiently integrate sensor data and reduce the effects of noise and uncertainty, providing a promising structure for future full-localization stages. Several experiments were conducted for the quantitative evaluation of the smart walker’s localization system, including: (i) uncalibrated encoder, (ii) calibrated encoder, and (iii) sensor fusion between encoder and gyroscope using the EIF. The results showed error reduction with the use of calibration and fusion over a path consisting of five laps around a 1.6-m-side square trajectory. The RMSE of the position decreased from 0.467 m to 0.073 m after calibration and subsequently to 0.040 m with the inclusion of sensory fusion with EIF, while the RMSE of the orientation decreased from 0.557 rad to 0.083 rad with calibration and finally to 0.036 rad with fusion. The adopted approach demonstrated that, even without external sources of absolute localization, it is possible to reliably estimate the walker’s trajectory and orientation, provided that the sensors are properly calibrated and effectively combined through the EIF. This strategy, which combines odometry with sensor fusion, constitutes an important step in developing a robust and reliable localization system applicable to real assistive scenarios.Doctoral Thesis Caracterização da microbiota de uma amostra de petróleo do pré-sal(Universidade Federal do Rio Grande do Norte, 2024-08-30) Freitas, Júlia Firme; Lima, Lucymara Fassarella Agnez; Lanza, Daniel Carlos Ferreira; Dalmolin, Rodrigo Juliani Siqueira; Ramos, Pablo Ivan; Abrahão, Jônatas SantosThe COVID-19 pandemic has highlighted the need to develop pathogen surveillance systems for the early detection and management of zoonotic diseases and pandemic threats. In the context of genomic surveillance, some studies have shown that wastewater treatment plants are critical points for the dissemination of genes, such as antimicrobial resistance genes (ARG), due to the high density and diversity of microbial communities and the presence of mobile genetic elements that facilitate horizontal gene transfer. Thus, sewage can reflect the population's health, and understanding the microbiota present in this waste can provide essential information for public health authorities. In this context, the present study aimed to evaluate the virosphere and bacteriosphere of wastewater from the city of Natal to identify the main pathogens present, antimicrobial resistance genes, and virulence factors and their correlations to obtain biomarkers for monitoring and provide subsidies for public policies. For this purpose, the study was conducted longitudinally, with weekly collections over a year (June 2021 to May 2022) at three of Natal's main water treatment plants. A flocculation protocol was used to concentrate viral particles, and viral DNA and RNA extraction was performed using commercial kits to obtain the virosphere. The water samples were also subjected to total DNA extraction to evaluate the microbial community. The weekly samples were combined by month and sequenced via the NextSeq 1000 platform. Several bioinformatics tools were used to access the taxonomic profile, assemble contigs and genomes, and identify resistance and virulence genes. Co-occurrence networks were obtained via Bray-Curtis dissimilarity analysis. The virosphere was stable throughout the year, mainly in viruses infecting microorganisms or plants. An alternation in the representation of viruses that infect animals was observed. Bacteriophages associated with genera Escherichia, Pseudomonas, and Caulobacter bacteria were among the most abundant. The Odontoglossum ringspot virus was identified as a potential biomarker of RNA viruses of agricultural importance. Among the DNA viruses that infect animals, members of the Poxviridae family were observed in the samples. Co-occurrence network analysis identified potential biomarkers such as the Volepox virus, Anatid herpesvirus 1, and Caviid herpesvirus 2. Among RNA viruses that affect animals, the genera Mamastrovirus, Rotavirus, and Norovirus were the most abundant pathogens. Additionally, members of the Coronaviridae family, including SARS-CoV-2, exhibited high centrality in the co-occurrence network, connecting even with unclassified viruses. Furthermore, we confirmed the presence of SARS-CoV-2 by qPCR. We observed an association between Coronaviridae sequences, rainfall, and the number of reported COVID-19 cases. The rarefaction curve showed that all samples reached species stability regarding the metagenome. Phylogenetic diversity analysis did not reveal significant differences in the richness and evenness of communities over the months, suggesting a similar taxonomic composition throughout the year. However, differences in the proportion of some taxa were observed between samples. The genus Aliarcobacter was the most predominant in all samples. Non-metric multidimensional scaling (NMDS) analysis revealed four distinct groups indicating seasonality. Canonical correlation analysis (CCA) allowed the evaluation of the relationship between the taxonomic profile and variations in rainfall and temperature, indicating the same groupings. However, these environmental variables individually did not have a statistically significant impact on microbial composition. Among the viral communities identified in the metagenome, the genus Paundivirus predominated in most samples. Between January and March, crAssphage was the predominant virus. 221 antimicrobial resistance genes (ARG) classified into 16 categories were identified, with multidrug resistance genes being the most abundant. The genes msrE, mphE, sulI, and tetC were more abundant, with significant enrichment in January, July, and December. Virulence factors genes (VFG) analysis revealed 213 genes, classified into 14 categories, with the most prevalent adhesion factors. The genes tapT and mrkC were the most abundant, with significant enrichment in January and April. Co-occurrence networks revealed that ARG showed significant co-occurrence with viruses, while few ARG co-occurred with VFG. CrAssphage was the only virus that co-occurred with VFG. NMDS analyses confirmed the direct proportional relationship between ARG and viruses and the inversely proportional relationship between ARG and VFG. A total of 95 MAG (metagenomeassembled genomes) were obtained, among which 69 resistance genes, mainly related to multidrug resistance, were identified. Additionally, 33 MAG are those with the highest number of ARG. The most recurrent gene was the adeF gene (associated with tetracycline and fluoroquinolone resistance). Of the 33 MAG obtained, the genera Tolumonas and Rivicola were the most represented. In conclusion, this study's findings highlight the microbial community's complexity and stability in wastewater environments and the presence and dynamics of antimicrobial resistance genes and virulence factors over time. These advances will significantly contribute to our preparedness and response to future threats. Additionally, our study contributes to the knowledge of microbial dynamics, offering insights that can contribute to the direction of future public health policies and interventions and identifying potential monitoring biomarkers.Master Thesis Uso de técnicas de detecção automatizada de erros em datasets supervisionados para correção de rótulos oriundos de pipelines de aprendizado fracamente supervisionado(Universidade Federal do Rio Grande do Norte, 2025-09-22) Leal, Nalbert Gabriel Melo; Araújo, Daniel Sabino Amorim de; https://orcid.org/0000-0001-5572-0505; http://lattes.cnpq.br/4744754780165354; https://orcid.org/0000-0003-3178-7793; http://lattes.cnpq.br/2378995337056252; Santos, Araken de Medeiros; http://lattes.cnpq.br/8059198436766378; Menezes Neto, Elias Jacob de; https://orcid.org/0000-0002-1153-8899; http://lattes.cnpq.br/9152955193794784; Xavier Júnior, João Carlos; http://lattes.cnpq.br/5088238300241110The high cost of data labeling for training machine learning models has motivated the development of weakly supervised learning (WSL), however, this approach frequently introduces label noise, affecting model performance. Among WSL techniques, data programming (DP) stands out by utilizing noisy sources (such as heuristics and pre-trained models) to perform automated, low-cost data labeling, resulting in potentially inaccurate labels that impact the end-model’s performance. The objective of this work is to evaluate the impact of automatic label error detection techniques when integrated into the data programming pipeline. An experiment was conducted to identify the impact of label error detection on the performance and cost of the DP pipeline. The impact of each technique on performance was evaluated using the Matthews correlation coefficient (MCC) metric (collected from the evaluation of the pipeline produced end-model), and the cost was measured by the pipeline’s execution time. The results demonstrate that, in most cases, the application of detection techniques significantly degraded the end-model performance. Only 4% of the pipelines implementing detection showed a statistically significant performance improvement superior to the experiment baselines. These improvements, when they occurred, were isolated and accompanied by a high computational cost. It is concluded that DP pipelines without detection techniques demonstrated a better performance-cost trade-off, proving to be a more efficient approach.Master Thesis Ferramenta computacional destinada a análise matemática de sistemas de geração de energia elétrica unifonte e híbridos(2025-07-21) Alves, Dennys Lopes; Pinheiro, Ricardo Ferreira; http://lattes.cnpq.br/4890839733220743; https://orcid.org/0000-0002-6275-5009; http://lattes.cnpq.br/1862473549371846; Oliveira, José Tavares de; http://lattes.cnpq.br/0617813879678041; Silva, Neilton Fidelis da; http://lattes.cnpq.br/4771027087761821; Lacerda, Estefane George Macedo de; http://lattes.cnpq.br/1763651349773729The growing demand for sustainable solutions and the use of renewable energy highlight the importance of training qualified professionals in these fields, a process that can be strengthened through specialized software. In this context, the main objective of this research is to develop a computational tool designed to systematize the mathematical analysis of electric power generation systems, both single-source and hybrid. Regarding methodological classification, this is an applied research study. The software was developed using the Python 3 programming language, in combination with the Flet framework. Among the resources provided, a key feature is its ability to process and analyze numerical data related to the aforementioned systems, generating tables, charts, and visual representations derived from the mathematical processing of information concerning solar irradiance, wind speed, and other related meteorological and energy variables. The results are made available through a graphical user interface, compatible with both desktop and web platforms. The main functionalities of the tool include the calculation of parameters related to wind power generation, such as power and power density available in the wind, turbine output power, and rotor swept area, and photovoltaic generation, such as energy produced by the module, power supplied by the modules, fill factor, inverter sizing factor, and efficiency, among others. Furthermore, the tool incorporates optimization techniques, specifically genetic algorithms and particle swarm optimization, applied to the configuration of hybrid power generation systems.