Principais Coleções do Repositório
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Eficiência relativa do gasto público com obras públicas ligadas à educação: uma aplicação da análise envoltória de dados (DEA)
(Universidade Federal de Lavras, 2026-03-17) Oliveira, Rogério Condé de; Vaz, Janderson Martins; Nascimento, Érica Suélen do; Paiva, Alysson Ribeiro
Considering that efficiency stems from the results obtained through efforts made, and in the case of public spending, for managers to optimize the delivery of public goods to society, they must deliver results with the minimum resources available for the implementation of a given project. Given the importance of delivering efficient public services, this study aimed to investigate the levels of relative efficiency in the application of public resources in federal government works related to education. As a guiding principle directing the activities that comprise the core of this study, a literature review was conducted on the concepts of public spending and its functions, an explanation of the concept of efficiency applied to public management, serving as the theoretical basis for this research. In addition, the concept of public works and their current legislation were defined. The nature of this study is basic or fundamental research, through a descriptive and exploratory study, with a quantitative approach. To this end, regarding the procedures, a bibliographic research and documentary analysis of secondary data were carried out. The data were available on the Obrasgov Panel. For the documentary analysis of secondary data, the efficiency of spending on public works financed with federal resources linked to education, registered between 2021 and 2024, was studied using Data Envelopment Analysis (DEA), VRS model, oriented towards outputs. The DMUs (Detailed Management Units) were each project of the executing agencies. The following input variables were used for the production frontier: committed values of the works, values paid for the works, planned completion time, and actual completion time. The output variable was the Percentage of Physical Execution of the works. The results showed that, in general, the federal resources applied/spent on public works linked to education fulfilled the distributive function of the State, with works being carried out throughout the territory promoting overall development. The scenario shows that the institutions that conducted the largest number of works are generally those with the greatest capacity to complete them. Of the works analyzed, 61% were within the efficiency frontier, reaching maximum relative efficiency. Thirteen percent of the projects were between the overall average efficiency and the frontier, remaining close to the frontier but with potential for improvement. Finally, 26% were below average, requiring process improvements. The main benchmark was DMU 123, conducted by UFLA, whose objective, according to the Painel Obrasgov website, was "the contracting of a specialized company for the construction of a new Central Warehouse to meet the demands of the Directorate of Materials and Assets of the Federal University of Lavras – UFLA". Thus, it is possible to conclude that there are several efficient projects that serve as a benchmark for the management of public resources. Therefore, this study will provide the Public Administration with subsidies that will assist in the efficient management of resources, contributing to improved transparency of spending and accountability of public resources. Keywords: Efficiency, Data Envelopment Analysis, Public Works, DEA.
Nanopartículas de lignina kraft: síntese, caracterização e aplicação em têxteis funcionais.
(Universidade Federal de Lavras, 2026-02-06) Ferreira, Cecilia Baldoino; Bianchi, Maria Lucia; Soares, Filippe Elias de Freitas; Freitas, Victor Augusto Araújo de; Ballotin, Fabiane Carvalho; Magalhães, Fabiano; Soares, Filippe Elias de Freitas
Kraft lignin, one of the main by-products of the pulp and paper industry, has attracted increasing interest as a renewable feedstock for the development of sustainable materials, in line with the principles of the circular economy and the Sustainable Development Goals. In this context, this thesis aimed to investigate the production, characterization, and application of kraft lignin nanoparticles (KLNP) as a strategy to valorize this macromolecule in functional materials. KLNP were produced using the solvent exchange method and characterized in terms of chemical composition, structural and thermal properties, and colloidal stability through spectroscopic, thermal, and microscopic techniques. The results demonstrated the formation of nanoparticles with nanometric dimensions, homogeneous size distribution, and good colloidal stability. The environmental safety of the KLNP was assessed through phytotoxicity assays using arugula seeds (Eruca sativa), with no significant adverse effects observed in the evaluated parameters. In a subsequent stage, the nanoparticles were applied to the functionalization of cotton fabrics to impart functional properties. The incorporation of KLNP led to a significant increase in the ultraviolet protection factor (UPF), as well as effective antibacterial activity against Escherichia coli and Staphylococcus aureus. A homogeneous deposition of nanoparticles on the fabric fibers and enhanced material retention after washing cycles were observed in the presence of a fixing agent. Overall, the results highlight the potential of KLNP as a sustainable and versatile alternative for the development of functional materials and advanced technological textiles. Keywords: sustainable materials; ultraviolet protection; antimicrobial activity; circular economy
Análise bayesiana de modelos não lineares no estudo da cinética de herbicidas
(Universidade Federal de Lavras, 2026-02-26) Azarias, Edilene Cristina Pedroso; Muniz, Joel Augusto; Silva, Edilson Marcelino; Pala, Luiz Otávio de Oliveira; Fernandes, Felipe Augusto; Silveira, Silvio de Castro; Silva, Edilson Marcelino
The agricultural sector requires attention due to its susceptibility to various interfering factors, among which weeds stand out. These plants compete with crops for water, nutrients, light, and space, in addition to serving as hosts for pathogens. One of the methods used for their control is the application of herbicides, whose dose–response relationship can be described using nonlinear models. In this thesis, three articles were developed with the objective of analyzing the dose–response relationship of the herbicides trifloxysulfuron-sodium and S-metolachlor applied to weeds of the genus Amaranthus, evaluating the susceptibility of these plants to each herbicide, and identifying the most appropriate model to represent the data. To this end, the parameters of nonlinear models were estimated from a Bayesian inference perspective, adopting maximum entropy prior distributions. To obtain the posterior distributions, the Markov Chain Monte Carlo (MCMC) method was employed, using the Gibbs sampling and Metropolis– Hastings algorithms. Model selection was performed based on the Deviance Information Criterion (DIC), as well as the Conditional Predictive Ordinate (CPO) and the Log Pseudo- Marginal Likelihood (LPML) criteria. In the first article, the nonlinear models of Groot, Weibull, and logistic were fitted. In the second article, it was considered that the assumption of normality does not always reflect the actual behavior of the data, which often lack symmetry. Thus, the Streibig logistic model was fitted under four specifications for the likelihood function — normal, Weibull, gamma, and exponential. In the third article, the Streibig logistic model was applied again, comparing the likelihood functions under the normal and Student’s t distributions. The results of this thesis indicate that the Bayesian approach, combined with maximum entropy prior distributions and different specifications for the data distribution, constitutes a consistent methodological alternative for the analysis of herbicide dose–response data, expanding the possibilities for applying this approach in the field. The comparison among the models revealed differences in the susceptibility of species of the genus Amaranthus. Furthermore, the consideration of distributions alternative to the normal proved useful for data modeling. Keywords: dose–response; Bayesian inference; maximum entropy; MCMC; likelihood.
Explorando curvas principais com a meta-heurística lobo cinzento para a classificação de dados sintéticos e de desempenho acadêmico dos estudantes no ENEM
(Universidade Federal de Lavras, 2026-03-13) Macêdo, Bruno da Silva; Barbosa, Bruno Henrique Groenner; Ferreira, Danton Diego; Lacerda, Wilian Soares; Vitor, Giovani Bernardes
Education is fundamental to a country’s development, and in Brazil, the need for improvements that can be driven by the use of Information Technology stands out. Through the National High School Exam (ENEM), the country’s largest educational exam and one of the main gateways to higher education, it is possible to assess aspects of the quality of education by constructing educational indicators based on student performance on the exam. To predict student performance on the exam based on variables present in the ENEM database, such as socioeconomic information, school characteristics, and participation data, Machine Learning (ML) techniques have been increasingly used in this context, allowing the identification of performance patterns, possible irregularities, and the customization of pedagogical strategies. The problem of predicting student performance on the ENEM has been investigated by several authors, but many have not explored other ML techniques, such as Principal Curves (PC). In recent years, the PC method has been applied in various areas, demonstrating potential in classification problems. In this context, this research aims to apply the K-segment PC extraction method to classify the academic performance of students who took the 2023 ENEM exam, considering class 0 for students who did not present the expected performance and class 1 for those with good performance on the exam, and to evaluate it on synthetic bases. Furthermore, the GreyWolf Optimizer (GWO) hyperparameter optimization method was applied to automatically determine the hyperparameter values for the K-segment PC method, a task that can be determined manually but is complex. The methodology comprises the steps of preparation, dimensionality reduction, class balancing, and transformation of the input variables from the 2023 ENEM database, including socioeconomic variables, school characteristics, and participant information, in addition to applying the GWO technique to optimize the model hyperparameters. The classification methods were evaluated using metrics such as accuracy, F1-Score, precision, recall, and Kappa coefficient. In experiments conducted on synthetic datasets, the method showed good performance in compact, elongated, spherical, and spiral datasets, with metrics greater than 0.9700. In experiments conducted using the ENEM 2023 database, the PC approach showed competitive results compared to the literature-referenced methods (Extreme Learning Machine, Naive Bayes, and Random Forest). Among the evaluated approaches, the worst result was observed with t-SNE, with an accuracy and recall of 0.7310, precision of 0.7314, F1-Score of 0.7309, and a Kappa coefficient of 0.4621 in the test set. While the Select K-Best method obtained the best results, with an accuracy and recall of 0.7603, precision of 0.7612, F1-Score of 0.7601, and a Kappa coefficient of 0.5206 in the test set, it outperformed the Naive Bayes method in this configuration. These results indicate that the proposed approach is promising for classifying academic performance, especially when combined with appropriate dimensionality reduction and optimization techniques. Keywords: Educational Data; Academic Performance; ENEM; Machine Learning; Pattern Recognition; Principal Curves; K-segments.
Ecologia da maturação do Queijo Minas Artesanal produzido na microrregião do Campo das Vertentes e sua influência na viabilidade do Staphylococcus aureus
(Universidade Federal de Lavras, 2025-10-31) Luz, Luiz Claudio Pepe; Piccoli, Roberta Hilsdorf Piccoli; Boari, Cleube Andrade; Silva, Monique Suela; Pereira, Alcilene de Abreu; Gonçalves, Michelli Carlota
Artisanal Mynas cheese is very well-known and highly valued, not only in Mynas Gerais but also in other Brazilian states. Efforts have been made to reduce the maturation time stipulated in the legislation. Although there have been years of research involving its manufacturing process, studies on the viability of pathogenic microorganisms and the microbial dynamics of maturation still need to be conducted, defining the best maturation time for these cheeses with the aim of ensuring safety and the sensory quality, highly appreciated, of the product to the consumer. Thus, the objective of this project was to evaluate the viability time of Staphylococcus aureus in cheese as well as to study the dynamics of the fermentation process involving lactic acid bacteria (LAB). Ten freshly made cheeses were collected from each of the two cheese factories in the Campo das Vertentes microregion for the evaluation of S. aureus and LAB during their maturation, transported to the laboratory, where they were matured, with 5 cheeses from the control group and 5 cheeses inoculated with S. aureus producing enterotoxin type A (108 CFU/g of S. aureus), analyzed at five maturation times (0, 7, 14, 21, and 28 days) using microbiological and physicochemical methods, and the metabolome profile was also monitored. The detection of enterotoxins was carried out, and the metataxonomic study was conducted on the control group cheeses based on the sequencing of the V3-V4 region of the 16S rRNA. The physicochemical analyzes showed a significant reduction in moisture in both cheeses from the two studied cheese factories. The cheeses from São João del Rei (SJDR) tended to have a higher initial moisture content, around 48%, compared to the cheeses from Coronel Xavier Chaves (CXC) at 42.8%. The pH of the CXC cheeses started at 6.67, reduced to 6.08, and gradually increased to 6.45, with the initial pH of the control cheese being significantly higher than that of the Inoculated cheese. Time had a highly significant effect on the count of S. aureus, which decreased over the maturation period, at time 0, initial counts were 7,40 e 7,32 log CFU/g for cheeses from the CXC and SJRD inoculated groups,respectively, and at 28 days of maturation, counts were 1,27 and 1,64. The average values of LAB counts also showed a significant decline over the maturation time for all cheese factories and groups, with a marked predominance of the genus Lactococcus, which represented approximately 95% of the sequences attributed to the metataxonomic profile. The PCoA (Bray-Curtis) indicated a clear separation between the bacterial communities, suggesting a distinct microbial composition from the other samples, indicating that the bacterial community of the SJDR cheese factory at time 7 underwent significant structural changes during maturation, compared to the other samples. The results showing the absence of Salmonella spp, Escherichia coli, and Listeria monocytogenes found in the evaluation of microbiological quality throughout the entire maturation period attested to the hygienic quality of the cheese factories in the Campo das Vertentes microregion. Keywords: ripening; lactic bacteria; microbial ecology; foodborne diseases; staphylococcal enterotoxin.