Federal University of Itajubá

This work aims to present a robust bi-objective optimization analysis in the end milling of duplex stainless steel UNS S32205. The study included design variables (cutting speed, feed per tooth, cutting depth, work penetration) and noise variables (flank wear, fluid flow, tool overhang length) to achieve results that are closer to the reality of the process. Response surface methodology and normal boundary intersection were applied to evaluate Ra roughness and material removal rate (MRR). The results showed that work penetration, cutting depth, and tool overhang length have no significant impact on Ra. Regarding MRR, only tool flank wear showed no significance for the response. Bi-objective optimization was performed, and a Pareto’s front was found with several optimal configurations, achieving up to a 50% improvement in the studied responses. This paper presents something unprecedented in the literature to date by analyzing the noise of the end milling process using UNS S32205.

In this paper we consider parabolic quasilinear nonautonomous evolution problems with variable exponents and homogeneous Neumann boundary conditions. We deduce a semilinear limit problem associated with this quasilinear problem and we prove results on the existence of solutions, in some sense, for both problems. We prove existence of global strong solutions and integral solutions, as well we prove the existence of the pullback attractors. Additionally, we prove results on asymptotic dynamics for these models, that is, we obtain results on continuity of the flow and upper semicontinuity of pullback attractors in the sense of Hausdorff semidistance.

The aim of the present study is to analyze working conditions in manual cotton harvesting on small properties, in Catuti region, Minas Gerais State, Brazil by analyzing collectors’ activity in irrigated and rainfed crops, as well as labor relationships between small farmers and pickers. The study followed a qualitative methodological approach based on field footages and systematic observations. Semi-structured interviews were carried out with cotton collectors, public authorities and with cotton farmers' cooperative representatives in Catuti-MG, as well as focus groups with cotton farmers. Labor relationships between cotton farmers and collectors are linked to the region’s socioeconomic aspects. Payment based on production technique is the basis for collectors' wage calculations. This technique, whether based on rainfed or irrigation, has straight influence on work processes and on pickers’ remuneration. These pickers develop strategies to increase their productivity and, consequently, their salary, as well as to protect their health.

The Brazilian steel sector, inserted in a scenario of crisis and competitiveness, is going through a moment when optimization procedures in the production process are well regarded. However, issues related to sustainability, such as controls on energy consumption and carbon dioxide ($\hbox {CO}_{2}$) emissions, are also fundamental to be considered as analysis variables since the demand for actions that preserve the ecosystem and aim at the conscientious consumption of resources has increased. An important technical tool that can help to address these factors together is Discrete Event Simulation (DES), a well-known computational simulation method to support decision-making. Still hardly used to explore issues related to sustainability, a DES model allows the representation of an existing system in a virtual space, providing an ideal environment for conducting experiments, without the need to make changes in practice. Thus, this paper applied the DES method in a steelmaker located in Brazil, in order to simulate the impact, after a possible change in the system, on the behavior of three analysis variables: production volume, electrical energy consumption, and $\hbox {CO}_{2}$ emission. The use of a discrete event simulation methodology and JaamSim®open-source software, provided a low-cost solution and allowed the simulation of results in the virtual scenario, after modification. As a result, in the alternative scenario, daily production increased by almost 87% and the total electrical energy consumption decreased by 38.2%, a total saving of 100,440.6 kilowatts per hour, which is equivalent to 71.2 metric tons of avoided $\hbox {CO}_{2}$ emission.

There has been a steep increase in investment in more affordable approaches to desalinated seawater using renewable energy sources. This paper proposes desalinating seawater with a pump and a motor-generator installed inside wind turbine nacelles, to desalinate seawater with reverse osmosis membranes without electricity. The technology was named Wind Desalination and Power (WDP), as the wind turbine can pump seawater and generate electricity. This study presents a comprehensive analysis and rationale behind the design, estimates its economic viability, and assesses the global potential of the proposed technology. Results demonstrate that the cost for seawater desalination with WDP is 0.64 USD/m³, which is 20% cheaper than conventional wind desalination. The paper shows that WDP has the potential to enhance water resilience in arid regions while at the same time contributing to grid stability and renewable energy integration.

Scanning microwave impedance microscopy (sMIM) has become a powerful tool for nanoscale characterization, utilizing microwave frequencies to probe the material properties of diverse systems with remarkable spatial resolution. This review offers an in-depth analysis of the foundational principles, technological advancements, and broad applications of sMIM. By harnessing near-field microwave interactions between a sharp metallic probe and the sample, sMIM enables simultaneous acquisition of both real (resistive) and imaginary (capacitive) components of the reflected signal, providing detailed insights into the local permittivity and conductivity of materials at the nanoscale. We address critical challenges, including impedance matching, probe–sample interactions, and the influence of environmental factors such as surface water layers and meniscus formation on resolution and contrast. Recent advancements in finite element modeling and the application of lumped-element circuit models have further enhanced the precision of signal interpretation, enabling more accurate analysis of complex systems. This review highlights sMIM’s wide-ranging applications, from material science and semiconductor diagnostics to biological systems, showcasing its ability to perform non-destructive, high-resolution imaging down to the single-digit nanometer scale. These capabilities position sMIM as an indispensable tool for advancing future innovations in nanotechnology and related fields.

Labyrinth seals (LSs) in turbomachinery are used to minimize leaks. This study presents an experimental setup designed to test and validate LS designs. The test bench (TB) described in this paper can evaluate different LS designs obtained through various methods to find better solutions to mitigate greenhouse gas (GHG) emissions. Prototypes with conventional geometry, such as straight-through and interlaced LSs, are initially implemented and tested. A measurement procedure and experimental methodology for collecting leakage data are defined. The experimental methodology includes a measurement TB that supports a pressure difference of up to 5 bar. The leakage rate is measured in g/s using instrumentation that corrects the mass flow rate based on pressure and temperature measurements. The experimental results are compared to the Computational Fluid Dynamics results. Thus, the setup given in this article is a new and versatile TB setup focused on leakage measurement, which allows being used for analyzing various types of LS (with or without rotor structures) and is effective for evaluating the performance of LSs, helping the development of new LS geometries that can reduce GHG emissions.

The displacement of the university from an industrial society to a post-industrial society and its new roles, require research into new organizational forms and new proposals to restructure higher education institutions so that the university can contribute to the economic and social growth of the nation. This paper proposes a reflection on the entrepreneurial university in the context of the Brazilian system for the evaluation of higher education (SINAES) in order to analyze the application of the metamodel of the entrepreneurial university as an institutional assessment tool for the reaccreditation of institutions. This paper reviews the literature on the entrepreneurial university and on the metamodel of the entrepreneurial university, in particular. It then presents a proposal for the application of the metamodel dimensions of the entrepreneurial university in the external and internal evaluation processes of higher education institutions.The results obtained in the present reflection are innovative because linking the entrepreneurial university dimensions with the dimensions of the external evaluation tool provide support in the identification and evaluation of programs, projects and actions in the field of entrepreneurship, enabling the identification of elements of entrepreneurial universities in Brazil.

The aim of this study is to develop a new tool to foster entrepreneurship, creativity and innovation in cities. The tool consists of transformative dimensions, able to synthesize, assemble and embrace the notions of entrepreneurial city, creative and innovative. Then it seeks to illustrate the operation of the city model proposed taking into account 51 Cities Master Plans in Brazil. The study is exploratory and adopts a reflexive methodology. The main innovative result is the entrepreneurial city model tool which meets five essential transformative dimensions: value proposition, customers, value configurations, strategic partnerships and revenue model. The entrepreneurial city model tool proposed aims to increase the dynamics understanding of the procedural flow in the city context, from the entrepreneurial activity perspective. This process flows dynamics can be expressed in the city, for example, in the planning, implementation and actions monitoring, programs, projects and public policies directed to notions of entrepreneurial city, creative and innovative. The innovative results of this research have several practical implications, among which are: (1) public management in the city; (2) public policy makers; (3) researchers and scholars; (4) human resource professionals.

Morphing wings represent a major advancement in aerospace engineering, capable of dynamically altering shape during flight to enhance performance and reduce fuel consumption. This study introduces a novel methodology for controlling morphing wings using Macro Fiber Composite (MFC) actuators configured in parallel at the trailing edge. By electrically stimulating the MFC actuators, we achieve continuous wing shape modulation, with a maximum deflection of 17 mm and a resultant force of 6.55 N. To optimize morphing control, we developed an Artificial Neural Network (ANN) based controller, trained on data from a detailed Finite Element (FE) model using Ansys Parametric Design Language (APDL). The ANN controller accurately predicts the required control voltages to achieve desired wing shapes, enhancing maneuverability and reducing wing weight. Our results demonstrate the feasibility of this approach, with the ANN controller achieving an accuracy of 89.9%. This research underscores the practical advantages of ANN-based controllers in morphing wing applications, offering a promising alternative to traditional wing control methods and contributing to improved aircraft performance and environmental sustainability.

Considering that many vitamins can be easily degraded, developing ways to avoid the degradation process and the availability of these vitamins is essential. Thus, this study aims to use layered zinc hydroxysalt (LHS) as a slow-release option that provides thermal stability for administering vitamins. Two compounds were synthesized, LHS/B3-1, which presented basal spacing of 9.63 Å, corresponding to a vitamin monolayer, and LHS/B3-2, which is a mixture of two phases, one with a basal distance of 9.92 Å and a secondary one with a basal distance of 15.43 Å, corresponding to a vitamin monolayer and bilayer, respectively. After intercalation, an increase of 175 °C in the thermal stability of vitamin B3 was observed, occasioned by the interaction with the layers of the matrix. Vitamin B3 was slowly released from LHS/B3-1 and LHS/B3-2, and 68.8% and 56.9% were released in 44 h, respectively. The release of the vitamin from LHS/B3-1 and LHS/B3-2 was divided into three stages. The results suggest that surface diffusion is initially involved in the release of B3 from intercalation products, where the anion dissociates from the surface of LHSs, followed by B3 release controlled by a diffusion process via intra-particle or surface diffusion. Graphical Abstract

The percentages of losses in water supply systems are alarming. Hydraulic modeling and simulation are widely used resources for evaluating network behavior and identifying better operating conditions and configurations. To this end, EPANET is a widely used software because of its open-source code and the possibility of programming in different languages. In this study, a methodology was proposed where two theoretical networks with different sizes and configurations were developed, but with pressures higher than the recommended range in the standard. The aim of the study was to apply and compare two algorithms developed in Python and coupled with EPANET: a random search and one involving evolutionary theory—genetic algorithms, aiming at aligning the network pressures within the range established in the standard. Nine scenarios of the GA were tested, varying recombination and mutation parameters. Both algorithms were able to adjust the network pressures to Brazilian standards, reducing the percentages of losses with relatively similar results. Among the studied scenarios, a greater influence of the mutation rate was observed in relation to the recombination rate, with lower losses in those with a mutation rate of 5%. Keywords: hydraulic simulation; water distribution networks; genetic algorithms

Welding is a widely used manufacturing process for permanently joining components. In fusion welding processes, high temperatures generate residual stresses (RS), which make the welded and thermally affected regions susceptible to failure. These stresses can superimpose on externally applied loads, making it crucial to determine RS to assess the forces borne by the component and prevent failures. Welding parameters, including the shielding gas flow rate (GFR), significantly influence the magnitude of RS. However, GFR has received limited attention in the literature regarding its overall impact. This study investigated the effect of GFR on RS in AWS ER70S-6 weld beads deposited on DIN EN 10025–2 S275JR steel plates using gas metal arc welding (GMAW) and critically refracted longitudinal waves (LCR) for stress measurement. Weld beads were deposited with GFR values of 12, 15, and 20 l/min, while other parameters were kept constant. Longitudinal RS distribution profiles were obtained for each specimen. The results showed a significant impact of GFR on the RS profile, with the highest RS values observed at 15 l/min. These findings can aid the industry in selecting welding parameters that minimize RS, thus improving the prediction of structural integrity and failure of welded components. The study emphasizes the importance of evaluating the influence of welding parameters on RS in welded joints and shows the viability of employing LCR technique to control the stresses in welds.

The large elderly population has naturally led to an increase in the number of orthopedic surgeries, such as hip replacement, highlighting the need to develop machining manufacturing technologies that meet the quality requirements of the prosthesis. ABNT 316L austenitic stainless steel is used in the manufacture of joint prostheses and, although it is considered a material with low machinability, it is an economical alternative to the application of other biomaterials such as titanium alloys and ceramics. This work presents the optimization of the turning of femoral heads for a total hip prosthesis. To this end, it has used the response surface methodology and the robust parameters design to model and optimize the main process responses: roughness and sphericity. The experiments were carried out based on a combined array considering three control variables: cutting speed, feed rate and depth of cut, and two noise variables: fixed length of the workpiece and cutting fluid flow. As quality characteristics, the surface finish and the sphericity of the femoral heads were analyzed using the mean roughness and total circularity deviation, respectively. Robust optimization was performed by combining mean square error and normal boundary intersection techniques. Therefore, the formulation of the optimization problem was to minimize the roughness in the turning process of ABNT 316L, limiting the sphericity to 10 µm. From the results obtained in this investigation, it was possible to generate components with surface and shape quality simultaneously. The noise variables had significant effects on the evaluated responses leading to their robustness.

The return to face-to-face classes in 2022, following the COVID-19 pandemic, has posed a challenge for educational institutions in formulating plans to reduce the impact on academic life. An alternative was therefore sought by adopting information systems to develop strategies for managing educational data. The aim of the research was to create a mini BI (Business Intelligence) to help with class scheduling at the Federal Institute of Southern Minas Gerais - Muzambinho campus. The methodology used was the CRISP-DM (Cross Industry Standard Process for Data Mining), made up of six stages carried out in two cycles, enabling it to provide managers with tables and dashboards for scheduling classes with the same number of teachers, providing coordinators with data on the number of students and the type of class. The solution contributed to faster and more assertive decision-making when it came to offering courses in 2022. In addition, the study offers a replicable model that can be applied by other institutions facing similar challenges, optimizing class scheduling and improving efficiency and academic quality.

Cavitation is a phenomenon that reduces the useful life of hydraulic machines, taking place in function of the variation of the pressure gradient at a constant temperature. In hydraulic turbines, cavitation occurs when the turbine operates beyond nominal conditions, generating abnormal vibrations, erosion to blades and other key components, thus resulting in stoppage for maintenance. This article proposes a cavitation monitoring system based on the analysis of vibration spectra via two Machine Learning (ML) models: a Multilayer Perceptron (MLP) neural network and a Radial Basis Function (RBF) neural network. Drawing upon vibration analysis and pressure coefficient parameter standards, such models are capable of identifying the vibratory state of a given machine, distinguishing its cavitating and non-cavitating states. Moreover, it is proposed that these models may estimate real conditions for turbine functioning, thus enabling planning for the most opportune moment to carry out turbine maintenance. Both ML models were evaluated through a series of experiments with data from a Francis turbine installed in Brazil, where vibration spectra and flow pressure coefficients were monitored; they identified cavitating and non-cavitating states with precision levels between 95% and 100%, thus demonstrating satisfactory performance and serving as an important step in the development of a system for monitoring hydropowers. Keywords: Machine Learning Model, Multilayer Perceptron (MLP); Radial Basis Function (RBF); Predictive maintenance; Cavitation; Hydraulic turbines; Vibration analysis

Studies of systematic review of the literature have grown as a strategy for understanding certain epistemological fields, considering that these follow specific protocols of analysis in order to compile approaches to various documental corpus. In these terms, this article addresses specifically a systematic review, aiming to know the state of the art about Helix (Triple, Quadruple, Quintuple) and compile theoretical and methodological perspectives for data analysis via statistical language and artificial intelligence. The procedural scope involved compiling the studies and providing a real contribution to the scientific field, operationalizing a systematic review of a quantitative nature, by means of meta-analysis via R software-with pre-programmed operations in Bibliometrix, which allows multiple evaluations in different perspectives in analogy to ChatGPT search results. A total of 21,180 articles were found, distributed in the Web of Science (WOS) and Scopus databases, and meeting exclusion criteria, 1,545 articles were considered for analysis. The results indicate an open and emerging field for studies on Innovation Helices. Furthermore, the results show that at certain stages of the systematic literature review, sometimes R Software performed better, sometimes ChatGPT. Therefore, the use of artificial intelligence can be a complementary and effective way to construct literature reviews. However, the researcher’s knowledge is essential to ensure the quality and scientific rigor of the research. The contribution of the research is threefold. First, it highlights the state of the art of Innovation Helices. Second, it presents the potential of artificial intelligence in literature reviews. Third, it presents a roadmap for data analysis using R software and ChatGPT to guide research and systematic literature reviews in different modalities.