Carlos Henrique Valério de Moraes

This study investigates the impact of different types of personal protective equipment (PPE), specifically Powered Air-Purifying Respirators (PAPRs) and traditional N95 masks with face shields, on the physiological stress responses of healthcare workers (HWs) during the COVID-19 pandemic. Utilizing an interventional randomized crossover trial design, the research encompasses a simulation phase with ten participants followed by field testing involving thirty frontline healthcare professionals in a tertiary-care hospital setting. Heart rate (HR) and movement data were collected through smartwatches, while trained observers recorded the duration and nature of various activities undertaken during simulations. Data analysis employed statistical techniques, including Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE), to explore potential correlations between PPE type, HR, and movement. Clustering validation measures such as the Calinski–Harabasz, Davies–Bouldin, and Silhouette scores were applied to evaluate the difference between each type of PPE. The results indicated no significant differentiation in HR responses between the two PPE types. However, because HR may lack the sensitivity to fully capture variations in cognitive load or stress, these findings should be interpreted as an exploratory baseline. Additionally, no clear distinctions were observed regarding individual user responses or the activities performed, even when considering movement data. Although the findings imply non-inferiority of the examined PPE, future research including heart rate variability as a more comprehensive indicator of stress would be informative. This research contributes valuable insights into PPE selection and its implications for healthcare worker performance and well-being in high-stress environments, ultimately aiming to inform guidelines and training programs to enhance healthcare delivery during infectious disease outbreaks.

This chapter presents a brief overview of the most recent optimization algorithms. The chapter starts by showing the difference between classical methods and modern methods. And then, some special types of problems solved by optimization algorithms are presented, including some open problems. After that, the initial modern optimization algorithms are listed; the recent optimization algorithms are shown with their main features. This section starts with the algorithms inspired by quantum mechanics and their solutions to intelligent systems. Also, this section shows other modern approaches merging intelligent techniques, neuroscience, data science, and machine learning.

Intelligent diagnostic system for power transformers.

The Gravitational Search Algorithm (GSA) is an optimization algorithm based on Newton's laws of gravity and dynamics. Introduced in 2009, the GSA already has several versions and applications. However, its performance depends on the values of its parameters, which are determined empirically. Hence, its generality is compromised, because the parameters that are suitable for a particular application are not necessarily suitable for another. This paper proposes the Gravitational Search Algorithm with Normalized Gravitational Constant (GSA-NGC), which defines a new heuristic to determine the initial gravitational constant of the GSA. The new heuristic is grounded in the Brans-Dicke theory of gravitation and takes into consideration the multiple dimensions of the search space of the application. It aims to improve the final solution and reduce the number of iterations and premature convergences of the GSA. The GSA-NGC is validated experimentally, proving to be suitable for various applications and improving significantly the generality, performance, and efficiency of the GSA.

Smart grids are a reality in distribution systems. They have assisted in the operation, control, and most of all, the protection of urban networks, significantly solving the contingencies of these networks. This paper treats the initial stage of implementing smart grid switching devices in distribution networks. In this stage, smart grid technologies need to operate with the traditional protection elements (such as fuses, reclosers, and sectionalizers). This fact can create trouble in the protection schemes because there are two distinctive philosophies. In some companies, especially those without substantial capital, these two protection philosophies can run together for many years. The most popular intelligent electronic devices (IEDs) available in the market are studied to verify their features and the possibility to incorporate techniques to allow the two philosophies to work together. After that, the proposed approach shows how the existing IEDs can interact with the traditional devices. Special functions can also be incorporated to inform the control center of an operational problem, increasing the observability of the network. With the proposed approach, the IEDs are transformed into intelligent agents. Practical examples using real distribution systems are presented and discussed, proving the efficacy of the proposed methodology.

The importance of power in society is indisputable. Virtually all economic activities depend on electricity. The electric power systems are complex, and move studies in different areas are motivated to make them more efficient and solve their operational problems. The smart grids emerged from this approach and aimed to improve the current systems and integrate electric power using alternative and renewable sources. Restoration techniques of these networks are being developed to reduce the impacts caused by the usual power supply interruptions due to failures in the distribution networks. This paper presents the development and evaluation of the performance of a multi-objective version of the Bacterial Foraging Optimization Algorithm for finding the minor handling switches that maximize the number of buses served, keeping the configuration radial system and within the limits of current in the conductors and bus voltage. An electrical system model was created, and routines were implemented for the network verification, which was used as a function of the Multi-Objective Bacterial Foraging Optimization Hybrid Algorithm. The proposed method has been applied in two distribution systems with 70 buses and 201 buses, respectively, and the algorithm’s effectiveness to solve the restoration problem is discussed.

With the increasing demand for differentiated virtual environments, the need for dynamic content generation arises, speeding up development, reducing productions costs and increase the product shelf life. In general, procedurally generated worlds, offer a greater range of details in the environment and experience customization without sacrificing performance, but this is only guaranteed when the quality of the content obtained satisfies the user in quality and diversity. Thus, the use of algorithms that are able to guarantee the quality of the generated experience without lacking diversity are required. This paper presents a technique that can be used to develop virtual environments both in its production, providing the developer with different design possibilities to be developed and refined, as well as execution, building unique simulations while interfacing with the user. Therefore, the technique developed makes use of morphological niches to ensure diversity of solutions, producing results that provide the best solutions within each group, surpassing in quality the existing techniques such as novelty search and local competition.

In a company, the manual checking of load data resulting from measurements is a repetitive process. It takes a long time and is subject to errors, since after one or two hours performing that task, it is difficult for the licensee's technician to identify the existing deviations in the large databases. Another aspect is that the data in those bases are usually distributed through tables on the screen. Such fact renders the task more complicated due to the lack of a better notion of sets such as possible rearrangements, for example, if those same data were displayed in graphics. The aim of the current work is to substitute the load monitoring previously done through manual checking by a computer system specially conceived to meet the necessities of Energias Brasil - Bandeirante. Because of the proposed methodological development and the implemented computer software, the data checking is practically automatic, eliminating errors that could not be identified. The computer software brings a new paradigm to check flaws in the data, making possible to relate them in many dimensions. The software performs a repetitive task based on pattern recognition techniques. Moreover, the program indicates possible flaws in measurement, easing the correction of figures and helping in the correct measurement of the company's load. This paper aims to present the methodology that was developed to automate the load monitoring and its computer implementation.

In a company, the manual checking of load data resulting from measurements is a repetitive process. It takes a long time and is subject to errors, since after one or two hours performing that task, it is difficult for the licensee's technician to identify the existing deviations in the large databases. Another aspect is that the data in those bases are usually distributed through tables on the screen. Such fact renders the task more complicated due to the lack of a better notion of sets such as possible rearrangements, for example, if those same data were displayed in graphics. The aim of the current work is to substitute the load monitoring previously done through manual checking by a computer system specially conceived to meet the necessities of Energias Brasil Bandeirante. Because of the proposed methodological development and the implemented computer software, the data checking is practically automatic, eliminating errors that could not be identified. The computer software brings a new paradigm to check flaws in the data, making possible to relate them in many dimensions. The software performs a repetitive task based on pattern recognition techniques. Moreover, the program indicates possible flaws in measurement, easing the correction of figures and helping in the correct measurement of the company's load. This paper aims to present the methodology that was developed to automate the load monitoring and its computer implementation