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Increasingly stringent pollutant emission regulations and a customer demand for a high-fuel economy drive the modern automotive industry to hurriedly solve the problem of decarbonization and powertrain efficiency, leading R&D towards alternative powertrain solutions and fuels. Electrification, today, plays the biggest role in the topic, with Mild Hybrid Electrified Vehicles (MHEVs) being the most cost-effective architectures, displaying dominance in smaller markets such as Brazil. One of the biggest challenges for HEVs' development is the complexity of the hybrid control system, knowing when to actuate the electric machine, and the optimum power delivery, plus the gearshift schedule becomes a hard optimization problem that plays a key role in powertrain efficiency and cost savings for the customer. This paper proposes the implementation of a genetic algorithm (GA) as a machine learning-based control strategy to determine the torque split and the gear engaged for each driving condition of an MHEV operation, aiming to optimize fuel consumption. A quasi-static model of the vehicle was developed in Matlab/Simulink version 2022b, the virtual vehicle was then tested following the FTP75 and HWFET driving cycles. Simulation results indicate that the control decisions taken by the GA are qualitatively coherent for all operation conditions, and even quantitatively coherent in some cases, and that the software has the potential to be used as a control strategy outside the simulation environment, in future steps of development. [ABSTRACT FROM AUTHOR]

The rising demand for energy and an still increasing world population are prompting the need to further improve power supply services upon a "zero interruption" target. This, combined with the process of shifting fossil fuel usage toward electrification, may escalate the economic and social impacts caused from interruptions. In this context, scheduled inspection plays a major role to manage risks. The challenge, however, is the trade-off of low O&M costs and high reliability. To solve that, this paper proposes a reliability-centered maintenance approach that combines heuristic and genetic algorithm (GA). The heuristic, based on benchmark practices, determines the cumulative total line length to be inspected to reach a targeted reliability level. The GA, on the other hand, yields a priority order list to portions of the grid to be inspected. The methodology relies on corporate systems historic data from outage management system, supervisory control and data acquisition, geographic information system and enterprise resource planning, typically available on power utilities. Tests are performed to a real 13.8 kV network in the South West of Brazil. Results demonstrate that the proposed methodology can significantly improve the decision-making by prioritizing feeder segments with higher load density, number of interruptions and voltage/current problems, up to a preset target, thus, improving reliability while limiting O&M costs. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of feature selection aiming to improve a flood forecasting model. The proposed model is carried out through a case study that uses 18 different time series of thirty-five years of hydrological data, forecasting the level of the Xingu River, in the Amazon rainforest in Brazil. We employ a Genetic Algorithm for the task of feature selection and exploit several different genetic parameters seeking to improve the accuracy of the prediction. The features selected by the Genetic Algorithm are used as input of a Linear Regression model that performs the forecasting. A statistical analysis verifies that the final model can predict the river level with high accuracy, which obtains a coefficient of determination equal to 0.988. Hence, the proposed Genetic Algorithm showed to be successful in selecting the most relevant features. [ABSTRACT FROM AUTHOR]

This paper proposes a novel dynamic pricing scheme for demand response with individualized tariffs by consumption profile, aiming to benefit both customers and utility. The proposed method is based on the genetic algorithm, and a novel operator called mutagenic agent is proposed to improve algorithm performance. The demand response model is set by using price elasticity theory, and simulations are conducted based on elasticity, demand, and photovoltaic generation data from Brazil. Results are evaluated considering the integration effects of renewable energy sources and compared with other two pricing strategies currently adopted by Brazilian utilities: flat tariff and time-of-use tariff. Simulation results show the proposed dynamic tariff brings benefits to both utilities and consumers. It reduces the peak load and average cost of electricity and increases utility profit and load factor without the undesirable rebound effect. [ABSTRACT FROM AUTHOR]

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• The paper investigates the efficiency of the Brazilian Rail Cargo System. • The methodology combined TOPSIS with a genetic algorithm to build optimized scenarios. • The significant attributes in scenarios of low and high performance were highlighted. • Transport of any kind of cargo, and shared railway are significant for the results. • Competition and diversification are key elements for high performance. A railway system plays a significant role in countries with large territorial dimensions. The Brazilian rail cargo system (BRCS), however, is focused on solid bulk for export. This paper investigates the extreme performances of BRCS through a new hybrid model that combines TOPSIS with a genetic algorithm for estimating the weights in optimized scenarios. In a second stage, the significance of selected variables was assessed. The transport of any type of cargo, a centralized control of the operation, and sharing the railway track pushing competition, and the diversification of services are significant for high performance. Public strategies are discussed. [ABSTRACT FROM AUTHOR]

Wind energy is a powerful resource contributing to the decarbonization of the electric grid. However, wind power penetration introduces uncertainty about the availability of wind energy. This article addresses the complementarity of remote offshore wind sites in Brazil, demonstrating that strategic distribution of wind farms can significantly reduce the seasonality and the risk of periods without generation and reduce dependence on fossil sources. Field observations, atmospheric reanalysis, and simplified optimization methods are combined to demonstrate generation improvement considering regions under environmental licensing and areas not yet considered for offshore development. Aggregated power results demonstrate that with the relocation of wind turbines, a 68% reduction of the grid seasonal variability is possible, with a penalty of only 9% of the generated energy. This is accomplished through optimization and the inclusion of the northern region, which presents negative correlations with all other stations. More specifically, the north and northeast of Brazil have large seasonal amplitudes. However, out-of-phase wind regimes with a strong negative correlation (R < −0.6) and high-capacity factors ( C F ) during the peak seasons occur in Jan-Feb-Mar in the north ( C F > 0.5) and in Aug-Sep-Oct in the northeast ( C F > 0.7). These complementary regimes allow for the introduction of the concept of Reserve Wind Power (RWP) plants, wind farms that can be viewed as "reserve sources" for energy security. These can replace the contracts of thermal reserve plants, with resulting economic and environmental advantages. Our analysis suggests that RWP plants can be 20 to 32% cheaper than thermal reserves in the current market. [ABSTRACT FROM AUTHOR]

Abstract: In Brazil, the National Cancer Institute (INCA) reports more than 50,000 new cases of the disease, with risk of 51 cases per 100,000 women. Radiographic images obtained from mammography equipments are one of the most frequently used techniques for helping in early diagnosis. Due to factors related to cost and professional experience, in the last two decades computer systems to support detection (Computer-Aided Detection – CADe) and diagnosis (Computer-Aided Diagnosis – CADx) have been developed in order to assist experts in detection of abnormalities in their initial stages. Despite the large number of researches on CADe and CADx systems, there is still a need for improved computerized methods. Nowadays, there is a growing concern with the sensitivity and reliability of abnormalities diagnosis in both views of breast mammographic images, namely cranio-caudal (CC) and medio-lateral oblique (MLO). This paper presents a set of computational tools to aid segmentation and detection of mammograms that contained mass or masses in CC and MLO views. An artifact removal algorithm is first implemented followed by an image denoising and gray-level enhancement method based on wavelet transform and Wiener filter. Finally, a method for detection and segmentation of masses using multiple thresholding, wavelet transform and genetic algorithm is employed in mammograms which were randomly selected from the Digital Database for Screening Mammography (DDSM). The developed computer method was quantitatively evaluated using the area overlap metric (AOM). The mean±standard deviation value of AOM for the proposed method was 79.2±8%. The experiments demonstrate that the proposed method has a strong potential to be used as the basis for mammogram mass segmentation in CC and MLO views. Another important aspect is that the method overcomes the limitation of analyzing only CC and MLO views. [Copyright &y& Elsevier]

The operation planning of the Brazilian power system is a great challenge due to its size of continental dimensions, large seasonal variations, and predominantly hydroelectric nature. Its operation requires careful planning to reconcile conflicting goals such as spill minimization during the rainy season, and minimization of generation deficits during the dry season, i.e., operation risk minimization. The optimization models for hydrothermal systems currently used to operate large systems do not include the electric network when determining the energy goals. This work aims at modeling a hydrothermal dispatch problem by means of genetic algorithms. The electrical constraints are included by an optimal power flow, which is solved monthly over the planning horizon. The fitness function is composed of criteria that look for using the reservoirs as much as possible during the rainy season, thus, minimizing spills and production costs and, at the same time, catering for the load and limits of energy exchange between the subsystems. The methodology was tested using the 33-bus system. [ABSTRACT FROM AUTHOR]