Entomological Society of Brazil

Biomass burning has become an increasingly important driver of air quality degradation in Minas Gerais, southeastern Brazil, particularly during the dry season, when fire activity intensifies, and atmospheric dispersion is suppressed. This work addresses recent evidence on the role of regional biomass burning in shaping fine particulate matter (PM₂.₅) concentrations, with a particular focus on the extreme air pollution episodes observed during 2024. The analysis integrates long-term records of fire hotspots, ground-based air quality monitoring, and key meteorological variables to elucidate the coupled processes linking fire activity, atmospheric dynamics, and urban air quality.Minas Gerais encompasses the third largest metropolitan region in Brazil and hosts major local atmospheric emission sources, including intensive mining activities, steelmaking, and a dense urban–industrial infrastructure. Despite the persistent contribution of these structural sources to baseline air pollution levels, fire occurrence in the state exhibits pronounced seasonality, with approximately two-thirds of annual hotspots concentrated between August and October. The year 2024 stands out as one of the most critical periods of the last decade, characterized by prolonged drought, anomalously high temperatures, and persistently low relative humidity. These conditions not only favored the ignition and spread of fires but also created a meteorological environment highly unfavorable to pollutant dispersion.Time-series analyses indicate that peaks in PM₂.₅ concentrations closely coincided with periods of increased fire frequency and intensity, particularly in the Metropolitan Region of Belo Horizonte. Statistical analyses reveal a moderate-to-strong positive association between PM₂.₅ levels and fire hotspot counts, and a consistent negative association with relative humidity. Notably, even in a region with significant local emissions from mining, steel production, and vehicular traffic, biomass burning emerged in 2024 as the dominant driver of exceedances of the national PM₂.₅ air quality standards established by CONAMA Resolution No. 506/2024. These findings demonstrate that regional-scale transport and accumulation of biomass-burning emissions can outweigh the influence of traditional urban and industrial sources during extreme events.Several monitoring stations recorded historically high PM₂.₅ concentrations in 2024, leading to recurrent violations of national air quality thresholds. The compounded effects of extreme meteorological conditions and biomass burning led to short-lived but severe pollution episodes that substantially deteriorated air quality across the metropolitan area. Beyond local emission sources, regional fire activity therefore represents a critical and recurrent contributor to urban particulate pollution, with direct implications for public health and regulatory compliance.Overall, the results reinforce the need for integrated air quality management strategies that combine fire prevention and control, regional-scale monitoring, and meteorological forecasting. Such approaches are particularly relevant under a changing climate, in which the frequency and severity of droughts, heatwaves, and associated biomass-burning events are expected to increase, thereby amplifying their impacts on air quality even in heavily industrialized urban regions.

Electric vehicles (EVs) rely on a battery pack as their primary energy source, making it a critical component for their operation. To guarantee safe and correct functioning, a Battery Management System (BMS) is employed, which uses variables such as State of Charge (SOC) to set charge/discharge limits and to monitor pack health. In this article, we propose a Multilayer Perceptron (MLP) network to estimate the SOC of a 14.8 V battery pack installed in a robotic vacuum cleaner. Both offline and online (real-time) tests were conducted under continuous load and with rest intervals. The MLP’s output is compared against two commonly used approaches: NARX (Nonlinear Autoregressive Exogenous) and CNN (Convolutional Neural Network). Performance is evaluated via statistical metrics, Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE), and we also assess computational cost using Operational Intensity. Finally, we map these results onto a Roofline Model to predict how the MLP would perform on an automotive-grade microcontroller unit (MCU). A generalization analysis is performed using Transfer Learning and optimization using MLP–Kalman. The best performers are the MLP–Kalman network, which achieved an RMSE of approximately 13% relative to the true SOC, and NARX, which achieved approximately 12%. The computational cost of both is very close, making it particularly suitable for use in BMS.

ABSTRACT The development of functional beverages that combine bioactive compounds with sensory appeal is of growing scientific and industrial interest. This study evaluated mixed beverages formulated with varying ratios of lychee juice and yerba mate infusion to optimize physicochemical properties, antioxidant activity, and sensory acceptance. Five formulations were prepared (lychee:mate – 90:10 to 10:90). Analyses included pH, titratable acidity, color, viscosity, total phenolic content (TPC), antioxidant activity (DPPH, FRAP, ABTS), and mineral composition. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) interpreted the data, whereas flash profile (FP) and consumer tests assessed sensory characteristics and acceptance. PCA explained 88% of the variance, distinguishing samples by lychee and mate content. Lychee‐rich beverages were lighter, sweeter, more viscous, and higher in citric acid, whereas mate‐rich ones had greater TPC, antioxidant activity, and minerals. FP associated lychee with fruity aroma, sweetness, and smooth texture, and mate with bitterness, astringency, and darker color. All formulations showed good acceptance (scores ≈6), with F2 (70:30 lychee‐to‐mate) achieving the highest ratings for aroma, flavor, texture, and overall acceptance. Thus, the 70:30 formulation was identified as optimal, balancing antioxidant potential with desirable sensory attributes.

Background:Muscle enhancement through autologous fat grafting has become an important aspect of body contouring procedures, targeting several muscle groups, including the rectus abdominis, deltoids, biceps, trapezius, longissimus, hamstrings, and vastus muscles.Methods:Patients who underwent the ultrasound-guided rectus abdominis fat transfer technique were evaluated using preoperative and postoperative magnetic resonance imaging (MRI) scans to assess the anatomy of the rectus abdominis muscles. Preoperative MRI measured the muscle thickness of the middle and lower rectus abdominis segments on both sides, as they were the most representative. Postoperative imaging was performed 6 months after surgery, assessing the total thickness of the rectus abdominis segments (including fat graft and muscle), the muscle thickness alone, and the remaining fat graft thickness.Results:The ultrasound-guided rectus abdominis fat transfer technique was performed on 20 patients between September 2022 and April 2023. Of these, 6 were men and 14 were women, with a mean age of 37.05 years (range: 25–57 y). Postoperative MRI revealed a statistically significant increase in the total thickness of the muscle segments (including muscle and fat) compared with preoperative measurements.Conclusions:Ultrasound-guided fat grafting is a safe and effective procedure currently used in abdominal etching. Long-term MRI assessments showed significant fat retention and rectus abdominis muscle enhancement.

Employing portable Energy-Dispersive X-Ray Fluorescence (pXRF) sensors and Machine Learning (ML) using calibration set sizes with reduced samples can save resources and time in measuring soil fertility attributes. The calibration of local models must have a sufficient number of samples to represent the spectral variation of the area related to the properties of interest, avoiding excess, which would be redundant, and lack, which would cause a loss of performance. From this perspective, this study investigated the impact of different calibration set sizes on the performance of Partial Least Square Regression (PLS) models using a pXRF sensor (0‐15 kV) to predict Exchangeable Potassium (K⁺), and Calcium (Ca²⁺), Cation Exchange Capacity (CEC), and Soil Organic Carbon (SOC). Two Brazilian tropical fields composed the pXRF spectral libraries. The Cambé and Toledo datasets comprised 386 and 205 soil samples, respectively. Applying the Kennard-Stone sampling selection algorithm, calibration set sizes ranged from 36 to 276 samples, with increments of 20, for the Cambé field, and from 20 to 140 samples, also with increments of 20, for the Toledo field. Results evaluated on validation sets indicate that calibration sample size can be optimized without compromising model accuracy. Established by quantitatively comparing the models RMSEP, the optimal number of calibration samples was attribute- and field-specific. For the Cambé library, the optimal calibration set size was 116, 136, 156 and 116 for the SOC, CEC, K + and Ca 2+ , respectively. For the Toledo library, the SOC, CEC, K + , and Ca 2+ had optimal sample numbers of 60, 100, 80, and 80, respectively. All these models presented satisfactory RPIQ values (≥ 1.7). Therefore, to encompass all the evaluated attributes, it is suggested 156 calibration samples as a reference for developing local libraries applying pXRF sensors in tropical soils with oxide-rich mineralogy. • Calibration sample size was optimized without compromising model accuracy. • The optimal number of calibration samples was attribute- and field-specific, ranging from 60 to 156. • 156 samples are the minimum XRF library size for simultaneous prediction of soil attributes.

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Chronic kidney disease (CKD) represents one of today's main public health problems. Serum creatinine measurement and estimation of the glomerular filtration rate (GFR) are the main tools for evaluating renal function. There are several equations to estimate GFR, and CKD-EPI equation (Chronic Kidney Disease - Epidemiology) is the most recommended one. There are still some controversies regarding serum creatinine measurement and GFR estimation, since several factors can interfere in this process. An important recent change was the removal of the correction for race from the equations for estimating GFR, which overestimated kidney function, and consequently delayed the implementation of treatments such as dialysis and kidney transplantation. In this consensus document from the Brazilian Societies of Nephrology and Clinical Pathology and Laboratory Medicine, the main concepts related to the assessment of renal function are reviewed, as well as possible existing controversies and recommendations for estimating GFR in clinical practice.