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ABSTRACT The goal of this paper is to discuss representations about grammar teaching of Portuguese L1 teachers. It draws on the exploratory study designed to identify the invariants and divergences in self-perceptions about L1 grammar teaching of trainees in different training situations: in-service teachers, pre-service teachers and bachelor’s students. The study focus on student’s and teacher’s acknowledgment of the Portuguese L1 Curriculum (Reis et al. 2009), which conveys a clear paradigm change in Portuguese language education, assuming that grammar teaching is based upon language awareness development. The results show an evident gap between students and teachers in what concerns the familiarity with the Portuguese L1 Curriculum and teaching strategies. However, some puzzling convergent points are the importance attributed to grammar and poor linguistics background. The results analysis draws attention to the relationship between teacher cognition and practice, highlighting fragilities in teacher education and potential conceptual changes that may lead to effective changes in classroom practices.

The increasing use of nonlinear loads in power systems introduces voltage and current components at non-fundamental frequencies, leading to harmonic distortion, which negatively impacts electrical and electronic devices. A common mitigation strategy involves identifying harmonic sources and installing filters nearby. However, due to the high cost of power quality (PQ) meters, comprehensive harmonic level monitoring across the entire power system is impractical. To address this, various methodologies for Harmonic State Estimation (HSE) have been developed, which estimate distortion levels on unmonitored system buses using data from a minimal set of monitored ones. Many HSE techniques rely on optimization algorithms with numerous tuning parameters, complicating their application. This paper proposes a novel methodology for fundamental frequency power flow and harmonic state estimation using the Jaya algorithm, which is characterized by fewer tuning parameters for easier adjustment. It also introduces a strategy to determine the minimal number of buses that need monitoring to achieve system observability. The methodology is validated on the IEEE-14 and IEEE-30 bus systems, demonstrating its effectiveness. The results of the proposed methodology are compared with those obtained using Evolutionary Strategies (ESs), highlighting its enhanced accuracy and computational efficiency. [ABSTRACT FROM AUTHOR]

Background: The study evaluated the effects of resistance exercise training and açaí supplementation on cardiac parameters in hypertensive animals. Methods: For this study, rats from the Wistar and SHR lines (spontaneously hypertensive rats) were used. The animals were divided into 5 groups: Wistar Control (C); Control Hypertensive (H); Trained Hypertensive (HT); Hypertensive and Supplemented with Açaí (HA); and Hypertensive Trained and Supplemented with Açaí (HAT). Resistance exercise training was carried out through climbing. The supplemented groups received 3 g of açaí/kg of body mass. The animals' systolic blood pressure (SBP), body mass, and physical test were measured at the beginning and end of the intervention. At the end, an echocardiographic analysis was performed. Histological analysis and oxidative stress of the LV were performed. Results: It was found that hypertensive animals showed an increase in SBP, and the treatments reduced this parameter. The trained groups achieved higher values of maximum carrying load. Hypertension increased the dimension of the left ventricular free wall in diastole and reduced ejection and shortening fractions. The trained groups showed improvement in ejection and shortening fractions. The H group increased the proportion of extracellular matrix and reduced the proportion of cells, with the HAT group attenuating this change. Cell diameter was greater in group H, and all treatments reduced this parameter. Hypertension increased the concentration of malondialdehyde and decreased catalase activity in LV. The treatments managed to mitigate this damage. Conclusions: It is concluded that the treatments managed to generate positive cardiovascular adaptations, and their combination enhanced these effects. [ABSTRACT FROM AUTHOR]

The aim of this study was to analyze the match-to-match variation in high-intensity demands from one Portuguese professional football team according to playing positions. Twenty-three male outfield professional football players were observed during eighteen matches of the Portuguese Second League. Time–motion data were collected using Global Positioning System (GPS) technology. Match running performance was analyzed based on the following three playing positions: defenders (DF), midfielders (MF), and forwards (FW). Repeated measures ANOVA was utilized to compare match running performance within each position role, and seasonal running variation. Practical differences were assessed using the smallest worthwhile change (SWC), coefficient of variation (CV), and twice the coefficient of variation (2CV). Significant differences were found among playing positions in total distance covered (F = 15.45, p < 0.001, η2 = 0.33), average speed (F = 12.79, p < 0.001, η2 = 0.29), high-speed running (F = 16.93, p < 0.001, η2 = 0.36), sprinting (F = 13.49, p < 0.001, η2 = 0.31), accelerations (F = 4.69, p = 0.001, η2 = 0.132), and decelerations (F = 12.21, p < 0.001, η2 = 0.284). The match-to-match running performance encompassed TD (6.59%), AvS (8.67%), HSRr (37.83%), SPR (34.82%), ACC (26.92%), and DEC (27.85%). CV values for total distance covered ranged from 4.87–6.82%, with forwards and midfielders exhibiting the greatest and smallest variation, respectively. Midfielders demonstrated the highest match-to-match variation for all other analyzed variables (8.12–69.17%). All playing positions showed significant variation in high-demanding variables (26.94–37.83%). This study presents the initial analysis of match-to-match variation in high-intensity demands within a Portuguese professional football team. Thus, the position's specificity and context can provide a helpful strategy for evaluating match-to-match running performance, and for recommending individualized training exercises based on the peak and high-intensity demands for each player's role within the game. [ABSTRACT FROM AUTHOR]

The aim of this study was to test a machine learning (ML) model to predict high-intensity actions and body impacts during youth football training. Sixty under-15, -17, and -19 sub-elite Portuguese football players were monitored over a 6-week period. External training load data were collected from the target variables of accelerations (ACCs), decelerations (DECs), and dynamic stress load (DSL) using an 18 Hz global positioning system (GPS). Additionally, we monitored the perceived exertion and biological characteristics using total quality recovery (TQR), rating of perceived exertion (RPE), session RPE (sRPE), chronological age, maturation offset (MO), and age at peak height velocity (APHV). The ML model was computed by a feature selection process with a linear regression forecast and bootstrap method. The predictive analysis revealed that the players' MO demonstrated varying degrees of effectiveness in predicting their DEC and ACC across different ranges of IQR. After predictive analysis, the following performance values were observed: DEC ( x ¯ predicted = 41, β = 3.24, intercept = 37.0), lower IQR (IQRpredicted = 36.6, β = 3.24, intercept = 37.0), and upper IQR (IQRpredicted = 46 decelerations, β = 3.24, intercept = 37.0). The player's MO also demonstrated the ability to predict their upper IQR (IQRpredicted = 51, β = 3.8, intercept = 40.62), lower IQR (IQRpredicted = 40, β = 3.8, intercept = 40.62), and ACC ( x ¯ predicted = 46 accelerations, β = 3.8, intercept = 40.62). The ML model showed poor performance in predicting the players' ACC and DEC using MO (MSE = 2.47–4.76; RMSE = 1.57–2.18: R2 = −0.78–0.02). Maturational concerns are prevalent in football performance and should be regularly checked, as the current ML model treated MO as the sole variable for ACC, DEC, and DSL. Applying ML models to assess automated tracking data can be an effective strategy, particularly in the context of forecasting peak ACC, DEC, and bodily effects in sub-elite youth football training. [ABSTRACT FROM AUTHOR]

Utilizing techniques for reducing multivariate data is essential for comprehensively understanding the variations and relationships within both biomechanical and physiological datasets in the context of youth football training. Therefore, the objective of this study was to identify the primary factors influencing training sessions within a standard microcycle among young sub-elite football players. A total of 60 male Portuguese youth sub-elite footballers (15.19 ± 1.75 years) were continuous monitored across six weeks during the 2019–2020 in-season, comprising the training days from match day minus (MD-) 3, MD-2, and MD-1. The weekly training load was collected by an 18 Hz global positioning system (GPS), 1 Hz heart rate (HR) monitors, the perceived exertion (RPE) and the total quality recovery (TQR). A principal component approach (PCA) coupled with a Monte Carlo parallel analysis was applied to the training datasets. The training datasets were condensed into three to five principal components, explaining between 37.0% and 83.5% of the explained variance (proportion and cumulative) according to the training day (p < 0.001). Notably, the eigenvalue for this study ranged from 1.20% to 5.21% within the overall training data. The PCA analysis of the standard microcycle in youth sub-elite football identified that, across MD-3, MD-2, and MD-1, the first was dominated by the covered distances and sprinting variables, while the second component focused on HR measures and training impulse (TRIMP). For the weekly microcycle, the first component continued to emphasize distance and intensity variables, with the ACC and DEC being particularly influential, whereas the second and subsequent components included HR measures and perceived exertion. On the three training days analyzed, the first component primarily consisted of variables related to the distance covered, running speed, high metabolic load, sprinting, dynamic stress load, accelerations, and decelerations. The high intensity demands have a high relative weight throughout the standard microcycle, which means that the training load needs to be carefully monitored and managed. [ABSTRACT FROM AUTHOR]

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