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Cup anemometers are frequently employed in the wind power industry for wind resource assessment at prospective wind farm sites. In this paper, we demonstrate a method for identifying faulty three cup anemometers. This method is applicable to cases where data is available from two or more anemometers at equal height and cases where data is available from anemometers at different heights. It is based on examining the Weibull parameters of the distribution generated from the difference between the anemometer’s reported measurements and utilizing a discriminant function technique to separate out the data corresponding to bad cup anemometers. For anemometers at different heights, only data from the same height pair combinations are compared. In addition, various preprocessing techniques are discussed to improve performance of the algorithm. These include removing data that corresponds to poor wind directions for comparing the anemometers and removing data that corresponds to frozen anemometers. These methods are employed on the data from the PHM 2011 Data Competition with results presented.

Abstract Background The assessment of cardiorespiratory fitness is important because it allows the identification of subgroups with poor health status and the targeting of effective intervention strategies to improve health. Objective To compare the cardiorespiratory capacity of children and adolescents living in a moderate altitude region of Peru with international studies and to develop reference values for the 6-min walk test (6MWT) according to age and sex. Methodology A descriptive cross-sectional study of schoolchildren from a region of moderate altitude in Peru was carried out. A total of 704 schoolchildren (400 males and 304 females) with an age range of 6 to 17 years were studied. Weight, standing height, waist circumference (WC), body mass index (BMI) and tri-ponderal mass index (TMI) were evaluated. The 6MWT was assessed in a straight line over a distance of 30 m. Percentiles were created through the LMS method [L (skewness: lambda), M (median: mu) and S (coefficient of variation: Mu)]. Results There were discrepancies in cardiorespiratory fitness performance with international studies by age and sex. The schoolchildren in the study reached stability and the highest number of meters in the last two age ranges (14 to 15 years: 698.1 m and 16 to 17 years 686.3 m in males). While females (14 to 15 years: 698.1 m and 16 to 17 years: 686.3 m). The proposed percentile values show ascending values as age advances. The cut-off points adopted are: low cardiorespiratory fitness 75. Conclusion We verified that the cardiorespiratory fitness evaluated by means of the 6MWT is ascending with the course of age. Even the performance with other countries is heterogeneous at early and middle ages, stabilizing during adolescence. The proposed reference values can be used to evaluate and monitor cardiorespiratory fitness during physical education classes.

In recent years, wearable sensors and mHealth applications are becoming an important part for recording and monitoring health parameters. The aim is to characterize bibli-ometric indicators on scientific productivity in the use of smart bracelets according to scientific journal, study topic and publication trend. A bibliometric study was per-formed. The data extracted from the articles were: year of publication, name of the journal, language of publication, country, type of study and subject matter. A total of 45 studies were identified (21 cross-sectional and 24 experimental), of which 43 were published in English, one in Spanish and one in German. A total of 32 scientific journals published eight research topics related to smartwatch use [active living with 17 studies (37.8%), followed by stress and anxiety with 7 studies (15.6%), nanogenerators (batteries) with 6 studies (13.3%), computing with 5 studies (11.1%), chronic diseases 4 studies (8.9%), COVID propagation 3 studies (6.7%), energy expenditure 2 studies (4.4%) and occupational health 1 study (2.2%)]. The journal Sensors (Basel) published the most articles in the last 6 years (n=7, 16%). The scientific productivity of both study types reflects a positive trend (cross-sectional studies R2= 0.82 and in experimental studies R2= 0.76). A total of 45 cross-sectional and experimental studies using smart-watches were verified in eight different study topics. The most investigated category was active living and the journal that published the highest number of articles was the journal Sensors (Basel).

Determining the classification of motor competence is an essential aspect of physical activity that must be carried out during school years. The objective is to evaluate motor competence in schoolchildren using smart bands, generate percentiles of the evaluation metrics, and classify motor performance through machine learning with hyperparameter optimization. A cross-sectional descriptive study was carried out on 764 schoolchildren (451 males and 313 females) aged 6 to 17 years. Five state schools in the city of Arequipa, Peru were evaluated. Weight, height, and waist circumference were assessed, and body mass index (BMI) was calculated. The tests evaluated in the schoolchildren measured walking and running for 6 minutes. These tests were carried out using smart bands, capturing cadence, number of steps, calories consumed, speed, stride, and heart rate. As a result, the percentiles were created through the LMS method [L (asymmetry: lambda), M (median: mu), and S (coefficient of variation: sigma)]. The cut-off points considered were p75 (above average). For classification, the machine-learning algorithms random forest, decision tree, support vector machine, naive Bayes, logistic regression, k-nearest neighbor, neural network, gradient boosting, XGBboost, LightGBM, and CatBoost were used, and the hyperparameters of the models were optimized using the RandomizedSearchCV technique. In conclusion, it was possible to classify motor competence with the tests carried out on schoolchildren, significantly improving the accuracy of the machine-learning algorithms through the selected hyperparameters, with the gradient boosting classifier being the best result at 0.95 accuracy and in the ROC-AUC curves with a 0.98. The reference values proposed in this study can be used to classify the walking motor competence of schoolchildren. Finally, the mobile software product built based on the proposed model was validated using the prototype of the Software Quality Systemic Model (SQSM) based on three specific categories: functionality, reliability, and usability, obtaining 77.09%. The results obtained can be used in educational centers to achieve the suggested recommendations for physical activity in schoolchildren.

(1) Background: Regular physical activity has multiple benefits. Therefore, school recess is a key tool to provide opportunities for schoolchildren to engage in extracurricular physical activity, have fun, play and interact with their peers. The aim is to provide reference data to quantify the number of steps that children and adolescents perform in a school recess using smart bands according to age range and sex. (2) Method: A descriptive cross-sectional study was carried out in 494 schoolchildren aged 6 to 17 years (292 males and 202 females). Weight, standing height and waist circumference (WC) were evaluated. The body mass index (BMI) was calculated. The quantification of the number of steps during school recess was performed using a smart band. (3) Results: Percentiles were constructed for the number of steps (number of steps/recess). The cut-off points considered were p75 (above average). The median values in both sexes decreased as the age range increased. Youth who walked fewer steps during recess (

El entrenamiento del salto es fundamental para obtener un óptimo desempeño en diversos deportes, siendo el método complejo, basado en el fenómeno de potenciación posactivación (PPA), uno de los de los más utilizados para el entrenamiento de esta cualidad. Numerosos estudios respaldan la existencia de PPA representada en las variaciones de un salto vertical, sin embargo, en la literatura no se describe un protocolo estándar de entrenamiento que genere dicho fenómeno. Este estudio piloto cuantitativo cuasi experimental tiene como objetivo determinar en cuál de las zonas de fuerza descritas por Naclerio se debe entrenar para que la PPA se exprese, en mayor medida, evidenciado en las variaciones de altura de un salto Counter Movement Jump (CMJ). Se utilizó un muestreo por conveniencia de veinticinco individuos, divididos aleatoriamente en cinco grupos, cada uno correspondiente a una zona de fuerza. Luego los sujetos fueron entrenados con sentadilla media según los parámetros de cada zona. Finalmente, se obtuvo la altura del salto basándose en la fórmula de Bosco a partir de los fotogramas de una cámara digital. Existieron dos zonas de fuerza que presentaron un aumento en la altura del salto posterior a la potenciación, la zona de fuerza potencia y explosiva, sin embargo solo la primera de estas mostró resultados significativos en dicho cambio (p