Medidas corporales y estimación de peso vivo en bucerros predestete en la provincia de Granma, Cuba

La investigación se realizó en la oriental provincia de Granma, área que destaca por sus resultados científicos relacionados con el comportamiento productivo de la especie bubalina en Cuba. El objetivo fue estimar el peso vivo a través de medidas corporales en bucerros desde el nacimiento hasta los ocho meses de edad. Se registraron datos de 1 302 animales, hembras y machos nacidos de 120 búfalas de la raza Buffalypso en el período 2008 a 2015, las medidas corporales: alto de la cruz (AC), longitud del cuerpo (LC), perímetro torácico (PT), perímetro abdominal (PA), ancho de la pelvis (AP), largo de la pelvis (LP) y ancho del tórax (AT) fueron medidas con cinta métrica en cm. Mientras el peso vivo (PV) fue determinado con plataforma digital, todas las maniobras se hicieron cada 30 días. Los modelos predictivos utilizados fueron: Quetélet, PV = (PT)2 (longitud de cuerpo) (87,5); Crevat, PV= (PT) (Longitud del cuerpo) (PA) (80) y Correa, PV= (PT)2(Longitud del cuerpo)/ 300. Los resultados comparativos por sexo arrojaron diferencias altamente significativas (P<0,001) para el PA y diferencias significativas (P<0,05) para el PT, PV, LP y LC a favor de los machos. EL modelo que mostró mejor ajuste (r2=0,96, P>0,001) combinó tres variables (PT, PA y LC), aunque el perímetro torácico solo mostró parámetros elevados (r2=0,94, P>0,001). Finalmente se concluye que las elevadas correlaciones entre las medidas corporales y el peso vivo, demuestran que las variables estudiadas pueden por si solas o combinadas explicar el comportamiento del peso vivo, pero la ecuación de predicción del PV (kg) a través de PT (cm) propuesta atribuye mayores ventajas para la práctica del pesaje. The research was carried out in the eastern province of Granma, an area that stands out for its scientific results related to the productive behavior of the buffalo species in Cuba. The objective was to estimate live weight through body measurements in calves from birth to eight months of age. Data were recorded on 1 302 animals, females and males born to 120 buffaloes of the Buffalypso breed in the period 2008 to 2015, body measurements: height at the withers (AC), body length (LC), thoracic perimeter (PT) , abdominal perimeter (PA), pelvic width (AP), pelvic length (LP) and chest width (AT) were measured with a tape measure in cm. While the live weight (PV) was determined with a digital platform, all the maneuvers were done every 30 days. The predictive models used were: Quetélet, PV = (PT) 2 (body length) (87.5); Crevat, PV = (PT) (Body length) (PA) (80) and Correa, PV = (PT) 2 (Body length) / 300. The comparative results by sex yielded highly significant differences (P <0.001) for the PA and significant differences (P <0.05) for the PT, PV, LP and LC in favor of males. The model that showed the best fit (r2 = 0.96, P> 0.001) combined three variables (PT, PA and LC), although the thoracic perimeter only showed elevated parameters (r2 = 0.94, P> 0.001). Finally, it is concluded that the high correlations between body measurements and live weight show that the variables studied can, alone or in combination, explain the behavior of live weight, but the prediction equation of LW (kg) through PT (cm ) proposal attributes greater advantages to the practice of weighing.

Accelerometer-based prediction of ground reaction force in head-out water exercise with different exercise intensity countermovement jump

Background Water jumping exercise is an alternative method to achieve maintenance of bone health and reduce exercise injuries. Clarifying the ground reaction force (GRF) of moderate and high cardiopulmonary exercise intensities for jumping movements can help quantify the impact force during different exercise intensities. Accelerometers have been explored for measuring skeletal mechanical loading by estimating the GRFs. Predictive regression equations for GRF using ACC on land have already been developed and performed outside laboratory settings, whereas a predictive regression equation for GRF in water exercises is not yet established. The purpose of this study was to determine the best accelerometer wear-position for three exercise intensities and develop and validate the ground reaction force (GRF) prediction equation. Methods Twelve healthy women (23.6 ± 1.83 years, 158.2 ± 5.33 cm, 53.1 ± 7.50 kg) were recruited as participants. Triaxial accelerometers were affixed 3 cm above the medial malleolus of the tibia, fifth lumbar vertebra, and seventh cervical vertebra (C7). The countermovement jump (CMJ) cadence started at 80 beats/min and increased by 5 beats per 20 s to reach 50%, 65%, and 80% heart rate reserves, and then participants jumped five more times. One-way repeated analysis of variance was used to determine acceleration differences among wear-positions and exercise intensities. Pearson’s correlation was used to determine the correlation between the acceleration and GRF per body weight on land (GRFVLBW). Backward regression analysis was used to generate GRFVLBW prediction equations from full models with C7 acceleration (C7 ACC), age, percentage of water deep divided by body height (PWDH), and bodyweight as predictors. Paired t-test was used to determine GRFVLBW differences between values from the prediction equation and force plate measurement during validation. Lin’s CCC and Bland–Altman plots were used to determine the agreement between the predicted and force plate-measured GRFVLBW. Results The raw full profile data for the resultant acceleration showed that the acceleration curve of C7 was similar to that of GRFv. The predicted formula was − 1.712 + 0.658 * C7ACC + 0.016 * PWDH + 0.008 * age + 0.003*weight. Lin’s CCC score was 0.7453, with bias of 0.369%. Conclusion The resultant acceleration measured at C7 was identified as the valid estimated GRFVLBW during CMJ in water.

ESTIMATING BODY FAT PERCENTAGE THROUGH BODY MASS INDEX AND HANDGRIP STRENGTH IN MIDDLE AND OLDER-AGED ASIAN ADULTS

Objectives: To determine the ability of handgrip strength combined with body mass index (BMI, kg/m2) to estimate body fat percentage (BF%) in middle-aged and older Asian adults. Methods: Middle-aged and older Asian adults (n=459, males=197) were randomly divided into a validation and model development group (n=303) and cross-validation group (n=156). A whole-body scan using dual energy x-ray absorptiometry measured BF%. Bland-Altman plots, standard error of the estimates, total errors and mean absolute errors were used to compare prediction equations. Stepwise regression analysis was used to determine a new prediction equation for middle-aged and older Asian adults. Right and left handgrip strength, age, sex and BMI were included in the analysis. Results: A previously developed prediction equation that included handgrip strength poorly predicted BF% in our current sample with the mean difference being -6.0 ± 4.2%. Predicted BF% values were significantly lower than measured BF% values (22.7% vs. 28.7%, p<0.05). A new prediction equation was developed that included sex, BMI, left handgrip strength and age. Validation of the new equation revealed a constant error of 0.2 ± 3.9% with there being no significant difference between measured and predicted BF% (28.2% vs. 28.5%, p=0.467). Previously developed BF% equations using BMI, but not handgrip strength, had similar constant errors and mean absolute errors compared to the new prediction equation. Conclusion: Handgrip strength does not appear to improve the estimation of body fat percentage from BMI prediction equations in middle and older-aged Asian adults.

Effect of Ovality Length on Collapse Strength of Imperfect Sandwich Pipes Due to Local Buckling

The effect of ovality length on imperfect sandwich pipes is investigated using the finite element method in the scenario of local buckling under external pressure. First, the finite element model of the imperfect sandwich pipelines is established in ANSYS and is validated by comparing the results from numerical simulation with those from experiments. Then, the effect of ovality features on the collapse strength of the sandwich pipes is studied. At last, based on the calculation results from 1200 cases, a prediction equation is proposed to represent the relationship between collapse strength and ovality length of imperfect sandwich pipes. Good agreement is achieved between the proposed equation and the calculation results, leading to the conclusion that the proposed simplified model can be an efficient tool in the evaluation of the local collapse strength of subsea sandwich pipes under external pressure.

Predicting the Ultimate Axial Capacity of Uniaxially Loaded CFST Columns Using Multiphysics Artificial Intelligence

The object of this research is concrete-filled steel tubes (CFST). The article aimed to develop a prediction Multiphysics model for the circular CFST column by using the Artificial Neural Network (ANN), the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the Gene Expression Program (GEP). The database for this study contains 1667 datapoints in which 702 are short CFST columns and 965 are long CFST columns. The input parameters are the geometric dimensions of the structural elements of the column and the mechanical properties of materials. The target parameters are the bearing capacity of columns, which determines their life cycle. A Multiphysics model was developed, and various statistical checks were applied using the three artificial intelligence techniques mentioned above. Parametric and sensitivity analyses were also performed on both short and long GEP models. The overall performance of the GEP model was better than the ANN and ANFIS models, and the prediction values of the GEP model were near actual values. The PI of the predicted Nst by GEP, ANN and ANFIS for training are 0.0416, 0.1423, and 0.1016, respectively, and for Nlg these values are 0.1169, 0.2990 and 0.1542, respectively. Corresponding OF values are 0.2300, 0.1200, and 0.090 for Nst, and 0.1000, 0.2700, and 0.1500 for Nlg. The superiority of the GEP method to the other techniques can be seen from the fact that the GEP technique provides suitable connections based on practical experimental work and does not rely on prior solutions. It is concluded that the GEP model can be used to predict the bearing capacity of circular CFST columns to avoid any laborious and time-consuming experimental work. It is also recommended that further research should be performed on the data to develop a prediction equation using other techniques such as Random Forest Regression and Multi Expression Program.

Forecasting process parameters on weld nugget hardness of filler added friction stir welded dissimilar aluminium alloys 5052 And 6082 joints

This article deals with the optimization of friction stir welding process parameters with filler ratios on dissimilar Aluminium alloy groups. For this purpose, 6 series Aluminium alloy 6082 and 5 series Aluminium alloy 5052 were taken. Microhardness property was conducted under various rotational speeds, welding speed, plunge depth, Center distance between the holes and filler mixing ratio. The Central Composite Design (CCD), the most commonly used Response Surface Methodology (RSM), is considered to develop the prediction equation. A validation analysis is carried out, and the results were compared with the relative impact of input parameters on weld nugget microhardness. It is observed that the increase in welding speed with plunge depth and filler ratio result in the increase of weld nugget microhardness up to a maximum value. The maximum weld nugget hardness of fabricated joint was obtained with the welding process parameters combination of 1000 rpm rotational speed, 125 mm/min welding speed, 0.15 mm plunge depth, 2 mm centre distance between the holes, and filler ratio of 95% Mg and 5% Cr.

Desain Alat Ukur Kadar Asam Urat Non-Invasive Dengan Sensor Near Infrared

Gout disease or commonly known as gout arthritis is a disease caused by the accumulation of monosodium urate crystals in the body. Monitoring blood uric acid levels is currently still using invasive techniques by taking blood samples to be included in the test strip, this invasive technique measurement requires money and time to come to health clinics so that many people cannot monitor blood uric acid levels on a regular basis. This study aims to develop a measuring instrument for monitoring blood uric acid levels using sensors by utilizing the absorption and reflectance of infrared rays so that gout sufferers and other individuals can monitor blood uric acid levels regularly and are easy to use. The method of developing non-invasive techniques focuses on monitoring blood uric acid levels using a NIR sensor with an IR LED with a wavelength of 940 nm as a transmitter and a photodiode as a detector at a wavelength of 700-1300 nm and a microcontroller as a minimum system for control. The first stage is measuring uric acid levels with invasive techniques on participants and at the same time measuring voltages with sensors, the results with invasive techniques are correlated with sensor output voltages to obtain non-linear equations in polynomial form, for conversion programs on the microcontroller. The second stage is measuring uric acid levels with invasive techniques and invasive measurements on participants at the same time. Both monitoring results were analyzed by simple ANOVA statistics and calculated SEP and RMSE to determine the accuracy of the prediction equation and its accuracy value.

Optimized Design of 3D Spatial Images Based on Kalman Filter Equation

This paper takes the advantageous ability of Kalman filter equation as a means to jointly realize the accurate and reliable extraction of 3D spatial information and carries out the research work from the extraction of 3D spatial position information from multisource remote sensing optical stereo image pairs, recovery of 3D spatial structure information, and joint extraction of 3D spatial information with optimal topological structure constraints, respectively. Taking advantage of the stronger effect capability of Wiener recovery and shorter computation time of Kalman filter recovery, Wiener recovery is combined with Kalman filter recovery (referred to as Wiener-Kalman filter recovery method), and the mean square error and peak signal-to-noise ratio of the recovered image of this method are comparable to those of Wiener recovery, but the subjective evaluation concludes that the recovered image obtained by the Wiener-Kalman filter recovery method is clearer. To address the problem that the Kalman filter recovery method has the advantage of short computation time but the recovery effect is not as good as the Wiener recovery method, an improved Kalman filter recovery algorithm is proposed, which overcomes the fact that the Kalman filter recovery only targets the rows and columns of the image matrix for noise reduction and cannot utilize the pixel point information among the neighboring rows and columns. The algorithm takes the first row of the matrix image as the initial parameter of the Kalman filter prediction equation and then takes the first row of the recovered image as the initial parameter of the second Kalman filter prediction equation. The algorithm does not need to estimate the degradation function of the degradation system based on the degraded image, and the recovered image presents the image edge detail information more clearly, while the recovery effect is comparable to that of the Wiener recovery and Wiener-Kalman filter recovery method, and the improved Kalman filter recovery method has stronger noise reduction ability compared with the Kalman filter recovery method. The problem that the remote sensing optical images are seriously affected by shadows and complex environment detail information when 3D spatial structure information is extracted and the data extraction feature edge is not precise enough and the structure information extraction is not stable enough is addressed. A global optimal planar segmentation method with graded energy minimization is proposed, which can realize the accurate and stable extraction of the topological structure of the top surface by combining the edge information of remote sensing optical images and ensure the accuracy and stability of the final extracted 3D spatial information.

Development and Validation of Prediction Formula of Wingate Test Peak Power From Force–Velocity Test in Male Soccer Players

Peak power of the Wingate anaerobic test (WAnT), either in W (Ppeak) or in W.kg–1 (rPpeak), has been widely used to evaluate the performance of soccer players; however, its relationship with force–velocity (F-v) test (e.g., whether these tests can be used interchangeably) has received little scientific attention so far. The aim of this work was to develop and validate a prediction equation of Ppeak and rPpeak from F-v characteristics in male soccer players. Participants were 158 adult male soccer players (sport experience 11.4 ± 4.5 years, mean ± standard deviation, approximately five weekly training units, age 22.6 ± 3.9 years, body mass 74.8 ± 7.8 kg, and height 178.3 ± 7.8 cm) who performed both WAnT and F-v test. An experimental (EXP, n = 79) and a control group (CON, n = 79) were used for development and validation, respectively, of the prediction equation of Ppeak and rPpeak from F-v test. In EXP, Ppeak correlated very largely with body mass (r = 0.787), fat-free mass (r = 0.765), largely with maximal power of F-v test (Pmax; r = 0.639), body mass index (r = 0.603), height (r = 0.558), moderately with theoretical maximal force (F0; r = 0.481), percentage of body fat (r = 0.471), fat mass (r = 0.443, p &lt; 0.001); rPpeak correlated with rPmax (largely; r = 0.596, p &lt; 0.001), theoretical maximal velocity (v0; moderately; r = 0.341, p = 0.002), F0 (small magnitude; r = 0.280, p = 0.012), BF (r = −0.230, p = 0.042), and fat mass (r = −0.242, p = 0.032). Ppeak in EXP could be predicted using the formula “44.251 + 7.431 × body mass (kg) + 0.576 × Pmax (W) – 19.512 × F0” (R = 0.912, R2 = 0.833, standard error of estimate (SEE) = 42.616), and rPpeak from “3.148 + 0.218 × rPmax (W.kg–1) + v0 (rpm)” (R = 0.765, R2 = 0.585, SEE = 0.514). Applying these formulas in CON, no bias was observed between the actual and the predicted Ppeak (mean difference 2.5 ± 49.8 W; 95% CI, −8.7, 13.6; p = 0.661) and rPpeak (mean difference 0.05 ± 0.71 W.kg–1; 95% CI, −0.11, 0.21, p = 0.525). These findings provided indirect estimates of Ppeak of the WAnT, especially useful in periods when this test should not be applied considering the fatigue it causes; in this context, the F-v test can be considered as an alternative of exercise testing for estimating the average Ppeak of a group of soccer players rather than for predicting individual scores when the interindividual variation of performance is small.