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.

Secure Cloud Auditability for Virtual Machines by Adaptive Characterization Using Machine Learning Methods

With the Present days increasing demand for the higher performance with the application developers have started considering cloud computing and cloud-based data centres as one of the prime options for hosting the application. Number of parallel research outcomes have for making a data centre secure, the data centre infrastructure must go through the auditing process. During the auditing process, auditors can access VMs, applications and data deployed on the virtual machines. The downside of the data in the VMs can be highly sensitive and during the process of audits, it is highly complex to permits based on the requests and can increase the total time taken to complete the tasks. Henceforth, the demand for the selective and adaptive auditing is the need of the current research. However, these outcomes are criticised for higher time complexity and less accuracy. Thus, this work proposes a predictive method for analysing the characteristics of the VM applications and the characteristics from the auditors and finally granting the access to the virtual machine by building a predictive regression model. The proposed algorithm demonstrates 50% of less time complexity to the other parallel research for making the cloud-based application development industry a safer and faster place.

Forecasting Economic Indicators of Transport Activity

The article discusses the methodological and practical aspects of forecasting the economic indicators of the transport sector at the level of a transport company and the type of economic activity. The development of forecasting methodology at the present time is analyzed. The necessity, features and main directions of development of the forecasting methodology for the type of economic activity are revealed. The methodological basis for forecasting the development of the transport sector is investigated and characterized. A method for forecasting transportation and storage as a type of economic activity under conditions of uncertainty is proposed and tested. Based on the results of the correlation analysis, subsets of predicted indicators and factors were formed that were optimal for constructing the corresponding linear regression models. Predictive regression models have been developed, their significance and statistical accuracy have been confirmed.

Influence of alloying systems on the properties of single crystal nickel-based superalloys

The purpose of the investigation was to obtain the predictive regression models that help correct the calculation of the mechanical properties of single crystal nickel-based superalloys without conducting prior experiments. The paper considers the influence of alloying elements on their tendency to form phases in foundry nickel-based superalloys. Using the elements influence on the phase formation, the coefficient Kc’ of the ratio of alloying elements for this class of alloys was set for the first time. We have revealed the short correlation of the ratio Kc’ with the dimensional misfit of γ and γ’ crystal lattices. Also, a high probability to predict the misfit for multicomponent nickel systems is shown, which significantly affected the strength properties. The regression models of correlation dependencies on the dimensional γ/γ’- misfit were offered to predict the short-term and long-term limits of the strength of alloys. We determined the operating temperature at which the misfit value should decrease to zero. The structure stability should increase because of the structural stresses minimizing. This has a positive effect on strength and plastic properties.

Properties of nickel-based superalloys of equiaxial crystallization

Purpose. The aim of the work is to obtain predictive regression models, with the help of which, it is possible to adequately calculate the mechanical properties of nickel-based superalloys of equiaxial crystallization, without carrying out preliminary experiments. Research methods. To find regularities and calculate the latest CALPHAD method was chosen, and modeling of thermodynamic processes of phase crystallization was performed. Results. As a result of experimental data processing, the ratio of alloying elements Kg¢ was proposed for the first time, which can be used to assess the mechanical properties, taking into account the complex effect of the main alloy components. The regularities of the influence of the composition on the properties of heat-resistant nickel alloys of equiaxial crystallization are established. The analysis of the received dependences in comparison with practical results is carried out. The relations well correlated with heat resistance, mismatch and strength of alloys are obtained. Scientific novelty. It is shown that for multicomponent nickel systems it is possible with a high probability to predict a mismatch, which significantly affects the strength characteristics of alloys of this class. The regularities of the influence of the chemical composition on the structure and properties of alloys are established. A promising and effective direction in solving the problem of predicting the main characteristics of heat-resistant materials based on nickel is shown Practical value. On the basis of an integrated approach for multicomponent heat-resistant nickel-based alloys, new regression models have been obtained that make it possible to adequately predict the properties of the chemical composition of the alloy, which made it possible to solve the problem of computational prediction of properties from the chemical composition of the alloy. This allows not only to design new nickel-based alloys, but also to optimize the composition of existing brands.

Numerical Investigation of Residual Displacement of Rocking Self-Centering Columns Under Cyclic Loading

Well-designed rocking self-centering (RSC) columns are capable of achieving small residual displacement. However, few studies conducted the quantitative analysis for the residual displacement of RSC columns. The residual displacement is the product of the struggle between the self-centering (SC) capacity and the energy dissipation (ED) capacity. In this study, a SC factor and an ED parameter were defined to reflect the SC and ED capacity of the RSC column, respectively. The influence of eight common design parameters on the SC factor and the ED parameter was explored using factorial analysis. Parametric analysis was performed to investigate the tendency of the SC factor and the ED parameter with the increase of maximum drift. According to the results of the parametric analysis, the effect of the SC factor and the ED parameter on the distribution of the residual drift was researched statistically. A simplified formula was proposed to calculate the upper limit of the residual drift. What is more, a set of predictive regression formulas was established to estimate the actual residual drift, these regression formulas have an applicable condition that the ED parameter should be larger than 0.75. When the ED parameter was less than 0.75, the residual drift is approximate to zero.

Kurtosis and Skewness on Lagged Market Risk Premium in Indonesian Market During Covid 19 Pandemic

This study investigates the impact of return distribution such as skewness and kurtosis on lagged market risk premium to risk premium in Indonesia capital market during COVID-19 pandemic. Data are monthly, from january to December 2020, and 674 firms. Panel data predictive regression is used The method For this study, I first looked for market risk premium and risk premium desripitives. Second, I used monthly panel data predictive regression from lagged market risk premium and risk premium in 2020. Third, I incorporate skewness and kurtosis simultaneously. Fourth, I exclude kurtosis or skewness in previous model. The results are market risk premium and risk premium having negative return. Risk premium has lower returns than market risk premium. The beta lagged market risk premium is significant to risk premium. The skewness and kurtosis market risk premium do not signify to risk premium together but significant separately. I can clonclude that the movement market risk premim and risk premium during COVID-19 pandemic are average negative. Beta lagged market risk premium can explain the future monthly risk premium. Contrary skewness and kurtosis, those can not be run together. When the model used to beta lagged market risk premium and skewness, partially the skewness was significant and the direction was positive. However, only beta lagged market risk premium and kurtosis were staying negative to the previous model. Incorporating lagged assumptive distribution only explain the risk premium under 1 % about 0.24%.