BIOMEDICINE ON HORMONE MEDIATION OF SPORTS IN ADOLESCENT HEIGHT DEVELOPMENT

ABSTRACT Introduction: Physical exercise is an important factor in regulating energy balance and body composition. Exercise itself is a kind of body stress. It involves the central nervous system, cardiovascular, respiratory, endocrine, and other systems. Sports have various effects on the hormones in adolescent height development. Objective: This article analyzes the effects of different time and load exercise training on the levels of serum testosterone, free testosterone, and cortisol in young athletes. Methods: The athletes’ blood samples were collected at the quiet time in the morning before each experiment, immediately after exercise, and at three time intervals the next morning. Then blood testosterone (T), free testosterone (FT), and corticosteroids (C) were measured. Results: One-time and one-day high-volume training can cause a decrease in serum testosterone and free testosterone levels and an increase in cortisol hormones in young athletes. The testosterone level of young athletes rises immediately after exercise. Conclusion: Hormonal changes after physical exercise provide a scientific basis for athlete exercise load prediction and exercise plan formulation. Level of evidence II; Therapeutic studies - investigation of treatment results.

VMD-WSLSTM Load Prediction Model Based on Shapley Values

Accurate short-term load forecasting can ensure the safe operation of the grid. Decomposing load data into smooth components by decomposition algorithms is a common approach to address data volatility. However, each component of the decomposition must be modeled separately for prediction, which leads to overly complex models. To solve this problem, a VMD-WSLSTM load prediction model based on Shapley values is proposed in this paper. First, the Shapley value is used to select the optimal set of special features, and then the VMD decomposition method is used to decompose the original load into several smooth components. Finally, WSLSTM is used to predict each component. Unlike the traditional LSTM model, WSLSTM can simplify the prediction model and extract common features among the components by sharing the parameters among the components. In order to verify the effectiveness of the proposed model, several control groups were used for experiments. The results show that the proposed method has higher prediction accuracy and training speed compared with traditional prediction methods.

Outlier analysis of sloshing impact loads on liquid ship cargo

This paper introduces an outlier analysis which can improve the convergence of the statistical analysis results of sloshing model test data. The paper classify possible outliers in the sloshing model test into three categories and present a treatment method for each outlier. The developed outlier analysis is adapted to the model test results for the cargo of the liquefied-natural-gas (LNG) carrier in operation. The results of the present new method are compared with those of the conventional procedure, particularly focusing on long-term sloshing prediction. Through this study, the effectiveness of the present method is observed, and it is found that the present method provides is robust and reliable results in the application of experimental data for load prediction.

Optimization of Load Forecasting in Smartgrid using Artificial Neural Network based NFTOOL and NNTOOL

The motivation behind the research is the requirement of error-free load prediction for the power industries in India to assist the planners for making important decisions on unit commitments, energy trading, system security & reliability and optimal reserve capacity. The objective is to produce a desktop version of personal computer based complete expert system which can be used to forecast the future load of a smart grid. Using MATLAB, we can provide adequate user interfaces in graphical user interfaces. This paper devotes study of load forecasting in smart grid, detailed study of architecture and configuration of Artificial Neural Network(ANN), Mathematical modeling and implementation of ANN using MATLAB and Detailed study of load forecasting using back propagation algorithm.

Metagenomics-Guided Assessment of Water Quality and Predicting Pathogenic Load

Antibiotic-resistant bacteria (ARB) are becoming more prevalent in the environment and are efficiently disseminating through contaminated wastewater resulting in resistome cycling. This chapter compares the bacterial profile of hospital effluents collected from rural, urban, and delta regions of West Bengal, India. Comparative metagenomics analysis identified pathogenic bacterial genera like pseudomonas, escherichia, staphylococcus, lactobacillus, prevotella, acinetobacter across the samples. Delta sample showed highest abundance of pseudomonas whereas rural sample had lower titre of all the common bacterial genera. Urban sample reflected more diversity of different genera in terms of abundance. Pathogenic load prediction revealed significant occurrence of diarrhea, irritable bowel syndrome, liver cirrhosis, ulcerative colitis in the disease network. This chapter proposes a monitoring programme for assessing wastewater health using a combination of culture independent and culture-dependent molecular techniques in order to prevent the spread of pollutants in tropical environments.