Mathematical modeling in behavior responses: The tendency-prediction based on a persistence model on real-time data

Real-time determination of water status upon simultaneous zebrafish exposure to sublethal concentrations of CuSO4

Water pollution from commonly occurring contaminants (metals, xenobiotics, etc.) is a serious global problem. Copper is a commonly occurring water contaminant. A variety of physiological and biological methods have been developed to monitor water quality. The assessment of biological responses is an effective method for identifying the harmful effects of contaminants on ecosystems. Fish is a highly recommended animal model in water quality monitoring. Swimming consistency (firmness) and respiratory metabolism (oxygen consumption rate, carbon dioxide excretion rate and respiratory quotient) are essential for fish to maintain body homeostasis toward coping with environmental stress. We exposed zebrafish to different concentrations (Treatment I-0.1 mg/L and Treatment II-1.58 mg/L) of CuSO₄. We have continuously quantified the strength of behavior (swimming consistency) and physiological (respiratory rates) biomarkers for ten days using an online monitoring system of swimming behavior and external respiration. Swimming consistency and respiratory rates of zebrafish (p<0.05) decreased in the CuSO₄-treated groups compared to the control group. Avoidance behavior has led to an endpoint behavior at copperiedus. The time-delayed toxic effect has resulted in CuSO₄ treatment groups. We checked for swimming consistency aberration on the artificial neural array, Self-organizing map (SOM). Circadian rhythms were influenced by prolonged exposure to CuSO₄ toxicity. A concentration- and duration-dependent behavior anomaly was noted in this study. Swimming behavior and respiratory metabolism patterns are sensitive non-invasive stress biomarkers for water quality monitoring studies.

Real-Time Regulation Model of Physical Fitness Training Intensity Based on Wavelet Recursive Fuzzy Neural Network

It has been widely used in signal processing, image processing, speech recognition and synthesis, pattern recognition, machine vision, machinery fault diagnosis and monitoring, and other scientific and technological fields and has achieved great results. The application potential in nonlinear system identification is increasing. According to the theory of “overload recovery” and “functional reserve”, the mathematical model of “load-fitness state” is established to understand the adaptation characteristics and individual characteristics of athletes to sports training. The model is used to simulate the values and time required to reach the maximum fitness state for four types of precompetition reduction plans and to provide a reference for the development of precompetition training plans. The data required for parameter estimation were the actual training data of six outstanding basketball athletes (mean age 18.2 ± 0.75, mean training years 4.6 ± 0.49). And the coaches' training plan was not intervened during the test. In order to further reduce the biaxial synchronization error of the sports platform and improve the stability of the system, the wavelet transformation capable of time-varying signal analysis and the recursive structure with dynamic capability were combined with the fuzzy neural network, and the learning ability of the neural network was used to learn and adjust the scaling and translation factors in the wavelet function, the mean and standard deviation in the fuzzy structure, and the connection weights between the layers, according to the biaxial synchronization. The simulation results show that the designed global sliding mode controller can improve the convergence speed of tracking error and ensure the single-axis tracking accuracy of the H-type motion platform compared with the traditional sliding mode controller, and the tracking accuracy and synchronization accuracy of the system can be further improved after adding the cross-coupled synchronization controller, but the improvement of synchronization control accuracy is not very satisfactory due to the fixed selection of the parameters of the cross-coupled controller. Further improvement is needed.