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Physical endurance is the time span between the beginning of physical activity by an individual and the termination because of exhaustion. Physical endurance involves a multifaceted behaviour which can be understood by complexities. Everyone performs physical activity in order to sustain-life. However, the number of activities done are largely subject to personal choice and varies from person to person as well as for a given person over time. Physical activity like meditation/exercises are positively related to physical fitness. One needs to understand relation between physical activity, exercise, physical fitness and health. These activities can be partitioned mutually exclusively into many different ways. This paper categorizes daily physical activity into three broad subdivisions based on amount of body movements taking place are: (i) light, (ii) moderate and (iii) high intensity. These three characterizations are considered to be mutually exclusive and sum up to total energy spent by an individual. The behavior of the three factors physical activity, heart and energy generated is analyzed with the help of Fast Lyapunov indicator (FLI), Dynamic Lyapunov indicator (DLI), Small alignment index (SALI). FLI’s increase for chaotic orbits for values of R=20, Q=70 for the case of high intensity exercises and to linearly regular orbits for values of R=5, Q=8 and R=10, Q=12 in the cases of light and moderate exercises respectively. SALI’s alters through non-zero value for R=20, Q=70 while it tends to zero for values of R=5, Q=8 and R=10, Q=12. DLI’s the largest Eigen values form a definite pattern/curve for n=2000 for values of R=5, Q=8 and n=100 for R=10, Q=12 respectively as the motion stays regular plus dispersed randomly as the motion is chaotic for n=60 and for R=20, Q=70.

Salinization of streams, rivers and other water sources threaten the civilizations, ecologies enduring constituent species that results in rendering the precious water unusable for human chores. Increase in salinity across the flow in streams and wet-lands have been mostly to raise a concern towards salt tolerance to various limits. Hence, it becomes important to monitor the acidity/alkalinity causing water parameters that can be referred to as salinity. The prime measure scale of salinity is the quality of potential-of-hydrogen (pH) present in river waters at two sample locations. Two locations that have been identified by CPCB as per the highly reported pollutants’ level found, have been analysed through artificial-intelligence (AI) conjucted with Multivariate Adaptive Regression Spline (MARS). The hybrid of wavelet neuro-fuzzy inferences with that of MARS (WNF-MARS) predicted with more accuracy. Simulation of performance measures: root meant square error (RMSE); mean absolute error (MAE); goodness-of-fit (R2) together with their execution time for the three prototypes provided remarkable results. RMSE outcomes diminish on the whole on applying the training and validating data division in Wavelet conjucted MARS and WNF-MARS as compared to studying the data through MARS. Goodness-of-fit statistic analysed the concentration levels of salinity in the river at the identified sites. Thus, it is observed from this study that the pH levels provide future estimation of inapt quality of water at the source, so that it prohibits the further-decay of water consumed in the ecosystem. Thus, these predictors would be helpful towards formulation of strategies for protection of vegetation and other required purposes.

Coronavirus disease so called as COVID-19 is an infectious disease and its spread takes place due to human interaction by their pathogen materials during coughing and sneezing. COVID-19 is basically a respiratory disease as evidence proved that a large number of infected people died due to short breathing. Most widely and uncontrollably spreading unknown viral genome infecting people worldwide was announced to be 2019-2020

The paper intends to deliver a structure of speed-control for electric DC motor widely being used in the electric rechargeable-battery vehicles. Electric vehicles are the need of the hour due to increasing environmental concerns and the dependency on fuels and oils. So as to promote this hybrid and electric vehicle technology and ensure its sustenance, the Ministry of Heavy Industry and Public Enterprises in the Gazette of India on 13th of March, 2015 approved the Scheme for Faster adoption and manufacture of (Hybrid &) Electric Vehicle in India referred as FAME-India under National Electric Mobility Mission (NEMM). This scheme intends to encourage the hybrid/electric motor driven vehicles in the market and also its manufacturing for the betterment of eco-system to be implemented over a period of six years till 2020. Electric-battery driven vehicle is sourced on the restricted electrical-energy delivered by the battery in circuit. Major contribution of this work is to propose control-strategy through Fuzzified-PID controller so that the performances of the electric vehicle is comparable to that of an internal combustion-engine vehicle. Feedback is the foundation of PID control. The target or the set point is compared with the resultant of the process. Then, correction is computed and applied for the difference identified. This procedure is carried on till the time recalculation is required. PID refers to the combined computation of proportional-integral-derivative. Controllers, in general do not apply all three mathematical functions. Maximum processes were being handled through the proportional-integral-terms. However, addition of derivative control for fine control plus to avoid overshoot are required. Following models: PID controller, hybrid Fuzzified-reasoning PID controllers for linear surfaces and non-linear control surfaces using n-D Lookup-Table data have been designed for a comparative study. It has been observed that hybrid model designed for non-linear control-surfaces provided better speed response and have zero steady state error. The simulation of these models is carried out using SIMULINK under varying state conditions.

Coronaviruses did not invite attention at a global level and responsiveness until the series of 2003-SARS contagion followed by year-2012 MERS plus, most recently, 2019-nCoV eruptions. SARS-CoV & MERS-CoV are painstaking, extremely pathogenic. Also, very evidently, both have been communicated from bats to palm-civets & dromedary camels and further transferred ultimately to humans. No country has been deprived of this viral genomic contamination wherever populaces reside and are interconnected. This study aimed to develop a mathematical model for calculating the transmissibility of this viral genome. The analysis aids the study of the outbreak of this Virus towards the other parts of the continent and the world. The parameters such as population mobility, natural history, epidemiological characteristics, and the transmission mechanism towards viral spread when considered into crowd dynamism result in improved estimation. This article studies the impact of time on the amount of susceptible, exposed, the infected person taking into account asymptomatic and symptomatic ones; recovered i.e., removed from this model and the virus particles existing in the open surfaces. The transition from stable phase to attractor phase happens after 13 days i.e.; it takes nearly a fortnight for the spread to randomize among people.Further, the pandemic transmission remains in the attractor phase for a very long time if no control measures are taken up. The attractor-source phase continues up to 385 days i.e., more than a year, and perhaps stabilizes on 386th day as per the Lyapunov exponent's analysis. The time series helps to know the period of the Virus's survival in the open sources i.e. markets, open spaces and various other carriers of the Virus if not quarantined or sanitized. The Virus cease to exist in around 60 days if it does not find any carrier or infect more places, people etc. The changes in LCEs of all variables as time progresses for around 400 days have been forecasted. It can be observed that phase trajectories indicate how the two variables interact with each other and affect the overall system's dynamics. It has been observed that for exposed and asymptomatically infected (y–z), as exposed ones (y) change from 0 to 100 the value of asymptomatically infected (z) increased upto around 58, at exposed ones (y) = 100, asymptomatically infected (z) has two values as 58 and 10 i.e. follows bifurcation and as exposed ones (y) changes values upto 180, the value of asymptomatically infected (z) decreases to 25 so for exposed ones (y) from 100 to 180, asymptomatically infected (z) varies from 58 to 25 to 10 follows bifurcation. Also, phase structures of exposed-symptomatically infected (y–u), exposed-removed (y–v), exposed-virus in the reservoir (y–w), asymptomatically infected-removed (z–v), symptomatically infected-removed (u–v) specifically depict bifurcations in various forms at different points. In case of asymptomatically infected-virus in the reservoir (z–w), at asymptomatically infected (z) = 10, the value of viruses in the reservoir (w) = 50, then as asymptomatically infected (z) increases to upto around 60. At this point, removed ones (v) increase from 50 to 70 and asymptomatically infected (z) decrease to 20 i.e., crosses the same value twice, which shows its limiting is known as limit cycle behavior and both the values tend to decrease towards zero. It shows a closed-loop limit cycle. Today, there has been no scientific revolution in the development of vaccination, nor has any antiviral treatment been successful, resulting in lack of its medication. Based on the phases, time series, and complexity analysis of the model's various parameters, it is studied to understand the variation in this pandemic's scenario.

Water is life and is the most precious resource on Earth. Earth consists of 70% of water, 2.5% of freshwater, and 1% of easily accessible freshwater; thus, only 0.007% of Earth’s water is accessible. The survival of life on Earth is directly proportional to the presence of water among other important resources. Water remains to be a natural resource with no replacement. In today’s era, where science and technology are growing every hour and innovating new technologies and devices to make life easier and comfortable, but no artificial intelligence could either replicate or replace the need for water on Earth. The present study deals with the qualitative exploration of water quality components like potential of hydrogen (pH), chemical oxygen demand (COD); biochemical oxygen demand (BOD); dissolved oxygen (DO) of Yamuna River at different sample sites. Various sample sites designated for highly reported pollutants using artificial intelligence through least squares support vector regression (LSSVR) and hybrid of wavelet and LSSVR. It is observed that hybrid of wavelet and least squares support vector regression (WLSSVR) predicted good quality accurately among the two prototypes simulated on the basis of the simulation errors which are root–mean-square error (RMSE); mean absolute error (MAE); coefficient of determination (R2); and execution time for both prototypes. RMSE values decrease overall on training and validating via WLSSVR as compared to LSSVR. It is observed that MAE values show a lesser decrease as it is in RMSE; on an average, MAE has lesser variability and R2 has a greater variability as per simulations. The simulation is carried out to analyze the level of various pollutants in the Yamuna River at different sites for the consideration of the quality of water. The observed pattern from the study may help for the future prediction of the quality of water parameters, so that it prohibits the further decay of water quality which may prove to be lethal to the environment. These forecasts may be helpful for the formulation of policies, planning, and execution for the protection of the environment and quality of water.

Significance of this research is to accomplish tentative study on highs and lows of particular S&P BSE stock prices using proposed intelligent models, multivariate adaptive regression spline (MARS) and M5 prime regression tree-(M5’). Anticipated models work to predict as there exists vitality for price instability. Daily highs and lows of the stock price data have been considered as the data set. This article discusses about computational ability of the MARS and M5’ regressions during the time period and also how better accuracy can be attained. M5’ constructs in two phases: growing and pruning which smoothen regression tree at nodes. MARS builds complex configuration of correlation among multiple responses. This can be helpful for investors to predict significant statistics for trading stocks listed on BSE.

This paper studies the impact of the decision taken by the Government of India to demonetize Rs.500 and Rs.1000 currency which meant that the legal tender of currency units was announced invalid from 9th November, 2016. The effect of demonetization on the stock market is studied for daily basis data of Opening prices of NSE Nifty India consumption from 1st April, 2016 to 31st July, 2017. The data has been divided into two time periods: before and after demonetization. Wavelet transformation improves the prediction by taking valuable information at various levels and improving the accuracy of forecasting. This is similar to the application of a set of a narrowband filter in conventional Fourier analysis. It is observed that the estimates made by Wavelet-Neural-Fuzzy models are more precise as compared to those by Artificial Neural Networks, Fuzzy and Wavelet-Neural models. The disintegration level in the wavelet transform may be computed with respect to the periodicity and seasonality of series. Estimated disintegrated subseries data from the wavelet analysis is provided as the input for discussed inference system. The results show that wavelet-neuro-fuzzy coupled model has less forecasting error as compared to other techniques. It is concluded that demonetization has temporary effect on the stock market and investors tend to look towards the plastic money. However, they were bound by the time due to various constraints imposed on cash flow in the market.

Physical exercise is any bodily activity that enhances or maintains physical fitness, overall health, and wellness. Fitness means being able to perform physical activity. It also means having the energy and strength to feel as good as possible. Getting more fit, even a little bit, can improve your health. Exercise is a way of life. It has an important role in the prevention and treatment of lifestyle-related diseases. Physiologically, the benefits of exercises can be explained as more avail-ability of oxygen to all tissues of body as it increases the alveolar ventilation and improves the strength of respiratory muscle and lung volumes by regular practice. In this paper, a mathematical model is developed to study the effects of exercise on the human body and on its two major components lungs and heart. The model uses non-linear differential equations which are time dependent under constant metabolic rate. A compartment model of breath function from lungs to tissues or body cells is discussed. The stability analysis by finding the equilibrium points, eigensystem and phase space was discussed. Further, for improving accuracy and efficiency of the model, time series analysis is also discussed. Lastly, statistical analysis comprising regression line, correlation, fractal dimension, Hurst exponent are also studied for the time series data generated from the non-linear differential equations of the proposed model. It is observed that lungs variable has exponential growth. The body cell and heart variable have chaotic behavior. It is concluded that excessive exercise or increase in time of exercising can be harmful as it leads to more intake of oxygen and then a sudden drop which may be life threatening.