Sudipta Basu*, Anirban Goswami, Proloy Banerjee and Shreya Bhunia
In this article it is tried to construct a stochastic model which looks a generalized stochastic version of Von Bertalanffy power law model and Richard’s model and one can use to describe biological growth phenomena according to the appropriate situation and suitability of this model. It is mainly constructed to explain growth dynamics of patients infected by COVID-19 in South Korea. Here it is attempted to find the expression of variable of interest at time t and also the MLE of growth rate parameter is worked out. This model is applied to a real life data of infected patients by COVID-19 in South Korea after observing the growth pattern. This model could be used to the data sets of other countries, where no lockdown was imposed as a precautionary measure to deal with this situation. Then a comparative study is made between some well-known models and special cases of the model, described here. It is found that the special cases of the model that is described in this article fits better to the data than others.
DOI: 10.37421/2090-0902.2023.14.407
The transport of contaminants emanating from localized sources such as factories and agricultural farms in porous media, is of hydro dispersion phenomena, and has been the major subject for more than four decades. Because of industrial and agricultural activities, inorganic wastes mainly non-biodegradable substances from oil spills, human wastes, fertilizers among others, percolates through porous media and eventually find their way to water bodies and food crops in the farms. Some of these substances are harmful to human health and gets to our bodies through the water we drink or the food we eat. This research study aims to formulate a particle tracking mathematical model of contaminant flow through a porous media. The governing equation of three-dimensional concentration distribution in fluid flow through porous media has been formulated using advection-dispersion equation. This equation has been solved analytically and numerically using three-dimensional finite difference algorithm. Simulation to validate solutions is done using data from agricultural chemicals as the source point. Results confirm that the concentration of one time source of contaminant decreases as it diffuses away from the source point with respect to distance and time. The plume evolved horizontally and vertically, with peak concentration at the source, and decays further and downwards due to degradation, reaction and sorption. Particle concentration tracking shows that concentration of 100 mg/l at a point source decreases to 0 mg/l, after a distance of 300 m. For a toxic chemical like sulphur dioxide, glyphosate, and trinidol, if released from a point near borehole or food crops less than 300 m, the contaminant can be traced to the drinking water and edible parts of the crop and accumulation in the body may be carcinogenic or cause kidney and liver infections. We recommend that for water pollution minimization, and safe food crops, the source of contaminant should be more than 300 m. Additional reaction methods can be used to decompose the contaminant before reaching unwanted places.
DOI: 10.37421/2090-0902.2023.14.432
This study is conducted upon static mechanics. In physics, static mechanics is a field of statics. Through static mechanics, we investigate the mechanism of the universe via mathematical interpretations and axiomatic interpretations. We found that the universal mechanism is static; we mean: all motion of bodies’ celestial bodies, quantum bodies in the universe is still. The implication of this finding is that all motion in the universe is an illusion; there is no motion of bodies.
John Franklin Ogilvie
We consider the quantum aspects of chemical and physical observations and practices, including quantum physics, quantum mechanics, quantum chemistry and the quantum laws of nature. The technical term quantum implies discrete -- the discreteness of a physical entity or an observable property. This term might appear in four legitimate scientific contexts -- quantum physics, quantum mechanics, quantum chemistry and quantum laws. As an extension of a previous report, we consider briefly each in turn.
Physics-based image formation models enable computationally obtaining meaningful information by processing other forms of information which can be acquired through measurements. In practical situations however, the inner functionalities of the system which create the impulse response function are usually unknown, and due to noise, measurements are unreliable. Before Deep Neural Networks (DNNs) taking over, Compressed Sensing (CS) techniques were primarily being used to address this lack of information by imposing assumptions into the problem. But this switch to DNNs came with the price of mass data acquisition for training to leap over the never-ending problem of algorithmic fidelity in CS methods. Recently, deep image prior and untrained or semi-trained networks, while leveraging the power of DNNs and algorithms, have become successful to be considered as potential answers to the desire of finding a cost-efficient yet powerful solution. In this paper, we briefly have a look at the recent breakthroughs conducted over this concept to solve various imaging problems.
Physical Mathematics received 686 citations as per Google Scholar report
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