The cosmological constant problem is localizated in the convergence between general relativity and quantum field theory, it is considered as a fundamental problem in modern physics. In this paper we describe a different point of view of this problem. We discuss how the problem could depend to different definition of the vacuum energy density.
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.
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 492 citations as per Google Scholar report
