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Molecular line observations of starless (prestellar) cores combined with a chemical evolution modeling and radiative transfer calculations are a powerful tool to study the earliest stages of star formation. However, conclusions drawn from such a modeling may noticeably depend on the assumed thermal structure of the cores. The assumption of isothermality, which may work well in chemo-dynamical studies, becomes a critical factor in molecular line formation simulations. We argue that even small temperature variations, which are likely to exist in starless cores, can have a non-negligible effect on the interpretation of molecular line data and derived core properties. In particular, ``chemically pristine isothermal cores (low depletion) can have centrally peaked C$^{18}$O and C$^{34}$S radial intensity profiles, while having ring-like intensity distributions in models with a colder center and/or warmer envelope assuming the same underlying chemical structure. Therefore, derived molecular abundances based on oversimplified thermal models may lead to a mis-interpretation of the line data.
Temperature, the central concept of thermal physics, is one of the most frequently employed physical quantities in common practice. Even though the operative methods of the temperature measurement are described in detail in various practical instruct
We examine the possibility of soft cosmology, namely small deviations from the usual framework due to the effective appearance of soft-matter properties in the Universe sectors. One effect of such a case would be the dark energy to exhibit a differen
In the present paper, we investigate the cosmographic problem using the bias-variance trade-off. We find that both the z-redshift and the $y=z/(1+z)$-redshift can present a small bias estimation. It means that the cosmography can describe the superno
This paper studies robust mean estimators for distributions with only finite variances. We propose a new loss function that is a function of the mean parameter and a robustification parameter. By simultaneously optimizing the empirical loss with resp
The intent recognition is an essential algorithm of any conversational AI application. It is responsible for the classification of an input message into meaningful classes. In many bot development platforms, we can configure the NLU pipeline. Several