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The purpose of this research is to primarily understand how crypto traders can use the Bitcoin as a hedge or safe haven asset to reduce their losses from crypto trading. The study also aims to provide insights to crypto investors (portfolio managers) who wish to maintain a crypto portfolio for the medium term and can use the Bitcoin to minimize their losses. The findings of this research can also be used by policymakers and regulators for accommodating the Bitcoin as a medium of exchange, considering its safe haven nature.

This study applies the cross-quantilogram (CQ) approach introduced by Han et al. (2016) to examine the safe-haven property of the Bitcoin against the other selected crypto assets. This method is robust for estimating bivariate volatility spillover between two markets given unusual distributions and extreme observations. The CQ method is capable of calculating the magnitude of the shock from one market to another under different quantiles. Additionally, this method is suitable for fat-tailed distributions. Finally, the method allows anticipating long lags to evaluate the strength of the relationship between two variables in terms of durations and directions simultaneously.

The Bitcoin acts as a weak safe haven asset for a majority of new crypto assets for the entire study period. These results hold even during greed and fear sentiments in the crypto market. The Bitcoin has the ability to protect crypto assets from sharp downturns in the crypto market and hence gives crypto traders some respite when trading in a highly volatile asset class.

This study is the first attempt to show how the Bitcoin can act as a true matriarch/patriarch for crypto assets and protect them during market turmoil. This study presents a clear and concise representation of this relationship via heatmaps constructed from CQ analysis, depicting the quantile dependence association between the Bitcoin and other crypto assets. The uniqueness of this study also lies in the fact that it assesses the protective properties of the Bitcoin not only for the entire sample period but also specifically during periods of greed and fear in the crypto market.

This paper aims to investigate in a self-designed closed loop reactor process conditions for thermal inactivation of B16 melanoma cells by microwave and conventional heating.

Besides control experiments (37°C), inactivation rate was determined in the range from 42°C to 46°C. Heating was achieved either by microwave radiation at 2.45 GHz or by warm water. To distinguish viable from dead cells, AnnexinV staining method was used and supported by field effect scanning electron microscopy (FE-SEM) imaging. Furthermore, numerical simulations were done to get a closer look into both heating devices. To investigate the thermal influence on cell inactivation and the differences between heating methods, a reaction kinetics approach was added as well.

Control experiments and heating at 42°C resulted in low inactivation rates. Inactivation rate at 44°C remained below 12% under conventional, whereas it increased to >70% under microwave heating. At 46°C, inactivation rate attained 68% under conventional heating; meanwhile, even 88% were determined under microwave heating. FE-SEM images showed a porous membrane structure under microwave heating in contrast to mostly intact conventional heated cells. Numerical simulations of both heating devices and a macroscopic Arrhenius approach could not sufficiently explain the observed differences in inactivation.

A combination of thermal and electrical effects owing to microwave heating results in higher inactivation rates than conventional heating achieves. Nevertheless, it was not possible to determine the exact mechanisms of inactivation under microwave radiation.

The compressive strength of concrete depends on many interdependent parameters; its exact prediction is not that simple because of complex processes involved in strength development. This study aims to predict the compressive strength of normal concrete and high-performance concrete using four datasets.

In this paper, five established individual Machine Learning (ML) regression models have been compared: Decision Regression Tree, Random Forest Regression, Lasso Regression, Ridge Regression and Multiple-Linear regression. Four datasets were studied, two of which are previous research datasets, and two datasets are from the sophisticated lab using five established individual ML regression models.

The five statistical indicators like coefficient of determination (R²), mean absolute error, root mean squared error, Nash–Sutcliffe efficiency and mean absolute percentage error have been used to compare the performance of the models. The models are further compared using statistical indicators with previous studies. Lastly, to understand the variable effect of the predictor, the sensitivity and parametric analysis were carried out to find the performance of the variable.

The findings of this paper will allow readers to understand the factors involved in identifying the machine learning models and concrete datasets. In so doing, we hope that this research advances the toolset needed to predict compressive strength.