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Quantum computation based on superconducting circuits utilizes superconducting qubits with Josephson tunnel junctions. Engineering high-coherence qubits requires materials optimization. In this work, we present two superconducting thin film systems, grown on silicon (Si), and one obtained from the other via annealing. Cobalt (Co) thin films grown on Si were found to be superconducting [EPL 131 (2020) 47001]. These films also happen to be a self-organised hybrid superconductor/ferromagnet/superconductor (S/F/S) structure. The S/F/S hybrids are important for superconducting $\pi$-qubits [PRL 95 (2005) 097001] and in quantum information processing. Here we present our results on the superconductivity of a hybrid Co film followed by the superconductivity of a CoSi$_2$ film, which was prepared by annealing the Co film. CoSi$_2$, with its $1/f$ noise about three orders of magnitude smaller compared to the most commonly used superconductor aluminium (Al), is a promising material for high-coherence qubits. The hybrid Co film revealed superconducting transition temperature $T_c$ = 5 K and anisotropy in the upper critical field between the in-plane and out-of-plane directions. The anisotropy was of the order of ratio of lateral dimensions to thickness of the superconducting Co grains, suggesting a quasi-2D nature of superconductivity. On the other hand, CoSi$_2$ film showed a $T_c$ of 900 mK. In the resistivity vs. temperature curve, we observe a peak near $T_c$. Magnetic field scan as a function of $T$ shows a monotonic increase in intensity of this peak with temperature. The origin of the peak has been explained in terms of parallel resistive model for the particular measurement configuration. Although our CoSi$_2$ film contains grain boundaries, we observed a perpendicular critical field of 15 mT and a critical current density of 3.8x10$^7$ A/m$^2$, comparable with epitaxial CoSi$_2$ films., Comment: 15 pages, 7 figures, 2 tables

Systematic variation is a common issue in metabolomics data analysis. Therefore, different scaling and normalization techniques are used to preprocess the data for metabolomics data analysis. Although several scaling methods are available in the literature, however, choice of scaling, transformation and/or normalization technique influence the further statistical analysis. It is challenging to choose the appropriate scaling technique for downstream analysis to get accurate results or to make a proper decision. Moreover, the existing scaling techniques are sensitive to outliers or extreme values. To fill the gap, our objective is to introduce a robust scaling approach that is not influenced by outliers as well as provides more accurate results for downstream analysis. Here, we introduced a new weighted scaling approach that is robust against outliers however, where no additional outlier detection/treatment step is needed in data preprocessing and also compared it with the conventional scaling and normalization techniques through artificial and real metabolomics datasets. We evaluated the performance of the proposed method in comparison to the other existing conventional scaling techniques using metabolomics data analysis in both the absence and presence of different percentages of outliers. Results show that in most cases, the proposed scaling technique performs better than the traditional scaling methods in both the absence and presence of outliers. The proposed method improves the further downstream metabolomics analysis. The R function of the proposed robust scaling method is available at https://github.com/nishithkumarpaul/robustScaling/blob/main/wscaling.R, Comment: 34 pages, 7 figures