Validation of the test for detecting SARS--CoV-2 antigens in the Polish population in patients with suspected SARS-CoV-2 infection.

In December 2019, the World Health Organization (WHO) reported that China had accumulated pneumonia of unclear etiology in Wuhan. According to WHO recommendations, in strictly defined situations, antigen tests can be implemented into the diagnostic algorithm to reduce the number of molecular tests performed and support the rapid identification and treatment of COVID-19 patients. According to WHO recommendations, the antigen test for diagnostic use should have a sensitivity of ≥ 80% and a specificity of ≥ 97% compared to molecular tests (NAAT). Based on the comparative analysis, the sensitivity and specificity of the SARS-CoV-2 Antigen ELISA test were determined concerning the RT-PCR reference method. The sensitivity of the SARS-CoV-2 Antigen ELISA was 100% (51/51) and the specificity was 98.84%. The obtained data demonstrate that the analyzed antigen test meets both the WHO and the Ministry of Health criteria.

Titanium anodizing in a choline dihydrogencitrate salt-oxalic acid deep eutectic solvent: a step towards green chemistry in surface finishing of titanium and its alloys.

Deep Eutectic Solvents (DESs) are "green" competitors for some conventional plating baths and electrolytes used for surface modification. Their use allows a material to be obtained with a structure different from that observed in conventional plating or finishing technologies. In this work the titanium anodizing process was investigated in a bath based on a choline dihydrogencitrate salt and oxalic acid (1 : 1 molar ratio) green solvent. Titanium anodized at the lowest voltage applied (10 V) was a deep yellow color, which turned to deep blue at 30 V. The surface morphology and topography of titanium, both anodized and untreated, were monitored by optical, scanning electron (SEM and HR-SEM) and atomic force (AFM) microscopy. Anodizing at 10 V produced a fine granular morphology of the oxide layer, while anodizing at 30 V led to the formation of a probably thicker and quite uneven oxide layer, characterized by a distinct and coarse granular morphology. The average size of the micro-nodules was higher than those at 10 V and porous structures have been also identified. According to X-ray photoelectron spectroscopy (XPS) the stoichiometric TiO2, regardless of the applied voltage during anodizing, was practically the only component of the oxide layer produced on titanium in the DES bath. At 10 V, the oxide layer was thicker (>10 nm) than the natural Ti passive layer (approx. 2.2 nm), which, apart from TiO2, also contained oxides of titanium at lower oxidation states, i.e. +2 and +3. Moreover, the XPS technique was supported by electrochemical impedance spectroscopy (EIS), especially in the context of the structure of the oxide layer and its interaction with a corrosive environment. The corrosion resistance of anodized titanium was assessed in 0.05 mol dm-3 solution of NaCl by the linear polarization resistance (LPR) technique and polarization curves. During interpretation of the impedance spectra, the layers produced by the anodizing process were described using the two-layer model. It was assumed that the inner layer formed directly on the surface of metallic titanium was responsible for the barrier properties (resistance of 2.8 MΩ cm2). The porous outer layer formed on it has a much lower corrosion resistance, i.e. 800-1300 Ω cm2.

Anomaly Detection in Asset Degradation Process Using Variational Autoencoder and Explanations.

Development of predictive maintenance (PdM) solutions is one of the key aspects of Industry 4.0. In recent years, more attention has been paid to data-driven techniques, which use machine learning to monitor the health of an industrial asset. The major issue in the implementation of PdM models is a lack of good quality labelled data. In the paper we present how unsupervised learning using a variational autoencoder may be used to monitor the wear of rolls in a hot strip mill, a part of a steel-making site. As an additional benchmark we use a simulated turbofan engine data set provided by NASA. We also use explainability methods in order to understand the model's predictions. The results show that the variational autoencoder slightly outperforms the base autoencoder architecture in anomaly detection tasks. However, its performance on the real use-case does not make it a production-ready solution for industry and should be a matter of further research. Furthermore, the information obtained from the explainability model can increase the reliability of the proposed artificial intelligence-based solution.