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Due to the importance of COVID-19 control, innovative methods for predicting cases using social network data are increasingly under attention. This study aims to predict confirmed COVID-19 cases using X (Twitter) social network data (tweets) and deep learning methods. We prepare data extracted from tweets by natural language processing (NLP) and consider the daily G-value (growth rate) as the target variable of COVID-19, collected from the worldometer. We develop and evaluate a time series mixer (TSMixer) predictive model for multivariate time series. The mean squared error (MSE) loss on the test dataset was 0.0063 for 24-month Gvalue prediction when using the MinMax normalization with recursive feature elimination (RFE) and average or min aggregation method. Our findings illuminate the potential of integrating social media data to enhance daily COVID-19 case predictions and are applicable also for epidemiological forecasting purposes. [ABSTRACT FROM AUTHOR]

Summary: This review article explores recent advancements in converting cheese whey (CW) into high‐value‐added products, focusing on producing biofuels, bioelectricity and different whey‐based food items, as well as other potential uses. Additionally, new integrated reutilising strategies are proposed to utilise CW to develop innovative products with various applications, mainly focusing on sustainable alternatives at the industrial level. CW could be utilised with the help of different processes, for example separation of main protein components from cheese whey, like separation and purification of α‐lactalbumin, production of polysaccharides (lactose and lactulose), volatile fatty acids (VFAs), other organic acids and bioethanol. Besides these products, CW could also be utilised in the fermentation process, in the production of alcoholic drinks (whey sprite and white whisky), in the production of whey protein concentrate and as poultry feed. Biotechnological approaches for whey utilisation are the most recent alternatives. They can produce important products for the industries, such as whey‐based products, bioplastics, biofuels, bioelectricity, organic acids, bioactive compounds and whey‐based food items. [ABSTRACT FROM AUTHOR]

Antiperovskites have become the main focus of research and development in energy harvesting technologies. This research aims to evaluate the structural, electronic, optical, and thermoelectric characteristics of K3CuO and K3AgO using DFT-based calculations with the WIEN2K program. The cubic phase and thermal stability assessment has been ascertained by computing the tolerance factor and formation energy. The band configurations of the investigated antiperovskites have documented direct band gap values. Both materials exhibit semiconducting properties, with band gap values of 0.82 and 0.56 eV, respectively. The various properties of these studies have significant consequences on a range of optoelectronic and thermoelectric technologies. In addition, the optical characteristics have also been elucidated to determine the feasibility of materials for optoelectronic devices. The optical aspects have revealed that these antiperovskites might be promising options for infrared detectors and other IR-based optoelectronic applications. Thermoelectric attributes have been examined, and Figure of merit (ZT) values of 0.59 and 0.63 are obtained, which suggest their suitability for thermoelectric systems. The results of this research offer an extensive understanding of forecasting the characteristics of these substances and suggest their suitability for optoelectronic and thermoelectric technologies. [ABSTRACT FROM AUTHOR]