Catarratto is one of the most common non-aromatic white grape varieties cultivated in Sicily (Southern Italy). In order to improve the aromatic expression of Catarratto wines a trial was undertaken to investigate the effect of yeast strain, nutrition and reduced glutathione. Variables included two Saccharomyces cerevisiae strains, an oenological strain (GR1) and one isolated from honey by-products (SPF52), three different nutrition regimes (Stimula Sauvignon Blanc™ (SS), Stimula Chardonnay™ (SC) and classic nutrition practice), and a specific inactivated yeast rich in reduced glutathione to prevent oxidative processes [Glutastar™ (GIY)] ensuing in ten treatments (T1-T10). Microbiological and chemical parameters demonstrated the aptitude of strain SPF52 to successfully conduct alcoholic fermentation. During fermentation, the Saccharomyces yeast populations ranged from 7 to 8 logarithmic CFU/mL. All wines had a final ethanol content ranging between 12.91 and 13.85% (v/v). The dominance of the two starter strainof Catarratto wines.The isoflavones daidzin and genistin, present in soybeans, can be transformed by the intestinal microbiota into equol and 5-hydroxy-equol, compounds with enhanced availability and bioactivity, although these are only produced by a fraction of the population. Hence, there is an interest in the production of these compounds, although, to date, few bacteria with biotechnological interest and applicability in food have been found able to produce equol. In order to obtain lactic acid bacteria able to produce equol, the daidzein reductase (dzr), dihydrodaidzein reductase (ddr), tetrahydrodaidzein reductase (tdr) and dihydrodaidzein racemase (ifcA) genes, from Slackia isoflavoniconvertens DSM22006, were cloned into the vector pNZTuR, under a strong constitutive promoter (TuR). DNQX supplier Lactococcus lactis MG1363, Lacticaseibacillus casei BL23, Lactiplantibacillus plantarum WCFS1, Limosilactobacillus fermentum INIA 584L and L. fermentum INIA 832L, harbouring pNZTuR.tdr.ddr, were able to produce equol from dihydrodaidzein, while L. fermentum strains showed also production of 5-hydroxy-equol from dihydrogenistein. The metabolization of daidzein and genistein by the combination of strains harbouring pNZTuR.dzr and pNZTuR.tdr.ddr showed similar results, and the addition of the correspondent strain harbouring pNZTuR.ifcA resulted in an increase of equol production, but only in the L. fermentum strains. This pattern of equol and 5-hydroxy-equol production by L. fermentum strains was also confirmed in cow's milk supplemented with daidzein and genistein and incubated with the different combination of strains harbouring the constructed plasmids. Bacteria generally recognized as safe (GRAS), such as the lactic acid bacteria species used in this work, harbouring these plasmids, would be of value for the development of fermented vegetal foods enriched in equol and 5-hydroxy-equol.Vibration signals from rotating machineries are usually of multi-component and modulated signals. Hilbert-Huang transform (HHT), hereby referring to the combination of empirical mode decomposition (EMD) and normalized Hilbert transform (NHT), is an effective method to extract useful information from the multi-component and modulated signals. However, sifting stopping criterion (SSC) that is crucial to the HHT performance has not been well explored for this sift-driven method in the past decades. This paper proposes the soft SSC, which can ease the mode-mixing problem in signal decomposition through the EMD and improve demodulation performance in signal demodulation. The soft SSC can adapt to input signals and determine the optimal iteration number of a sifting process by tracking this sifting process. Extensive simulations show that the soft SSC can enhance the performance of the HHT in signal decomposition, signal demodulation, and the estimation of the instantaneous amplitude and frequency over the existing state-of-the-art SSCs. Finally, the improved HHT with the soft SSC is demonstrated on the fault diagnosis of wheelset bearings.Despite the increased sensor-based data collection in Industry 4.0, the practical use of this data is still in its infancy. In contrast, academic literature provides several approaches to detect machine failures but, in most cases, relies on simulations and vast amounts of training data. Since it is often not practical to collect such amounts of data in an industrial context, we propose an approach to detect the current production mode and machine degradation states on a comparably small data set. Our approach integrates domain knowledge about manufacturing systems into a highly generalizable end-to-end workflow ranging from raw data processing, phase segmentation, data resampling, and feature extraction to machine tool anomaly detection. The workflow applies unsupervised clustering techniques to identify the current production mode and supervised classification models for detecting the present degradation. A resampling strategy and classical machine learning models enable the workflow to handle small data sets and distinguish between normal and abnormal machine tool behavior. To the best of our knowledge, there exists no such end-to-end workflow in the literature that uses the entire machine signal as input to identify anomalies for individual tools. Our evaluation with data from a real multi-purpose machine shows that the proposed workflow detects anomalies with an average F1-score of almost 93%.In this research, a novel control method is proposed for 3-Phase Induction Motor (3-PIM) drives with star-connected under Single-Phase Open-Circuit (SPOC) fault using modified Rotor Field-Oriented Control (RFOC) strategy. The standard RFOC strategy is designed based on the d-q model of 3-PIM during normal mode. This strategy cannot be applied for a 3-PIM under SPOC fault as the model of a healthy 3-PIM is different from the model of a faulty 3-PIM. The use of standard RFOC strategy for Vector Control (VC) of a 3-PIM under SPOC fault increases the electromagnetic torque ripples during this fault. To overcome this problem, two unbalanced rotational transformations are used. A Current Transformation Matrix (CTM) is obtained based on the concept of maintaining value of Magnetic Motive Force (MMF) of the faulted machine like the healthy machine. Then, a Voltage Transformation Matrix (VTM) is introduced by using the presented CTM and according to the Single-Phase Induction Motor (SPIM) model. Based on the results, faulted machine RFOC equations were obtained similar to the healthy machine RFOC equations by using the CTM and VTM.