Background: Adherence to Helicobacter pylori (H. pylori) eradication treatment is a cornerstone for achieving adequate treatment efficacy.Objective: To determine which factors influence compliance with treatment.Methods: A systematic prospective non‐interventional registry (Hp‐EuReg) of the clinical practice of European gastroenterologists. Compliance was considered adequate if ≥90% drug intake. Data were collected until September 2021 using the AEG‐REDCap e‐CRF and were subjected to quality control. Modified intention‐to‐treat analyses were performed. Multivariate analysis carried out the factors associated with the effectiveness of treatment and compliance.Results: Compliance was inadequate in 646 (1.7%) of 38,698 patients. The non‐compliance rate was higher in patients prescribed longer regimens (10‐, 14‐days) and rescue treatments, patients with uninvestigated dyspepsia/functional dyspepsia, and patients reporting adverse effects. Prevalence of non‐adherence was lower for first‐line treatment than for rescue treatment (1.5% vs. 2.2%; p < 0.001). Differences in non‐adherence in the three most frequent first‐line treatments were shown: 1.1% with proton pump inhibitor + clarithromycin + amoxicillin; 2.3% with proton pump inhibitor clarithromycin amoxicillin metronidazole; and 1.8% with bismuth quadruple therapy. These treatments were significantly more effective in compliant than in non‐compliant patients: 86% versus 44%, 90% versus 71%, and 93% versus 64%, respectively (p < 0.001). In the multivariate analysis, the variable most significantly associated with higher effectiveness was adequate compliance (odds ratio, 6.3 [95%CI, 5.2–7.7]; p < 0.001).Conclusions: Compliance with Helicobacter pylori eradication treatment is very good. Factors associated with poor compliance include uninvestigated/functional dyspepsia, rescue‐treatment, prolonged treatment regimens, the presence of adverse events, and the use of non‐bismuth sequential and concomitant treatment. Adequate treatment compliance was the variable most closely associated with successful eradication.

Early failure detection in gear systems reduces unplanned downtime and associated maintenance costs in rotating machinery. Although numerous indicators can be extracted from vibration signals, selecting the most relevant ones remains challenging. This study proposes a methodology for selecting time-domain features to classify fault severity levels in spur gearboxes. Vibration signals are acquired using six accelerometers and processed to extract 64 statistical condition indicators (CIs). The most informative subset of CIs is identified and selected through a wrapper-based selection approach and artificial intelligence tools. The selected features are then evaluated based on the classification accuracy and the area under the curve (AUC) in receiver operating characteristic (ROC) achieved using Random Forest (RF) and K-nearest neighbours (K-NN) models, with performance exceeding 98%. Additionally, the effect of sensor position and inclination on signal quality and classification performance is analysed using factorial analysis of variance (ANOVA) and multiple comparison tests. The results confirm the robustness of the selected CIs and the minimal influence of sensor placement variability, supporting the practical applicability of the proposed approach in industrial settings. The methodology offers a structured framework for selecting condition indicators in vibration signals, experimentally validated using multiple sensors and fault severity levels, and it is both automated and straightforward to implement.

Benzothiadiazole (BTH) is a functional analogue of the phytohormone salycilic acid (SA) involved in the plant immune response. NahG tomato plants are unable to accumulate SA, which makes them hypersusceptible to several pathogens. Treatments with BTH increase the resistance to bacterial, fungal, viroid, or viral infections. In this study, metabolic alterations in BTH-treated Money Maker and NahG tomato plants infected by citrus exocortis viroid (CEVd) were investigated by nuclear magnetic resonance spectroscopy. Using multivariate data analysis, we have identified defence metabolites induced after viroid infection and BTH-treatment. Glycosylated phenolic compounds include gentisic and ferulic acid accumulated in CEVd-infected tomato plants, as well as phenylalanine, tyrosine, aspartate, glutamate, and asparagine. Besides, an increase of γ-aminobutyric acid (GABA), glutamine, adenosine, and trigonelline, contributed to a clear discrimination between the metabolome of BTH-treated tomato leaves and their corresponding controls. Among them, GABA was the only metabolite significantly accumulated in both genotypes after the chemical treatment. In view of these results, the addition of GABA was performed on tomato plants infected by CEVd, and a reversion of the NahG hypersusceptibility to CEVd was observed, indicating that GABA could regulate the resistance to CEVd induced by BTH.

Spur gearboxes are an integral component in the operation of rotary machines. Hence, the early determination of the severity level of a failure is crucial. This manuscript delineates a methodology for selecting essential mother wavelets and filters from the wavelet transform (WT) to process the vibration signal within the time‐frequency domain, aiming to ascertain the severity level of failures in spur gearboxes. Initially, information is garnered from the gearbox through vibration signals in the time domain, utilising six accelerometers. Subsequently, the signal is partitioned into various levels, and information from each level is extracted using diverse mother wavelets and their respective filters. The signal is segmented into sub‐bands, from which the condition state is ascertained using an energy operator. After that, the appropriate level of wave decomposition is determined through ANOVA tests and post‐hoc Tukey analyses, evaluating performance in failure classification via the Random Forest (RF) model. Upon establishing the decomposition level, the analysis proceeds to identify which mother wavelets and filters are most suitable for determining the severity level of different types of failure in spur gearboxes. Moreover, this study investigates the impact of sensor positioning and inclination on acquiring the vibration signal. This aspect is explored through factorial ANOVA tests and multiple comparisons of the data derived from the sensors. The RF classification model achieved exceedingly favourable results (accuracy 96% and AUC 98%), with minimal practical influence from the positioning and inclination of a sensor, thereby affirming the proposed methodology's suitability for this type of analysis.

In the industry, gearboxes used for their efficiency in power transmission are critical components, making early failure detection essential. This work aims to determine the ranking of condition indicators (CIs) to extract information from the vibration signal in the frequency domain for a spur gearbox and to assess the accuracy of the classification model for the failure severity level. In laboratory conditions, the tooth breakage of a pinion with different severity levels was simulated in a spur gearbox. Four accelerometers were installed in the gearbox in a vertical position to obtain the vibration signal. First, information was extracted from the vibration signal through 25 CIs in the accelerometers. Using artificial intelligence, the ranking of 10 CIs was carried out. Subsequently, the random forest (RF) algorithm was used to determine the accuracy in classifying the failure severity level. Finally, an ANOVA test was conducted to determine if there were significant differences in classification accuracy for the four accelerometers. The results concluded that the CIs selected through the ranking are optimal for determining the failure severity level of tooth breakage in a spur gearbox and that the sensor placement affects the classification accuracy.