Functional beverages enriched with herbal extracts are gaining popularity due to their potential health benefits. Tilia cordata flowers are known for their antioxidant and antimicrobial properties, making them a promising additive in food and beverage formulations. Our study aimed to develop ready-to-drink iced teas enriched with T. cordata flower extracts and to evaluate their antioxidant, antimicrobial, and sensory characteristics as functional food products. Fresh T. cordata flowers were analyzed for metal contents. Phenolic acid profiles in ethanolic and aqueous extracts were determined using HPLC-MS. Antioxidant activity was evaluated using DPPH radical scavenging, conjugated diene, and iron ion chelation assays. Antimicrobial effects were tested against Staphylococcus aureus, Bacillus cereus, and Listeria monocytogenes. Sensory analysis was conducted using AI-based facial expression recognition to assess consumer responses. Metal analysis revealed low concentrations of Mn, Zn, Cu, and Fe, with no detectable Pb, Cd, or Ni. Ethanolic extracts showed significantly higher levels of phenolic acids than aqueous extracts. Iced teas containing both types of extracts demonstrated strong antioxidant activity, with ethanolic formulations having the highest levels of phenols and flavonoids. Antimicrobial tests confirmed activity in both teas, with ethanolic extracts showing stronger effects. Sensory analysis indicated positive emotional responses and consumer acceptance for both formulations. Iced teas enriched with T. cordata extracts exhibited significant antioxidant and antimicrobial properties, confirming their potential as functional beverages. The use of AI-driven sensory evaluation proved effective in capturing consumer preferences, supporting its application in product development. These findings suggest commercial viability for industrial production.
BACKGROUND Peptic ulcer disease continues to pose a major clinical challenge worldwide. A better understanding of molecular mechanisms underlying gastric mucosal damage could open new avenues for targeted interventions. The interleukin-33 (IL-33)/suppression of tumorigenicity 2 (ST2) signaling pathway is an important regulator of inflammation and epithelial injury. AIM To investigate the effect of ST2 deletion on multiple pathways of inflammation and epithelial cell death in an experimental model of acute gastric injury. METHODS Acute gastric damage was induced in ST2(-/- )and wild-type BALB/c mice by oral administration of 80% ethanol, followed by macroscopic and histological evaluation. Gastric tissue and serum were analyzed by quantitative polymerase chain reaction, enzyme-linked immunosorbent assay, immunohistochemistry, and flow cytometry to assess cytokine production, immune cell recruitment, inflammatory signaling, and cell death pathways. Recombinant IL-33 was administered intraperitoneally in selected groups to confirm functional relevance. RESULTS ST2 deletion ameliorates acute gastric injury in mice, as evidenced by reduced macroscopic lesions and more preserved mucosal architecture. This was associated with decreased infiltration of neutrophils, macrophages, dendritic cells, eosinophils, CD8+ T cells, and ILC2s, along with reduced production of pro-inflammatory cytokines (IL-1 beta, tumor necrosis factor-alpha, IL-17 and interferon-gamma). Moreover, ST2 gene deficiency downregulated nuclear factor kappa B (NF-kappa B) and NOD-like receptor family, pyrin domain containing 3 (NLRP3) inflammasome signaling pathways in gastric tissue, leading to diminished release of IL-1 beta, tumor necrosis factor-alpha and interferon-gamma in gastric-infiltrating neutrophils and macrophages. In addition, ST2 deletion limited epithelial cell apoptosis, while recombinant IL-33 administration significantly exacerbated gastric mucosal injury, confirming the pathogenic role of IL-33/ST2 signaling. CONCLUSION Our study provides evidence that ST2 gene deficiency alleviates acute gastric injury effectively by suppressing inflammation mainly via repression of NF-kappa B and NLRP3 inflammasome signaling, and concurrently downregulating epithelial cell death. Obtained data suggest that targeting IL-33/ST2 axis represent a promising therapeutic strategy for acute gastric ulcer disease.
Traditional fault diagnosis methods often suffer from performance degradation under new working conditions due to distribution shifts between the source and target domains. To bridge this gap in cross-domain fault diagnosis (CDFD), the domain adaptation (DA) technique leverages transfer learning to align feature distributions, which facilitates knowledge transfer from labeled source domains to unlabeled target domains. Although existing studies on DA have demonstrated efficacy, they still face significant challenges due to abrupt domain shifts and insufficient feature discrimination. To overcome these problems, this study proposes a dynamic evolution mechanism to construct a sequence of hybrid domains that gradually evolves from the source to the target domain. This strategy establishes a smooth transition path to mitigate abrupt domain shifts. Additionally, a dual-path feature extraction structure empowered by wavelet packet transform (WPT) is introduced. This structure decomposes input signals into high-frequency and low-frequency components to enhance discriminative feature representation. The experimental results on rolling bearing and gearbox datasets demonstrate the effectiveness and generalization performance of the proposed method.
Purpose In response to ever-increasing customers’ expectations for easily-accessible relevant information, Tripadvisor introduced an AI-powered feature that summarizes customer hotel reviews – AI Review Summaries (RSs). This study aims to explore how persuasion is manifested linguistically in these texts by focusing on metadiscourse features and their rhetorical effects. Design/methodology/approach This study is corpus-based and draws on Hyland’s (2005) interpersonal model of metadiscourse, slightly modified to cater for the specificities of the genre under investigation. RSs of 358 hotels in three most prominent global destinations were analyzed using a combination of top-down and bottom-up approaches, together with software-assisted and manual methods. Quantitative analysis is supplemented with functional interpretation. Findings The results point to the abundance of items that enhance the rational, credible and affective appeals, with slighter predominance of interactive over interactional categories and transitions and hedges being the most prevalent markers. These results directly reflect an RS overall purpose, on the one hand, and, on the other, could be seen as a reflection of their algorithmic nature. The analysis has also revealed the repetitive use of the same metadiscourse patterns coupled with the lexical non-diversity, which could be a potential hindrance to the intended persuasive effects. Originality/value This paper extends existing research on metadiscourse on AI-generated texts by examining texts from tourism domain. In doing so, it contributes to the study of AI hotel RSs by offering a discourse-oriented analytical perspective, thereby shedding light on the rhetorical nature of this emerging genre. Viewing metadiscourse as a component of persuasive rhetoric, the study shows how these resources contribute to the central purpose of the genre.
Poka-Yoke is one of the fundamental Lean tools used to prevent or detect errors. However, the existing theoretical framework and classification models are neither sufficiently systematised nor confirmed by empirical research. This study therefore provides the first comprehensive evaluation of three classification models based on: function (I), principle (II), and device type (III). The first two models are the most commonly used in the relevant literature, while the third was developed by the author and improved through the research conducted for a clearer understanding and more practical application. The models were empirically tested using two criteria: classification accuracy assessment and ease of application assessment. The research included 21 examples of PY solutions from literature and industrial practice, evaluated by 30 experts of various profiles. Statistical analysis of the data confirmed the existence of significant differences between the models. Classification models III was found to be the most accurate and simplest to use, thus confirming its practical value in modern industrial practice. This study contributes to the development of a theoretical and practical framework for the PY method, offering empirically based recommendations for standardisation and wider implementation. These recommendations create the conditions for more effective error management, thereby increasing process reliability and ensuring compliance with Industry 4.0 requirements.
