Tomas Bata University in Zlín (TBU), (Czech Univerzita Tomáše Bati ve Zlíně), is a Czech public university in the Moravian city of Zlín, comprising six faculties offering courses in technology, economics, humanities, arts and health care. The university was named after the entrepreneur Tomáš Baťa, the founder of the shoe industry in Zlín. With a current student population of more than 9,200, TBU is among the medium-sized Czech universities.
Electrospinning is a breakthrough technique in materials science that enables the fabrication of polymer yarns with extraordinary nanoscale precision previously unattainable. Nanofiber-based yarns produced by the electrospinning process show unique structural and functional properties differing from traditional materials, such as an incredibly high surface area, mechanical properties leading to significantly increased durability, and architectures that can be designed according to specific requirements. This review details the transformative influence of electrospinning on yarn technology, emphasizing its incredible potential to go beyond the conventional constraints that have previously limited this field and thus fuel innovation in a vast range of applications across many industries. Thanks to innovative nanotechnology applications and high-end materials engineering, electrospun nanofibrous yarns have seriously redefined standards in many important areas, such as environmental sustainability, energy solutions, wearable technology, and biomedicine. However, significant challenges persist, requiring further scale-up and process optimization. This review emphasizes advancements made so far in the art of the electrospinning process for yarn development in pivotal positions for next-generation progress. The future of nanofiber-based yarns and their integration underscores the importance of such yarns in solving current global challenges.
Purpose Anchoring on the social exchange theory (SET), this study aims to examine the impact of artificial intelligence (AI) on customer behavior within the e-commerce sector. This study investigates the interconnections between perceived benefits of AI, customer trust, customer satisfaction and electronic word of mouth (eWOM). Design/methodology/approach A mixed-methods approach was used in the Vietnamese e-commerce context. A quantitative survey of 291 respondents was conducted to examine the proposed relationships, while qualitative research involving semistructured interviews with 10 participants provided deeper insights. Thematic analysis of the interview data enriched and validated the quantitative findings, offering a holistic understanding of the phenomena. Findings The quantitative analysis indicates that perceived benefits of AI do not directly affect eWOM; however, they significantly influence eWOM indirectly via trust and customer satisfaction, which serve as full mediators. The qualitative findings reveal three primary themes: the impact of AI’s perceived advantages on trust and satisfaction, the indirect connection between AI’s benefits and eWOM and the mediating functions of trust and satisfaction within this framework. Originality/value This research addresses a gap in existing literature by analyzing the influence of AI on consumer behavior in a developing market, specifically focusing on Vietnam as a case study. The integration of quantitative and qualitative methods provides a comprehensive framework for analyzing AI-mediated customer interactions. The findings contribute to the theoretical framework of SET within the realm of AI, while also offering practical insights for businesses and policymakers. They emphasize strategies aimed at improving customer trust, satisfaction and eWOM in the context of e-commerce through the application of AI.
The objective of this study was to evaluate the effect of citrus fiber (1% wt/wt) on the physicochemical, rheological, textural, tribological, and sensory properties of quark cheese spreads with different fat content (10%, 15%, and 20% wt/wt). Physicochemical properties, including pH, DM, and water activity, were not affected by citrus fiber addition. Furthermore, rheological and textural analyses showed that citrus fiber increased samples' hardness and viscoelastic moduli, probably by reinforcing the protein-polysaccharide matrix, especially in low-fat variants. Tribological measurements indicated that higher fat improved lubrication, whereas fiber-containing quark cheese spread samples exhibited slightly higher coefficients of friction due to increased structural rigidity. Additionally, color analysis revealed minor shifts toward darker and more yellow tones when citrus fiber was used. Moreover, sensory evaluation confirmed that citrus fiber addition did not affect most of the tested organoleptic attributes, with slight improvement in flavor. The findings indicate that incorporating citrus fiber into quark cheese spreads can improve their functional properties, facilitate fat reduction, and promote the sustainable utilization of byproducts in dairy products.
The rapid advancements in artificial intelligence (AI) and its deployment by firms are important factors in attracting investors. By using the data of A-share Chinese listed firms from 2016 to 2022, this study attempts to estimate the possible linkage of AI adoption by firms in appealing the Foreign Institutional Investors (Foreign II's). The study findings indicate the following: (1) AI is positively linked to Foreign II's. (2) The heterogeneity analysis shows that this linkage is stronger in new firms. (3) Firm performance significantly improves the relationship between AI and Foreign II's. (4) In contrast, firm age negatively influences this relationship. After employing several robustness tests, the study findings remain validated. Overall, we provide important insights and actionable references to support the strategic incorporation of AI into organizational operations, aiming to attract foreign investments.
We propose a dual-state-feedback delayed resonator (DFDR) by incorporating an additional acceleration-based feedback into the classical DR design. The optimal tuning of its feedback parameters is guided by two objectives: enhancing vibration suppression at a specified target frequency and maintaining overall system stability. First, we extend the Advanced Clustering with Frequency Sweeping (ACFS) methodology from the delay-only domain to the combined delay-gain domain, enabling a rigorous and complete stability analysis where feedback gains and delays interact. Second, we develop the optimal parameter tuning procedure and demonstrate that the proposed DFDR achieves improved stability margins, enhanced robustness to frequency variations, and superior vibration suppression performance compared to the classical DR. These results highlight the practical potential of DFDR as an effective and robust solution for active vibration suppression systems.
