Search Results (3,330)

In the context of China’s increasingly competitive agricultural product branding, authenticity has become a pivotal mechanism for shaping consumer trust and willingness to pay. This study takes Perceived Brand Authenticity (PBA) as its focal construct and builds a chained mediation framework incorporating experiential quality (EQ) and consumer trust. Employing a dual-evidence strategy that combines structural discovery and causal validation, the study integrates Jaccard similarity clustering and PLS-SEM to examine both behavioral patterns and psychological mechanisms. Drawing on 636 valid survey responses from across China, the results reveal clear segmentation in channel choice, certification concern, and premium acceptance by gender, age, income, and education. Younger and highly educated consumers rely more on e-commerce and digital traceability, while middle-aged, older, and higher-income groups emphasize geographical indications and organic certification. The empirical analysis confirms that PBA has a significant positive effect on EQ and consumer trust, and that the chained mediation pathway “PBA → EQ → Trust → Purchase Intention” robustly captures the transmission mechanism of authenticity. The findings demonstrate that verifiable and consistent authenticity signals not only shape cross-group consumption structures but also strengthen trust and repurchase intentions through enhanced experiential quality. The core contribution of this study lies in advancing an evidence-based framework for sustainable agricultural branding. Theoretically, it reconceptualizes authenticity as a measurable governance mechanism rather than a rhetorical construct. Methodologically, it introduces a dual-evidence approach integrating Jaccard clustering and PLS-SEM to bridge structural and causal analyses. Practically, it proposes two governance tools—“evidence density” and “experiential variance”—which translate authenticity into actionable levers for precision marketing, trust management, and policy regulation. Together, these insights offer a replicable model for authenticity governance and consumer trust building in sustainable agri-food systems. Full article

There has been a recent shift in the global wine market towards reduced-alcohol wines. Muscadine grapes (Vitis rotundifolia) have become a popular choice in many emerging markets; however, their suitability in reduced-alcohol wine production has not been extensively tested. In this study, methods to reduce ethanol in muscadine wine were compared to determine differences in chemical and sensory attributes and consumer preference. The methods evaluated included full fermentation time with Saccharomyces cerevisiae (control), reduced fermentation time with Saccharomyces cerevisiae (stopped fermentation), fermentation with Saccharomycodes ludwigii yeast (instead of Saccharomyces cerevisiae), and vacuum distillation. The control and distilled wines were fermented for 121 h, Saccharomycodes ludwigii for 45 h, and the stopped fermentation wine for 3 h. Yeast and sugar levels were monitored throughout the fermentation processes using brix measurements and yeast counts. After the fermentation, the color, pH, volatiles, and titratable acidity (TA) were measured. The results showed that Saccharomycodes ludwigii fermented more slowly than Saccharomyces cerevisiae, and that both the stopped fermentation and Saccharomycodes ludwigii wines had lower titratable acidity with a more intense color. The total concentration of volatile compounds for the Saccharomycodes ludwigii wine and the stopped wine were lower than for the distilled and control wines. A consumer panel (n = 92) judged the wine samples on chemical qualities and overall preference. The distilled wine was perceived as more alcoholic compared to the other reduced-alcohol wines. The results showed that the stopped fermentation and Saccharomycodes ludwigii wines were preferred by consumers over the control and vacuum-distilled wines. Full article

This study investigates semantic text segmentation enhanced by large language model (LLM) embeddings. We assess how effectively embeddings capture semantic coherence and topic closure by integrating them into both classical clustering algorithms and a modified graph-based methods. In addition, we propose a simple magnetic clustering algorithm as a lightweight baseline. Experiments are conducted across multiple datasets and embedding models, with segmentation quality evaluated using the boundary segmentation metric. Results demonstrate that LLM embeddings improve segmentation accuracy, highlight dataset-specific difficulties, and reveal how contextual window size and embedding choice affect performance. These findings clarify the strengths and limitations of embedding-based approaches to segmentation and provide insights relevant to retrieval-augmented generation (RAG). Full article

While international cross-listing locations in host countries have been identified as integral to firm valuation gains, the influence of the home country information environment on firm financial market integration remains underexplored. This study examined how political stability, government effectiveness, and voice and accountability influence cross-listing destination choices amongst emerging-market firms seeking enhanced valuation gains. Using data on cross-listed firms from BRICS countries between 2000 and 2020, the study employed generalized linear models (GLMs), including probit and robit specifications, to analyze this relationship. The researchers found that stronger political stability; government effectiveness; and voice and accountability in home countries significantly increase the likelihood of BRICS firms cross-listing on advanced exchanges characterized by higher valuation gains. These results indicate that reduced political risk, effective government policy implementation and greater media freedom in BRICS emerging market countries facilitate cross-listing firms’ access to more efficient global capital markets by reducing asymmetric information, and help overcome traditional market segmentation barriers. Contrary to the conventional emphasis that home country proximity is significant for cross-listing valuation gains, these results highlight the signaling mechanism of home country governance quality as an appealing factor for firm cross-listing location in advanced exchange markets. Policymakers in emerging markets should consider governance reforms that enhance domestic firm competitiveness in global financial markets for higher valuation gains. Full article

We address surrogate-assisted multi-objective optimization for computationally expensive structural designs. The testbed is an axisymmetric laminated composite shell whose geometry, ply angles, and plywise materials are optimized to simultaneously (i) maximize separation of selected natural frequencies from a known excitation and (ii) minimize material cost. To reduce high-fidelity (HF) finite element evaluations, we develop a deep neural network surrogate framework with three variants: an HF-only baseline; a multi-fidelity (MF) pipeline using an auxiliary refinement network to convert abundant low-fidelity (LF) data into pseudo-HF labels for a single-fidelity evaluator; and a cascaded ensemble that emulates HF responses and then maps them to pseudo-experimental targets. During optimization, only surrogates are queried—no FEM calls—while final designs are verified by FEM. Pareto-front quality is quantified primarily by a normalized relative hypervolume indicator computed against an envelope approximation of the True Pareto Front, complemented where appropriate by standard indicators. A controlled training protocol and common validation regime isolate the effect of fidelity strategy from architectural choices. Results show that MF variants markedly reduce HF data requirements and improve Pareto-front quality over the HF-only baseline, offering a practical route to scalable, accurate design under strict computational budgets. Full article

Background: Recurrent urinary tract infections (rUTIs) significantly impair women’s quality of life, making antimicrobial prophylaxis a critical preventative strategy. This retrospective observational study aimed to characterize antibiotic prophylaxis patterns, relapse rates, comparative efficacy of different agents, and tolerability in 908 women (663 postmenopausal, 245 premenopausal) with rUTIs managed at a tertiary university hospital. Methods: Data from medical records (January 2022–December 2024) were analyzed. Patients were stratified by menopausal status. We assessed antibiotic usage, relapse rates (per 100 patient-months), and adverse events. Comparative efficacy of nitrofurantoin-based versus fosfomycin/other prophylaxis was evaluated for rUTIs caused by E. coli, E. faecalis, or E. coli ESBL using weighted and matched analyses to control for covariates. Results: Continuous antimicrobial prophylaxis was the primary strategy, with nitrofurantoin being most frequently used. Premenopausal women showed a greater tendency for intermittent or combined prophylactic approaches. Postmenopausal women exhibited a higher overall crude relapse rate (5.54/100 p-m) compared to premenopausal women (3.14/100 p-m), with E. coli being the most common causative agent in relapses. For rUTIs caused by E. coli, E. faecalis, or E. coli ESBL, nitrofurantoin-based prophylaxis demonstrated significantly lower adjusted relapse rates than fosfomycin/other regimens (rate ratio: 0.47 for postmenopausal, 0.35 for premenopausal women). This observed efficacy for nitrofurantoin was robust against potential unmeasured confounding. Prophylaxis was generally well-tolerated (3.0% gastrointestinal adverse events overall); however, premenopausal women reported a higher adverse event incidence. Conclusions: Our findings strongly suggest that nitrofurantoin is an effective prophylactic choice for rUTIs caused by common uropathogens (E. coli, E. faecalis, E. coli ESBL), particularly in postmenopausal women. The diverse prophylactic strategies highlight the need for individualized care. While generally well-tolerated, adverse event profiles vary between menopausal groups, necessitating careful monitoring. Full article

This study presents the deployment of Machine Learning (ML) models on low-cost edge devices (ESP32) for real-time water quality prediction in tilapia aquaculture. A compact monitoring and control system was developed with low-cost sensors to capture key environmental parameters under field conditions in Northern Thailand. Three ML models—Multiple Linear Regression (MLR), Decision Tree Regression (DTR), and Random Forest Regression (RFR)—were evaluated. RFR achieved the highest accuracy (R² > 0.80), while MLR, with moderate performance (R² ≈ 0.65–0.72), was identified as the most practical choice for ESP32 deployment due to its computational efficiency and offline operability. The system integrates sensing, prediction, and actuation, enabling autonomous regulation of dissolved oxygen and pH without constant cloud connectivity. Field validation demonstrated the system’s ability to maintain DO within biologically safe ranges and stabilize pH within an hour, supporting fish health and reducing production risks. These findings underline the potential of Edge AIoT as a scalable solution for small-scale aquaculture in resource-limited contexts. Future work will expand seasonal data coverage, explore federated learning approaches, and include economic assessments to ensure long-term robustness and sustainability. Full article

Generative pre-trained transformers (GPTs) have rapidly entered educational contexts, raising questions about their impact on pedagogy, workload, and professional practice. While their potential to automate resource creation, planning, and administrative tasks is widely discussed, little empirical evidence exists regarding their use in vocational education (VE). This study explores how VE educators in England are currently engaging with AI tools and the implications for workload and teaching practice. Data were collected through a survey of 60 vocational teachers from diverse subject areas, combining quantitative measures of frequency, perceived usefulness, and delegated tasks with open qualitative reflections. Descriptive statistics, cross-tabulations, and thematic analyses were used to interpret responses about the application and allocation of work given by teachers to GPTs. Findings indicate cautious but positive adoption, with most educators using AI tools infrequently (0–10 times per month), yet rating them highly useful (average 4/5) for supporting workload. Resource and assessment creation dominated reported uses, while administrative applications were less common. The choice architecture framing indicates that some GPTs guide teachers to certain resources over others and the potential implications of this are discussed. Qualitative insights highlighted concerns around quality, overreliance, and the risk of diminishing professional agency. The study concludes that GPTs offer meaningful workload support but require careful integration, critical evaluation, and professional development to ensure they enhance rather than constrain VE pedagogy. Full article

Research Problem and Gap: Live streaming sales rely heavily on streamers, with both genuine and AI-generated virtual streamers gaining popularity. However, these streamer types possess contrasting capabilities. Genuine streamers are superior at building trust and reducing product valuation uncertainty but have limited reach, while virtual streamers excel at broad audience engagement but are less effective at mitigating uncertainty, often leading to higher product return rates. This trade-off creates a critical strategic gap; that is, brand firms lack clear guidance on whether to invest in genuine or virtual streamers or adopt a hybrid approach for their live channels. Objective and Methods: This study addresses this gap by developing a theoretical analytical model to determine a monopolistic brand firm’s optimal streamer strategy among three options: using only a genuine streamer, only a virtual streamer, or a combination of the two (hybrid approach). The researchers model consumer utility, factoring in uncertainty and the streamers’ differential impact on reach, to derive optimal decisions on pricing and streamer selection. Results and Findings: The analysis yields several key findings with direct managerial implications. First, while a hybrid strategy leverages the complementary strengths of both streamer types, its success depends on employing high-quality streamers; in other words, this strategy does not justify settling for inferior talent of either type. Second, employing a virtual streamer requires a moderate price reduction to compensate for higher consumer uncertainty and prevent high profit-eroding return rates. Third, a pure strategy (only genuine or only virtual) is optimal only when that streamer type has a significant cost advantage. Otherwise, the hybrid strategy tends to be the most profitable. Moreover, higher product return costs directly diminish the viability of virtual streamers, making a genuine or hybrid strategy more attractive for products with expensive return processes. Conclusions: The results provide a clear framework for brand firms—that is, the choice of streamer is a strategic decision intertwined with pricing and product return costs. Firms should pursue a hybrid strategy not as a compromise but as a premium approach, use targeted pricing to mitigate the risk of virtual streamers, and avoid virtual options altogether for products with high return costs. Full article

The value of personal data can only be unlocked through efficient circulation. This study explores a multi-party collaborative mechanism for personal-data trading, aiming to improve data quality and market vitality via incentive-compatible institutional design, thereby supporting the high-quality development of the digital economy. Symmetry enhancement refers to the use of strategies and mechanisms to narrow the information gap among data controllers, operators, and demanders, enabling all parties to facilitate personal-data transactions on relatively equal footing. Drawing on evolutionary-game theory, we construct a tripartite dynamic-game model that incorporates data controllers, data operators, and data demanders. We analyze how initial willingness, payoff structures, breach costs, and risk factors (e.g., data leakage) shape each party’s strategic choices (cooperate vs. defect) and their evolutionary trajectories, in search of stable equilibrium conditions and core incentive mechanisms for a healthy market. We find that (1) the initial willingness to cooperate among participants is the foundation of a virtuous cycle; (2) the net revenue of data products significantly influences operators’ and demanders’ propensity to cooperate; and (3) the severity of breach penalties and the potential losses from data leakage jointly affect the strategies of all three parties, serving as key levers for maintaining market trust and compliance. Accordingly, we recommend strengthening contract enforcement and trust-building; refining the legal and regulatory framework for data rights confirmation, circulation, trading, and security; and promoting stable supply–demand cooperation and market education to enhance awareness of data value and compliance, thereby stimulating individuals’ willingness to authorize the use of their data and maximizing its value. Full article

This paper introduces the Gesture-Based Physical Stability Classification and Rehabilitation System (GPSCRS), a low-cost, non-invasive solution for evaluating physical stability using an Arduino microcontroller and the DFRobot Gesture and Touch sensor. The system quantifies movement smoothness, consistency, and speed by analyzing “up” and “down” hand gestures over a fixed period, generating a Physical Stability Index (PSI) as a single metric to represent an individual’s stability. The system focuses on a temporal analysis of gesture patterns while incorporating placeholders for speed scores to demonstrate its potential for a comprehensive stability assessment. The performance of various machine learning and deep learning models for gesture-based classification is evaluated, with neural network architectures such as Transformer, CNN, and KAN achieving perfect scores in recall, accuracy, precision, and F1-score. Traditional machine learning models such as XGBoost show strong results, offering a balance between computational efficiency and accuracy. The choice of model depends on specific application requirements, including real-time constraints and available resources. The preliminary experimental results indicate that the proposed GPSCRS can effectively detect changes in stability under real-time conditions, highlighting its potential for use in remote health monitoring, fall prevention, and rehabilitation scenarios. By providing a quantitative measure of stability, the system enables early risk identification and supports tailored interventions for improved mobility and quality of life. Full article

Background/Objectives: In view of demographic change and the increase in chronic illnesses, home care poses a considerable challenge. Telemedical technologies offer considerable potential for improving the quality of care and relieving the burden on family caregivers. With this study, we aim to develop appropriate training strategies for the use of telemedical applications in home care, focusing on the specific requirements of patients with dementia, heart failure, diabetes mellitus, chronic obstructive pulmonary disease, and stroke. Methods: A comprehensive survey was conducted among 31 family caregivers to record their experience with digital technologies and to analyze caregiver acceptance of these technologies and barriers to their use. The survey comprised 29 questions, including a mix of multiple-choice, Likert scale, and open-ended questions. The internal consistency of the questionnaire was high (Cronbach’s alpha = 0.8876). Results: The results show that although 32% of respondents already use digital technologies, there is a significant need for training and support. Key barriers identified include a lack of technical skills (cited by 45% of respondents), limited access to suitable devices (38%), and privacy concerns (35%). In addition, 90% of respondents expressed a willingness to participate in training programs. Conclusions: Based on the survey results, evidence-based recommendations are provided for the design of training programs tailored to the individual needs of family caregivers. Through a targeted combination of e-learning modules, webinars, and practical exercises, family caregivers can be empowered to take full advantage of telemedical technologies and thus significantly improve the quality of care at home. The results underscore the importance of overcoming technical barriers and providing comprehensive training to ensure the effective use of telemedicine in home care. Full article

Aroma and flavor are central to consumer perception, product acceptance, and market positioning of animal-derived foods such as meat, milk, and eggs. These sensory traits arise from volatile organic compounds (VOCs) formed via lipid oxidation (e.g., hexanal, nonanal), Maillard/Strecker chemistry (e.g., pyrazines, furans), thiamine degradation (e.g., 2-methyl-3-furanthiol, thiazoles), and microbial metabolism, and are modulated by species, diet, husbandry, and post-harvest processing. Despite extensive research on food volatiles, there is still no unified framework spanning meat, milk, and eggs that connects production factors with VOC pathways and links them to sensory traits and consumer behavior. This review explores how production systems, feeding strategies, and processing shape VOC profiles, creating distinct aroma “fingerprints” in meat, milk, and eggs, and assesses their value as markers of quality, authenticity, and traceability. We have also summarized the advances in analytical techniques for aroma fingerprinting, with emphasis on GC–MS, GC–IMS, and electronic-nose approaches, and discuss links between key VOCs and sensory patterns (e.g., grassy, nutty, buttery, rancid) that influence consumer perception and willingness-to-pay. These patterns reflect differences in production and processing and can support regulatory claims, provenance verification, and label integrity. In practice, such markers can help producers tailor feeding and processing for flavor outcomes, assist regulators in verifying claims such as “organic” or “free-range,” and enable consumers to make informed choices. Integrating VOC profiling with production data and chemometric/machine learning pipelines can enable robust traceability tools and sensory-driven product differentiation, supporting transparent, value-added livestock products. Thus, this review integrates production variables, biochemical pathways, and analytical platforms to outline a research agenda toward standardized, transferable VOC-based tools for authentication and label integrity. Full article

This study focuses on the challenges of resident mobility in low-density areas. Amid China’s rapid urbanization, rural landscapes and travel patterns are undergoing significant transformation. Using Lanzhou’s rural areas as a representative case study, this research employs questionnaire surveys to collect data. It applies a multi-nominal logit (MNL) model to examine factors influencing travel mode choices and utilizes structural equation modeling (SEM) to assess travel satisfaction—a composite metric derived from residents’ subjective evaluations of convenience, cost, time, and comfort. Findings indicate that private cars and public transportation are the primary travel modes. The MNL model reveals that age and destination accessibility significantly influence travel choices. SEM path analysis further shows that annual household income has a direct positive effect on satisfaction, while age exerts an indirect negative influence through mediating variables. Female satisfaction levels were significantly lower than those of males. Both road density and perceived infrastructure quality significantly enhanced satisfaction, while destination accessibility may exert a slight negative indirect effect by increasing travel expectations. The study theoretically enriches research on rural travel patterns and provides practical insights into rural transportation planning and infrastructure development. Full article

Background/Objectives: We aimed to test whether home-based low-intensity interval training (LIIT) could be equally or more effective than traditional continuous walking advice (TWA) in a population hospitalized and healed from severe COVID-19. Methods: This pragmatic nonrandomized controlled trial (NCT04615390) enrolled patients admitted to intensive care units due to COVID-19 who at discharge from the hospital were given a choice between either a home-based LIIT program or TWA. The former received a structured LIIT walking (1:1 walk:rest ratio per 10 times) to be performed at a prescribed progressively increasing speed maintained with a metronome. The latter received TWA according to the guidelines (30 min or moderate intensity activity, 5 days/week). Outcome measures, collected at baseline, at the end of the 3-month training and at the 6-month follow-up, included 6 min walking distance (primary), lower limb strength, quality of life, depression and cognitive status. Results: From a total of 85 enrolled patients, 69 of them (LIIT n = 32; TWA n = 37) completed the study. Home exercise was safely executed with an 82% adherence for the LIIT group and 64% adherence for TWA. After the 3-month program, both groups significantly improved the 6MWD (LIIT: +87 m vs. TWA +42 m; p < 0.001) with a significant difference that was also maintained at follow-up (LIIT: +138 m vs. TWA +69 m; p < 0.001). No other significant between-group differences were noted. However, patients in the LIIT group significantly improved in the majority of the outcomes, while patients of TWA improved in only the primary outcome and the physical component of quality of life. Conclusions: Compared with TWA, LIIT walking was feasible, safe and associated with more favorable multidimensional recovery in COVID-19 survivors after hospitalization for severe pneumonitis. Full article