Search Results (4,120)

Blood collection tubes are widely used medical devices. Inaccurate citrate anticoagulant concentration can influence the results of coagulation tests. The producer’s expertise and responsibility are considered the quality safeguards. However, the tubes undergo changes during their lifecycle, partly due to storage conditions, and the end user or a third party has no comprehensive insight. A methodology is necessary to reveal the tube’s inherent characteristics. We provide insight into the anionic–cationic composition and pH of anticoagulant solutions in commercial tubes using high-performance ion exchange chromatography on a purified water model, making the anticoagulant volume accuracy assessment possible through a direct dye-dilution method. The results revealed differences between the tubes of two producers, Greiner BIO-ONE (A and A(nr)) and BD (C). Tube C has the most accurate anticoagulant amount. Both brands contain buffered citrate. The method of buffer preparation is not a source of interferant for the spectrometric method of the tubes’ quality evaluation. Acetate, formate, chloride, nitrite, sulfate, oxalate, bromide, and nitrate impurities were determined in anticoagulant solutions, all in tube A and some in the others. Tubes C exhibit the highest contamination with cations. Full article

This paper examines how digital infrastructure affects agricultural upgrading in China’s edible fungi industry, focusing on the divergence between output expansion and unit value enhancement. Using a balanced panel of 28 Chinese provinces from 2019 to 2024, we apply a Two-Stage Least Squares (2SLS) approach, instrumenting digital infrastructure with the 1984 provincial fixed-line telephone penetration rate (first-stage F-statistic = 82.15) to address endogeneity concerns. The results reveal a clear asymmetry between quantity and quality outcomes. Digital infrastructure significantly increases total output (coefficient = 1.540, p < 0.01), primarily through improved market coordination rather than productivity gains. However, it produces no statistically discernible effect on unit output value. This divergence suggests that agricultural digitalization follows a stage-dependent pattern: basic connectivity effectively relaxes constraints on production scaling but is insufficient on its own to shift producers toward higher-value activities. Consequently, scale expansion may proceed without corresponding value creation, raising concerns for long-term economic and environmental sustainability. Achieving genuine agricultural upgrading therefore requires complementary investments in institutional capacity, downstream processing, and brand development alongside digital infrastructure deployment. Full article

Universal adhesives (UAs) exhibit considerable versatility; however, no single commercial product has attained recognition as a clinical gold standard. This study evaluated the functional performance, retention, and marginal integrity of various UA brands in non-carious cervical lesion (NCCL) restorations and examined the effects of different adhesion strategies. A search of electronic databases was conducted for randomized clinical trials (RCTs) published between 2015 and 2025. Only RCTs that assessed the retention and marginal integrity of UAs with follow-ups of 18–48 months, using the USPHS/FDI criteria, were included. This review was registered with PROSPERO (CRD420251026490) and adhered to PRISMA 2020 and PICOS guidelines. Risk of bias was evaluated using the RoB 2 tool; statistical significance was defined as p < 0.05. Of 251 records screened, 23 met the eligibility criteria, resulting in the inclusion of 21 RCTs. Sixteen UA brands exhibited no clear differences in performance outcomes. Etch-and-rinse (ER) and selective enamel-etching (SEE) strategies achieved higher retention rates (median up to 100%; USPHS, p < 0.001), while the self-etch (SE) approach demonstrated lower and more variable retention (median 87.0%). Marginal integrity remained consistently high across all strategies (median 100%; p > 0.05). Although ER and SEE strategies significantly enhance long-term retention, no UA brand showed consistent superiority to be considered a gold standard. Full article

This study develops and tests an association-based model explaining how consumers interpret AI-enabled personalization in fashion e-commerce and how these interpretations relate to behavioral intentions. Integrating perspectives from Social Exchange Theory, the Antecedents of Trust Model, Self-Determination Theory, Psychological Contract Breach Theory, and Surveillance Capitalism, we examine the joint associations of perceived personalization, transparency, data control, and privacy concerns with brand trust, perceived surveillance, privacy violation perceptions, and purchase intention. Using PLS-SEM with data from 664 online shoppers, we find that personalization, transparency, and data control are each positively associated with brand trust, while personalization and privacy concerns are positively associated with surveillance perceptions. Brand trust is negatively associated with both surveillance and privacy violation perceptions, and privacy violation is negatively associated with purchase intention. Data control is directly associated with lower surveillance perceptions, whereas transparency operates indirectly through brand trust. Mediation analysis reveals that surveillance is associated with lower purchase intention only indirectly through privacy violation (full mediation), identifying perceived privacy violation as the central psychological pathway in the personalization-privacy paradox. Multi-group analysis identifies segment-level variations by gender and education: personalization is a stronger trust cue for men, while transparency is a stronger trust cue for women; trust buffers violation more strongly for higher-educated consumers. The results highlight a trust-first personalization strategy in which relevance must be paired with meaningful transparency and data-control features to mitigate surveillance and violation appraisals, supporting positive consumer outcomes in fashion e-commerce. Full article

Electric vehicles (EVs) have fundamentally changed the noise, vibration, and harshness (NVH) landscape of automotive powertrains. In the absence of masking internal-combustion-engine noise, harmonic components such as gear whine, electric-motor orders, and inverter-related tones become more perceptible and more critical to vehicle refinement. This review synthesizes the current state of the art in harmonic NVH engineering for electric drivetrains, focusing on the interactions between gear geometry, manufacturing variability, electromechanical coupling, structural transfer, and human sound perception. Classical mechanisms of gear-mesh excitation are revisited together with emerging EV-specific challenges, including long-wavelength flank deviations, ghost orders, lightweight housing dynamics, and psychoacoustic sound-quality requirements. The review further examines recent progress in predictive and data-driven approaches, including machine-learning-based gear-noise modeling, digital-twin concepts, and virtual NVH assessment workflows. Overall, the literature shows that harmonic NVH engineering in EVs is evolving from a conventional gear-noise problem into a multidisciplinary system-level task integrating gear dynamics, manufacturing science, structural acoustics, electric-drive control, psychoacoustics, and data-driven optimization. This review provides a structured synthesis of these developments and identifies key research gaps and future directions for the next generation of refined electric drivetrains. Full article

The use of invasive plant species for papermaking presents both environmental and economic opportunities, particularly for companies seeking to introduce sustainable materials. This study examined whether paper made from cellulose fibres of Japanese knotweed is suitable for printing business elements such as logos in specific red colours. The physical, mechanical, and optical properties of the paper were compared with those of standard office and commercial Xerox paper. Two printing techniques—electrophotography and inkjet printing—were tested, and the colour differences (CIE colour difference, ΔE) between the reference logo and the prints, with and without the International Colour Consortium (ICC) colour profile, were evaluated. The results showed that the low whiteness and high porosity of the knotweed paper negatively affected colour reproduction, especially in inkjet printing, where even manually optimised profiles did not yield satisfactory results (minimum ΔE > 23). Electrophotography performed better but still had limitations. It was concluded that Japanese knotweed paper is not suitable for professional reproduction of demanding colour elements without additional processing, although it has potential for sustainable applications with lower visual requirements. Full article

Geographical origin identification of fragrant pears is crucial for ensuring fruit quality, protecting regional brand value, and maintaining market order. However, pears from different origins often exhibit highly similar appearance and physicochemical properties, making rapid and nondestructive identification challenging for traditional methods. This study proposes a hyperspectral origin identification method based on an enhanced one-dimensional convolutional neural network (ECNN-1D) incorporating an Efficient Channel Attention (ECA) mechanism, using visible–near-infrared (Vis–NIR) and short-wave infrared (SWIR) spectral data. To address the technical challenges of highly similar spectra, redundant features, and complex information distribution, ECNN-1D enhances discriminative spectral feature representation, overcoming limitations of conventional machine learning and standard deep learning models in feature extraction and classification stability. Systematic comparisons with machine learning models (LDA, RF, KNN, SVM) and deep learning models (VGG-1D, ResNet-1D, CNN-1D) showed that while all models performed well on Vis–NIR spectra, ECNN-1D achieved the highest test accuracy of 98.94% and F1 score of 98.95% on the more challenging SWIR spectra, outperforming other approaches. These results indicate that ECNN-1D enables high-precision, nondestructive origin identification of fragrant pears, with potential cost advantages, providing a reliable technical solution for fruit traceability and quality supervision. Full article

Delivery services have drawn much attention and become of topmost significance in urban areas by presenting online food delivery selections for a diversity of dishes from a wide range of restaurants, decreasing both travel and waiting times. Customer data analysis acts as a cornerstone in corporate strategy, allowing enterprises to gather and interpret user feedback and helping them to make informed decisions that drive future business development. However, major knowledge gaps remain due to the scarcity of literature review studies on these delivery services, hindering a complete understanding of customer satisfaction in this sector. Furthermore, there has been little systematic research on managerial response tactics to online consumer complaints and negative reviews. Researchers have contributed by applying artificial intelligence, including deep learning and machine learning models, to analyze customer sentiment and understand customer brand perceptions. This study presents a Hybrid Deep Neural Network Model for Customer Satisfaction Analysis (HDNNM-CSA), with the aim of developing an efficient model which is capable of accurately classifying customer satisfaction levels in delivery apps based on textual responses provided by customer experience managers. To achieve this, the model initially pre-processes text data using text cleaning, emoji removal, normalization, tokenization, stop word removal, and stemming to clean and standardize the unstructured text data for further analysis. Following this, term frequency–inverse document frequency-based word embedding is utilized to transform the pre-processed text into meaningful feature representations. Lastly, an ensemble architecture involving bidirectional long short-term memory, temporal convolutional, and graph convolutional networks is deployed to classify customer satisfaction levels with managers’ responses. A series of experimental analyses are performed, and the results are examined for numerous features. A comparative analysis demonstrates the enhanced performance of the HDNNM-CSA technique with respect to existing approaches. Full article

Considering that consumers have dual reference effects of price and goodwill; that is, consumers have psychological expectations for price and brand goodwill when making consumption decisions. A difference game model with reference effects is established for a closed-loop supply chain composed of a manufacturer, a retailer and a recycler, and a bidirectional cost-sharing contract is adopted for coordination. At the same time, the impact of dual reference effects and the bidirectional cost sharing contract on supply chain members’ profits are further analyzed by numerical simulation. We find that: (1) The impact of the price reference effect and the goodwill reference effect on supply chain decisions and market demand exhibits significant cost interval dependence. Notably, within a specific cost interval and under the influence of the dual reference effects, the market exhibits a phenomenon of “high price, high demand.” (2) The price reference effect influences the power structure of the supply chain. Specifically, when the price reference effect exceeds a certain threshold, the retailer’s profit surpasses the manufacturer’s profit. (3) The bidirectional cost-sharing contract coordinates the discrete dynamic closed-loop supply chain under dual reference effects. Consequently, it achieves a double Pareto improvement in supply chain members’ profits and brand goodwill. Full article

Hypertension is a major health issue, both globally and in Japan. Potassium-rich foods help prevent hypertension by promoting sodium excretion. We evaluated whether Dadacha-mame (a local potassium-rich edamame variety) improves sodium excretion and influences dietary behavior. This 2-week stratified randomized exploratory trial included 54 (mean age: 20.5 ± 1.4 years; 53.7% females) adults assigned to an intervention group [15 g/day of dried Dadacha-mame; n = 27] or a control group [usual diet; n = 27]. Urinary sodium-to-potassium (Na/K) ratios were compared. Shifts in the dietary behavior-change stages (pre-contemplation and contemplation) were assessed at baseline and post-intervention. No significant between-group baseline differences were observed. While no overall effect on the Na/K ratio was observed, subgroup analysis showed a significant reduction in the Na/K ratio in the intervention group (4.39 vs. 5.91 in Control, p = 0.027). The intervention prompted positive dietary behavior changes, with the proportion of participants in the pre-contemplation stage decreasing from 50.0% at baseline to 33.3% post-intervention. Dietary intervention with Dadacha-mame can significantly improve sodium excretion in individuals at a higher risk of hypertension and encourage healthy dietary behaviors. Utilizing local potassium-rich foods may be a valuable public health strategy, creating added value for agricultural products and benefiting community health. Full article

Despite the growing importance of Environmental, Social, and Governance (ESG) performance in shaping brand perception and consumer trust, limited empirical evidence exists on how ESG indicators translate into measurable digital consumer engagement outcomes, particularly in ethically driven markets such as the vegan food sector. This study addresses this gap by examining how ESG performance translates into digitally observable consumer engagement and frames this relationship as a strategic performance measurement and decision-support problem. Building on the sector’s reliance on ethical positioning, trust, and online visibility, we integrate ESG indicators with digital marketing and web analytics metrics (e.g., traffic and engagement proxies) for a panel of five leading vegan food firms [Nestlé SA (Vevey, Switzerland), Kellanova (Chicago, IL, USA), Beyond Meat Inc. (El Segundo, CA, USA), Danone SA (Paris, France), and Conagra Brands Inc. (Chicago, IL, USA)], using data from the Semrush web analytics platform and the Eikon Refinitiv ESG database for the period January–December 2024. We employ a mixed-method design combining descriptive analytics with correlation analysis and simple linear regression to estimate the direction and strength of ESG–digital performance links, and we extend inference through Fuzzy Cognitive Mapping (FCM) using the MentalModeler platform to simulate “what-if” scenarios that support managerial foresight under digital uncertainty. Results indicate that stronger ESG profiles are associated with more favorable digital outcomes, with specific ESG mechanisms (e.g., human-capital and environmental initiatives) aligning with deeper engagement signals. The FCM scenarios further suggest that coordinated ESG improvements can amplify digital traction and reinforce sustainable brand growth. The proposed framework contributes to strategic management by operationalizing an ESG-enabled digital performance measurement system and a lightweight Decision Support System (DSS) that can guide resource allocation, KPI monitoring, and risk-aware positioning in sustainability-oriented markets. Full article

Building reliable vehicle detection models for intelligent transportation systems calls for large, well-annotated datasets—yet gathering and labelling real traffic data remains both costly and labour-intensive. This paper introduces Traffic Synth, an automated pipeline that generates synthetic training datasets by altering real traffic camera images rather than constructing entirely artificial scenes. The system begins by detecting vehicles through instance segmentation and removing them from the frame. It then places new vehicles directly into the cleared regions using diffusion-based inpainting, all while retaining the original road layout, lighting, and camera perspective. Doing so preserves the realistic scene context while broadening the visual variety of vehicles in the dataset. To ensure that the resulting traffic looks physically plausible, we incorporate a lane-aware prompting mechanism that matches each vehicle’s orientation to the direction of travel as seen from the camera. The system further draws on a weighted vehicle brand database that mirrors the makes and colours commonly found on European roads to better match actual deployment conditions. Class-specific mask processing—involving anisotropic scaling and relative dilation—rounds out the pipeline by improving generation quality across different vehicle size categories. The final output is a set of images with automatically generated annotations in a standard object detection format. Full article

As AI-powered e-commerce platforms grow more capable of predicting customer wants, a critical question remains unexplored: what makes customers perceive these experiences positively? The rapid integration of artificial intelligence (AI) into e-commerce platforms is reshaping how customers search for, evaluate, and experience digital services. However, empirical research has not kept pace with clarifying which platform-level factors most effectively shape customer experience (CX) in AI-driven environments. This study validated the Trust, Autonomy, Personalization, and Customer Engagement (TAPE) framework as a comprehensive set of CX drivers in intelligent commerce. Using survey data from 400 active e-commerce users, we employed a multi-stage approach combining exploratory factor analysis, confirmatory factor analysis, and covariance-based structural equation modeling (SEM) with bootstrapped mediation testing. All four TAPE drivers demonstrated significant positive reflective associations with CX, with personalization and engagement emerging as the strongest contributors. CX was strongly associated with customer satisfaction, loyalty, and brand equity, and mediated the effects of all four dimensions on these strategic outcomes, with model comparison evidence supporting full mediation. The study contributes theoretically by integrating and empirically validating four established CX dimensions within the AI-enabled e-commerce context, and by demonstrating the central mediating role of CX in converting intelligent platform features into user-perceived strategic value. Managerially, the TAPE framework provides actionable guidance for designing transparent, adaptive, and engaging AI-driven customer journeys that enhance both experience quality and long-term brand outcomes. Full article

This study examines the roles of startup hubs within the cultural and creative industries (CCIs) and their implications for cultural innovation and tourism in European cities. Despite the growing importance of CCIs in urban development and destination branding, few studies have explored the organisational, social and communicative dynamics of cultural startup hubs. To address this gap, a comparative mixed-methods approach is applied to analyse 91 incubated startups in three European hubs: 104factory (Paris, France), Makerversity (London, UK) and A Lab (Amsterdam, The Netherlands). This study integrates structural variables (sustainability and institutionalisation), social variables (gender representation in leadership) and communication variables (activity and engagement on Instagram). The results reveal distinct organisational models, from highly institutionalised structures to more flexible, community-oriented approaches, with notable differences in terms of sustainability and gender distribution. In terms of communication, greater engagement is associated with content focused on community, identity and collective creativity, rather than promotional strategies. These findings highlight the role of startup hubs as hybrid intermediaries that not only support cultural entrepreneurship, but also contribute to the symbolic positioning and tourist appeal of the cities in which they are located. This study offers theoretical and practical insights for the development of more inclusive, sustainable and effectively communicative cultural ecosystems. Full article