Search Results (856)

Background/Objectives: Accurate data subcategorising is vital for reliability and traceability in the training and validation of all artificial intelligence (AI) models. Methods: In this paper we show the complexity of clinical and technical features likely to affect the appearance and interpretation of mammography images and in turn affect the output of AI software used to aid clinical decisions. Results: Using mammography as a case study, the equitability covers screened population characteristics (e.g., women’s age and ethnicity) and image acquisition key factors (e.g., brand of system, exposure factors, image processing). We examine some studies and available datasets of mammography images, summarising the metadata available. Conclusions: We recommend that, where possible, AI models are trained and evaluated using data that includes subcategories based on these features, ensuring increased equitability in the data and coverage of image heterogeneities; or, where not possible, that the subcategories for which the AI model is valid are clearly defined. Such practices can easily be implemented in a wide range of AI applications but are illustrated here with mammography. Clinical Relevance: Reliable AI holds invaluable potential for both clinical efficiency and accuracy in diagnosis. With appropriately categorised training data, a reduction in subjective assessment can be achieved, leading to trustworthy and rapid assessment. Full article

This study evaluated oxidative damage, antioxidant-related, and nitric oxide (NO)-related chemical markers in commercial bee pollen using two extraction systems. For this purpose, a total of 30 bee pollen samples from different brands and/or manufacturers were examined; each sample was subjected to aqueous extraction and ethanol/water (80:20, v/v). The levels of reduced glutathione (GSH), malondialdehyde (MDA), and NO metabolites, comprising nitrite and nitrate and expressed as total NOx, were determined in pollen extracts using spectrophotometric methods. The data were analyzed using descriptive statistics, group comparisons, and Spearman correlation analysis. The findings showed that ethanol/water (80:20, v/v) extraction provided significantly higher measured GSH equivalents and total NOx levels than aqueous extraction. Mean GSH equivalents were 79.12 ± 25.54 µmol/g in the aqueous extracts and 189.77 ± 45.09 µmol/g in the ethanol/water extracts, and the difference was found to be statistically significant (p < 0.001). NO metabolite levels were determined as 5.97 ± 8.34 and 366.70 ± 85.10 µmol/g, respectively, and the difference between the extraction methods was found to be statistically significant (p < 0.001). In contrast, MDA levels did not show a significant difference between the two extraction systems (p = 0.391). A significant positive correlation was observed between GSH and NO metabolites in the combined dataset (r = 0.660, p < 0.001). The findings suggest that 80% ethanolic bee pollen extract may represent a promising source of NO metabolites for future investigation as a natural alternative to synthetic sodium nitrite in processed meat products. However, this hypothesis requires validation through microbiological, technological, sensory, and shelf-life studies. Full article

The development of more accessible perfumes (non-counterfeit replicas) that offer a similar scent to luxury perfume brands is an emerging trend in the perfume market. In this context, it is important to develop simple, rapid, cost-effective, and environmentally friendly alternative analytical tools capable of differentiating original perfumes from their non-counterfeit replicas. In this work, three vibrational spectroscopic techniques, namely near-infrared (NIR), mid-infrared (MIR), and Raman spectroscopy, were evaluated for the discrimination between original perfume samples and their non-counterfeit replicas. Several original perfume samples and their non-counterfeit replicas were scanned through these vibrational spectroscopic techniques and analyzed with two chemometric tools: principal component analysis (PCA) and hierarchical cluster analysis (HCA). Different pre-processing techniques were also tested in PCA and HCA to attest to the robustness of the findings. The results revealed that all vibrational spectroscopic techniques tested demonstrated high efficiency in differentiating original perfumes from their non-counterfeit replicas. NIR spectroscopy revealed the least similarity among the samples across all pre-processing techniques. In this context, these vibrational spectroscopic techniques are a rapid, cost-effective, non-destructive, and interesting alternative for discriminating between original perfume samples and non-counterfeit replicas. Although further studies, including the application of supervised classification methods to a larger sample set, are needed to attest to the robustness of these results, these preliminary results are very interesting and promising. To the best of our knowledge, this was the first time that vibrational spectroscopic techniques were applied and compared in the analysis of perfume and non-counterfeit replicas. Full article

Lightweight detectors for steel surface defects still struggle to balance robust feature representation with deployment efficiency when defects exhibit weak textures, fine details, and large variation in scale and shape. To narrow this gap, this study develops an incremental lightweight enhancement of YOLO11n rather than proposing a brand-new detection framework. The method combines two coordinated changes. First, an improved lightweight channel-aware fusion module with a dilated branch, termed LCAF-D, is inserted into the neck to strengthen context-aware multi-scale aggregation at low additional cost. Second, a hierarchical Mish activation strategy is introduced only in selected deep backbone layers and neck downsampling layers so that nonlinear modeling is strengthened without disturbing shallow low-level feature extraction. Experiments on NEU-DET indicate that the proposed design provides competitive lightweight detection performance. The benefit becomes more evident as the input resolution increases. Under the 640 × 640 setting, YOLO11n + LCAF-D + Mish reaches 77.1% mAP@0.5 and 44.3% mAP@0.5:0.95, exceeding the YOLO11n baseline. Five-seed repeated experiments further show slightly better mean accuracy with stable variation, and runtime benchmarking shows that the method keeps lightweight characteristics with only modest increases in parameters, GFLOPs, latency, and GPU memory. Additional evaluation on GC10-DET under the same 640 × 640 protocol also gives an overall improvement over the baseline, although the gains remain category-dependent rather than universal. Overall, the method is best understood as a competitive lightweight engineering refinement for steel surface defect detection, particularly when the input resolution is sufficient to preserve subtle defect details. Full article

This article presents a comprehensive ranking of popular Multi-Criteria Decision Analysis (MCDA) methods, assessed across six dimensions of visibility and usage: general web presence, academic mentions, number of academic publications, citation impact, reported academic applications, and real-world (industrial and public sector) use cases. Rather than evaluating theoretical performance or methodological robustness, this study focuses on visibility and adoption, the extent to which an MCDA method has achieved prominence through name recognition, dissemination, academic use, and documented practical application. The observed ranking patterns are consistent with the possibility that method popularity is partly shaped by branding, dissemination dynamics, and cumulative visibility, rather than by demonstrated methodological superiority alone. AHP emerges as the most dominant method across all six criteria, followed by TOPSIS, PROMÉTHÉE, and VIKOR. Newer or more recently visible methods such as BWM demonstrate rapid academic growth, while software-supported methods such as PAPRIKA illustrate how dedicated tools can strengthen practical visibility. By contrast, established methods like ÉLECTRE and MACBETH, while academically respected, exhibit more niche or regionally concentrated use. The study also touches on the meta-cognitive irony that MCDA methods, designed to reduce bias and promote rational deliberation, may themselves be adopted in ways consistent with mechanisms such as availability, familiarity, and brand recognition. This raises concerns about the criteria by which methodological choices are made, particularly in academic modelling and industrial decision-support contexts. Ultimately, the findings suggest that while branding plays a legitimate role in method dissemination, it should not overshadow considerations of methodological transparency, theoretical coherence, and decision-context fit. By distinguishing between methodological performance and visibility/adoption, this article contributes to a more reflective discourse on the selection, teaching and development of MCDA methods and techniques. The ranking should therefore be interpreted as a comparative map of visibility and adoption, not as an assessment of methodological quality, theoretical soundness, or decision-analytic performance. Full article

Conversion Rate Optimisation (CRO) has emerged as a central strategic priority in e-commerce management, yet its positioning within the broader interactive marketing paradigm remains theoretically underdeveloped. Interactive marketing, defined as a multi-directional value creation process through active customer connection, engagement, participation, and interaction, provides a critical lens through which the effectiveness and efficiency of digital marketing tactics can be understood, as each tactic mediates a distinct form of consumer brand interactivity. The academic literature, however, remains fragmented; no unified comparative framework exists that simultaneously assesses both effectiveness and efficiency of digital marketing tactics within an interactive marketing context. Drawing on the classical effectiveness and efficiency framework and interactive marketing theory, this study addresses this gap through a cross-sectional quantitative survey of 302 digital marketing professionals, evaluating a broad range of tactics across both dimensions using a validated psychometric instrument. The findings reveal that Email Marketing consistently dominates across effectiveness and efficiency assessments, reflecting its permission-based structure and its capacity to sustain ongoing consumer–brand dialogue. Search Engine Marketing exhibits the most pronounced divergence between effectiveness and efficiency, consistent with auction-driven cost dynamics that constrain interactive value creation. Attribution modelling difficulty emerges as the primary structural barrier to CRO implementation, revealing a systemic challenge to evidence-based resource allocation in multi-channel interactive environments. An exploratory factor analysis identifies a three-factor taxonomy of tactic effectiveness, distinguishing Paid Conversion tactics, data-driven optimisation tools, and organic or relationship-based channels, each representing a qualitatively distinct mode of consumer–brand interaction. This study advances interactive marketing theory by providing the first empirically validated effectiveness–efficiency framework for e-commerce CRO, and offers actionable guidance for cross-channel budget allocation decisions in interactive digital environments. Full article

Objective: The objective of this study was to evaluate the chromatic effect of two post-curing protocols, (1) manufacturer-recommended standard post-curing (SPC) and (2) standard post-curing plus an additional post-curing cycle (APC), on three types of 3D-printed resins for provisional crowns. Methods: Ninety provisional crowns were printed (n = 30 per resin type-Power Resins Temp, Freeprint Temp, and NextDent C&B) and divided into two groups: SPC (19 min at 30 °C, n = 15 per group) and APC (19 min + 19 min at 30 °C, n = 15 per group). Color coordinates (L*, a*, and b*) were recorded using a spectrophotometer before and after each post-curing protocol to calculate the total color difference (ΔE). Data were analyzed using paired t-test, Wilcoxon signed-rank test, and one-way ANOVA (α = 0.05). Results: After APC, statistically significant changes were observed in the b* coordinate, indicating a lower yellowing tendency for Freeprint Temp (p = 0.018) and NextDent C&B (p = 0.023). No significant changes were found in L* and a* coordinates. However, the total color difference (ΔE) remained below the clinical acceptability threshold (ΔE < 3.3) in all groups. Statistical analysis showed no significant differences in ΔE between the resin brands (p = 0.914). Conclusions: An additional post-curing protocol is a safe procedure from a chromatic standpoint, as it does not compromise the chromatic esthetic outcomes of 3D provisional resins. Clinical Significance: The additional post-curing protocol does not compromise the immediate aesthetic appearance of 3D-printed provisional resins, confirming its optical safety during laboratory processing. Nevertheless, validating its long-term mechanical stability and behavior under simulated oral conditions remains necessary prior to full clinical implementation. Full article

Consumer loyalty has become crucial in the sustainable fashion market for continued growth and sustainable competitive advantage. Building upon the theory of loyalty and the Stimulus–Organism–Response (S-O-R) framework, this study investigates how green brand quality, green brand price, and green brand image influence green consumer loyalty. Additionally, this study evaluates the mediating role of green brand trust. Data were collected via an online survey of 340 sustainable fashion consumers. Structural equation modeling (SEM) was used to test direct and mediating hypotheses. The findings reveal that green brand quality and green brand image influenced green consumer loyalty. However, green product price had no effect. Additionally, green brand trust strongly influenced green consumer loyalty. It also played a mediating role between quality, image, and loyalty. Ultimately, this study positions brand image, quality, and trust as foundational mechanisms in sustainable consumer behavior, especially green consumer loyalty. Full article

This study examines how perceived institutional diversity, equity, and inclusion, online brand community engagement, and service quality shape student advocacy in higher education through brand attitude and student-university identification. Drawing on the S-O-R framework and social identity theory, the study develops a model in which institutional signals influence student advocacy through evaluative and identity-based mechanisms. Data were collected from 217 students enrolled at a university in Greece and analyzed using partial least squares structural equation modeling. The findings show that perceived institutional diversity, equity, and inclusion, online brand community engagement, and service quality all positively influence brand attitude. Perceived institutional diversity, equity, and inclusion and online brand community engagement also positively influence student-university identification, whereas the effect of service quality on student-university identification is not significant. Both brand attitude and student-university identification positively influence student advocacy, with student-university identification emerging as the stronger predictor. In addition, brand attitude mediates the effects of all three institutional signals on student advocacy, whereas student-university identification mediates only the effects of perceived institutional diversity, equity, and inclusion and online brand community engagement. The study highlights the importance of inclusive institutional values and digitally mediated engagement in shaping supportive student behavior in higher education. Full article

Background: Multiple myeloma (MM) is a mature B-cell disorder characterized by the excessive production of monoclonal immunoglobulins. It is the second most common hematological cancer and predominantly affects older adults. In 2022, there were approximately 188,000 MM new cases worldwide. The disease is associated with a relapsing–refractory course. First-line therapies are often insufficient, making additional treatment options necessary. For patients refractory to lenalidomide and proteasome inhibitors (bortezomib and/or carfilzomib), pomalidomide combined with dexamethasone and an additional active agent can be used as a therapeutic strategy. Objective: This study was conducted to evaluate the bioequivalence and tolerability of two pomalidomide hard capsule formulations to support regulatory approval of a branded generic product in Latin American countries. Methods: An open-label, randomized, single-dose, two-treatment, two-sequence, two-period crossover study was conducted in healthy male subjects. Participants received a single oral dose of the test product, Xetrane^® 4 mg hard capsule (Laboratório LKM S.A., Argentina), and the reference product, Imnovid^® 4 mg hard capsule (Celgene International Sàrl), with a 7-day washout period. Blood samples were collected over 48 h post-dose. Plasma pomalidomide concentrations were determined using a validated LC-MS/MS method, and pharmacokinetic parameters were estimated using non-compartmental analysis. Findings: Thirty-four subjects were enrolled, and 29 completed the study. Geometric mean ratios (90% confidence intervals) for Cmax and AUC_0–t were 106.24% (100.77–112.00) and 94.67% (91.61–97.82), respectively. Both formulations were well tolerated. Bioequivalence between Xetrane^® and Imnovid^® was demonstrated in accordance with regulatory criteria. NCT07694011. Full article

The aim of the article is to analyze the impact of selected corporate social responsibility (CSR) initiatives in the area of human rights on the decisions regarding employer choice made by representatives of Generation Z. A survey was conducted among students at Polish universities, who belong to Generation Z, including both those currently employed and those soon to enter the labor market, and builds on previous research and analyses conducted by the authors. The study focused on analyzing selected CSR initiatives related to respect for human rights and their potential impact on the respondents’ choice of employer. The varied results in the statistical models point to the complex nature of the relationships under study. The application of various analytical methods has shown that the impact of the analyzed variables is not always direct or linear. The results confirm the significance of all the CSR initiatives analyzed, and the evaluation of these initiatives varies both among employed and unemployed respondents and according to educational background. Respondents’ willingness to accept employment increases under the influence of factors such as salary and opportunities for professional development, while the impact of CSR is minimal. The results indicate that CSR initiatives in the area of human rights are perceived by employed members of Generation Z, as well as those who will soon enter the labor market, as a factor that has only a minor influence on employment decisions. The findings provide practical guidance for employers on shaping CSR strategies and employer branding initiatives tailored to the needs and values of Generation Z. Full article

The Circular Economy (CE) offers an alternative to the traditional “take–make–dispose” model and is increasingly important in hospitality due to the sector’s high resource consumption. This systematic review analyzes 159 studies published between 1995 and 2024, sourced from ABI, BSP, and WoS, and consolidates scattered knowledge on CE practices in hotels. It outlines five strategies for transitioning to CE: energy efficiency, renewable energy, water management, waste reduction, and carbon footprint reduction. These are supported by green procurement, local supply chains, eco-certifications, and regenerative branding, all of which help hotels fulfill stakeholder expectations and support local economies. Guest engagement—through nudges, incentives, co-creation, and digital tools for monitoring and feedback—is becoming increasingly important. The review shows that CE adoption is driven by internal factors such as leadership, staff training, culture, and resources, as well as external factors such as regulation, institutional pressure, and consumer demand for authenticity. Effective collaboration among managers, staff, suppliers, policymakers, and certifiers is essential for achieving systemic adoption. CE is moving from operational efficiency to broad innovation benefiting the environment, economy, and society, despite financial, regulatory, and behavioral challenges. Future research should prioritize establishing measurement standards, addressing scalability issues, and evaluating the global effectiveness of implementation. Full article

This paper develops and evaluates a predictive analytics framework for influencer marketing return on investment (ROI), integrating hybrid deep learning architectures with trust-aware modelling to address the dual purpose of (a) developing a rigorous evaluation framework for influencer campaign performance and (b) examining the effectiveness of influencer marketing predictors. The concept of influencer marketing has quickly grown to be one of the most effective mediums within the contemporary digital advertising landscape. Due to the growing number of brands dedicating huge amounts of budgets to social media partnerships, the importance of data-driven approaches that can predict the outcomes of campaigns and, consequently, ensure the best possible return on investment (ROI) has become urgent. This paper introduces a machine learning system that can be used to forecast the sales of products promoted by influencer marketing campaigns based on campaign-level features, including type of platform, influencer type, type of campaign, time of the year, number of engagements, estimated reach, and campaign duration. A publicly available influencer marketing ROI dataset was trained and tested on an XGBoost regression model with a coefficient of determination ($R^2$) of 0.95 indicating high predictive power and generalization. The results show that engagement metrics and estimated reach are some of the most impactful factors in sales performance, and additional contextual factors like platform selection, type of campaign, and timing of the year also moderate results. In addition to predictive modelling, this paper explains how artificial intelligence (AI) can be strategically integrated throughout the influencer marketing lifecycle. With the inclusion of AI-based analytics, marketers will be able to leverage their intuitive decision-making processes with quantifiable and replicable measures and approaches that can lead to true consumer trust and lasting brand resonance. The framework proposed can provide practitioners and researchers with a scalable basis for implementing intelligent systems in the context of influencer marketing. Recent computer science research further demonstrates that AI-driven frameworks spanning generative content modelling, AI-powered CRM architectures for understanding consumer preferences on social media, and parasocial-trust models of influencer engagement provide strong methodological complements to the predictive approach developed here, while governance and project management considerations for deploying such systems are increasingly addressed in the literature. Concurrently, a growing body of influencer marketing research examines how platform affordances shape information-seeking and trust, how influencer attributes and social satisfaction mediate purchase intention, how influencer marketing drives sustainable consumption, and how social media measurably shapes health-related behaviours all of which motivate the predictive and trust-modelling objectives of this work. Full article

Food festivals have become important instruments of regional tourism development, destination branding, agricultural promotion, and cultural policy. Yet the authenticity of food festivals is often evaluated through visitor perception or promotional discourse, leaving limited room for scalable assessment across large festival systems. This study develops a Location Quotient (LQ)-based framework for measuring agricultural embeddedness as a supply-side structural condition that can support objective material authenticity claims. Using a dataset of 277 South Korean regional food festivals, the study examines how featured food materials are connected to regional agricultural specialization and how this structural connection compares with budget scale, infrastructure, and material-duplication factors in explaining administrative visitor attendance and portfolio-level governance patterns. The results show that agricultural embeddedness is unevenly distributed across the national festival portfolio. Festival budget is the strongest predictor of log-transformed visitor attendance, whereas agricultural embeddedness is not significantly associated with visitor attendance after budget and infrastructure controls are included. Material-level duplication is useful as a portfolio diagnostic for identifying crowded food categories where place-product credibility may become strategically important. Scenario-based portfolio comparisons provide planning benchmarks suggesting that targeted restructuring and reallocation would be associated with higher average agricultural embeddedness under specified portfolio assumptions, although these scenarios should not be interpreted as causal forecasts. The study contributes to food tourism, event governance, and destination branding research by offering a reproducible way to assess the material grounding of food festival authenticity claims and by shifting policy discussion from festival proliferation toward portfolio quality. Full article

Universities are increasingly expected to contribute not only to teaching and research, but also to public-sector innovation, regional development, and digitally enabled governance. This article examines how higher education institutions organize that contribution by comparing two university-based transfer architectures: Smart-EDU Hub @ SNSPA in Bucharest and the distributed transfer portfolio of RheinMain University of Applied Sciences and Arts (HSRM). Using a qualitative comparative case-study design based on the document analysis of internal strategy and regulatory documents, institutional webpages, and European policy frameworks, the study analyzes the mission framing, organizational form, program architecture, trust infra-structure, and scaling logic. The documentary analysis indicates that Smart-EDU Hub is formally presented and institutionally organized as a centralized, branded, mission-led platform that bundles conferences, courses, projects, visiting scholars, and publication channels under a recognizable public-facing identity. HSRM, by contrast, is documented as a distributed transfer portfolio linking transfer strategy, dialogue formats, digitally supported teaching, administrative digitalization, continuing education, and AI support services. The comparison should therefore be read as an analysis of formal and publicly documented transfer architectures, not as an evaluation of actual institutional performance, stakeholder experience, or societal impact. The article contributes to Administrative Sciences by conceptualizing university transfer for smart governance as a public-management and governance-design problem. It develops an analytical hybrid transfer-architecture framework in which a visible hub is combined with distributed specialist nodes, shared quality assurance, and explicit safeguards for ethics, cybersecurity, and trustworthy AI. Full article