Search Results (327)

A smart device unlocking scheme based on ultrasonic gesture recognition is proposed, allowing users to unlock their devices by customizing the unlock code through gesture movements. This method utilizes ultrasound to detect multiple consecutive gestures, identifying micro-features within these gestures for authentication. To enhance recognition accuracy, an unsupervised segmentation algorithm is employed to accurately segment the gesture feature region and extract the time-frequency domain data of the gestures. Additionally, two-stage data enhancement techniques are applied to generate user-specific data based on a small sample size. Finally, the user-specific model is deployed to mobile devices via transfer learning for on-device, real-time inference. Experimental validation on a commercial smartphone (Redmi K50) demonstrates that the entire authentication pipeline, from signal acquisition to decision, processes 8 types of gestures in a sequence in sequence in approximately 1.2 s, with the core model inference taking less than 50 milliseconds. This ensures that the raw biometric data (ultrasonic echoes) and the recognition results never leave the user’s device during authentication, thereby safeguarding privacy. It is important to note that while model training is performed offline on a server to leverage greater computational resources for personalization, the deployed system operates fully in real time on the edge device. Experimental results demonstrate that our system achieves accurate and robust identity verification, with an average five-fold cross-validation accuracy rate of up to $93.56\%$, and it shows robustness across different environments. Full article

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

Cardiovascular disease (CVD) is a major cause of mortality around the world. This underscores the critical need to implement effective predictive tools to inform clinical decision-making. This study aimed to compare the predictive performance of ensemble learning algorithms, including Bagging, Random Forest, Extra Trees, Gradient Boosting, and AdaBoost, when applied to a clinical dataset comprising patients with CVD. The methodology entailed data preprocessing and cross-validation to regulate generalization. The performance of the model was evaluated using a variety of metrics, including accuracy, F1 score, precision, recall, Cohen’s Kappa, and area under the curve (AUC). Among the models evaluated, Bagging demonstrated the best overall performance (accuracy ± SD: 93.36% ± 0.22; F1 score: 0.936; AUC: 0.9686). It also reached the lowest average rank (1.0) in Friedman test and was placed, together with Extra Trees (accuracy ± SD: 90.76% ± 0.18; F1 score: 0.916; AUC: 0.9689), in the superior statistical group (group A) according to Nemenyi post hoc test. The two models demonstrated a high degree of agreement with the actual labels (Kappa: 0.87 and 0.83, respectively), thereby substantiating their reliability in authentic clinical contexts. The findings substantiated the preeminence of aggregation-based ensemble methods in terms of accuracy, stability, and concordance. This underscored the prominence of Bagging and Extra Trees as optimal candidates for cardiovascular diagnostic support systems, where reliability and generalization were paramount. Full article

Background: Authentic leadership is characterized by the authenticity and self-awareness of the leader, who acts with transparency and promotes positive outcomes in clinical practice and team management. In Portugal, there isn’t a tool available to assess nurses’ perceptions of authentic leadership in nursing. This study aimed to translate and cross-culturally adapt the Authentic Nurse Leadership Questionnaire (ANLQ) for the Portuguese context and to evaluate its psychometric properties. This instrument assesses nurses’ perceptions of the authentic leadership exercised by their leader. Methods: A methodological, descriptive, cross-sectional study with a quantitative approach was conducted. The translation and cross-cultural adaptation process followed the recommendations of internationally recognized guidelines. The Authentic Nurse Leadership Scale—Portuguese version (ANLS-PT) was administered to a sample of 406 nurses from various functional units in three primary healthcare centers. Exploratory and confirmatory factor analysis techniques were used. Reliability was established through a test–retest administration to 22 nurses at two different times, with a two-week interval. The internal consistency of the scale was assessed using Cronbach’s Alpha. Results: An instrument with 29 items and 3 dimensions was obtained, explaining 68.3% of the total variance. The identified dimensions were Caring and Decision-Making, Self-Awareness, and Relational Integrity and Ethics. The overall instrument showed an internal consistency of 0.97. Conclusions: The ANLS-PT proved to be a valid, reliable, and robust tool for assessing authentic leadership in the Portuguese cultural context and can be used in various nursing practice contexts. Full article

With the rapid growth of digital interactions, safeguarding user privacy on websites has become a critical concern. This paper introduces a comprehensive framework that integrates both technical and policy-based factors to assess a website’s level of privacy protection. The framework employs a scoring system that evaluates key technical elements, such as HTTP security headers, email authentication protocols (SPF, DKIM, DMARC), SSL/TLS certificate usage, domain reputation, DNSSEC, and cookie practices. In parallel, it examines the clarity and GDPR compliance of privacy policies. The resulting score reflects not only the technical strength of a website’s defenses but also the transparency with which data processing practices are communicated to users. To demonstrate its effectiveness, the framework was applied to two similarly sized private hospitals, generating comparative privacy scores under a unified metric. The results confirm the framework’s value in producing measurable insights that enable cross-organizational privacy benchmarking. By combining policy evaluation with technical analysis, this work addresses a significant gap in existing research and offers a reproducible, extensible methodology for assessing website privacy posture from a visitor’s perspective. Full article

This study examines community-based wellness tourism (CBWT) in Ubud, Bali, focusing on ownership structures, community participation, and the role of local traditions. Using a qualitative design, the data were collected through semi-structured interviews with wellness stakeholders and field observations of spas and yoga centers. The findings reveal that spas are predominantly locally owned and staffed, ensuring value retention and skill development, while flagship yoga and retreat centers are dominated by non-local actors, creating risks of economic leakage and weaker cultural stewardship. Community involvement is strong in operations but limited in planning and governance, highlighting a policy–practice gap. Integrating Balinese traditions, such as Usada Bali and Melukat, could enhance authenticity but requires careful protection against commodification. The findings reveal that locally owned spas contribute to SDG 1 (No Poverty) and SDG 8 (Decent Work and Economic Growth) through local value retention, employment creation, and skill development, while non-local dominance of yoga and retreat centers risks economic leakage and weakened cultural guardianship. The study also identifies gaps in governance and planning, underscoring the need for inclusive participation and capacity building to align with SDG 11 (Sustainable Cities and Communities). Integrating Balinese traditions, such as Usada Bali and Melukat, highlights the opportunities for safeguarding cultural heritage, provided that protocols against commodification are enforced. To address these challenges, the study proposes a strategic framework emphasizing governance reform through a quadruple-helix model, shared-equity ownership, standardized human capital development, and protocol-based cultural guardianship. Despite the limitations of this being a single-case, cross-sectional study, the findings contribute to wellness tourism research by shifting attention from visitor demands to governance and equity. The study offers practical strategies for institutionalizing CBWT in Ubud while providing a transferable model for destinations seeking to balance growth with tradition. Full article

The architectural settlements of the Dong people are the core representatives of China’s Dong culture. The unique architectural forms created by the Dong people, such as stilted houses, drum towers, and wind-and-rain bridges, demonstrate the wisdom of the Dong people in adapting to mountainous environments and their exquisite construction techniques. However, with the acceleration of urbanization and the impact of tourism development, Dong village architecture is facing multiple challenges, including settlement hollowing-out, the discontinuity of traditional craftsmanship, and the destruction of authenticity. This study proposes a series of protection and restoration strategies by integrating relevant domestic and international theories and practical experiences based on the formal characteristics, cultural value, and current issues of Dong village settlement architecture. It emphasizes the principle of holistic protection, advocates for the combination of authentic restoration and adaptive renewal, and aims to achieve the inheritance of cultural heritage through means such as digital technology, community participation mechanisms, and cross-regional collaborative protection. Furthermore, this study explores the path toward balancing traditional architecture with modern needs, intending to provide theoretical support and a practical reference for the sustainable protection of Dong village settlement architecture and the continuation of cultural memory. Full article

The Zero Trust Architecture (ZTA) model has emerged as a foundational cybersecurity paradigm that eliminates implicit trust and enforces continuous verification across users, devices, and networks. This study presents a systematic literature review of 74 peer-reviewed articles published between 2016 and 2025, spanning domains such as cloud computing (24 studies), Internet of Things (11), healthcare (7), enterprise and remote work systems (6), industrial and supply chain networks (5), mobile networks (5), artificial intelligence and machine learning (5), blockchain (4), big data and edge computing (3), and other emerging contexts (4). The analysis shows that authentication, authorization, and access control are the most consistently implemented ZTA components, whereas auditing, orchestration, and environmental perception remain underexplored. Across domains, the main challenges include scalability limitations, insufficient lightweight cryptographic solutions for resource-constrained systems, weak orchestration mechanisms, and limited alignment with regulatory frameworks such as GDPR and HIPAA. Cross-domain comparisons reveal that cloud and enterprise systems demonstrate relatively mature implementations, while IoT, blockchain, and big data deployments face persistent performance and compliance barriers. Overall, the findings highlight both the progress and the gaps in ZTA adoption, underscoring the need for lightweight cryptography, context-aware trust engines, automated orchestration, and regulatory integration. This review provides a roadmap for advancing ZTA research and practice, offering implications for researchers, industry practitioners, and policymakers seeking to enhance cybersecurity resilience. Full article

As 6G networks become increasingly complex and heterogeneous, effective classification of network slicing is essential for optimizing resources and managing quality of service. While recent advances demonstrate high accuracy under controlled laboratory conditions, a critical gap exists between algorithm performance evaluation under idealized conditions and their actual effectiveness in realistic deployment scenarios. This study presents a comprehensive comparative analysis of two distinct preprocessing methodologies for 6G network slicing classification: Pure Raw Data Analysis (PRDA) and Literature-Validated Realistic Transformations (LVRTs). We evaluate the impact of these strategies on algorithm performance, resilience characteristics, and practical deployment feasibility to bridge the laboratory–reality gap in 6G network optimization. Our experimental methodology involved testing eleven machine learning algorithms—including traditional ML, ensemble methods, and deep learning approaches—on a dataset comprising 10,000 network slicing samples (expanded to 21,033 through realistic transformations) across five network slice types. The LVRT methodology incorporates realistic operational impairments including market-driven class imbalance (9:1 ratio), multi-layer interference patterns, and systematic missing data reflecting authentic 6G deployment challenges. The experimental results revealed significant differences in algorithm behavior between the two preprocessing approaches. Under PRDA conditions, deep learning models achieved perfect accuracy (100% for CNN and FNN), while traditional algorithms ranged from 60.9% to 89.0%. However, LVRT results exposed dramatic performance variations, with accuracies spanning from 58.0% to 81.2%. Most significantly, we discovered that algorithms achieving excellent laboratory performance experience substantial degradation under realistic conditions, with CNNs showing an 18.8% accuracy loss (dropping from 100% to 81.2%), FNNs experiencing an 18.9% loss (declining from 100% to 81.1%), and Naive Bayes models suffering a 34.8% loss (falling from 89% to 58%). Conversely, SVM (RBF) and Logistic Regression demonstrated counter-intuitive resilience, improving by 14.1 and 10.3 percentage points, respectively, under operational stress, demonstrating superior adaptability to realistic network conditions. This study establishes a resilience-based classification framework enabling informed algorithm selection for diverse 6G deployment scenarios. Additionally, we introduce a comprehensive explainable artificial intelligence (XAI) framework using SHAP analysis to provide interpretable insights into algorithm decision-making processes. The XAI analysis reveals that Packet Loss Budget emerges as the dominant feature across all algorithms, while Slice Jitter and Slice Latency constitute secondary importance features. Cross-scenario interpretability consistency analysis demonstrates that CNN, LSTM, and Naive Bayes achieve perfect or near-perfect consistency scores (0.998–1.000), while SVM and Logistic Regression maintain high consistency (0.988–0.997), making them suitable for regulatory compliance scenarios. In contrast, XGBoost shows low consistency (0.106) despite high accuracy, requiring intensive monitoring for deployment. This research contributes essential insights for bridging the critical gap between algorithm development and deployment success in next-generation wireless networks, providing evidence-based guidelines for algorithm selection based on accuracy, resilience, and interpretability requirements. Our findings establish quantitative resilience boundaries: algorithms achieving >99% laboratory accuracy exhibit 58–81% performance under realistic conditions, with CNN and FNN maintaining the highest absolute accuracy (81.2% and 81.1%, respectively) despite experiencing significant degradation from laboratory conditions. Full article

The rapid advancement of generative models, such as GAN and diffusion architectures, has enabled the creation of highly realistic forged images, raising critical challenges in key domains. Detecting such forgeries is essential to prevent potential misuse in sensitive areas, including healthcare, financial documentation, and identity verification. This study addresses the problem by deploying a vision transformer (ViT)-based multiclass classification framework to identify image forgeries across three distinct domains: invoices, human faces, and medical images. The dataset comprises both authentic and AI-generated samples, creating a total of six classification categories. To ensure uniform feature representation across heterogeneous data and to effectively utilize pretrained weights, all images were resized to 224 × 224 pixels and converted to three channels. Model training was conducted using stratified K-fold cross-validation to maintain balanced class distribution in each fold. Experimental results of this study demonstrate consistently high performance across three folds, with an average training accuracy of 0.9983 (99.83%), validation accuracy of 0.9620 (96.20%), and test accuracy of 0.9608 (96.08%), along with a weighted F1 score of 0.9608 and exceeding 0.96 (96%) for all classes. These findings highlight the effectiveness of ViT architectures for cross-domain forgery detection and emphasize the importance of preprocessing standardization when working with mixed datasets. Full article

This study introduces a machine learning–driven extended reality (XR) interaction framework that leverages electroencephalography (EEG) for decoding consumer intentions in immersive decision-making tasks, demonstrated through functional food purchasing within a simulated autonomous vehicle setting. Recognizing inherent limitations in traditional “Preference vs. Non-Preference” EEG paradigms for immersive product evaluation, we propose a novel and robust “Rest vs. Intention” classification approach that significantly enhances cognitive signal contrast and improves interpretability. Eight healthy adults participated in immersive XR product evaluations within a simulated autonomous driving environment using the Microsoft HoloLens 2 headset (Microsoft Corp., Redmond, WA, USA). Participants assessed 3D-rendered multivitamin supplements systematically varied in intrinsic (ingredient, origin) and extrinsic (color, formulation) attributes. Event-related potentials (ERPs) were extracted from 64-channel EEG recordings, specifically targeting five neurocognitive components: N1 (perceptual attention), P2 (stimulus salience), N2 (conflict monitoring), P3 (decision evaluation), and LPP (motivational relevance). Four ensemble classifiers (Extra Trees, LightGBM, Random Forest, XGBoost) were trained to discriminate cognitive states under both paradigms. The ‘Rest vs. Intention’ approach achieved high cross-validated classification accuracy (up to 97.3% in this sample), and area under the curve (AUC > 0.97) SHAP-based interpretability identified dominant contributions from the N1, P2, and N2 components, aligning with neurophysiological processes of attentional allocation and cognitive control. These findings provide preliminary evidence of the viability of ERP-based intention decoding within a simulated autonomous-vehicle setting. Our framework serves as an exploratory proof-of-concept foundation for future development of real-time, BCI-enabled in-transit commerce systems, while underscoring the need for larger-scale validation in authentic AV environments and raising important considerations for ethics and privacy in neuromarketing applications. Full article

This study models how designable cues in digital heritage promotion shape advocacy through affect and memory. Relying on the stimulus–organism–response paradigm, we argue that three stimuli, Visual Sensory Appeal (VSA), Narrative Immersion (NI), and Perceived Authenticity (PA), trigger Emotional Engagement (EE) and become Destination Memory (DM), leading to Intention to Recommend (IR). A cross-sectional quantitative design with an online self-report survey was employed. Using Structural Equation Modeling (SEM) we modeled 653 usable responses to test hypothesized stimulus–organism–response processes and Multi-Group Analysis (MGA) tested heterogeneity across gender, age, education, recent contact, cultural-travel frequency, preservation interest, prior heritage experience, and technology use. Direct associations revealed VSA was a strong predictor of IR, and EE and DM predicted IR positively. NI and PA were not incrementally directly affecting IR. Mediation tests revealed partial mediation for VSA (through EE and DM) and complete mediation for NI and PA; across all stimuli, DM far surpassed EE, suggesting memory consolidation as the overall mechanism. MGA revealed systematic segmentation: women preferred visual and authenticity approaches; men used affective conversion, narrative, and authenticity-to-memory more; young adults preferred story/memory levers; higher education made authenticity pathways legitimate; exposure, experience, sustainability interest, and technology use further conditioned strength of paths. Results sharpen S–O–R accounts by ranking visual design as a proximal driver and placing EE on DM as the central channel through which narrative and authenticity have their influence. In practice, the research supports visually consistent, memory-backed, segment-specific strategies for sustainable, inclusive heritage communication. Full article

Edible insects, particularly Tenebrio molitor (Linnaeus) (mealworm) and Zophobas morio (Fabricius) (superworm), have drawn increasing attention as alternative protein sources. This study aims to develop an accurate molecular detection method for T. molitor, an EU-approved food species, and to differentiate it from Z. morio, which remains unapproved for human consumption in the EU. The process enables precise and sensitive identification methods by optimizing singleplex and duplex PCR techniques targeting 16S rRNA and COI gene regions. The DNA of T. molitor was detected in various food matrices, including pastries, chocolate, and porridge, while avoiding cross-reactivity with Z. morio, Gryllus asimilis, and Locusta migratoria. The detection limit for both singleplex and duplex PCR was 10 pg of DNA, ensuring robustness against inhibitory effects from complex food matrices. The developed approach ensures reliable detection and compliance with EU regulations regarding insect-based foods, providing a critical tool for food authentication and preventing adulteration. The key advancements of this approach lie in its improved specificity and sensitivity, allowing for the ability to detect complex food matrices. An applied perspective was evaluated using real commercial food products. Full article

In a world of rapidly globalizing food markets, biodiversity, authenticity, and the safety of food products have become a universal concern. DNA barcoding is a widely used molecular-based method that can identify biological material and is used for the traceability of both raw materials and ingredients in processed food. In the present study, contacted within the framework of the BioValue Horizon Project, which promotes the role of agrobiodiversity in sustainable food systems, DNA barcoding using the ITS and rbcL markers was employed as a proof-of-concept approach to reveal the biodiversity and authenticity of ten commercial plant-based products. Following successful DNA amplification and sequencing using six products as a proof-of-concept, a diverse range of plant genera and species were identified, verifying biodiversity. A strong correlation between ITS and rbcL-based markers was demonstrated, supporting their combined use for reliable species-level biodiversity assessment. Finally, heat map analysis of label contents and sequencing-based genera identification confirmed high concordance between label claims and sequencing results in most cases, though undeclared species and absent labeled taxa were also detected, highlighting potential mislabeling or cross-contamination. Full article

This study investigates the effects of species, geographical origin, and processing on the proximate composition of frog leg meat, with a focus on developing predictive models for processing status. Data were systematically compiled from 18 published studies, yielding 32 entries across 10 edible frog species and multiple processing methods. Proximate composition parameters (moisture, protein, fat, ash) were compared between processed and unprocessed samples, and classification models were trained using moisture content as the primary predictor. Logistic regression and several machine learning algorithms, including Stochastic Gradient Descent, Support Vector Machine, Random Forest, and Decision Tree, were benchmarked under a Leave-One-Study-Out (LOSO) cross-validation framework. Results demonstrated that moisture content alone was sufficient to accurately distinguish processing status, with a critical threshold of ~73% separating processed from unprocessed frog legs. Logistic regression achieved perfect specificity and precision (100%) with an overall accuracy of 96.8%, while other classifiers also performed strongly (>90% accuracy). These findings confirm moisture as a species- and origin-independent marker of processing, offering a simple, rapid, and cost-effective tool for authenticity verification and quality control in frog meat and potentially other niche protein products. Future work should expand sample coverage, validate thresholds across processing types, and integrate biochemical and sensory quality assessments. Full article