Search Results (474)

Heterogeneous electronic medical record (EMR) systems and institution-specific data structures continue to limit interoperability and large-scale utilization of healthcare data. Although Health Level Seven (HL7) Fast Healthcare Interoperability Resources (FHIR) has been adopted as an international standard, existing conversion approaches often require extensive preprocessing, high implementation costs, or deep system-specific expertise, restricting their applicability, particularly in small and medium-sized hospitals. To address these constraints, we propose a lightweight EMR-to-HL7 FHIR conversion library optimized for small- and medium-sized healthcare providers that operate with limited system resources. Methods: The library adopts a modular architecture comprising data preprocessing, reference management, structural transformation using transform maps, terminology translation, and validation modules. The proposed approach was implemented using the HL7 Application Programming Interface (HAPI) FHIR and evaluated with anonymized EMR data extracted from multiple hospitals in South Korea, with performance and validation results compared against a conventional HAPI FHIR client-based conversion method. Results: This study proposes a standardized FHIR-based medical data conversion library that enables the efficient transformation of diverse EMR data structures into interoperable FHIR. The proposed library achieved approximately 30% lower single-request conversion latency compared to a conventional HAPI FHIR client-based conversion pipeline under identical hardware and runtime conditions. Conclusions: The proposed conversion method provides a lightweight and adaptable solution for EMR-to-FHIR transformation, improving interoperability with reduced implementation effort and supporting scalable medical data exchange across diverse healthcare environments. Full article

The rapid growth of mobile payment systems has positioned Near Field Communication (NFC) as a core enabling technology. However, conventional NFC protocols primarily emphasize transmission efficiency rather than robust authentication and privacy protection, which exposes users to threats such as eavesdropping, replay, and tracking attacks. In this study, a lightweight and privacy-preserving authentication protocol is proposed for NFC-based mobile payment services. The protocol integrates anonymous authentication, replay resistance, and tracking protection while maintaining low computational overhead suitable for resource-constrained devices. A secure offline session key generation mechanism is incorporated to enhance transaction reliability without increasing system complexity. Formal security verification using the Scyther tool (version 1.1.3) confirms resistance against major attack vectors, including impersonation, man-in-the-middle, and replay attacks. Comparative performance analysis further demonstrates that the proposed scheme achieves superior efficiency and stronger security guarantees compared with existing approaches. These results indicate that the protocol provides a practical and scalable solution for secure and privacy-aware NFC mobile payment environments. Full article

Background/Objectives: Adequate hydration is essential for health; however, water consumption behaviors are influenced not only by physiological needs but also by hydration knowledge and perceptions of drinking water quality. Empirical evidence examining these factors in Eastern European populations remains limited. This study aimed to assess hydration knowledge, water consumption patterns, and attitudes toward drinking water quality among adults in Romania, and to examine their associations with daily water intake and water source preferences. Methods: A cross-sectional study was conducted between November 2024 and November 2025 among adults residing in Romania. Data were collected from 165 participants using an anonymous, self-developed, paper-based questionnaire administered in person to adult patients attending routine visits in four primary care clinics in Arad, Romania, using a convenience sampling approach. The questionnaire assessed sociodemographic characteristics, hydration knowledge, water consumption behaviors, and attitudes toward drinking water quality. Descriptive statistics, chi-square tests, correlation analyses, and multivariable linear and logistic regression models were applied to identify factors associated with daily water intake, adequate hydration (≥2 L/day), and bottled water consumption. Results: Hydration knowledge was moderate overall and was significantly associated with education level and gender. Higher hydration knowledge was positively correlated with daily water intake (r = 0.21, p = 0.006) and was independently associated with higher intake and adequate hydration (OR = 1.28, 95% CI: 1.10–1.49; p = 0.002). Greater trust in tap water was also positively associated with daily intake (r = 0.27, p = 0.001) and adequate hydration (OR = 1.31, 95% CI: 1.12–1.54; p < 0.001). Lower trust in tap water and stronger beliefs regarding bottled water were significant predictors of bottled water use as the primary drinking water source. Education level emerged as a consistent predictor across multiple hydration-related outcomes. Conclusions: Hydration knowledge and perceptions of drinking water quality are key, modifiable factors associated with water consumption behaviors. Educational strategies integrated into primary care and transparent communication regarding tap water safety may support adequate and sustainable hydration among adults. Full article

As a core support for the integration of new energy and smart grids, Vehicle-to-Grid (V2G) networks face a core contradiction between user privacy protection and transaction security traceability—a dilemma that is further exacerbated by issues such as the quantum computing vulnerability of traditional cryptography, cumbersome key management in stateful ring signatures, and conflicts between revocation mechanisms and privacy protection. To address these problems, this paper proposes a post-quantum revocable linkable ring signature scheme based on SPHINCS+, with the following core innovations: First, the scheme seamlessly integrates the pure hash-based architecture of SPHINCS+ with a stateless design, incorporating WOTS+, FORS, and XMSS technologies, which inherently resists quantum attacks and eliminates the need to track signature states, thus completely resolving the state management dilemma of traditional stateful schemes; second, the scheme introduces an innovative “real signature + pseudo-signature polynomially indistinguishable” mechanism, and by calibrating the authentication path structure and hash distribution of pseudo-signatures (satisfying the Kolmogorov–Smirnov test with $D\le 0.05$), it ensures signer anonymity and mitigates the potential risk of distinguishable pseudo-signatures; third, the scheme designs a KEK (Key Encryption Key)-sharded collaborative revocation mechanism, encrypting and storing the $(I,pk,RID)$ mapping table in fragmented form, with KEK split into $KE{K}_1$ (held by the Trusted Authority, TA) and $KE{K}_2$ (held by the regulatory node), with collaborative decryption by both parties required to locate malicious users, thereby resolving the core conflict of privacy leakage in traditional revocation mechanisms; fourth, the scheme generates forward-secure linkable tags based on one-way private key updates and one-time random factors, ensuring that past transactions cannot be traced even if the current private key is compromised; and fifth, the scheme adopts hash commitments instead of complex cryptographic commitments, simplifying computations while efficiently binding transaction amounts to signers—an approach consistent with the pure hash-based design philosophy of SPHINCS+. Security analysis demonstrates that the scheme satisfies the following six core properties: post-quantum security, unforgeability, anonymity, linkability, unframeability, and forward secrecy, thereby providing technical support for secure and anonymous payments in V2G networks in the quantum era. Full article

Background/Objectives: Cellulite is a common aesthetic condition in women, traditionally assessed using visual inspection and palpation-based scales that are inherently subjective. Therefore, image-based methods that may support standardized severity grading are of growing interest. To evaluate infrared thermography as an imaging-based method for grading cellulite severity and to perform methodological validation of a newly developed thermographic classification scale by comparing it with clinical palpation and anthropometric parameters. Methods: This retrospective, non-interventional study analyzed anonymized clinical and thermographic data from 81 women with clinically assessed cellulite. Cellulite severity was evaluated using the Nürnberger–Müller palpation scale and a newly developed five-point thermographic scale based on skin surface temperature differentials and histogram pattern analysis. The associations between the assessment methods were evaluated using ordinal statistical measures, and agreement was assessed using weighted Cohen’s kappa statistics. Results: Thermographic grading demonstrated high agreement with palpation-based assessment, with a percentage agreement of 93.8% and an almost perfect agreement based on weighted Cohen’s κ. A strong ordinal association was observed between the methods. Thermography consistently classified a subset of cases as one grade higher compared with palpation. No statistically significant associations were observed between thermographic grade and body mass index or waist-to-hip ratio. Conclusions: Infrared thermography enables image-based grading of cellulite severity and shows a strong concordance with established palpation scales. The proposed thermographic classification provides preliminary methodological validation of an imaging-based grading approach. Further multicenter studies involving multiple assessors and diverse populations are required to assess reproducibility, specificity, and potential clinical applicability. Full article

Prostate cancer is one of the most lethal cancers in the male population, and accurate localization of intraprostatic lesions on MRI remains challenging. In this study, we investigated methods for improving prostate cancer segmentation on T2-weighted pelvic MRI using cascaded neural networks. We used an anonymized dataset of 400 multiparametric MRI scans from two centers, in which experienced radiologists had delineated the prostate and clinically significant cancer on the T2 series. Our baseline approach applies 2D and 3D segmentation networks (UNETR, UNET++, Swin-UNETR, SegResNetDS, and SegResNetVAE) directly to full MRI volumes. We then introduce additional stages that filter slices using DenseNet-201 classifiers (cancer/no-cancer and prostate/no-prostate) and localize the prostate via a YOLO-based detector to crop the 3D region of interest before segmentation. Using Swin-UNETR as the backbone, the prostate segmentation Dice score increased from 71.37% for direct 3D segmentation to 76.09% when using prostate detection and cropped 3D inputs. For cancer segmentation, the final cascaded pipeline—prostate detection, 3D prostate segmentation, and 3D cancer segmentation within the prostate—improved the Dice score from 55.03% for direct 3D segmentation to 67.11%, with an ROC AUC of 0.89 on the test set. These results suggest that cascaded detection- and segmentation-based preprocessing of the prostate region can substantially improve automatic prostate cancer segmentation on MRI while remaining compatible with standard segmentation architectures. Full article

The increasing deployment of interconnected devices in Smart Cities and Industrial Internet of Things (IIoT) environments has significantly enhanced operational efficiency, automation, and real-time data analytics. However, this rapid digitization also introduces complex security and privacy challenges, particularly in the handling of sensitive data across heterogeneous and resource-constrained networks. This review explores the current landscape of privacy-preserving protocols designed for Smart City and IIoT infrastructures. We examine state-of-the-art approaches including lightweight cryptographic schemes, secure data aggregation, anonymous communication protocols, and blockchain-based frameworks. The paper also analyzes practical trade-offs between security, latency, and computational overhead in real-world deployments. Open research challenges such as secure interoperability, privacy in federated learning, and resilience against AI-driven cyberattacks are discussed. Finally, the paper outlines promising research directions and technologies that can enable scalable, secure, and privacy-aware network infrastructures for future urban and industrial ecosystems. Full article

Background/Objectives: Accurate identification of dental malocclusions from routine clinical photographs can be time-consuming and subject to interobserver variability. A YOLOv11-based deep learning approach is presented and evaluated for automatic malocclusion detection on routine intraoral photographs, testing the hypothesis that training on a structured annotation protocol enables reliable detection of multiple clinically relevant malocclusions. Methods: An anonymized dataset of 5854 intraoral photographs (frontal occlusion; right/left buccal; maxillary/mandibular occlusal) was labeled according to standardized instructions derived from the Index of Orthodontic Treatment Need (IOTN) A total of 17 clinically relevant classes were annotated with bounding boxes. Due to an insufficient number of examples, two malocclusions (transposition and non-occlusion) were excluded from our quantitative analysis. A YOLOv11 model was trained with augmented data and evaluated on a held-out test set using mean average precision at IoU 0.5 (mAP50), macro precision (macro-P), and macro recall (macro-R). Results: Across 15 analyzed classes, the model achieved 87.8% mAP50, 76.9% macro-P, and 86.1% macro-R. The highest per-class AP₅₀ was observed for Deep bite (98.8%), Diastema (97.9%), Angle Class II canine (97.5%), Anterior open bite (92.8%), Midline shift (91.8%), Angle Class II molar (91.1%), Spacing (91%), and Crowding (90.1%). Moderate performance included Anterior crossbite (88.3%), Angle Class III molar (87.4%), Head bite (82.7%), and Posterior open bite (80.2%). Lower values were seen for Angle Class III canine (76%), Posterior crossbite (75.6%), and Big overjet (75.3%). Precision–recall trends indicate earlier precision drop-off for posterior/transverse classes and comparatively more missed detections in Posterior crossbite, whereas Big overjet exhibited more false positives at the chosen threshold. Conclusion: A YOLOv11-based deep learning system can accurately detect several clinically salient malocclusions on routine intraoral photographs, supporting efficient screening and standardized documentation. Performance gaps align with limited examples and visualization constraints in posterior regions. Larger, multi-center datasets, protocol standardization, quantitative metrics, and multimodal inputs may further improve robustness. Full article

The rapid growth in Internet-based services has increased the demand for user data to enable personalized and adaptive digital experiences. These services typically require users to disclose various types of personal information, which are organized into user profiles and used to tailor content, recommendations, and accessibility settings. However, achieving an effective balance between personalization accuracy and user data protection remains a persistent and complex challenge. Excessive data disclosure raises the risk of re-identification and privacy breaches, while excessive anonymization can significantly diminish personalization and overall service quality. In this paper, we address this trade-off by proposing a context-aware learning-based data generalization framework that preserves user privacy while maintaining the functional usefulness of personal data. We first conduct a systematic classification of user data commonly collected into five main categories: demographic, location, accessibility, preference, and behavior data. To generalize these data categories dynamically and adaptively, we use a Multi-Layer Perceptron (MLP) model that learns patterns across heterogeneous data types. Unlike traditional rule-based generalization techniques, the MLP-based approach captures nonlinear relationships, adapts to heterogeneous data distributions, and scales efficiently with large datasets. The proposed MLP-based generalization method reduces the granularity of personal data, preserving privacy without significantly compromising information usefulness. Experimental results show that the proposed method reduces the risk of re-identification to approximately 35%, compared to non-anonymized data, where the re-identification risk is about 80–90%. These findings highlight the potential of learning-based data generalization as a strategy for privacy-preserving personalization in modern Internet services. They also show how the proposed generalization method can be applied in practice to transform user data while maintaining both utility and confidentiality. Full article

Background: Adult females with Autism Spectrum Disorder (ASD) and Attention Deficit Hyperactivity Disorder (ADHD) are frequently underdiagnosed due to gender bias, overlapping symptoms, and limited awareness among healthcare professionals. The scarcity of research on this subject—particularly in the UK context—underscores the need for further investigation. Accordingly, the aim of this study was to explore the lived experiences of adult females receiving a late diagnosis of ASD and/or ADHD and to identify key barriers within the UK diagnostic pathway. This study addresses a critical knowledge gap by examining the factors contributing to delayed diagnosis within the United Kingdom. Study Design and Method: The study employed a qualitative approach, utilising an anonymous online questionnaire survey comprising nine open-ended questions. Responses were obtained from 52 UK-based females aged 35–65 years who had either received or were awaiting a diagnosis of ASD and/or ADHD. Data were analysed thematically within a constructivist framework. Findings: The analysis revealed three overarching themes: (i) limited understanding and lack of empathy among healthcare professionals, (ii) insufficient post-diagnostic support, with most participants reporting no follow-up care, and (iii) a complex, protracted diagnostic process, often involving waiting periods exceeding three years. Gender bias and frequent misdiagnosis were recurrent issues, contributing to significant psychological distress. These findings underscore the need for systemic reforms and align closely with gaps identified in the existing literature. Conclusions: The findings emphasise the urgent need for gender-sensitive diagnostic frameworks, enhanced professional training, and a person-centred approach to care. Key recommendations include shortening diagnostic waiting times, strengthening healthcare professionals’ knowledge base, and ensuring equitable and consistent post-diagnostic support. Full article

Background/Objectives: The present study aimed to examine the associations among achievement motivation, meaning in life, and well-being among video game players and to investigate differences between players with approach- and avoidance-oriented motivations. Methods: The sample consisted of 296 university students who reported playing video games (192 men and 104 women), aged 18 to 35 years (M = 22.62; SD = 2.64). Participants completed a battery of self-report measures, including the Achievement Goal Questionnaire, the Meaning in Life Questionnaire, and the WHO-5 Well-Being Index, administered anonymously. Results: Mediation analyses revealed that meaning in life was a significant mediator in the relationship between approach-oriented mastery goals and well-being (Ind = 0.07; 95% CI [0.02, 0.12]). However, no significant mediation effect was found for approach-oriented performance goals (Ind = 0.04; 95% CI [−0.01, 0.09]). Independent-samples t-tests indicated that participants with approach-oriented motivations reported significantly higher levels of meaning in life (t(294) = 4.44; p < 0.001), presence of meaning (t(294) = 5.74; p < 0.001), and well-being (t(294) = 5.52; p < 0.001) compared to those with avoidance-oriented motivations. Conclusions: The findings suggest that approach-oriented achievement motivations among players are positively associated with meaning in life and are indirectly associated with higher well-being, whereas avoidance-oriented motivations are associated with lower levels of well-being. These results carry potential implications for game design, education, and psychotherapy. Full article

Data governance for responsible AI systems remains challenged by the lack of automated tools that can apply robust privacy-preserving techniques without destroying analytical value. We propose AnonymAI, a novel methodological framework that integrates LLM-based intelligent agents, the mathematical guarantees of differential privacy, and an automated workflow to generate anonymized datasets for analytical applications. This framework produces data tables with formally verifiable privacy protection, dramatically reducing the need for manual classification and the risk of human error. Focusing on the protection of tabular data containing sensitive personal information, AnonymAI is designed as a generalized, replicable pipeline adaptable to different regulations (e.g., General Data Protection Regulation) and use-case scenarios. The novelty lies in combining the contextual classification capabilities of LLMs with the mathematical rigor of differential privacy, enabling an end-to-end pipeline from raw data to a protected, analysis-ready dataset. The efficiency and formal guarantees of this approach offer significant advantages over conventional anonymization methods, which are often manual, inconsistent, and lack the verifiable protections of differential privacy. Validation studies, covering both controlled experiments on four types of synthetic datasets and broader tests on 19 real-world public tables from various domains, confirmed the applicability of the framework, with the agent-based classifier achieving high overall accuracy in identifying confidential columns. The results demonstrate that the protected data maintains high value for statistical analysis and machine learning models, highlighting AnonymAI’s potential to advance responsible data sharing. This work paves the way for trustworthy and scalable data governance in AI through a rigorously engineered automated anonymization pipeline. Full article

This study presents the on-site public evaluation of Notre-Dame Whispers, a geolocated audio-guided tour that explores the sonic history of the Cathédrale Notre-Dame de Paris. The experience combines binaural reproduction, embodied storytelling, and historically informed soundscapes to immerse visitors in the cathedral’s past auditory environments. Drawing on virtually recreated acoustics, it reconstructs key components of Notre-Dame’s sound heritage, including the medieval construction site, early polyphonic chant, and the contemporary urban soundscape. An on-site evaluation was conducted to assess visitor engagement, usability, and the perceived authenticity of the reconstructed soundscapes. A mixed-methods approach integrated questionnaire responses, semi-structured interviews, and anonymized user analytics collected through the mobile application. Results indicate a high level of immersion, with participants particularly valuing the spatialised audio design and narrative depth. However, challenges were identified regarding GPS-based triggering reliability and the difficulty of situational interpretation in complex spatial environments. These findings offer insights into public reception of immersive heritage audio experiences and inform future developments in digital cultural mediation. Full article

Constructing reusable accident-text corpora is hindered by anonymization, heterogeneous sources, and sparse labels, which complicate cross-document event linking. We propose a spatiotemporal lattice-constrained approach that encodes administrative hierarchies and temporal granularity, defines domain-informed consistency criteria, instantiates spatial/temporal relations via a subset of RCC-8 and Allen’s interval algebra, estimates anchor weights via smoothing with monotonic projection, and fuses signals using a constrained monotonic network with explicit probability calibration. An active-learning decision rule—combining maximum probability with a probability-gap criterion—supports scalable automatic labeling, and controlled augmentation leverages instruction-tuned LLMs under lattice constraints. Experiments show competitive ranking (Hit@1 = 41.51%, Hit@5 = 77.33%) and discrimination (ROC-AUC = 87.34%), with the best F1 (62.46%). The method yields the lowest calibration errors (Brier = 0.14; ECE = 1.97%), maintains performance across sources, and exhibits the smallest F1 fluctuation across thresholds (Δ = 1.7%). In deployment-oriented analyses, it auto-labels 77.7% of cases with 97.51% accuracy among high-confidence outputs while routing 22.3% to review, where the true-positive rate is 81.46%. These findings indicate that integrating structured constraints with calibrated probabilistic fusion enables accurate, auditable, and scalable event linking for accident-corpus construction. Full article

This study evaluates the effectiveness of Jordan’s judiciary in overseeing electoral appeals within the framework of a constitutional monarchy. Adopting a mixed-methods approach, it combines doctrinal legal analysis of key constitutional provisions and Election Law No. 4 of 2022 with a comparative examination of electoral adjudication in Tunisia, Egypt, and Lebanon. The study is further strengthened by a structured content analysis of 120 appellate rulings issued between 2015 and 2023 and by qualitative insights drawn from anonymized interviews with judicial personnel engaged in electoral dispute resolution. Although Jordan’s legal framework formally empowers the judiciary to adjudicate electoral disputes, five structural limitations persist: narrow standing rules, rigid evidentiary thresholds, judicial reluctance to exercise investigatory powers, opaque reasoning in judgments, and the absence of specialized electoral courts. These constraints reflect systemic tensions between formal judicial independence and the realities of constrained discretion in hybrid regimes. An empirical analysis of 127 Jordanian electoral appeal cases from 2013 to 2020 reveals that a mere 7% of disputed electoral outcomes were overturned, whereas 73% of allegations were disregarded due to insufficient evidence. Furthermore, it is noteworthy that only 31% of rulings were publicly accessible, in stark contrast to the 89% accessibility rate observed in Tunisia. By identifying and addressing these systemic limitations, the study contributes to ongoing discourse on institutional reform and democratic resilience. In doing so, it underscores the importance of robust electoral justice mechanisms for sustaining public trust, rule of law, and inclusive governance—principles central to political and institutional sustainability as reflected in Sustainable Development Goal 16. Full article