Search Results (269)

Background/Objectives: The Rett Syndrome Behavior Questionnaire (RSBQ) is a widely used caregiver-reported instrument for assessing behavioral and neurological features of Rett syndrome (RTT). However, a validated Korean version has not been available. This study aimed to translate the RSBQ into Korean (K-RSBQ) and to evaluate its psychometric properties in a Korean RTT population. Methods: The RSBQ was translated and back-translated using standardized procedures and refined through a Delphi process. Primary caregivers of individuals with clinically diagnosed RTT completed an online survey including the K-RSBQ and the Childhood Autism Rating Scale (CARS). Test–retest reliability was assessed in a subset of caregivers who completed the questionnaire twice within one week, and inter-rater reliability was evaluated when an additional caregiver was available. Results: Sixty-six primary caregivers participated. The K-RSBQ demonstrated high internal consistency for the total score (Cronbach’s α = 0.912) and moderate-to-high consistency across most subscales. Test–retest reliability for the total score was moderate (weighted κ = 0.594), while inter-rater reliability between primary and secondary caregivers was generally low. The hand behavior subscale showed low and non-significant test–retest reliability. The K-RSBQ total score exhibited a low-to-moderate correlation with the CARS total score, and the general mood subscale showed a moderate correlation with the CARS emotional response item. Caregivers reported minimal difficulty in understanding the questionnaire items. Conclusions: The K-RSBQ demonstrates acceptable internal consistency and test–retest reliability when administered to primary caregivers, with preliminary evidence supporting its construct validity. Although limitations exist regarding criterion validation and inter-rater agreement, the K-RSBQ represents a feasible and culturally adapted tool for assessing RTT-related behavioral features in Korean clinical and research settings. Full article

Rett syndrome (RTT) is a rare neurodevelopmental disorder that causes severe motor and cognitive impairments, limiting voluntary communication. Gaze-based technologies and virtual reality (VR) offer innovative ways to assess and enhance attention, happiness, and learning in individuals with minimal motor control. This study investigated and compared visual-attentional and emotional engagement in girls with RTT and typically developing (TD) peers during exploration of a virtual forest presented in 2D and immersive 3D (VR) formats across four progressively complex tasks. Twelve girls with RTT and 12 TD peers completed eye-tracking tasks measuring reaction time, fixation duration, disengagement events, and observed happiness. Girls with RTT showed slower responses and more disengagements overall, but VR significantly improved attentional efficiency in both groups, resulting in faster reaction times (η²_p = 0.36), longer fixations (η²_p = 0.31), and fewer disengagements (η²_p = 0.27). These effects were stronger in the RTT group. Both groups also showed greater happiness in VR settings (RTT: p = 0.011; TD: p = 0.015), and in participants with RTT, peaks in attention coincided with peak happiness, indicating a link between happiness and cognitive engagement. Immersive VR thus appears to enhance attention and affect in RTT, supporting its integration into personalized neurorehabilitation. Full article

In 2025, there is still no ubiquitous, accurate, infrastructure-free indoor positioning system. Among existing approaches, WiFi-based positioning is highly promising as it leverages existing infrastructure. However, its performance is severely affected by WiFi signal variability and environmental dynamics. Thus, this paper presents a novel approach that combines WiFi Round Trip Time and Received Signal Strength measurements with the Conformal Prediction (CP) framework to achieve robust uncertainty-aware indoor positioning. Our proposed method does not only accurately estimate the user position, but also provides two prediction regions: a rectangular region and a circular region. We systematically evaluate our method across three real-world testbeds, which achieves a positioning accuracy of 0.6 m, while generating prediction regions with theoretical coverage guarantees for circular regions and marginal coverage guarantees for rectangular regions. To the best of our knowledge, this is one of the first work to enable uncertainty quantification on top of state-of-the-art WiFi ranging signals. Full article

This paper explores a stochastic model of noisy observations with a sparse true signal structure. Such models arise in a wide range of applications, including signal processing, anomaly detection, and performance monitoring in telecommunication networks. As a motivating example, we consider round-trip time (RTT) data, which characterize the transit time of network packets, where rare, anomalously large values correspond to localized network congestion or failures. The focus is on the asymptotic properties of the mean-square risk associated with thresholding procedures. Upper bounds are obtained for the mean-square risk when using the theoretically optimal threshold. In addition, a central limit theorem and a strong law of large numbers are established for the empirical risk estimate. The results provide a theoretical basis for assessing the effectiveness of thresholding methods in localizing rare anomalous components in noisy data. Full article

The rapid convergence of artificial intelligence (AI), cloud computing, and 5G communication has positioned extended reality (XR) as a core technology bridging the physical and virtual worlds. Encompassing virtual reality (VR), augmented reality (AR), and mixed reality (MR), XR has demonstrated transformative potential across sectors such as healthcare, industry, education, and defense. However, the compact architecture and limited computational capabilities of XR devices render conventional cryptographic authentication schemes inefficient, while the real-time transmission of biometric and positional data introduces significant privacy and security vulnerabilities. To overcome these challenges, this study introduces PXRA (PUF-based XR authentication), a lightweight and secure authentication and key distribution protocol optimized for cloud-assisted XR environments. PXRA utilizes a physically unclonable function (PUF) for device-level hardware authentication and offloads elliptic curve cryptography (ECC) operations to the cloud to enhance computational efficiency. Authenticated encryption with associated data (AEAD) ensures message confidentiality and integrity, while formal verification through ProVerif confirms the protocol’s robustness under the Dolev–Yao adversary model. Experimental results demonstrate that PXRA reduces device-side computational overhead by restricting XR terminals to lightweight PUF and hash functions, achieving an average authentication latency below 15 ms sufficient for real-time XR performance. Formal analysis verifies PXRA’s resistance to replay, impersonation, and key compromise attacks, while preserving user anonymity and session unlinkability. These findings establish the feasibility of integrating hardware-based PUF authentication with cloud-assisted cryptographic computation to enable secure, scalable, and real-time XR systems. The proposed framework lays a foundation for future XR applications in telemedicine, remote collaboration, and immersive education, where both performance and privacy preservation are paramount. Our contribution lies in a hybrid PUF–cloud ECC architecture, context-bound AEAD for session-splicing resistance, and a noise-resilient BCH-based fuzzy extractor supporting up to 15% BER. Full article

Background: Frontal sinus (FS) injuries carry high morbidity; however, currently, there is no universally agreed-upon treatment approach for frontal sinus and frontobasal trauma. Objective: This study sets out to evaluate surgical outcomes in frontal reconstruction, looking at how fracture patterns and operative variables impact complication rates. Methods: This was a retrospective cross-sectional study which identified a cohort of 137 patients between the years 2015 and 2022 who sustained frontal sinus fractures at a level one major trauma centre in Central London. The electronic patient record (EPR) and pre-operative computed tomography (CT) were analysed to assess the following factors: patient demographics, injury parameters, surgical technique, and complications. Statistical tests included Pearson’s chi square for categorical variables/nominal data. Mann–Whitney U and Kruskal–Wallis H tests were also used to analyse continuous variables. Results: Overall, 12 of the 91 patients who were treated surgically had major complications (n = 12, 13.2%). In total, 5.5% (n = 5) had return to theatre (RTT) for cerebrospinal fluid (CSF) leaks, 5.5% for infection and 2.2% (n = 2) for haematoma or bleeding. FS fracture complexity was predictive of RTT (p = 0.015) and CSF leak (p = 0.015). Frontobasal complexity was predictive of post-operative infection (p = 0.047). Neurosurgical operative involvement and cranialisation was predictive of post-operative infection, CSF leak, and RTT. Conclusions: Understanding risk profiles in the management of FS fractures is vital in order to help clinicians mitigate these risks and also to better educate patients, including during the consent process. Further research could look at the medical and social risk factors that increase complication rates in this patient cohort. Full article

Indoor positioning using commodity WiFi has gained significant attention; however, achieving sub-meter accuracy across diverse layouts remains challenging due to multipath fading and Non-Line-Of-Sight (NLOS) effects. In this work, we propose a hybrid Graph–Temporal Convolutional Network (GTCN) model that incorporates Access Point (AP) geometry through graph convolutions while capturing temporal signal dynamics via dilated temporal convolutional networks. The proposed model adaptively learns per-AP importance using a lightweight gating mechanism and jointly exploits WiFi Received Signal Strength (RSS) and Round-Trip Time (RTT) features for enhanced robustness. The model is evaluated across four experimental areas such as lecture theatre, office, corridor, and building floor covering areas from 15 m × 14.5 m to 92 m × 15 m. We further analyze the sensitivity of the model to AP density under both LOS and NLOS conditions, demonstrating that positioning accuracy systematically improves with denser AP deployment, especially in large-scale mixed environments. Despite its high accuracy, the proposed GTCN remains computationally lightweight, requiring fewer than ${10}^5$ trainable parameters and only tens of MFLOPs per inference, enabling real-time operation on embedded and edge devices. Full article

Hemophilia B is a hereditary bleeding disorder caused by mutations localized throughout the F9 gene. Existing gene therapy products containing AAV vectors have significant limitations. Replacement therapy with coagulation factor FIX infusions is not an optimal way of treatment, as patients still have periodic bleeding and require frequent transfusions. Moreover, approximately 5% of adult patients with hemophilia B develop inhibitory antibodies to recombinant forms of FIX. Therefore, it is important to develop universal CRISPR/Cas gene therapy approaches for F9 editing using non-viral delivery systems to enable gene reversion to a functional sequence at an early stage of disease development and establishment of the patients’ immune system. In this study, a unique approach of F9 prime-editing was tested for the first time. This method is estimated to edit 7.3% of pathogenic F9 mutation types. Specifically, it targets the gene region encoding amino acids 374 V to 408 Q, which accounts for approximately 9.35% of patients with hemophilia B. An advantage of this gene therapy approach is the absence of the need to change Primer Binding Site (PBS) or Reverse Transcriptase Template (RTT) sequences until going from preclinical to clinical trials, as well as the introduction of gain of function mutations in order to compensate for the low prime-editing frequencies and enhance the effect of treatment in vivo. Full article

This study investigates the desulfurization and smelting reduction processes of zinc leaching residue and analyzes the kinetics of the desulfurization and smelting reduction processes of zinc leaching residue. The results showed that, when the desulfurization temperature was 1623 K and the desulfurization time was 27 min, the desulfurization rate of zinc leaching residue was over 95%. When the melting reduction temperature is 1773 K and the melting reduction time is 40 min, the zinc reduction rate is over 98%, and when the melting reduction time is 100 min, the iron reduction rate is over 98%. The desulfurization process of zinc leaching residue is jointly controlled by mass transfer diffusion and interfacial chemical reactions (1373~1623 K), and the kinetic equation is [1 − (1 − x)1/3]2 = 21,897.11 × exp[−202.881/RT]t. The smelting reduction process of desulfurization slag is jointly controlled by interfacial chemical reactions and mass transfer diffusion (1673~1773 K). The kinetic equation of the zinc smelting reduction process is 1 − 2R/3 − (1 − R)2/3 = 4185.03 × exp[−194.78/RT]t. The kinetic equation of the iron smelting reduction process is 1 − (1 − R)1/3 = 8672.22 × exp[−207.13/RT]t. Full article

This study investigated the impact of thawing methods on the roasting quality and flavor of reduced-salt marinated large yellow croaker to optimize processing protocols for frozen products. Three thawing methods, low-temperature thawing (LTT), room-temperature thawing (RTT), and flowing-water thawing (FWT), were systematically evaluated. Freshly marinated (FM) and non-thawed (WT) samples served as controls. Key parameters, including thawing efficiency, physicochemical properties, texture, color, sensory attributes, and volatile organic compounds (VOCs), were analyzed. The results showed that FWT achieved the fastest thawing (14.67 min), significantly outperforming RTT (32.57 min) and LTT (591 min) (p < 0.05). Moisture content and springiness remained stable across treatments (p > 0.05). For color parameters, lightness (L*), yellowness (b*), and browning index (BI) showed no significant variations (p > 0.05), while the total color difference (ΔE) was significantly affected by thawing methods (p < 0.05). FWT exhibited the lowest salt retention (3.49 g/100 g), a 18.8% reduction compared to WT (4.30 g/100 g). Texture analysis revealed that FWT samples maintained optimal hardness and chewiness, with sensory scores second only to WT. Volatile profiling identified distinct “thermal–oxygen–temporal” effects, referring to the respective influences of heating conditions, oxidative environments, and processing time on flavor compound formation. RTT and WT treatments significantly increased the relative 1-propanethiol and 5-methyl-2-furanmethanol (>10% increase) contents, respectively, and markedly reduced the 2-butanol levels (<0.3%) due to volatilization losses. GC-IMS and electronic nose analysis established a robust correlation network among three major VOC clusters (aldehydes/alcohols, esters/acid/sulfides, and ketones), with sensory scores showing strong positive correlations with the alkane- and aromatic-sensitive sensors (W5C/W1C) of the electronic nose (r > 0.90) and negative correlations with other sensors (r < −0.70). These findings demonstrate that FWT offers the best balance of efficiency, salt reduction, and sensory quality, making it a superior method for reduced-salt marinated large yellow croaker industrial applications. Full article

The growing demand for ultra-reliable and low-latency communication (URLLC) in private 5G environments, such as smart campuses and industrial networks, has highlighted the limitations of conventional Wireline access gateway function (W-AGF) architectures that depend heavily on centralized 5G core (5GC) processing. This paper introduces a novel Centralized Unit (CU)-based Zone-Access Gateway Function (Z-AGF) architecture designed to enhance handover performance and enable Local Breakout (LBO) within Non-Public Networks (NPNs) for non-5G capable (N5GC) devices. The proposed design integrates W-AGF functionalities with the Open Radio Access Network (O-RAN) framework, leveraging the F1 Application Protocol (F1AP) as the primary interface between Z-AGF and CU. By performing local breakout (LBO) locally at the Z-AGF, latency-sensitive traffic is processed closer to the edge, reducing the backhaul load and improving end-to-end latency, throughput, and jitter performance. The experimental results demonstrate that Z-AGF achieves up to 45.6% latency reduction, 69% packet loss improvement, 85.6% reduction of round-trip time (RTT) for local communications under LBO, effective local offloading with quantified throughput compared to conventional W-AGF implementations. This study provides a scalable and interoperable approach for integrating wireline and wireless domains, supporting low-latency, highly reliable services within the O-RAN ecosystem and accelerating the adoption of localized next-generation 5G services. Full article

Advancements in Non-Terrestrial Network (NTN) technology facilitate ubiquitous network access for users, whereas satellite-based Quantum Key Distribution (QKD) offers a viable solution for long-distance quantum key exchange in scenarios lacking terrestrial network infrastructure. This study explores the feasibility and practical utility of integrating NTN technology with satellite-based QKD and proposes a novel quantum-enhanced security framework for next-generation space–terrestrial networks. We have developed and deployed the first-of-its-kind 5G-enabled (fifth generation mobile communication) NTN prototype system leveraging satellite-based QKD key encryption. This system comprises a quantum satellite system, a communication satellite system, a 5G network infrastructure, and end-to-end encryption/decryption modules, aiming to validate the feasibility and usability of the proposed quantum-encrypted NTN security framework. Comprehensive tests and performance evaluations were carried out on the testbed constructed based on this prototype system, which collected critical Quality of Experience (QoE) metrics, including Round-Trip Time (RTT) and jitter, during user-plane ping measurements. Experimental results demonstrate that the integration of quantum encryption capabilities incurs an RTT overhead of 5 ms (0.75%), a necessary trade-off for systems incorporating supplementary quantum-encrypted transmission. Concurrently, the deployment of Virtual Private Network (VPN) infrastructure mitigates network jitter by 50%. These results hold critical theoretical and practical implications for the development of next-generation NTN security frameworks enabled by satellite-based QKD. Full article

The Indoor Positioning System (IPS) is used to locate devices and people in smart environments. In recent years, position determination methods have evolved from simple Received Signal Strength Indicator (RSSI) measurements to more advanced approaches such as Channel State Information (CSI), Round Trip Time (RTT), and Angle of Arrival (AoA), increasingly combined with Machine Learning (ML). This article presents a systematic review of the literature on ML-based IPS using IEEE 802.11 Wireless LAN (Wi-Fi) and Bluetooth Low Energy (BLE), including studies published between 2020 and 2024 under the Preferred Reporting Items for Systematic Reviews and Meta-Analyse (PRISMA) methodology. This study examines the techniques used to collect measurements and the ML models used, and discusses the growing use of Deep Learning (DL) approaches. This review identifies some challenges that remain for the implementation of these systems, such as environmental variability, device heterogeneity, and the need for calibration. Future research should expand ML applications to RTT and AoA, explore hybrid multimetric systems, and design lightweight, adaptive DL models. Advances in wireless standards and emerging technologies are also expected to further enhance accuracy and scalability in next-generation IPS. Full article

Rett syndrome (RTT) presents with a wide range of symptoms spanning various clinical areas. Capturing symptom change as the disorder progresses is challenging. Wearable sensors offer a non-invasive and objective means of monitoring disease states in neurodevelopmental disorders. The goal of this study was to conduct a systematic literature review to critically appraise the literature on the use of wearable sensors in individuals with RTT. The PRISMA criteria were used to search four databases without time restriction and identified 226 records. After removing duplicates, the titles and abstracts of 184 records were screened, 147 were excluded, and 37 were assessed for eligibility. Ten (10) articles remained, and a further two were included after additional searching. In total, 12 articles were included in the final analysis. The sample size ranged from 7 to 47 subjects with an age range of 1 to 41 years. Different wearable biosensor devices were used across studies, with the Empatica E4 wearable device being most frequently used in 33% (4/12) of the studies. All the studies demonstrated a high methodological quality with a low risk of bias. Evidence from wearable sensors, combined with machine learning methods, enabled the prediction of different sleep patterns and clinical severity in RTT. Given the small sample size and the limitations of available data for training machine learning models, we highlight areas for consideration. The review emphasises the need to enhance research on the application of wearable sensors in epilepsy and gastrointestinal manifestations/morbidity in RTT. Increased electrodermal activity (EDA), % of maximum heart rate (HRmax%) and the heart rate to low-frequency power (HR/LF) ratio were identified as physiological measures potentially associated with disease states. Based on the evidence synthesis, the role of physiological parameters and their association with symptom management in RTT is discussed. Full article

Rett syndrome (RTT) is a severe neurodevelopmental disorder caused primarily by mutations in the gene encoding the methyl-CpG-binding protein 2 (Mecp2). Mecp2 binds to methylated cytosines, playing a crucial role in chromatin organization and transcriptional regulation. At the neurobiological level, RTT is characterized by dendritic spine dysgenesis and altered excitation–inhibition balance, drawing attention to the mechanisms that scale from mutations in a nuclear protein to altered neuronal connectivity. Although Mecp2 dysfunction disrupts multiple neuronal processes, emerging evidence highlights altered calcium (Ca²⁺) signaling as a central contributor to RTT pathophysiology. This review explores the link between Mecp2 and Ca²⁺ regulation by highlighting how Mecp2 affects Ca²⁺-dependent transcriptional pathways, while Ca²⁺ modulates Mecp2 function by inducing post-translational modifications. We discuss this crosstalk in light of evidence from RTT models, with a particular focus on the brain-derived neurotrophic factor BDNF-miR132-Mecp2 axis and the dysregulation of ryanodine receptors (RyRs). Additionally, we examine how these perturbations contribute to the reduced structural plasticity and the altered activity-driven gene expression that characterizes RTT. Understanding the intersection between Mecp2 function and Ca²⁺ homeostasis will provide critical insights into RTT pathogenesis and potential therapeutic targets aimed at restoring neuronal connectivity. Full article