Search Results (245)

The evolution of 3GPP non-terrestrial networks (NTNs) is enabling new avenues for broadband connectivity via satellite, especially within the scope of 5G. The parallel rise in satellite mega-constellations has further fueled efforts toward ubiquitous global Internet access. This convergence has fostered collaboration between mobile network operators and satellite providers, allowing the former to leverage mature space infrastructure and the latter to integrate with terrestrial mobile standards. However, integrating these technologies presents significant architectural challenges. This study investigates 5G NTN architectures using satellite mega-constellations, focusing on transparent architectures where Starlink is employed to relay the backhaul, midhaul, and new radio (NR) links. The performance of these architectures is assessed through a testbed utilizing OpenAirInterface (OAI) and Open5GS, which collects key user-experience metrics such as round-trip time (RTT) and jitter when pinging the User Plane Function (UPF) in the 5G core (5GC). Results show that backhaul and midhaul relays maintain delays of 50–60 ms, while NR relays incur delays exceeding one second due to traffic overload introduced by the RFSimulator tool, which is indispensable to transmit the NR signal over Starlink. These findings suggest that while transparent architectures provide valuable insights and utility, regenerative architectures are essential for addressing current time issues and fully realizing the capabilities of space-based broadband services. Full article

Background/Objectives: Rett syndrome (RTT) is a rare neurodevelopmental disorder that affects movement and communication skills primarily in females. This study aimed to synthesize the research from the last two decades regarding the verbal and nonverbal communication abilities, assessment procedures, and intervention approaches for individuals with RTT. Methods: A structured literature search was conducted using the Embase, Scopus, and PubMed databases. Fifty-seven studies were selected and analyzed based on inclusion criteria. The data were categorized into four domains (verbal communication skills, nonverbal communication skills, assessment procedures, and intervention approaches). Results: The findings indicated a wide variety of communicative behaviors across the RTT population, including prelinguistic signals, regression in verbal output, and preserved nonverbal communicative intent. Moreover, the results highlighted the importance of tailored assessments (Inventory of Potential Communicative Acts, eye tracking tools, and Augmentative and Alternative Communication) to facilitate functional communication. The individualized intervention approaches were found to be the most effective in improving communicative participation. Conclusions: The current review provides an overview of the current evidence with an emphasis on the need for personalized and evidence-based clinical practices. Additionally, it provided guidance for professionals, clinicians, and researchers seeking to improve the quality of life for individuals with RTT. Full article

Visually impaired individuals face significant challenges in navigating safely and independently, particularly under adverse weather conditions such as fog. To address this issue, we propose YOLO-Extreme, an enhanced object detection framework based on YOLOv12, specifically designed for robust navigation assistance in foggy environments. The proposed architecture incorporates three novel modules: the Dual-Branch Bottleneck Block (DBB) for capturing both local spatial and global semantic features, the Multi-Dimensional Collaborative Attention Module (MCAM) for joint spatial-channel attention modeling to enhance salient obstacle features and reduce background interference in foggy conditions, and the Channel-Selective Fusion Block (CSFB) for robust multi-scale feature integration. Comprehensive experiments conducted on the Real-world Task-driven Traffic Scene (RTTS) foggy dataset demonstrate that YOLO-Extreme achieves state-of-the-art detection accuracy and maintains high inference speed, outperforming existing dehazing-and-detect and mainstream object detection methods. To further verify the generalization capability of the proposed framework, we also performed cross-dataset experiments on the Foggy Cityscapes dataset, where YOLO-Extreme consistently demonstrated superior detection performance across diverse foggy urban scenes. The proposed framework significantly improves the reliability and safety of assistive navigation for visually impaired individuals under challenging weather conditions, offering practical value for real-world deployment. Full article

Accurate and scalable indoor localization is a key enabler of intelligent automation in smart environments and industrial systems. In this paper, we present SelfLoc, a self-supervised indoor localization system that combines IEEE 802.11az Round Trip Time (RTT) and Received Signal Strength Indicator (RSSI) data to achieve fine-grained positioning using commodity Wi-Fi infrastructure. Unlike conventional methods that depend heavily on labeled data, SelfLoc adopts a contrastive learning framework to extract spatially discriminative and temporally consistent representations from unlabeled wireless measurements. The system integrates a dual-contrastive strategy: temporal contrasting captures sequential signal dynamics essential for tracking mobile agents, while contextual contrasting promotes spatial separability by ensuring that signal representations from distinct locations remain well-differentiated, even under similar signal conditions or environmental symmetry. To this end, we design signal-specific augmentation techniques for the physical properties of RTT and RSSI, enabling the model to generalize across environments. SelfLoc also adapts effectively to new deployment scenarios with minimal labeled data, making it suitable for dynamic and collaborative industrial applications. We validate the effectiveness of SelfLoc through experiments conducted in two realistic indoor testbeds using commercial Android devices and seven Wi-Fi access points. The results demonstrate that SelfLoc achieves high localization precision, with a median error of only 0.55 m, and surpasses state-of-the-art baselines by at least 63.3% with limited supervision. These findings affirm the potential of SelfLoc to support spatial intelligence and collaborative automation, aligning with the goals of Industry 4.0 and Society 5.0, where seamless human–machine interactions and intelligent infrastructure are key enablers of next-generation smart environments. Full article

There are workloads that do not need the total data ordering enforced by the Transmission Control Protocol (TCP). For example, Virtual Network Computing (VNC) has a sequence of pixel-based updates in which the order of rectangles can be relaxed. However, VNC runs over the TCP and can have higher latency due to unnecessary blocking to ensure total ordering. By using Quick UDP Internet Connections (QUIC) as the underlying protocol, we are able to implement a partial order delivery approach, which can be combined with Forward Error Correction (FEC) to reduce data latency. Our earlier work on consistency fences provides a mechanism and semantic foundation for partial ordering. Our new evaluation on the Emulab testbed, with two different synthetic workloads for streaming and non-streaming updates, shows that our partial order and FEC strategy can reduce the blocking time and inter-delivery time of rectangles compared to total delivery. For one workload, partially ordered data with FEC can reduce the 99-percentile message-blocking time to 0.4 ms versus 230 ms with totally ordered data. That workload was with 0.5% packet loss, 100 ms Round-Trip Time (RTT), and 100 Mbps bandwidth. We study the impact of varying the packet-loss rate, RTT, bandwidth, and CCA and demonstrate that partial order and FEC latency improvements grow as we increase packet loss and RTT, especially with the emerging Bottleneck Bandwidth and Round-Trip propagation time (BBR) congestion control algorithm. Full article

Rett syndrome (RTT) is an X-linked neurodevelopmental disorder marked by neurological regression, autonomic dysfunction, seizures, and significant sleep and breathing abnormalities. About 80% of affected individuals, especially young children, experience sleep disturbances such as insomnia, sleep-disordered breathing, nocturnal vocalizations, bruxism, and seizures. Breathing irregularities during sleep—like apnea, alternating hyperventilation, and hypoventilation—are common, with both obstructive and central sleep apnea identified through polysomnography. This review focuses on the prevalent sleep disorders in children with Rett syndrome and highlights current recommendations for the management of sleep disorders. Full article

We propose a modeling approach for position estimation based on the observed radio propagation in an environment. The approach is purely similarity-based and therefore free of explicit physical assumptions. What distinguishes it from classical related methods are probabilistic position estimates. Instead of just providing a point estimate for a given signal sequence, our model returns the distribution of possible positions as continuous probability density function, which allows for appropriate integration into recursive state estimation systems. The estimation procedure starts by using a kernel to compare incoming data with reference recordings from known positions. Based on the obtained similarities, weights are assigned to the reference positions. An arbitrarily chosen density estimation method is then applied given this assignment. Thus, a continuous representation of the distribution of possible positions in the environment is provided. We apply the solution in a Particle Filter (PF) system for smartphone-based indoor localization. The approach is tested both with radio signal strength (RSS) measurements (Wi-Fi and Bluetooth Low Energy RSSI) and round-trip time (RTT) measurements, given by Wi-Fi Fine Timing Measurement. Compared to distance-based models, which are dedicated to the specific physical properties of each measurement type, our similarity-based model achieved overall higher accuracy at tracking pedestrians under realistic conditions. Since it does not explicitly consider the physics of radio propagation, the proposed model has also been shown to work flexibly with either RSS or RTT observations. Full article

Despite deep learning-based object detection techniques showing promising results, identifying items from low-quality images under unfavorable weather settings remains challenging because of balancing demands and overlooking useful latent information. On the other hand, YOLO is being developed for real-time object detection, addressing limitations of current models, which struggle with low accuracy and high resource requirements. To address these issues, we provide an Adaptive Enhancement Algorithm YOLO (AEA-YOLO) framework that allows for an enhancement in each image for improved detection capabilities. A lightweight Parameter Prediction Network (PPN) containing only six thousand parameters predicts scene-adaptive coefficients for a differentiable Image Enhancement Module (IEM), and the enhanced image is then processed by a standard YOLO detector, called the Detection Network (DN). Adaptively processing images in both favorable and unfavorable weather conditions is possible with our suggested method. Extremely encouraging experimental results compared with existing models show that our suggested approach achieves 7% and more than 12% in mean average precision (mAP) on the PASCAL VOC Foggy artificially degraded and the Real-world Task-driven Testing Set (RTTS) datasets. Moreover, our approach achieves good results compared with other state-of-the-art and adaptive domain models of object detection in normal and challenging environments. Full article

Background/Objectives: Rett Syndrome (RTT) is a rare neurodevelopmental disorder that affects girls and is characterized by severe motor and cognitive impairments, the loss of purposeful hand use, and communication difficulties. Children with RTT, especially those aged 5 to 9 years, often struggle to develop the foundational skills necessary for adaptive functioning, such as eye contact, object tracking, functional gestures, turn-taking, and basic communication. These abilities are essential for cognitive, social, and motor development and contribute to greater autonomy in daily life. This study aimed to explore the feasibility of a structured telerehabilitation program and to provide preliminary observations of its potential utility for young girls with RTT, addressing the presumed challenge of engaging this population in video-based interactive training. Methods: The intervention consisted of 30 remotely delivered sessions (each lasting 90 min), with assessments at baseline (A), after 5 weeks (B1), and after 10 weeks (B2). Quantitative outcome measures focused on changes in eye contact, object tracking, functional gestures, social engagement, and responsiveness to visual stimulus. Results: The findings indicate that the program was feasible and well-tolerated. Improvements were observed across all measured domains, and participants showed high levels of engagement and participation throughout the intervention. While these results are preliminary, they suggest that interactive digital formats may be promising for supporting foundational learning processes in children with RTT. Conclusions: This study provides initial evidence that telerehabilitation is a feasible approach for engaging young girls with RTT and supporting adaptive skill development. These findings may inform future research and the design of controlled studies to evaluate the efficacy of technology-assisted interventions in this population. Full article

Rett syndrome (RTT) is a multisystem neurological disorder. Pathogenic changes in the MECP2 gene that codes for methyl-CpG-binding protein 2 (MeCP2) in RTT lead to a loss of previously established motor and cognitive skills. Unravelling the mechanisms of neurological regression in RTT is complex, due to multiple components of the neural epigenome being affected. Most evidence has primarily focused on deciphering the complexity of transcriptional machinery at the molecular level. Little attention has been paid to how epigenetic changes across the neural epigenome in RTT lead to neurological regression. In this narrative review, we examine how pathogenic changes in MECP2 can disrupt the balance of the RTT neural epigenome and lead to neurological regression. Environmental and genetic factors can disturb the balance of the neural epigenome in RTT, modifying the onset of neurological regression. Methylation changes across the RTT neural epigenome and the consequent genotoxic stress cause neurons to regress into a senescent state. These changes influence the brain as it matures and lead to the emergence of specific symptoms at different developmental periods. Future work could focus on epidrugs or epi-editing approaches that may theoretically help to restore the epigenetic imbalance and thereby minimise the impact of genotoxic stress on the RTT neural epigenome. Full article

Classical Rett syndrome (RTT) is a neurodevelopmental disorder caused by mutations in the MECP2 gene, resulting in a devastating phenotype associated with a lack of gene expression control. Mouse models lacking Mecp2 expression with an RTT-like phenotype have been developed to advance therapeutic alternatives. Environmental enrichment (EE) attenuates RTT symptoms in patients and mouse models. However, the mechanisms underlying the effects of EE on RTT have not been fully elucidated. We housed male hemizygous Mecp2-null (Mecp2^-/y) and wild-type mice in specially conditioned cages to enhance sensory, cognitive, social, and motor stimulation. EE attenuated the progression of the RTT phenotype by preserving neuronal cytoarchitecture and neural plasticity markers. Furthermore, EE ameliorated defects in neuromuscular junction organization and restored the motor deficit of Mecp2^-/y mice. Treatment with plasma from young WT mice was used to assess whether the increased activity could modify plasma components, mimicking the benefits of EE in Mecp2^-/y. Plasma treatment attenuated the RTT phenotype by improving neurological markers, suggesting that peripheral signals of mice with normal motor function have the potential to reactivate dormant neurodevelopment in RTT mice. These findings demonstrate how EE and treatment with young plasma ameliorate RTT-like phenotype in mice, opening new therapeutical approaches for RTT patients. Full article

The pandemic period significantly impacted professional football, leading to mandatory SARS-CoV-2 testing and quarantine. Our study aimed to examine the factors influencing time of recovery after a positive test, including return-to-training (RTT) and return-to-first-match (RTFM) of male football players in a first-division Hungarian team between 8 May 2020 and 30 June 2022. Infection was determined using mandatory RT-PCR testing 3 times per week, which later decreased to 1 to 2 times per week, in 55 elite players. A self-administered questionnaire was utilized based on the U.S. Department of Health and Human Services symptom list and modified with relevant factors of return-to-play in football. The incidence of SARS-CoV-2-positive players in the three consecutive years was 5.26; 21.43 and 45.71%. Mild symptoms were present in test-positive players, completing the questionnaire (n = 31), predominantly loss of smell and dry cough. Post-infection fatigue levels correlated with the perceived performance decline. In players with precisely documented dates (n = 18), the average RTT was 18.7 days, while the RTFM was 67.3 days. Older players returned to training faster than their younger counterparts and the RT-PCR Ct number had a weak negative correlation with RTFM. Mental support was provided by family and friends in 68% of the players. This study highlights the variability in return-to-play timelines and the role of age, symptom severity and mental help in recovery and emphasizes the need for individualized rehabilitation in elite football. Full article

Introduction: Asthma and chronic obstructive pulmonary disease (COPD) are chronic respiratory illnesses frequently accompanied by anxiety and depression. These psychological symptoms often go undetected due to their overlap with somatic complaints. According to the regulatory theory of temperament (RTT), biologically based temperament traits may influence emotional responses to chronic illness. This study examined whether RTT-defined temperament traits predict depression and anxiety severity in patients with asthma and/or COPD. Material and Methods: The study included 210 adult patients with asthma and/or COPD recruited from a university hospital and pulmonology clinics. Individuals with a prior history of mental illness were excluded. Participants completed three validated questionnaires: the Formal Characteristics of Behavior–Temperament Inventory (FCB-TI), the Beck Depression Inventory (BDI), and the State–Trait Anxiety Inventory (STAI). Additional demographic and clinical data were collected. Multiple linear regression was used to assess the predictive value of six temperament traits for depression, state anxiety, and trait anxiety. A significance threshold of α = 0.05 was used in all statistical tests. Results: Temperament structure significantly predicted all three mental health outcomes: depression (R² = 0.37), state anxiety (R² = 0.45), and trait anxiety (R² = 0.35). Briskness negatively correlated with all outcomes, while emotional reactivity showed a positive correlation. No significant associations were found for the remaining four traits. Socioeconomic and lifestyle factors were not significant predictors. Conclusions: Temperament traits, particularly briskness and emotional reactivity, significantly influence depression and anxiety severity in asthma and COPD. Temperament assessment may serve as a low-cost, telemedicine-compatible tool to identify at-risk patients and support integrated, personalized care. Full article

Rett syndrome (RTT) is a rare X-linked dominant neurodevelopmental disorder caused by pathogenic variants in the methyl-CpG-binding protein 2 (MECP2) gene, which encodes a methyl-CpG-binding protein (MeCP2) that acts as a repressor of gene expression, crucial in neurons. Dysfunction of MeCP2 due to its pathogenic variants explains the clinical features of RTT. Here, we performed histological and RNA analyses on a post-mortem brain sample from an RTT patient carrying the p.Arg106Trp missense mutation. This patient is part of a cohort of 56 genetically and clinically characterized RTT patients, for whom we provide an overview of the mutation landscape. In the RTT brain specimen, RT-PCR analysis detected preferential transcription of the mutated mRNA. X-inactivation studies revealed a skewed X-chromosome inactivation ratio (95:5), supporting the transcriptional findings. We also mapped the MECP2 transcript in control human brain regions (temporal cortex and cerebellum) using the RNAscope assay, confirming its high expression. This study reports the MECP2 transcript representation in a post-mortem RTT brain and, for the first time, the in situ MECP2 transcript localization in a human control brain, offering insights into how specific MECP2 mutations may differentially impact neuronal functions. We suggest these findings are crucial for developing RNA-based therapies for Rett syndrome. Full article

Networked microgrids (NMG) are gaining popularity as an example of smart grids (SG), where power networks are integrated with communication technologies. Communication technologies enable NMGs to be monitored and controlled via communication networks. However, ensuring that communication networks in NMGs satisfy quality of delivery (QoD) metrics such as the round trip time (RTT) of NMG control data is necessary. This paper addresses the communication network types and communication technologies used in NMGs. We present various NMG deployments to demonstrate real-life applicability in different contexts. We develop a real-time NMG testbed using real hardware, such as Cisco 4331 Integrated Services Routers (ISR). We evaluate QoD in NMG control data by measuring RTT under varying relative network congestion levels. The results reveal that high-variance background traffic leads to greater RTTs, surpassing the industrial communication response time requirement specified by the European Telecommunications Standards Institute (ETSI) by over 25 times. Full article