Search Results (409)

This study emphasizes how the Global Navigation Satellite System (GNSS) receiving antenna technology transcends the boundaries of traditional ceramics manufacturing techniques, expanding their design options and improving the functional attributes of ceramic components for GPS, Galileo, GLONASS, and BeiDou applications. Ceramics exhibit exceptional material characteristics, such as excellent thermal resistance, outstanding electrical insulation, considerable hardness, and notable wear resistance, making them suitable for GNSS technology, due to their capacity to form intricate shapes and microstructures for applications in aerospace, electronics, and automotive sectors. The research systematically outlines the impact of advanced ceramic-based structures upon various antenna design, technology and types, relevant to their particular applications: antennas with alumina substrates, antennas that use FR4 substrate, antennas that use a PCB substrate, antennas with a dielectric ceramic backing, and antennas employing different concepts of Rogers substrates. This study also highlights temperature-stable ceramics, which represent a novel development in research, crucial for improving GNSS technology due to their ability to retain a consistent dielectric constant over a broad temperature range; these ceramics eliminate frequency variations in patch and dielectric resonator antennas, guaranteeing precise signal reception, even in extreme outdoor and satellite conditions. Full article

Space weather events triggered by solar activity impact critical technologies like the Global Navigation Satellite System (GNSS) by causing atmospheric imbalances that alter ionospheric electron density. This study investigates the upper atmosphere response to the severe geomagnetic storms of October 2024, focusing on the coupling and compositional exchange between the ionosphere and thermosphere. Data were analysed from two mid-latitude African stations, Rabat (RABT) and Hermanus (HNUS), using GNSS-Total Electron Content (TEC) measurements alongside thermospheric circulation observations from NASA-GOLD and solar wind indices from OMNIWeb. The October 2024 storm, which reached a minimum Dst of −333 nT, drove a negative ionospheric storm phase marked by TEC depletions exceeding 50 TECU. This response was driven by storm-time thermospheric upwelling of N₂-rich air, which lowered the O/N₂ ratio and accelerated plasma loss via charge-exchange reactions. Furthermore, a distinct hemispheric asymmetry was observed, as the equatorward thermospheric circulation in the Northern Hemisphere arrived before that of the Southern Hemisphere. Direct post-processing of the Earth-Centred Earth-Fixed (ECEF) coordinates using RTKLIB single-point position revealed that, while positioning accuracy significantly degraded at HNUS with errors increasing by up to 270%, it counterintuitively improved at RABT, where errors reached their minimum during the main and early recovery phases of the storm. These findings highlight that the technological impact of severe space weather is determined not just by storm magnitude but by the specific sign and spatial structure of the regional ionospheric response. Full article

The high-precision, continuous monitoring of the surface water level is of great importance for water resource management and the conservation of ecological systems. This study proposes a GNSS-IR-based water-level estimation method using NMEA observations collected from low-cost GNSS receivers. First, the NMEA-recorded satellite elevation angle, azimuth angle, and signal-to-noise ratio (SNR) are processed using time-series characteristics for improving the resolution and applicability of these GNSS observations. Then, the multi-frequency GNSS signal-based reflector height inversion models are developed by making use of the Lomb–Scargle periodogram method. Finally, the Velocity Pausing Particle Swarm Optimization (VPPSO) algorithm is employed to calculate the reflector height estimation and thus the water level. Two experimental data sets collected in two different environments were used to test the proposed method. The experimental results show that the root mean square error (RMSE) of the water-level estimation error is less than 6 cm for the proposed method when the in situ ones are in the range of 196.4 cm to 296.1 cm. This study provides a theoretical and technical foundation for the development of the low-cost GNSS-IR water-level measuring instrument. Full article

In the world of electronics, sensors are more than just components—they are the eyes, ears, and touchpoints of modern technology. From self-driving cars that rely on LiDAR and ultrasonic sensors to navigate complex environments, smart watches that detect your every move and heartbeat, to advanced brain chip implants that can sense your thoughts and translate them into physical moves with the assistance of exoskeletons, sensors bridge the gap between the physical world and digital systems. The rapid arrival of advanced Artificial Intelligence (AI) and Large Language Models (LLMs) has transformed almost every part of technology, especially data processing. However, the development of sensors remains a vitally important topic. Sensors form the foundation of innovation in electronics; novel sensors provide reliable data across a broad range of application areas and are a foundation for intelligent systems. Notably, knowing the capabilities and limitations of each sensor type is crucial for selecting the right sensor for a specific application, troubleshooting issues, and optimizing system performance. This book, entitled “Advanced Sensors for Real-Time Monitoring Applications II”, demonstrates developments of real sensors for a range of applications, including descriptions of fundamental principles of operation, concepts, theory, and practical validation of the results, as well as a review of current state-of-the-art and future directions. Full article

The quality of Global Navigation Satellite System (GNSS) observations on smartphones is highly susceptible to multipath and non-line-of-sight (NLOS) effects in urban environments, resulting in complex and highly variable observation errors. These challenges highlight the necessity of a reliable stochastic model to ensure robust and unbiased parameter estimation. However, conventional empirical stochastic models, such as elevation-dependent or signal-to-noise ratio (SNR)-based weighting schemes, are often insufficient to capture the rapidly changing stochastic behavior of observations in dense urban environments. To overcome this limitation, an adaptive GNSS stochastic model based on a deep neural network (DNN) is developed by integrating SNR, satellite elevation angle, and post-fit pseudorange residuals, which provide a strong indicator of observation quality and environmental context. Specifically, a fully connected DNN is designed to use SNR, satellite elevation angle, and post-fit pseudorange residual as input features, representing signal strength, satellite geometry, and residual information, respectively, and to learn their nonlinear relationship with measurement uncertainty. The network output is then used to adaptively update the diagonal elements of the measurement noise covariance matrix, thereby realizing epoch-wise adaptive weighting within the Kalman filtering process. The proposed DNN-based stochastic model, together with several conventional models, was evaluated using GNSS observations collected by a low-cost u-blox ZED-F9P receiver (u-blox AG, Thalwil, Switzerland) and a Samsung Galaxy S21+ smartphone (Samsung Electronics Co., Ltd., Suwon, Republic of Korea) during vehicle experiments in dense urban canyons. The code-based single point positioning (SPP) results demonstrate that the DNN-based model consistently outperforms traditional stochastic models under both open-sky and urban conditions. The improvement is particularly pronounced for smartphone observations in severely obstructed environments. The proposed DNN-based model reduces the 3D RMSE from 14.25 m, 13.68 m, and 13.05 m, obtained with the elevation-, SNR-, and integrated elevation–SNR-based models, respectively, to 8.94 m, representing an improvement of approximately 35%. A similar improvement is observed for the u-blox ZED-F9P receiver, where the 3D RMSE decreases from 5.71 m, 4.69 m, and 5.15 m to 3.10 m. These results suggest the effectiveness of the proposed DNN-based stochastic model in mitigating complex observation errors and improving positioning accuracy, providing a promising solution for reliable positioning of low-cost GNSS receivers in challenging urban environments. Full article

Background: Although pre-exposure prophylaxis (PrEP) has demonstrated strong clinical efficacy in preventing HIV infection among men who have sex with men (MSM), real-world utilization remains suboptimal. In South Korea, MSM constitute a major population within the domestic HIV epidemic; however, PrEP uptake has not increased pro-portionally to awareness. This discrepancy has been conceptualized as the “awareness–uptake gap,” reflecting multi-level barriers beyond individual knowledge. Purpose: This integrative review aimed to compare PrEP awareness, acceptance, and utilization among MSM populations in South Korea and international settings, and to identify structural, institutional, and psychosocial determinants contributing to the awaness, uptake gap. The study further sought to derive practical implications for nursing practice and health policy. Methods: An integrative literature review was conducted following Whittemore and Knafl’s five-step methodology and reported in line with PRISMA guidance. Electronic searches were performed in PubMed, Google Scholar, RISS, ScienceON, and DBpia for peer-reviewed studies published between 2015 and 2025 in English or Korean. The final search was completed on 31 January 2026. A total of 5952 records were identified, and 187 studies met the inclusion criteria after screening and duplicate removal. Quality appraisal was conducted using AXIS, Newcastle-Ottawa Scale, RoB 2.0, CASP, and MMAT according to study design, and the findings were synthesized within an environmental–structural–individual framework. Results: The included studies consistently showed that awareness of PrEP exceeded actual uptake. Across settings, the awareness–uptake gap was shaped by policy environment, service accessibility, stigma, privacy concerns, economic burden, institutional complexity, and provider preparedness. Comparative evidence from China, Thailand, Belgium and France, Brazil, and West Africa further suggested that awareness alone did not ensure uptake when service pathways were fragmented, culturally unsafe, or poorly understood. Conclusions: Closing the awareness–uptake gap requires integrated policy and practice strategies that extend beyond cost reduction. Strengthening confidentiality systems, simplifying service pathways, and enhancing provider competency—particularly through nurse-centered PrEP navigation and counseling models—may support more sustainable PrEP expansion among MSM populations in global settings. Full article

The global shift toward decentralized generation has established standalone energy supply systems as a vital solution for remote regions. However, the integration of intermittent renewable sources and the inherent lack of rotational inertia in power electronic interfaces create significant challenges for frequency stability. This study addresses these issues by introducing an original Two-Tier Fractional-Order PID (TTFOPID) controller designed for robust Load Frequency Control (LFC) in a hybrid system comprising solar, diesel, biodiesel, and battery energy storage (BESS). The research utilizes the Multi-Objective Imperialist Competitive Algorithm (MOICA), enhanced with an attractive and repulsive assimilation phase, to navigate the high-dimensional parameter space. A unique framework is established to simultaneously tune controller gains and high-level system parameters, specifically BESS sizing and droop settings. Results demonstrate that the MOICA-tuned TTFOPID provides superior performance, achieving a 72% improvement in the Integral of Time-Weighted Absolute Error (ITAE) compared to NSGA-II and a 56% improvement in the Integral of the Square of Control (ISC) compared to MOPSO. Furthermore, robustness analysis validates the controller’s stability against significant parametric variations. The study concludes that the integrated TTFOPID-MOICA approach provides a superior pathway for stabilizing autonomous energy supply systems while protecting hardware longevity through optimized control effort. Full article

Accurate vessel speed prediction is essential for maritime traffic supervision, navigational safety, and intelligent coastal management. However, due to the nonlinear, time-varying, and context-dependent characteristics of vessel motion in nearshore waters, conventional single-model approaches often fail to provide sufficiently accurate forecasts. To address this issue, this study proposes a hybrid deep learning framework for Automatic Identification System (AIS)-based nearshore vessel speed prediction and risk warning, integrating a temporal convolutional network (TCN), an attention mechanism, and a bidirectional long short-term memory network (BiLSTM) into a unified architecture. The core novelty of this framework is its task-oriented sequential design, in which TCN extracts local temporal patterns and multi-scale sequence features from historical AIS observations, the attention mechanism adaptively emphasizes informative representations, and BiLSTM models bidirectional contextual dependencies in vessel motion sequences; on this basis, a speed-risk warning process is constructed by combining the predicted speed with electronic-fence threshold constraints. Experiments conducted on real AIS data from coastal waters show that the proposed method obtains lower mean absolute error (MAE), mean squared error (MSE), and root mean square error (RMSE) as well as a higher coefficient of determination (R²) than several benchmark models. The results illustrate that the proposed framework effectively improves vessel speed prediction accuracy within the studied coastal area and provides practical support for proactive maritime supervision and nearshore safety management. Full article

Background: Liberia modernized vaccination data systems in 2023–2025 by piloting a District Health Information System (DHIS2)-based Digital Vaccination Registry (Electronic Immunization Registry, EIR) to address the limitations of paper-based workflows and of a proprietary COVID-19 electronic platform (offline gaps, lack of unique identifiers, performance issues and cost). Objective: To assess a pilot platform by evaluating training, registry use and device management, utility for routine immunization, vaccine logistics and Adverse Events Following Immunization (AEFI) data, and routine immunization data quality in the DHIS2 mobile application compared with paper registers. Methods: Using the Public Health Informatics Institute’s Collaborative Requirements Development Methodology, stakeholders defined requirements, trained users and implemented a pilot. Mixed methods were used; a mini data audit was performed, and qualitative data were collected across 19 facilities in Montserrado, Gbarpolu and Grand Bassa. Seventy-eight health workers were trained to use the DHIS2 mobile application. Results: The future state design replaces paper aggregation steps with real-time mobile entry to a national registry and dashboard. Dual entry persisted during high-volume periods. The mini data audit found discrepancies between facility paper registers and DHIS2-EIR entries for child enrollment data and, Bacillus Calmette Guérin and Diphtheria–Pertussis–Tetanus dose administration records Participants attributed these discrepancies to internet and device problems and challenges navigating the system. Participants requested a training manual, improved connectivity at point of service, integration with supportive supervision, additional staff and system features (field to record hospital number, automated next visit date, and vaccination status prompts). Conclusions: Lessons from the pilot will inform country-wide implementation, including planned linkage with electronic birth and death registration to enable a unique child identifier and reduce manual errors and delays. Full article

Blind and visually impaired individuals face persistent challenges when navigating unfamiliar environments, where unseen obstacles compromise their safety and independence. Although many electronic travel aids have been proposed, most remain impractical for daily use—they often rely on bulky or costly hardware, require external processing, or provide unintuitive feedback. This work presents a wearable stereo-vision-based vibrotactile system for real-time obstacle detection and navigation assistance. The device combines an off-the-shelf stereo camera integrated with a simultaneous localization and mapping framework to perceive spatial geometry and detect obstacles in the user’s path. Two stereo-matching methods were implemented to estimate depth: a block-based algorithm optimized for low-latency performance and a semi-global approach providing denser depth maps. Detected obstacles are translated into distinct vibration patterns delivered through four skin-contact body-mounted actuators encoding both direction and distance. The system was evaluated with blindfolded sighted, visually impaired, and blind participants. Both stereo approaches supported reliable real-time guidance and high obstacle-avoidance rates, demonstrating robust performance on affordable, wearable hardware. These findings confirm the feasibility of real-time tactile guidance using commercially available components, marking a concrete step toward accessible navigation support that enhances safety and autonomy for blind and visually impaired individuals. Full article

This study used Global Navigation Satellite System (GNSS) observations from the China Crustal Movement Observation Network (CMONOC) and the Kunming Continuously Operating Reference Station (KMCORS) network to investigate ionospheric response characteristics over China during the geomagnetic storm of 4–6 November 2023, and to assess their impacts on CORS-based real-time kinematic (RTK) positioning performance in the low-latitude Kunming region. A quantitative assessment was conducted by integrating regional two-dimensional dTEC (%) maps over China, BeiDou Navigation Satellite System (BDS) Geostationary Earth Orbit (GEO) total electron content (TEC), the rate of TEC index (ROTI), and RTK positioning solutions to evaluate ionospheric disturbances, irregularity activity, and associated degradation in positioning performance. Results indicate that, during geomagnetic storms, ionospheric responses over China exhibit pronounced phase-dependent and latitudinal variations. During the second geomagnetic storm on 5–6 November, positive responses were dominant at mid-to-high latitudes, whereas alternating positive and negative responses were observed at low latitudes. During the recovery phase, the Kunming region successively experienced a positive ionospheric storm lasting approximately 10 h, followed by a negative ionospheric storm lasting about 7 h, with relative TEC variations reaching a maximum of approximately 90%. The GEO TEC time series was consistent with the temporal evolution of the two-dimensional dTEC (%), while ROTI increased markedly during the disturbance enhancement period (21:00 UT on 5 November to 07:00 UT on 6 November 2023). During periods of enhanced ionospheric response and irregularities, RTK positioning performance was observed to deteriorate markedly. The fixed-solution rate at medium-to-long baseline stations decreased from nearly 100% to close to 0%, accompanied by an increase in vertical positioning errors to approximately 20 cm, whereas short-baseline stations were only minimally affected. These results indicate that ionospheric disturbances during geomagnetic storms exert a pronounced impact on CORS-based RTK positioning services in the Kunming region, with the magnitude of this impact being closely related to baseline length. Full article

Geomagnetic navigation is a promising alternative for positioning and localization of UAV swarm systems in GNSS-denied environments. However, strong and heterogeneous electromagnetic interference generated by onboard power, propulsion, and electronic subsystems severely degrades magnetic measurement fidelity, limiting the achievable accuracy of cooperative UAV swarm navigation. To address this challenge, this paper proposes PG-TLNet, a physics-guided aeromagnetic interference compensation framework based on the extended Tolles–Lawson (T–L) model. By integrating onboard state information (current, voltage, and attitude) with magnetic measurements through physics-consistency constraints and a lightweight multi-branch convolutional neural network, the framework enables robust real-time compensation under strong and time-varying interference while remaining suitable for resource-constrained UAV nodes. Experimental validation using multiple scalar magnetometers under heterogeneous interference conditions, with amplitudes up to 1000 nT, shows that PG-TLNet consistently outperforms the conventional T–L model across all sensing nodes, maintaining residual magnetic interference at approximately 0–30 nT under long-duration and highly dynamic operations. The proposed method achieves an improvement ratio (IR) of up to 15 with an end-to-end inference latency below 94 μs. These results indicate that PG-TLNet meets the practical measurement fidelity requirements for geomagnetic navigation in GNSS-denied environments. By ensuring reliable and consistent magnetic measurements at the individual UAV node level, the proposed framework establishes a practical sensing foundation for geomagnetic navigation and distributed magnetic sensing in UAV swarm systems operating in GNSS-denied environments. Full article

Global Positioning System (GPS)-derived ionospheric electron concentration measurements provide a powerful observational framework for seismo-electromagnetic studies, enabling quantitative investigation of lithosphere–atmosphere–ionosphere coupling processes through statistically detectable perturbations in ionospheric electron concentration. We analyze GPS-derived Vertical Total Electron Content (VTEC) variations associated with the 14 August 2021 Haiti earthquake ($M_w$ 7.2) and the 11 January 2022 Cyprus earthquake ($M_w$ 6.6) using data from nearby International GNSS (Global Navigation Satellite System) Service (IGS) stations located within their respective earthquake preparation zones. VTEC time series spanning 45 days before and 7 days after each event are processed to remove the diurnal component, yielding residuals that isolate short-term ionospheric variability. Anomaly detection is performed using three statistical frameworks: a Gaussian mean, standard deviation model, a robust median/median absolute deviation (MAD) model, and a distribution-free quantile-based model. Daily “occurrence” and “energy” indices are constructed to quantify the frequency and cumulative strength of detected anomalies, respectively. While the indices exhibit similar temporal patterns across all methods, they indicate frequent anomaly detection, limiting statistical selectivity. To address this, both indices are normalized by their median values and filtered using a 95% quantile threshold, retaining only extreme deviations. This procedure substantially reduces background fluctuations and isolates a small number of statistically significant anomaly peaks. For both earthquakes, enhanced anomaly activity is identified in the weeks preceding the events, whereas post-event peaks coincide with periods of elevated meteorological and geomagnetic activity. The results demonstrate that normalization combined with robust statistical methods is essential for discriminating significant ionospheric TEC anomalies from background variability. Full article

Abnormal refraction phenomena such as atmospheric ducts due to temperature inversions or rapid decreases in humidity often happen in the lower troposphere over the sea and coastal area, which can make low-elevation signals in the duct layer propagate beyond the horizon, and the ray trajectories extend horizontally over long distances. This paper uses ray tracing technology based on a second-order Taylor approximation to accurately predict the low-elevation ray trajectories within atmospheric ducts. The meteorologic parameters at the heights traversed by the rays are extracted to accumulate atmospheric absorption attenuation by line-by-line calculations, and a high-precision prediction method for atmospheric absorption attenuation in over-the-horizon propagation links is established; meanwhile, we also implement visualization of atmospheric absorption attenuation changes along the ray trajectories in atmospheric duct environments. By comparing the results of the atmospheric absorption attenuation models for horizontal terrestrial paths in the ITU-R P.676 recommendation and GJB Z87-1997 in atmospheric duct environments, we found that the high-precision model proposed in this paper can improve the prediction accuracy of atmospheric absorption attenuation by about 15% in surface ducts and 28% in elevated ducts, significantly improving the propagation performance of low-elevation signals under atmospheric ducts and other abnormal refraction conditions for electronic systems such as surveillance, detection, communication, and navigation. Full article

Background: Family and friend caregivers provide essential support across health and social care systems but remain inconsistently identified, assessed, and supported in routine practice. Although numerous caregiver needs’ assessment instruments exist, many focus on burden, distress, or preparedness rather than explicitly eliciting caregiver-defined support needs, limiting their utility for care planning, care transitions, and system integration. Methods: We conducted a rapid scoping review to identify and characterize caregiver needs’ assessment tools developed for family and friend caregivers. Searches were conducted in MEDLINE, PsycINFO, CINAHL, Web of Science, Health and Psychosocial Instruments, and the Cochrane Library. Eligible studies described the development, validation, or implementation of instruments designed to assess caregiver needs. Data were extracted on tool characteristics, domains assessed, administration methods, and implementation-relevant features. Item-level content analysis distinguished caregiver-defined support needs from related constructs, including burden, strain, preparedness, and care-recipient monitoring. Results: Forty-three studies describing caregiver needs’ assessment instruments were included (19 instruments; 17 instrument families). Tools varied widely in length, administration, and conceptual framing. Seven domains of caregiver-defined support needs were identified: caregiver health and self-care; emotional and psychological support; information, communication, and navigation; practical and instrumental support; social and relational support; autonomy and life participation; and spiritual, cultural, and existential support. Information and navigation needs were most frequently assessed, while autonomy and spiritual domains were least consistently represented. Many instruments demonstrated construct drift, assessing stressors or impacts rather than explicitly eliciting caregiver-defined support needs. Few tools were designed for longitudinal reassessment, workflow integration, or documentation within electronic medical records. Conclusions: Existing caregiver needs’ assessment tools inadequately support routine, system-integrated caregiver-centered care. Advancing caregiver-centered practice requires tools that explicitly elicit caregiver-defined support needs and are designed for workflow integration, longitudinal use, and interdisciplinary care pathways. Full article