Search Results (72)

This paper studied the general mechanism of spatial-frequency-shift (SFS) super-resolution imaging based on multiplex illumination modulation. The theory of SFS joint intensity was first proposed. Experiments on parallel slots with discrete spatial frequency (SF) distribution and V-shape slots with continuous SF distribution were carried out, and their real-space images and k-space images were obtained. The influence of single illumination with different SFS and mixed illumination with various combinations on SFS super-resolution imaging was analyzed. The phenomena of sample SF coverage were discussed. The SFS super-resolution imaging characteristics based on low-coherence illumination and highly localized light fields were discovered. The phenomenon of image magnification during SFS super-resolution imaging process was discussed. The differences and connections between the SF spectrum of objects and the k-space images obtained in SFS super-resolution imaging process were explained. This provides certain support for optimization of high-throughput SFS super-resolution imaging. Full article

Background and Objectives: Urinary incontinence (UI) is a prevalent condition that significantly affects quality of life but remains underreported. Understanding the factors that influence patients’ decisions to seek medical consultation is essential for improving care-seeking behavior and ensuring timely intervention. This study aimed to identify the facilitators of seeking medical consultation among individuals with UI in a Saudi secondary care setting. Materials and Methods: A cross-sectional study was conducted from December 2024 to April 2025 among adult patients with UI attending urology and urogynecology outpatient clinics at a single tertiary center. Participants completed a structured, self-administered questionnaire that comprised sociodemographic data, the ICIQ-UI SF, and 33 potential motivators for seeking care, categorized into six domains. Results: A total of 241 participants were included in the study. The 33-item scale demonstrated excellent internal consistency (Cronbach’s α = 0.945). The most influential domains were daily and physical impact, followed by emotional and psychological factors. Top facilitators included interference with prayers (66.8%), use of pads (62.2%), social limitations (63.9%), frequent clothing changes (64.7%), and fear of worsening symptoms (63.5%). Cultural factors, such as access to same-sex specialists (52.2%), were also prominent. Logistic regression identified age, marital status, and motivators from several domains as significant predictors. Key independent predictors included prayer interference, leakage frequency, and gender-concordant care. Conclusions: Help-seeking for UI is influenced by physical, emotional, social, and cultural factors. Enhancing patient education, addressing sociocultural sensitivities, and promoting physician-led discussions foster earlier care-seeking and improve health outcomes in populations with traditionally low treatment uptake. Full article

Global climate warming has exacerbated extreme snowfall events. The Southeastern Tibet (ST) region has become a high-incidence area for avalanches due to its unique topographical and climatic conditions. However, current research has paid insufficient attention to the thresholds for avalanches triggered by extreme snowfall. Therefore, the aim of this study is to construct the I-D (intensity-duration) thresholds for avalanche events triggered by extreme snowfall in southeastern Tibet, providing a scientific basis for disaster prevention and mitigation work in this region. Based on the snowfall data from 1951 to 2020, this study calculated four extreme snowfall indices, namely SF1d, SF90p, SF95p, and SF99p, to determine extreme snowfall events. And 33 avalanche events during this period were verified through the confusion matrix. This study found that the intensity of extreme snowfall events in southeastern Tibet has increased while the frequency has decreased. The I-D threshold parameters α (from 5.79 to 14.88) and β (from −2.81 to −0.66) within the study area were determined, and the overall threshold is I = 9.29 × D^−2.27 (D represents the duration of snowfall, with the unit being days.). It was also found that extreme snowfall in the study area has a significant positive correlation in with the ST. The terrain has a greater impact on the snowfall intensity, but its regulation on the duration of events is limited. Overall, in southeastern Tibet, if the single-day snowfall exceeds 12.38 mm (the regional average value of the SF1d index) or the cumulative snowfall within the previous 30 days exceeds 64.85 mm (the regional average value of the three indices of SF90p, SF95p, and SF99p), it can be considered that an extreme snowfall event has occurred. At the same time, the threshold of I = 9.29 × D^−2.27 can be used to forecast avalanches triggered by extreme snowfall events in the entire region. Full article

Real-time ionospheric products can accelerate the convergence of real-time precise point positioning (PPP) to improve the real-time positioning services of global navigation satellite systems (GNSSs), as well as to achieve continuous monitoring of the ionosphere. This study applied an extended Kalman filter (EKF) to total electron content (TEC) modeling, proposing a regional real-time EKF-based ionospheric model (REIM) with a spatial resolution of 1° × 1° and a temporal resolution of 1 h. We examined the performance of REIM through a 7-day period during geomagnetic storms. The post-processing model from the China Earthquake Administration (IOSR), CODG, IGSG, and the BDS geostationary orbit satellite (GEO) observations were utilized as reference. The consistency analysis showed that the mean deviation between REIM and IOSR was 0.97 TECU, with correlation coefficients of 0.936 and 0.938 relative to IOSR and IGSG, respectively. The VTEC mean deviation between REIM and BDS GEO observations was 4.15 TECU, which is lower than those of CODG (4.68 TECU), IGSG (5.67 TECU), and IOSR (6.27 TECU). In the real-time single-frequency PPP (RT-SF-PPP) experiments, REIM-augmented positioning converges within approximately 80 epochs, and IGSG requires 140 epochs. The REIM-augmented east-direction positioning error was 0.086 m, smaller than that of IGSG (0.095 m) and the Klobuchar model (0.098 m). REIM demonstrated high consistencies with post-processing models and showed a higher accuracy at IPPs of BDS GEO satellites. Moreover, the correction results of the REIM model are comparable to post-processing models in RT-SF-PPP while achieving faster convergence. Full article

The Finite Difference Time-Domain (FDTD) method is a powerful tool for electromagnetic field analysis. In this work, we develop a variation of the algorithm to accurately calculate antenna, microwave circuit, and target scattering problems. To improve efficiency, a single-field (SF) FDTD method is proposed as a numerical solution to the time-domain Helmholtz equations. New formulas incorporating resistors and voltage sources are derived for the SF-FDTD algorithm, including hybrid implicit–explicit and weakly conditionally stable SF-FDTD methods. The correctness of these formulas is verified through numerical simulations of a newly designed dual-band wearable antenna with an artificial magnetic conductor (AMC) structure. A novel antenna fed by a coplanar waveguide with a compact size of 15.6 × 20 mm² has been obtained after being optimized through an artificial intelligent method. A double-layer, dual-frequency AMC structure is designed to improve the isolation between the antenna and the human body. The simulation and experiment results with different bending degrees show that the antenna with the AMC structure can cover two frequency bands, 2.4 GHz–2.48 GHz and 5.725 GHz–5.875 GHz. The gain at 2.45 GHz and 5.8 GHz reaches 5.3 dBi and 8.9 dBi, respectively. The specific absorption rate has been reduced to the international standard range. In particular, this proposed SF-FDTD method can be extended to analyze other electromagnetic problems with fine details in one or two directions. Full article

Although the traditional Carrier-to-Code Leveling (CCL) method can provide ideal slant total electron content (STEC) observables for establishing ionospheric models, it must rely on dual-frequency (DF) receivers, which results in high hardware costs. In this study, an ionosphere-weight (IW) single-frequency (SF) precise point positioning (PPP) method for extracting STEC observables is proposed, and multi-global navigation satellite system (GNSS)-integrated processing is adopted to improve the spatial resolution of the ionospheric model. To investigate the advantages of this novel method, 41 European stations are used to establish the regional ionospheric model, and both low- and high-solar-activity conditions are considered. The results show that the IW SFPPP-derived regional ionospheric model has a significantly better quality of vertical total electron content (VTEC) than the CCL method when using the final global ionospheric map (GIM) as a reference, especially in areas with sparse monitoring stations. Compared with the CCL method, the RMS VTEC accuracy of the IW SFPPP method can be improved by 17.4% and 12.7% to 1.09 and 2.83 total electron content unit (TECU) in low- and high-solar-activity periods, respectively. Regarding GNSS carrier-phase-derived STEC variation (dSTEC) as the reference, the dSTEC accuracy of the IW SFPPP method is comparable to that of the CCL method, and its RMS values are about 1.5 and 2.8 TECU in low- and high-solar-activity conditions, respectively. This indicates that the proposed method using SF-only observations can achieve the same external accord accuracy as the CCL method in regional ionospheric modeling. Full article

Accurate measurement of running parameters, including the step length (SL), step frequency (SF), and velocity, is essential for optimizing sprint performance. Traditional methods, such as 2D video analysis and inertial measurement units (IMUs), face limitations in precision and practicality. This study introduces and evaluates two methods for estimating running parameters using real-time kinematic global navigation satellite systems (RTK GNSS) with 100 Hz sampling. Method 1 identifies mid-stance phases via vertical position minima, while Method 2 aligns with the initial contact (IC) events through vertical velocity minima. Two collegiate sprinters completed a 400 m sprint under controlled conditions, with RTK GNSS measurements validated against 3D video analysis and IMU data. Both methods estimated the SF, SL, and velocity, but Method 2 demonstrated superior accuracy, achieving a lower RMSE (SF: 0.205 Hz versus 0.291 Hz; SL: 0.143 m versus 0.190 m) and higher correlation with the reference data. Method 2 also exhibited improved performance in curved sections and detected stride asymmetries with higher consistency than Method 1. These findings highlight RTK GNSS, particularly the velocity minima approach, as a robust, drift-free, single-sensor solution for detailed per-step sprint analysis in outdoor conditions. This approach offers a practical alternative to IMU-based methods and enables training optimization and performance evaluation. Full article

Integrating infrared and visible-light images facilitates a more comprehensive understanding of scenes by amalgamating dual-sensor data derived from identical environments. Traditional CNN-based fusion techniques are predominantly confined to local feature emphasis due to their inherently limited receptive fields. Conversely, Transformer-based models tend to prioritize global information, which can lead to a deficiency in feature diversity and detail retention. Furthermore, methods reliant on single-scale feature extraction are inadequate for capturing extensive scene information. To address these limitations, this study presents GLMAFuse, an innovative dual-stream encoder–decoder network, which utilizes a multi-scale attention mechanism to harmoniously integrate global and local features. This framework is designed to maximize the extraction of multi-scale features from source images while effectively synthesizing local and global information across all layers. We introduce the global-aware and local embedding (GALE) module to adeptly capture and merge global structural attributes and localized details from infrared and visible imagery via a parallel dual-branch architecture. Additionally, the multi-scale attention fusion (MSAF) module is engineered to optimize attention weights at the channel level, facilitating an enhanced synergy between high-frequency edge details and global backgrounds. This promotes effective interaction and fusion of dual-modal features. Extensive evaluations using standard datasets demonstrate that GLMAFuse surpasses the existing leading methods in both qualitative and quantitative assessments, highlighting its superior capability in infrared and visible image fusion. On the TNO and MSRS datasets, our method achieves outstanding performance across multiple metrics, including EN (7.15, 6.75), SD (46.72, 47.55), SF (12.79, 12.56), MI (2.21, 3.22), SCD (1.75, 1.80), VIF (0.79, 1.08), Qbaf (0.58, 0.71), and SSIM (0.99, 1.00). These results underscore its exceptional proficiency in infrared and visible image fusion. Full article

Wildfires pose a severe threat to ecological systems, human life, and infrastructure, making early detection critical for timely intervention. Traditional fire detection systems rely heavily on single-sensor approaches and are often hindered by environmental conditions such as smoke, fog, or nighttime scenarios. This paper proposes Adaptive Multi-Sensor Oriented Object Detection with Space–Frequency Selective Convolution (AMSO-SFS), a novel deep learning-based model optimized for drone-based wildfire and smoke detection. AMSO-SFS combines optical, infrared, and Synthetic Aperture Radar (SAR) data to detect fire and smoke under varied visibility conditions. The model introduces a Space–Frequency Selective Convolution (SFS-Conv) module to enhance the discriminative capacity of features in both spatial and frequency domains. Furthermore, AMSO-SFS utilizes weakly supervised learning and adaptive scale and angle detection to identify fire and smoke regions with minimal labeled data. Extensive experiments show that the proposed model outperforms current state-of-the-art (SoTA) models, achieving robust detection performance while maintaining computational efficiency, making it suitable for real-time drone deployment. Full article

To achieve a near-zero magnetic field environment, the use of permalloy sheets with high-performance magnetic properties is essential. However, mainstream welding processes for magnetically shielded rooms (MSRs), such as argon arc welding and laser welding, can degrade the magnetic properties of the material. Additionally, neglecting the anisotropy of permalloy sheets can introduce unpredictable errors in the evaluation of MSR performance. To address this issue, this paper proposes a modified model for calculating the shielding factor (SF) of MSRs that incorporates the anisotropic magnetic characteristics of permalloy sheets. These characteristics were measured using a two-dimensional single sheet tester (2D-SST). A high-precision measurement system was developed, comprising a 2D-SST (to generate two-dimensional magnetic fields and sense the induced B and H signals) and a control system (to apply in-phase 2D excitation signals and amplify, filter, and record the B and H data). Hysteresis loops were tested at low frequencies (0.1–9 Hz) and under different magnetization states (0.1–0.6 T) in two orientations—parallel and perpendicular to the annealing magnetic field—to verify anisotropy under varying conditions. Initial permeability, near-saturation magnetization, and basic magnetization curves (BM curves) were measured across different directions to provide parameters for simulations and theoretical calculations. Based on these measurements and finite element simulations, a mathematical model was developed to adjust the empirical coefficient λ used in theoretical SF calculations. The results revealed that the ratio of empirical coefficients in different directions is inversely proportional to the ratio of magnetic permeability in the corresponding directions. A verification group was established to compare the original model and the modified model. The mean squared error (MSE) between the original model and the finite element simulation was 49.97, while the MSE between the improved model and the finite element simulation was reduced to 0.13. This indicates a substantial improvement in the computational accuracy of the modified model. Full article

The multi-global navigation satellite system (GNSS) undifferenced and uncombined precise point positioning (UU-PPP), as a high-precision ionospheric observables extraction technology superior to the traditional carrier-to-code leveling (CCL) method, has received increasing attention. In previous research, only dual-frequency (DF) or multi-frequency (MF) observations are used to extract slant ionospheric delay with the UU-PPP. To reduce the cost of ionospheric modeling, the feasibility of extracting ionospheric observables from the multi-GNSS single-frequency (SF) UU-PPP was investigated in this study. Meanwhile, the between-satellite single-differenced (SD) method was applied to remove the effects of the receiver differential code bias (DCB) with short-term time-varying characteristics in regional ionospheric modeling. In the assessment of the regional real-time (RT) between-satellite SD ionospheric model, the internal accord accuracy of the SD ionospheric delay can be better than 0.5 TECU, and its external accord accuracy within 1.0 TECU is significantly superior to three global RT ionospheric models. With the introduction of the proposed SD ionospheric model into the multi-GNSS kinematic RT SF-PPP, the initialization speed of vertical positioning errors can be improved by 21.3% in comparison with the GRAPHIC (GRoup And PHase Ionospheric Correction) SF-PPP model. After reinitialization, both horizontal and vertical positioning errors of the SD ionospheric constrained (IC) SF-PPP can be maintained within 0.2 m. This proves that the proposed SDIC SF-PPP model can enhance the continuity and stability of kinematic positioning in the case of some GNSS signals missing or blocked. Compared with the GRAPHIC SF-PPP, the horizontal positioning accuracy of the SDIC SF-PPP in kinematic mode can be improved by 37.9%, but its vertical positioning accuracy may be decreased. Overall, the 3D positioning accuracy of the SD ionospheric-constrained RT SF-PPP can be better than 0.3 m. Full article

Differential Code Bias (DCB) is a crucially systematic error in satellite positioning and ionospheric modeling. This study aims to estimate the BeiDou-3 global navigation satellite system (BDS-3) satellite DCBs by using the single-frequency (SF) uncombined Precise Point Positioning (PPP) model. The experiment utilized BDS-3 B1 observations collected from 25 International GNSS Service (IGS) stations located at various latitudes during March 2023. The results reveal that the accuracy of estimating B1I-B3I DCBs derived from single receiver exhibits latitude dependence. Stations in low-latitude regions show considerable variability in the root mean square (RMS) of absolute offsets for satellite DCBs estimation, covering a wide range of values. In contrast, mid- to high-latitude stations demonstrate a more consistent pattern with relatively stable RMS values. Moreover, it has been observed that the stations situated in the Northern Hemisphere display a higher level of consistency in the RMS values when compared to those in the Southern Hemisphere. When incorporating estimates from all 25 stations, the RMS of the absolute offsets in satellite DCBs estimation consistently remained below 0.8 ns. Notably, after excluding 8 low-latitude stations and utilizing data from the remaining 17 stations, the RMS of absolute offsets in satellite DCBs estimation decreased to below 0.63 ns. These enhancements underscore the importance of incorporating a sufficient number of mid- and high-latitude stations to mitigate the effects of ionospheric variability when utilizing SF observations for satellite DCBs estimation. Full article

Body composition measurement plays an important role in the nutritional diagnosis and treatment of diseases. In the past 30 years, the detection of body composition based on bioelectrical impedance analysis (BIA) has been widely used and explored in a variety of diseases. With the development of technology, bioelectrical impedance analysis has gradually developed from single-frequency BIA (SF-BIA) to multi-frequency BIA (multi-frequency BIA, MF-BIA) and over a range of frequencies (bioimpedance spectroscopy, BIS). As the clinical significance of nutrition management in chronic kidney disease has gradually become prominent, body composition measurement by BIA has been favored by nephrologists and nutritionists. In the past 20 years, there have been many studies on the application of BIA in patients with CKD. This review describes and summarizes the latest research results of BIA in nutritional management of patients with CKD including pre-dialysis, hemodialysis, peritoneal dialysis and kidney transplantation, in order to provide reference for the application and research of BIA in nutritional management of chronic kidney disease in the future. Full article

Computer-assisted total knee arthroplasty (CAS) remains controversial. Some authors defend that its improvement in knee alignment and positioning positively impacts arthroplasty survival rates, while others have stated that there is minimal or no difference compared to the conventional technique (cTKA). This paper features a retrospective, single-center, single-surgeon study, evaluating CAS surgery vs. regular cTKA in patients who consecutively underwent surgery between 2015 and 2017 (60 CAS patients vs. 59 cTKA). Data collection includes surgery duration, length of stay, blood loss and both preoperative and postoperative clinical outcome evaluation using WOMAC, SF-12, Forgotten Joint Score and VAS. Radiograph evaluation includes the tibiofemoral angle, posterior condylar offset and its ratio, and notching frequency and measurement. A total of 119 patients were included: 60 in the CAS group and 59 in the cTKA. Mean follow-up was 5.61 years (Max 7.83–Min 5.02 years). No clinically relevant preoperative differences were observed between the groups. Postoperatively, both groups showed similar functional results (WOMAC, SF-12, FJS, KSS, and VAS) with similar complication rates. The CAS group had an increased surgery time by a mean of 12 min (107.02 ± 15.22 vs. 95.32 + 13.87; p = 0.00) as well as a higher notching frequency and size (40% vs. 13.60%; p = 0.013; 1.239 mm ± 1.7604 vs. 0.501 mm ± 1.4179; p = 0.031). CAS obtained similar functional, radiological, and complication rates to cTKA at the expense of increasing surgery time and notching frequency and size. Full article

Infrared and visible image fusion aims to generate a single fused image that not only contains rich texture details and salient objects, but also facilitates downstream tasks. However, existing works mainly focus on learning different modality-specific or shared features, and ignore the importance of modeling cross-modality features. To address these challenges, we propose Dual-branch Progressive learning for infrared and visible image fusion with a complementary self-Attention and Convolution (DPACFuse) network. On the one hand, we propose Cross-Modality Feature Extraction (CMEF) to enhance information interaction and the extraction of common features across modalities. In addition, we introduce a high-frequency gradient convolution operation to extract fine-grained information and suppress high-frequency information loss. On the other hand, to alleviate the CNN issues of insufficient global information extraction and computation overheads of self-attention, we introduce the ACmix, which can fully extract local and global information in the source image with a smaller computational overhead than pure convolution or pure self-attention. Extensive experiments demonstrated that the fused images generated by DPACFuse not only contain rich texture information, but can also effectively highlight salient objects. Additionally, our method achieved approximately 3% improvement over the state-of-the-art methods in MI, Qabf, SF, and AG evaluation indicators. More importantly, our fused images enhanced object detection and semantic segmentation by approximately 10%, compared to using infrared and visible images separately. Full article