Search Results (34)

Although numerous observational and theoretical studies have examined the mean and turbulent structure of the tropical cyclone boundary layer (TCBL) over the open ocean, there have been comparatively fewer studies that have examined the kinematic and thermal structure of the TCBL across the land–ocean interface. This study examines the impact of different continental environments on the thermodynamic evolution of the TCBL during the landfall transition using high-resolution, full-physics numerical simulations. During landfall, the changes in the wind field within the TCBL due to the development of the internal boundary layer (IBL), combined with the formation of a surface cold pool, generates a pronounced thermal asymmetry in the boundary layer. As a result, the maximum thermodynamic boundary layer height occurs in the rear-right quadrant of the storm relative to its motion. In addition, azimuthal and vertical advection by the mean flow lead to enhanced turbulent kinetic energy (TKE) in front of the vortex (enhancing dissipative heating immediately onshore) and onshore precipitation to the left of the storm track (stabilizing the environment). The strength and depth of thermal asymmetry in the boundary layer depend on the contrast in temperature and moisture between the continental and storm environments. Dry air intrusion enhances cold pool formation and stabilizes the onshore boundary layer, reducing mechanical mixing and accelerating the decay of the vortex. The temperature contrast between the continental and storm environments establishes a coastal baroclinic zone, producing stronger baroclinicity and inflow on the left of the track and weaker baroclinicity on the right. The resulting gradient imbalance in the front-right quadrant triggers radial outflow through a gradient adjustment process that redistributes momentum and mass to restore dynamical balance. Therefore, the surface thermodynamic conditions over land play a critical role in shaping the evolution of the TCBL during landfall, with the strongest asymmetries in thermodynamic boundary layer height emerging when there are large thermal contrasts between the hurricane and the continental environment. Full article

When a CMOS (Complementary Metal–Oxide–Semiconductor) imaging system operates at a high frame rate or a high line rate, the exposure time of the imaging system is limited, and the acquired image data will be dark, with a low signal-to-noise ratio and unsatisfactory sharpness. Therefore, in order to improve the visibility and signal-to-noise ratio of remote sensing images based on CMOS imaging systems, this paper proposes a low-light remote sensing image enhancement method and a corresponding ZYNQ (Zynq-7000 All Programmable SoC) design scheme called the BGIR (Bilateral-Guided Image Restoration) algorithm, which uses an improved multi-scale Retinex algorithm in the HSV (hue–saturation–value) color space. First, the RGB image is used to separate the original image’s H, S, and V components. Then, the V component is processed using the improved algorithm based on bilateral filtering. The image is then adjusted using the gamma correction algorithm to make preliminary adjustments to the brightness and contrast of the whole image, and the S component is processed using segmented linear enhancement to obtain the base layer. The algorithm is also deployed to ZYNQ using ARM + FPGA software synergy, reasonably allocating each algorithm module and accelerating the algorithm by using a lookup table and constructing a pipeline. The experimental results show that the proposed method improves processing speed by nearly 30 times while maintaining the recovery effect, which has the advantages of fast processing speed, miniaturization, embeddability, and portability. Following the end-to-end deployment, the processing speeds for resolutions of 640 × 480 and 1280 × 720 are shown to reach 80 fps and 30 fps, respectively, thereby satisfying the performance requirements of the imaging system. Full article

In practical array signal processing applications, direction-of-arrival (DOA) estimation often suffers from degraded accuracy under low signal-to-noise ratio (SNR) and limited snapshot conditions. To address these challenges, we propose an off-grid DOA estimation method based on Fast Variational Bayesian Inference (OGFVBI). Within the variational Bayesian framework, we design a fixed-point criterion rooted in root-finding theory to accelerate the convergence of hyperparameter learning. We further introduce a grid fission and adaptive refinement strategy to dynamically adjust the sparse representation, effectively alleviating grid mismatch issues in traditional off-grid approaches. To address frequency dispersion in wideband signals, we develop an improved subspace focusing technique that transforms multi-frequency data into an equivalent narrowband model, enhancing compatibility with subspace DOA estimators. We demonstrate through simulations that OGFVBI achieves high estimation accuracy and resolution while significantly reducing computational time. Specifically, our method achieves more than 37.6% reduction in RMSE and at least 28.5% runtime improvement compared to other methods under low SNR and limited snapshot scenarios, indicating strong potential for real-time and resource-constrained applications. Full article

Trajectory planning for autonomous vehicles is essential for ensuring driving safety, passenger comfort, and operational efficiency. Collision avoidance constraints introduce significant computational complexity due to their inherent non-convex and nonlinear characteristics. Previous research has proposed the drivable corridor (DC) method, which transforms complex collision avoidance constraints into linear inequalities by constructing time-varying rectangular corridors within the spatiotemporal domains, thereby enhancing optimization efficiency. However, the DC construction process involves repetitive collision detection, leading to an increased computational burden. To address this limitation, this study proposes a novel approach that integrates grid-based obstacle representation with dynamic grid merging to accelerate collision detection and dynamically constructs the DC by adaptively adjusting the expansion strategies according to available spatial dimensions. The feasibility and effectiveness of the proposed method are validated through simulation-based evaluations conducted over 100 representative scenarios characterized by diverse and unstructured environmental configurations. The simulation results indicate that, with appropriately selected grid resolutions, the proposed approach achieves up to a 60% reduction in trajectory planning time compared to conventional DC-based planners while maintaining robust performance in complex environments. Full article

Sparse Synthetic Aperture Radar (SAR) imaging has garnered significant attention due to its ability to suppress azimuth ambiguity in under-sampled conditions, making it particularly useful for high-resolution wide-swath (HRWS) SAR systems. Traditional compressed sensing-based sparse SAR imaging algorithms are hindered by range–azimuth coupling induced by range cell migration (RCM), which results in high computational cost and limits their applicability to large-scale imaging scenarios. To address this challenge, the approximated observation-based sparse SAR imaging algorithm was developed, which decouples the range and azimuth directions, significantly reducing computational and temporal complexities to match the performance of conventional matched filtering algorithms. However, this method requires iterative processing and manual adjustment of parameters. In this paper, we propose a novel deep neural network-based sparse SAR imaging method, namely the Self-supervised Azimuth Ambiguity Suppression Network (SAAS-Net). Unlike traditional iterative algorithms, SAAS-Net directly learns the parameters from data, eliminating the need for manual tuning. This approach not only improves imaging quality but also accelerates the imaging process. Additionally, SAAS-Net retains the core advantage of sparse SAR imaging—azimuth ambiguity suppression in under-sampling conditions. The method introduces self-supervision to achieve orientation ambiguity suppression without altering the hardware architecture. Simulations and real data experiments using Gaofen-3 validate the effectiveness and superiority of the proposed approach. Full article

Background/Objectives: Clostridioides difficile infection (CDI) poses a significant healthcare challenge, with recurrence rates reaching 30%, leading to substantial morbidity and costs. Fidaxomicin (FDX) and bezlotoxumab (BEZ) have shown potential in reducing recurrence; however, real-world data on the efficacy of their combination in high-risk CDI patients remain limited. This study aimed to evaluate the efficacy and safety of FDX + BEZ compared with FDX alone in CDI patients with recurrence risk factors. Methods: CDI patients with ≥two recurrence risk factors treated with FDX alone or FDX + BEZ were analyzed. Sixteen factors were evaluated as risk factors for recurrent CDI based on findings from previous studies. Patients with FDX treatment duration <10 days or other CDI treatment prior to FDX were excluded. Outcomes included recurrence within 2 months, global and clinical cure rates, and adverse events. Univariate and multivariate analyses were performed to evaluate efficacy. Results: Among 82 patients, the FDX + BEZ group (n = 30) demonstrated significantly higher global (86.7% vs. 65.4%; p < 0.05) and clinical cure rates (90.0% vs. 69.2%; p < 0.05) compared with the FDX-alone group (n = 52), despite more severe cases in the combination group. Recurrence rates were non-significantly lower in the FDX + BEZ group (3.3% vs. 11.5%). Combination therapy also accelerated diarrhea resolution without additional adverse events. Multivariate analysis identified FDX + BEZ as significantly associated with improved clinical cure (adjusted odds ratio 4.167; 95% CI: 1.029–16.885). Conclusions: FDX + BEZ therapy offers superior efficacy and safety in CDI patients with recurrence risk factors, presenting a promising strategy for optimizing CDI management. Full article

2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) is highly toxic with potential impacts on aging. While previous studies have linked TCDD exposure to reduced telomere length and altered sperm DNA methylation (DNAm) age, its relationship with epigenetic aging remains unclear. This study investigated the association between serum TCDD levels and epigenetic clocks derived from DNAm in whole blood in older adults. Using data from the 1999–2002 National Health and Nutrition Examination Survey, we analyzed 589 participants aged 50 to 79 years with available blood TCDD and DNA methylation measures. Blood TCDD levels were measured by high-resolution gas chromatography/isotope-dilution high-resolution mass spectrometry. The six DNAm-based epigenetic clocks included Horvath Age, Hannum Age, SkinBlood Age, Pheno Age, Grim Age, and Grim Age2. Multivariable regression analysis showed significant associations between TCDD levels and Horvath Age, Hannum Age, Pheno Age, Grim Age, and Grim Age2. However, when using lipid-adjusted TCDD levels, significant associations remained only for PhenoAge (β = 0.73; SE, 0.31; p = 0.0258) and Grim Age2 (β = 0.44; SE, 0.21; p = 0.0472). The strongest non-linear trends were observed for PhenoAge, Grim Age, and Grim Age2, suggesting a threshold-dependent impact of TCDD on DNAm aging processes. Our findings suggest that TCDD exposure is associated with accelerated epigenetic aging, particularly in mortality-related clocks, with a dose-dependent and non-linear pattern. Full article

Ultrafast diagnostic technology has caused breakthroughs in fields such as inertial confinement fusion, particle accelerator research, and laser-induced phenomena. As the most widely used tool for ultrafast diagnostic technology, investigating the characteristics of streak cameras in the imaging process and streak tubes’ complex physical processes is significant for its overall development. In this work, the imaging process of a streak camera is modeled and simulated using Geant4-based Monte Carlo simulations. Based on the selected phosphor screen P43 (Gd₂O₂S: Tb) and charged coupled device (CCD) sensor parameters, Monte Carlo simulation models of phosphor screens and CCD sensors (We refer to the sensor parameters of the US company onsemi’s KAF-50100 sensor, but some adjustments are made during the simulation), implemented with the toolkit Geant4, are used to study the electron beam to generate fluorescence on phosphor and photoelectrons on CCD sensors. The physical process of a high-energy electron beam hitting a phosphor screen and imaging on the CCD camera is studied. Meanwhile, merits such as the luminous efficiency of the selected phosphor, spatial resolution of the phosphor screen, and spatial resolution of the selected CCD sensor are analyzed. The simulation results show that the phosphor screen and CCD sensor simulation models can accurately simulate the selected components’ performance parameters with the imaging process’ simulation results precisely reflecting the distribution of output electrons in the streak image tube. References for simulation and device selection in the subsequent research on streak cameras can be provided. Full article

Color transfer, an essential technique in image editing, has recently received significant attention. However, achieving a balance between holistic color style transfer and local detail refinement remains a challenging task. This paper proposes an innovative color transfer method, named BHL, which stands for Balanced consideration of both Holistic transformation and Local refinement. The BHL method employs a statistical framework to address the challenge of achieving a balance between holistic color transfer and the preservation of fine details during the color transfer process. Holistic color transformation is achieved using optimal transport theory within the generalized Gaussian modeling framework. The local refinement module adjusts color and texture details on a per-pixel basis using a Gaussian Mixture Model (GMM). To address the high computational complexity inherent in complex statistical modeling, a parameter estimation method called the unit-wise Riemannian information gradient (uRIG) method is introduced. The uRIG method significantly reduces the computational burden through the second-order acceleration effect of the Fisher information metric. Comprehensive experiments demonstrate that the BHL method outperforms state-of-the-art techniques in both visual quality and objective evaluation criteria, even under stringent time constraints. Remarkably, the BHL method processes high-resolution images in an average of 4.874 s, achieving the fastest processing time compared to the baselines. The BHL method represents a significant advancement in the field of color transfer, offering a balanced approach that combines holistic transformation and local refinement while maintaining efficiency and high visual quality. Full article

Appropriate traffic cooperation at intersections plays a crucial part in modern intelligent transportation systems. To enhance traffic efficiency at intersections, this paper establishes a cooperative motion optimization strategy that adjusts the trajectories of autonomous vehicles (AVs) based on risk degree. Initially, AVs are presumed to select any exit lanes, thereby optimizing spatial resources. Trajectories are generated for each possible lane. Subsequently, a motion optimization algorithm predicated on risk degree is introduced, which takes into account the trajectories and motion states of AVs. The risk degree serves to prevent collisions between conflicting AVs. A cooperative motion optimization strategy is then formulated, incorporating car-following behavior, traffic signals, and conflict resolution as constraints. Specifically, the movement of all vehicles at the intersection is modified to achieve safer and more efficient traffic flow. The strategy is validated through a simulation using SUMO. The results indicate a 20.51% and 11.59% improvement in traffic efficiency in two typical scenarios when compared to a First-Come-First-Serve approach. Moreover, numerical experiments reveal significant enhancements in the stability of optimized AV acceleration. Full article

This paper presents an analog delay-locked loop (DLL) with a digital coarse lock and error compensation, designed to enhance locking speed in duty-cycled operation while ensuring reliability. To accelerate coarse locking speed and prevent coarse lock failure, the proposed DLL combines a low-resolution digital-to-analog converter (DAC) with an analog method for accurate lock range identification, efficiently handling scenarios where the DAC’s limited resolution could lead to failure. Additionally, it enables the rapid control of voltage adjustments by disconnecting a loop filter during the coarse lock, eliminating the need for a buffer. The DLL improves the coarse lock process reliability by compensating for potential false lock errors caused by circuit non-idealities, such as residual RC delay and amplifier offset. Furthermore, it reuses the previously identified DAC input for the duty-cycled operation to significantly reduce relock time. To mitigate the risk of potential false lock resulting from changes in locking conditions, it can update the previous DAC input upon relocking, ensuring more reliable relocking. The proposed DLL, implemented in a 28 nm CMOS process, reduces initial lock and relock times by an average of 49.3% and 65.9% at a supply voltage of 0.5 V, and 42.4% and 70.2% at 1 V, respectively, compared to the conventional analog DLL. Full article

Immune-mediated diarrhea represents a serious complication of checkpoint inhibitor therapy, especially following ipilimumab-based treatment. Efficient diagnosis and control of diarrhea remains an ongoing challenge. We developed an accelerated management paradigm for patients with ipilimumab-induced diarrhea. Patients who developed significant diarrhea (>five loose stools/day) were presumed to be developing immune colitis. Therapy was interrupted and patients were treated with a methylprednisolone dose pack. If diarrhea was not completely resolved, high-dose steroids and infliximab were promptly added. Only non-responding patients underwent further evaluation for infection or other causes of diarrhea. A total of 242 patients were treated with ipilimumab-based regimens. Forty-six developed significant diarrhea (19%) and thirty-four (74.4%) had a rapid resolution of diarrhea following glucocorticosteroid and infliximab treatment. The median time to resolution of diarrhea was only 8.5 ± 16.4 days. Accelerated treatment for presumed immune-mediated diarrhea resulted in the rapid control of symptoms in the majority of patients. There were no intestinal complications or deaths. Immunosuppressive therapy for diarrhea did not appear to decrease the remission rate or survival. After the control of diarrhea, most patients were able to continue their planned immunotherapy. Further testing in 11/46 patients with unresponsive diarrhea revealed additional diagnoses, allowing their treatment to be adjusted. Full article

In the field of oilfield fracturing development, a profound understanding of the evolution and propagation of damage during the fracturing process is crucial for preventing well water coning and channeling. This study aimed to unravel the complexity of damage evolution during fracturing and elucidate the causes of well water flooding phenomena. To accurately describe the damage propagation laws, a damage constitutive model considering compaction and post-peak correction parameters was established in this research. The model, through parameter adjustment, enhances the precision of stress calculation during the rock compaction phase and accounts for the stress degradation pattern subsequent to damage. This model was applied to simulate the damage evolution under various conditions in oil layer profiles and wellbore cross-sections, including the impact of different perforation angles, natural fracture patterns, and the ratio of longitudinal to transverse boundary pressures. The research concludes that well water channeling and flooding are primarily caused by damage propagation and the connectivity with adjacent water-bearing formations. The proposed rock damage constitutive model demonstrated an accuracy improvement of more than 3% compared to previous studies. Additionally, the study discovered that when the angle between the perforation section and the formation exceeds 30°, the risk of fracture propagation into adjacent layers increases, leading to an elevated risk of post-fracturing water flooding. The presence of natural fractures in the oil layer provides a conduit for damage propagation, accelerating the process of damage in the oil layer. Furthermore, the perforation angle and the ratio of boundary pressure loads during the fracturing process were identified as the main factors influencing the direction change of fracture propagation. The conclusions drawn from this study provide a scientific basis for preventing post-fracturing water channeling and flooding issues and offer new perspectives for the development of well fracturing technology, aiding in the resolution of water flooding problems associated with well fracturing. Full article

The detection of faults during an operational process constitutes a crucial objective within the framework of developing a control system to monitor the structure of industrial mechanisms. Even minor faults can give rise to significant consequences that require swift resolution. This research investigates the impact of overtension in the tooth belt transmission and heating of the screw transmission worm on the vibration signals in a robotic system. Utilizing FFT techniques, distinct frequency characteristics associated with different faults were identified. Overtension in the tooth belt transmission caused localized oscillations, addressed by adjusting the acceleration and deceleration speeds. Heating of the screw transmission worm led to widespread disturbances affecting servo stress and positioning accuracy. A fuzzy logic algorithm based on spectral analysis was proposed for adaptive control, considering the vibration’s frequency and amplitude. The simulation results demonstrated effective damage mitigation, reducing wear on the mechanical parts. The diagnostic approach, relying on limited data, emphasized the feasibility of identifying transmission damage, thereby minimizing maintenance costs. This research contributes a comprehensive and adaptive solution for robotic system diagnostics and control, with the proposed fuzzy logic algorithm showing promise for efficient signal processing and machine learning applications. Full article

A top-down design methodology and implementation of a time domain sensor is presented in this paper. The acceleration resolution of the time domain sensor is equal to the time-measurement accuracy divided by the sensor sensitivity. Combined with the sensitivity formula, the acceleration resolution is proportional to the vibration amplitude, the time-measurement accuracy, and the third power of the resonant frequency. According to the available time-measurement accuracy and the desired acceleration resolution, the parameters including the vibration amplitude and the resonant frequency were theoretically calculated. The geometrical configuration of the time domain sensor device was designed based on the calculated parameters. Then, the designed device was fabricated based on a standard silicon-on-insulator process and a matched interface circuit was developed for the fabricated device. Experimental results demonstrated that the design methodology is effective and feasible. Moreover, the implemented sensor works well. In addition, the acceleration resolution can be tuned by adjusting the time-measurement accuracy and the vibration amplitude. All the reported results of this work can be expanded to other time domain inertial sensors, e.g., a gyroscope or tilt sensor. Full article