Search Results (210)

To address the limited overlapping bandwidth across polarization modes in conventional multi-polarized antennas, this paper proposes a wideband multi-polarized microstrip antenna with high polarization isolation. Based on the theory of orthogonal dual-circular polarization synthesis, the proposed antenna achieves left-hand circular polarization (LHCP) and right-hand circular polarization (RHCP) under a single-port excitation mode, and can generate arbitrary linear polarization (LP) by simply adjusting the phase when dual-fed. For verification, a prototype operating at the C-band is designed, fabricated, and measured. The measured results agree well with the simulations. For linear polarization, the measured 10 dB bandwidth ranges from 4 GHz to 8 GHz (relative bandwidth of 66.7%), with polarization isolation exceeding 26 dB. For circular polarization, the measured bandwidth (for 10 dB return loss and 3 dB axial ratio) spans 4.1–8 GHz (relative bandwidth of 64.5%), with polarization isolation greater than 15 dB. The linear polarization gain is slightly higher than the circular polarization gain, with a maximum gain of 4.3 dB. The proposed antenna simultaneously features multi-polarization, a wide bandwidth, a low profile (0.03 λ₀), and high polarization isolation, which can meet the urgent demand for multi-polarized antennas in modern multi-functional integrated wireless systems, such as communication systems, radar, and unmanned aerial vehicles (UAVs). Full article

Aiming at the speed chattering problem caused by high-frequency square wave injection in permanent magnet synchronous motors (PMSMs) during low-speed operation (200–500 r/min), this study intends to improve the rotor position estimation accuracy of sensorless control systems as well as the system’s ability to resist harmonic interference and sudden load changes. The goal is to enhance the control performance of traditional control schemes in this scenario and meet the requirement of stable low-speed operation of the motor. First, the study analyzes the harmonic error propagation mechanism of high-frequency square wave injection and finds that the traditional PI phase-locked loop (PI-PLL) is susceptible to high-order harmonic interference during demodulation, which in turn leads to position estimation errors and periodic speed fluctuations. Therefore, the extended state observer phase-locked loop (ESO-PLL) is adopted to replace the traditional PI-PLL. A third-order extended state observer (ESO) is used to uniformly regard the system’s unmodeled dynamics, external load disturbances, and harmonic interference as “total disturbances”, realizing real-time estimation and compensation of disturbances, and quickly suppressing the impacts of harmonic errors and sudden load changes. Meanwhile, a dynamic pole placement strategy for the speed loop is designed to adaptively adjust the controller’s damping ratio and bandwidth parameters according to the motor’s operating states (loaded/unloaded, steady-state/transient): large poles are used in the start-up phase to accelerate response, small poles are switched in the steady-state phase to reduce errors, and a smooth attenuation function is used in the transition phase to achieve stable parameter transition, balancing the system’s dynamic response and steady-state accuracy. In addition, high-frequency square wave voltage signals are injected into the dq axes of the rotating coordinate system, and effective rotor position information is extracted by combining signal demodulation with ESO-PLL to realize decoupling of high-frequency response currents. Verification through MATLAB/Simulink simulation experiments shows that the improved strategy exhibits significant advantages in the low-speed range of 200–300 r/min: in the scenario where the speed transitions from 200 r/min to 300 r/min with sudden load changes, the position estimation curve of ESO-PLL basically overlaps with the actual curve, while the PI-PLL shows obvious deviations; in the start-up and speed switching phases, dynamic pole placement enables the motor to respond quickly without overshoot and no obvious speed fluctuations, whereas the traditional fixed-pole PI control has problems of response lag or overshoot. In conclusion, the “ESO-PLL + dynamic pole placement” cooperative control strategy proposed in this study effectively solves the problems of harmonic interference and load disturbance caused by high-frequency square wave injection in the low-speed range and significantly improves the accuracy and robustness of PMSM sensorless control. This strategy requires no additional hardware cost and achieves performance improvement only through algorithm optimization. It can be directly applied to PMSM control systems that require stable low-speed operation, providing a reliable solution for the promotion of sensorless control technology in low-speed precision fields. Full article

In surgical navigation-assisted pedicle screw fixation, cross-source pre- and intra-operative point clouds registration faces challenges like significant initial pose differences and low overlapping ratio. Classical algorithms based on feature descriptor have high computational complexity and are less robust to noise, leading to a decrease in accuracy and navigation performance. To address these problems, this paper proposes a coarse-to-fine registration framework. In the coarse registration stage, a Point Matching algorithm based on Curvature Feature Learning (CFL-PM) is proposed. Through CFL-PM and Farthest Point Sampling (FPS), the coarse registration of overlapping regions between the two point clouds is achieved. In the fine registration stage, the Iterative Closest Point (ICP) is used for further optimization. The proposed method effectively addresses the challenges of noise, initial pose and low overlapping ratio. In noise-free point cloud registration experiments, the average rotation and translation errors reached 0.34° and 0.27 mm. Under noisy conditions, the average rotation error of the coarse registration is 7.28°, and the average translation error is 9.08 mm. Experiments on pre- and intra-operative point cloud datasets demonstrate the proposed algorithm outperforms the compared algorithms in registration accuracy, speed, and robustness. Therefore, the proposed method can achieve the precise alignment of the surgical navigation-assisted pedicle screw fixation. Full article

Hastelloy X exhibits outstanding thermal fatigue resistance, making it a promising material for repairing 50CrVA landing gear via directed energy deposition (DED). However, the substantial differences in composition and thermophysical properties between 50CrVA and Hastelloy X pose challenges by affecting interfacial microstructure and surface quality. This study investigates the effect of DED process parameters (laser power p, powder feed rate f, scanning speed v, and overlap rate) on the dilution ratio (η), microscopic morphology, surface flatness (ζ), and porosity of Hastelloy X claddings on a 50CrVA substrate. An optimization methodology integrating thermal–flow coupled simulation models and orthogonal experiments is developed to fabricate high-quality claddings. Furthermore, the corrosion–wear performance of the claddings is evaluated. The results indicate that the η of a single track increases with higher p or lower f, while it first increases and then decreases with the increase in v. Ablation marks tend to occur at excessive p or insufficient f, while low v causes surface ripples. The ζ of a single layer initially improves and subsequently deteriorates with increasing overlap rate. Porosity is significantly influenced by p and f. The optimal p, f, v, and overlap rate are 1600 W, 2.4 g/min, 240 mm/min, and 55%, respectively. The wear resistance of the cladding is nearly identical to that of the substrate, while corrosion resistance is significantly improved. This work provides a theoretical foundation for high-performance repair of 50CrVA landing gear in aircraft. Full article

The fabrication of microfluidic components using low-cost Fused Deposition Modeling (FDM) presents an attractive alternative to conventional manufacturing methods, yet achieving microscale dimensional accuracy remains a significant challenge. This study investigates the influence of five key FDM parameters (nozzle temperature, bed temperature, printing speed, flow rate, and infill overlap) on the dimensional accuracy of microchannels printed with PETG and TPU filaments. A Taguchi L27 orthogonal array was employed to systematically evaluate the effects of these parameters on width and depth deviations across sub-millimeter microchannel geometries. Results show that for PETG, optimal dimensional fidelity was achieved at 240 °C nozzle temperature, 70 °C bed temperature, 30 mm/s speed, 100% flow rate, and 15% overlap, enabling reliable channel widths down to 100 µm. TPU exhibited greater variability due to its elasticity, with optimal settings found at 220 °C, 60 °C bed temperature, 30 mm/s, 100% flow rate, and 25% overlap. Signal-to-noise ratio and ANOVA analyses revealed flow rate and printing speed as dominant factors for both materials. The findings provide a reproducible optimization framework for microscale FDM fabrication and highlight material-specific process sensitivities critical to functional microfluidic device performance. Full article

The direction-of-arrival (DOA) estimation of multiple linear frequency modulation (LFM) signals typically requires the construction of a spatial time–frequency distribution (STFD) matrix via linear transforms or quadratic time–frequency distributions (QTFD) before joint spatial time–frequency estimation. Extensive research has been conducted on DOA estimation of LFM signals with overlapped instantaneous frequency (IF) trajectories and significantly different chirp rates. However, when LFM signals have the same chirp rate and slightly different initial frequencies with parallel and close IF trajectories, their linear transforms suffer from low resolution and quadratic distributions and are affected by cross-terms, both of which reduce accuracy. To address this problem, this study proposes a DOA estimation algorithm based on QTFD and the Hough transform. First, QTFD is used to improve the resolution and apply both spatial and directional smoothing to eliminate cross-terms. Second, the Hough transform is employed for IF estimation instead of threshold filtering to enhance accuracy. Finally, DOA results are obtained via time–frequency filtering and the multiple signal classification (MUSIC) algorithm. Experiments show that for two LFM signals at a −5 dB signal-to-noise ratio (SNR), the proposed algorithm improves accuracy by approximately 43.2% compared to similar algorithms and effectively estimates the DOA in underdetermined cases. Thus, the proposed algorithm enhances the DOA estimation accuracy for multiple LFM signals, is robust to noise, and expands the application scenarios of joint spatial time–frequency estimation. Full article

Background: Diabetes is a leading cause of death globally and is strongly associated with aging-related conditions, such as cognitive decline. Both diabetes and cognitive impairment share overlapping biological mechanisms, including vascular damage and insulin resistance. While each condition independently affects health outcomes, the impact of their coexistence on mortality risk among older Hispanic adults remains understudied. This study aimed to examine the impact of the combination of diabetes and low cognitive performance on mortality risk among this marginalized population. Methods: Data were drawn from 636 Hispanic adults aged 60 years and older who participated in NHANES 1999–2002, with mortality follow-up through 2019. Cox proportional hazards models were used to estimate hazard ratios (HRs) for all-cause mortality, adjusting for sociodemographic and health-related variables. Results: Among participants, 23.3% had diabetes, and 54.9% had low cognitive performance. The combination of diabetes and low cognitive performance was associated with a significantly elevated risk of all-cause mortality (HR = 2.36; 95% CI: 1.70–3.28). No statistically significant increase in mortality risk was observed for either condition alone (i.e. diabetes or cognitive impairment). Conclusions: Coexisting diabetes and cognitive impairment in older Hispanic adults were associated with more than a twofold increase in mortality risk. These findings underscore the need for culturally appropriate, interdisciplinary strategies to address the dual burden of diabetes and cognitive decline in aging minority populations. Full article

Internet of Things (IoT) applications are rapidly adopting low-power wide-area network (LPWAN) technology due to its ability to provide broad coverage for a range of battery-powered devices. LoRaWAN has become the most widely used LPWAN solution due to its physical layer (PHY) design and regulatory advantages. Because LoRaWAN has a broad communication range, the coverage of the gateways might overlap. In LoRa technology, packets can be received concurrently by multiple gateways. Subsequently, the network server selects the packet with the highest receiver strength signal indicator (RSSI). However, this method can lead to the exhaustion of channel availability on the gateways. The optimisation of configuration parameters to reduce collisions and enhance network throughput in multi-gateway LoRaWAN remains an unresolved challenge. This paper introduces a novel low-complexity model for ZBMG-LoRa, mitigates the collisions using channel utilisiation, and categorises nodes into distinct groups based on their respective gateways. This categorisation allows for the implementation of optimal settings for each node’s subzone, thereby facilitating effective communication and addressing the identified issue. By deriving key performance metrics (e.g., network throughput, energy efficiency, and probability of effective delivery) from configuration parameters and network size, communication reliability is maintained. Optimal configurations for transmission power and spreading factor are derived by our method for all nodes in LoRaWAN networks with multiple gateways. In comparison to adaptive data rate (ADR) and other related state-of-the-art algorithms, the findings demonstrate that the novel approach achieves higher packet delivery ratio and better energy efficiency. Full article

The settlements of the Great Wall are the product of the overlap of ancient Chinese agricultural civilization and non-agricultural civilization. The structure of the settlement system is of great value for understanding the law of defense engineering and social spatial organization. The Great Wall, built by a non-agricultural civilization, is an important part of the development history of the Chinese civilization. Its uniqueness reflects the relationship between institution and space. However, the archaeological remains and related research methods for non-agricultural Great Wall settlements are not perfect. This paper takes the typical case of the Great Wall built by a non-agricultural civilization (Linhuang Lu settlements of the Jin Great Wall) as the object and integrates spatial analysis and social network analysis. It aims to explore the structure of the settlement system. The settlements of Linhuang Lu show non-random distribution characteristics. They can be divided into four levels. The number ratio from high-level to low-level settlements is 70:30:10:1. Through the weighted Voronoi and social network analysis of human connection and geographical connection, this paper clarifies the structural characteristics of spatial association and social association of settlements. Combined with accessibility and geographical environment, the Linhuang Lu settlements were finally divided into 10 Meng’an defense units and 12 Mouke defense units. Quantitative analysis of the settlement system structure shows the hierarchical management of nature and military by non-agricultural civilization. This provides an empirical basis for the reconstruction of the military defense system of the Great Wall of the Jin Dynasty and further explores the applicability of the research paradigm. This paper has methodological innovation value for solving the problem of spatial cognition of settlement heritage. Full article

Laser beam scanning (LBS) projection systems based on MEMS micromirrors offer advantages such as compact size, low power consumption, and vivid color performance, making them well suited for applications like AR glasses and portable projectors. Among various scanning methods, raster scanning is widely adopted; however, it suffers from artifacts such as dark bands between adjacent scanning lines and non-uniform distribution of the scanning trajectory relative to the original image. These issues degrade the overall viewing experience. In this study, we address these problems by introducing random variations to the slow-axis driving signal to alter the vertical offset of the scanning trajectories between different scan cycles. The variation is defined as an integer multiple of 1/8 of the fast-axis scanning period ($1/{\mathrm{f}}_{\mathrm{h}}$) Due to the temporal integration effect of human vision, trajectories from different cycles overlap, thereby enhancing the scanning fill factor relative to the target image area. The simulation and experimental results demonstrate that the maximum ratio of non-uniform line spacing is reduced from 7:1 to 1:1, and the modulation of the scanned display image is reduced to 0.0006—below the human eye’s contrast threshold of 0.0039 under the given experimental conditions. This method effectively addresses scanning display artifacts without requiring additional hardware modifications. Full article

Background/Objectives: Despite overlapping inflammatory responses and frequent culture-negative results in severe burn patients, early and accurate sepsis diagnosis remains challenging. We aimed to evaluate the diagnostic performance of seven candidate biomarkers and their clinical utility, particularly in culture-negative cases. Methods: We conducted a prospective diagnostic accuracy study (January 2021–December 2022; N = 221) in the burn intensive care unit, applying a two-step feature selection to 41 candidate variables. Seven top biomarkers—presepsin, procalcitonin (PCT), albumin, C-reactive protein (CRP), prothrombin time (PT), hematocrit (Hct), and D-dimer—were measured at the moment of clinical sepsis suspicion, concurrently with blood cultures and prior to empirical antibiotic administration, within ±2 h of Sequential Organ Failure Assessment (SOFA). Diagnostic performance was evaluated using a Receiver Operating Characteristic (ROC) curve analysis to determine the area under the curve (AUC), Youden index-derived cut-offs, decision curve analysis, and Net Reclassification Improvement (NRI). Results: Presepsin achieved the highest overall AUC (0.810; 95% CI, 0.742–0.878) and outperformed other markers in culture-negative cases (AUC, 0.846 vs. 0.604; p = 0.015). In the decision curve analysis, presepsin and PCT maintained the largest net benefits at high thresholds, although PT, D-dimer, and Hct also retained smaller positive benefits. Patients were stratified into high- vs. low-risk groups for survival analysis using Youden index cut-offs; Cox regression confirmed PCT (Hazard Ratio 3.78; p < 0.001) and PT (HR 2.12; p = 0.018) as a significant mortality predictor, with presepsin showing borderline significance (HR 3.14; p = 0.055). Conclusions: The high rate of culture-negative sepsis reflects early antibiotic use suppressing culture yield rather than resistance patterns alone. Presepsin’s rapid rise and preserved accuracy under pre-sampling antibiotics suggest its value for early sepsis detection and antimicrobial stewardship. Future work will incorporate polymicrobial and multidrug-resistant bloodstream infection profiles to refine biomarker utility. Full article

Background/Objectives: Chronic kidney disease (CKD) and osteoporosis are critical public health concerns, particularly among older adults, due to their prevalence and associated complications. While CKD-related disruptions in bone mineral metabolism are believed to increase osteoporosis risk, this relationship remains unclear in diverse populations such as Korea. Methods: This longitudinal cohort study utilized data from the Korean National Health Insurance Service Health Screening Cohort (2002–2019), including 13,622 patients with newly diagnosed CKD and 54,488 matched controls. CKD was defined as having at least two outpatient or inpatient records with ICD-10 codes N18 or N19 and/or evidence of dialysis treatment claims, following a 1-year washout period to exclude prevalent cases. Individuals with a prior history of osteoporosis or incomplete baseline data were excluded. Propensity score overlap weighting was applied, and adjusted hazard ratios (HRs) with 95% confidence intervals (CIs) were calculated using Cox proportional hazards models, with subgroup analyses based on demographic and clinical factors. Results: CKD was not associated with an increased risk of osteoporosis. On the contrary, CKD patients exhibited an 18% lower risk of developing osteoporosis compared to controls (HR = 0.82, 95% CI: 0.77–0.87, p < 0.001). This inverse association was more pronounced among women, non-smokers, individuals with low alcohol consumption, and those with a higher comorbidity burden. Conclusions: These findings suggest that certain subgroups of CKD patients may have a reduced risk of osteoporosis, highlighting the importance of individualized risk assessment and tailored preventive strategies. Full article

The fatty acid (FA) profile of beef is a key indicator of nutritional quality. This study assessed the ability of Near Infrared Spectroscopy (NIRS) to predict the FA profile in beef samples from southern Chile. A total of 81 FAs were analyzed, and 38% of the calibration models achieved RPD ≥ 2.5 (Ratio of Performance to Deviation). Strong predictive performance was observed for major FAs, particularly SFA and MUFA, with R²p > 0.90 (Coefficient of Determination) for palmitic (16:0). Although PUFA and some CLA isomers showed lower predictive accuracy—likely due to low concentrations and spectral overlap—minor FA such as 9c,11t-18:2 (CLA, rumenic acid) was accurately predicted. External validation confirmed that 77% of FAs showed no significant differences from gas chromatography, highlighting the robustness of NIRS for most compounds analyzed here. NIRS effectively captured FAs related to grass-based diets, such as trans-vaccenic acid and specific CLA isomers. NIRS works as a practical, rapid, and non-destructive tool for FA profiling, with potential uses in nutritional labeling and quality control; however, its application depends on the prior development of robust calibration models, which must be tailored to the specific matrix and analytical objectives. Full article

Copper alloys are critical heat sink materials for fusion reactor divertors due to their high thermal conductivity (TC) and strength, yet their performance under extreme particle bombardment and heat fluxes in future tokamaks requires enhancement. While neutron-induced transmutation helium affects the properties of copper, the atomistic mechanisms linking helium bubble size to thermal transport remain unclear. This study employs non-equilibrium molecular dynamics (NEMD) simulations to isolate the effect of bubble diameter (10, 20, 30, 40 Å) on TC in copper, maintaining a constant He-to-vacancy ratio of 2.5. Results demonstrate that larger bubbles significantly impair TC. This reduction correlates with increased Kapitza thermal resistance and pronounced lattice distortion from outward helium diffusion, intensifying phonon scattering. Phonon density of states (PDOS) analysis reveals diminished low-frequency peaks and an elevated high-frequency peak for bubbles >30 Å, confirming phonon confinement and localized vibrational modes. The PDOS overlap factor decreases with bubble size, directly linking microstructural evolution to thermal resistance. These findings elucidate the size-dependent mechanisms of helium bubble impacts on thermal transport in copper divertor materials. Full article

Automated surface defect detection in steel manufacturing is pivotal for ensuring product quality, yet it remains an open challenge owing to the extreme heterogeneity of defect morphologies—ranging from hairline cracks and microscopic pores to elongated scratches and shallow dents. Existing approaches, whether classical vision pipelines or recent deep-learning paradigms, struggle to simultaneously satisfy the stringent demands of industrial scenarios: high accuracy on sub-millimeter flaws, insensitivity to texture-rich backgrounds, and real-time throughput on resource-constrained hardware. Although contemporary detectors have narrowed the gap, they still exhibit pronounced sensitivity–robustness trade-offs, particularly in the presence of scale-varying defects and cluttered surfaces. To address these limitations, we introduce MBY (MBDNet-Attention-YOLO), a lightweight yet powerful framework that synergistically couples the MBDNet backbone with the YOLO detection head. Specifically, the backbone embeds three novel components: (1) HGStem, a hierarchical stem block that enriches low-level representations while suppressing redundant activations; (2) Dynamic Align Fusion (DAF), an adaptive cross-scale fusion mechanism that dynamically re-weights feature contributions according to defect saliency; and (3) C2f-DWR, a depth-wise residual variant that progressively expands receptive fields without incurring prohibitive computational costs. Building upon this enriched feature hierarchy, the neck employs our proposed MultiSEAM module—a cascaded squeeze-and-excitation attention mechanism operating at multiple granularities—to harmonize fine-grained and semantic cues, thereby amplifying weak defect signals against complex textures. Finally, we integrate the Inner-SIoU loss, which refines the geometric alignment between predicted and ground-truth boxes by jointly optimizing center distance, aspect ratio consistency, and IoU overlap, leading to faster convergence and tighter localization. Extensive experiments on two publicly available steel-defect benchmarks—NEU-DET and PVEL-AD—demonstrate the superiority of MBY. Without bells and whistles, our model achieves 85.8% mAP@0.5 on NEU-DET and 75.9% mAP@0.5 on PVEL-AD, surpassing the best-reported results by significant margins while maintaining real-time inference on an NVIDIA Jetson Xavier. Ablation studies corroborate the complementary roles of each component, underscoring MBY’s robustness across defect scales and surface conditions. These results suggest that MBY strikes an appealing balance between accuracy, efficiency, and deployability, offering a pragmatic solution for next-generation industrial quality-control systems. Full article