Search Results (2,864)

The article presents a new AM (Additive Manufacturing) process development, necessary to repair parts made from Aluminum 6061 material, with T6 treatment. The laser Directed Energy Deposition (DED) and Extreme High-Speed Directed Energy Deposition (EHLA) capabilities are evaluated for repairing Al large components. To optimize the process parameters, single-track depositions were analyzed for both laser-powder DED (feed rate of 2 m/min) and EHLA (feed rate 20 m/min) for AlSi10Mg and Al6061 powders. The cross-sections of single tracks revealed the bonding characteristics and provided laser-powder DED, a suitable parameter selection for the repair. Three damage types were identified on the Al component to define the specification of the repair process and to highlight the capabilities of laser-powder DED and EHLA in repairing intricate surface scratches and dents. Our research is based on variation of the powder mass flow and beam power, studying the influence of these parameters on the weld bead geometry and bonding quality. The evaluation criteria include bonding defects, crack formation, porosity, and dilution zone depth. The bidirectional path planning strategy was applied with a fly-in and fly-out path for the hatching adjustment and acceleration distance. Samples were etched for a qualitative microstructure analysis, and the HV hardness was tested. The novelty of the paper is the new process parameters for laser-powder DED and EHLA deposition strategies to repair large Al components (6061 T6), using AlSi10Mg and Al6061 powder. Our experimental research tested the defect-free deposition and the compatibility of AlSi10Mg on the Al6061 substrate. The readers could replicate the method presented in this article to repair by laser-powder DED/EHLA large Al parts and avoid the replacement of Al components with new ones. Full article

Structural collapses caused by accidents or disasters could create unexpected radiation shielding, resulting in sharp gradients within the radiation field. Traditional radiation mapping methods often fail to accurately capture these complex variations, making the rapid and precise localization of radiation sources a significant challenge in emergency response scenarios. To address this issue, based on standard Gaussian process regression (GPR) models that primarily utilize a single Gaussian kernel to reflect the inverse-square law in free space, a novel multi-kernel Gaussian process regression (MK-GPR) model is proposed for high-fidelity radiation mapping in environments with physical obstructions. MK-GPR integrates two additional kernel functions with adaptive weighting: one models the attenuation characteristics of intervening materials, and the other captures the energy-dependent penetration behavior of radiation. To validate the model, gamma-ray distributions in complex, shielded environments were simulated using GEometry ANd Tracking 4 (Geant4). Compared with conventional methods, including linear interpolation, nearest-neighbor interpolation, and standard GPR, MK-GPR demonstrated substantial improvements in key evaluation metrics, such as MSE, RMSE, and MAE. Notably, the coefficient of determination (R²) increased to 0.937. For practical deployment, the optimized MK-GPR model was deployed to an RK-3588 edge computing platform and integrated into a mobile robot equipped with a NaI(Tl) detector. Field experiments confirmed the system’s ability to accurately map radiation fields and localize gamma sources. When combined with SLAM, the system achieved localization errors of 10 cm for single sources and 15 cm for dual sources. These results highlight the potential of the proposed approach as an effective and deployable solution for radiation source investigation in post-disaster environments. Full article

Real-time wildlife monitoring on edge devices poses significant challenges due to limited power, constrained bandwidth, and unreliable connectivity, especially in remote natural habitats. Conventional object detection systems often transmit redundant data of the same animals detected across multiple consecutive frames as a part of a single event, resulting in increased power consumption and inefficient bandwidth usage. Furthermore, maintaining consistent animal identities in the wild is difficult due to occlusions, variable lighting, and complex environments. In this study, we propose a lightweight hybrid tracking framework built on the YOLOv8m deep neural network, combining motion-based Kalman filtering with Local Binary Pattern (LBP) similarity for appearance-based re-identification using texture and color features. To handle ambiguous cases, we further incorporate Hue-Saturation-Value (HSV) color space similarity. This approach enhances identity consistency across frames while reducing redundant transmissions. The framework is optimized for real-time deployment on edge platforms such as NVIDIA Jetson Orin Nano and Raspberry Pi 5. We evaluate our method against state-of-the-art trackers using event-based metrics such as MOTA, HOTA, and IDF1, with a focus on detected animals occlusion handling, trajectory analysis, and counting during both day and night. Our approach significantly enhances tracking robustness, reduces ID switches, and provides more accurate detection and counting compared to existing methods. When transmitting time-series data and detected frames, it achieves up to 99.87% bandwidth savings and 99.67% power reduction, making it highly suitable for edge-based wildlife monitoring in resource-constrained environments. Full article

To accurately estimate Zenith Tropospheric Delay (ZTD) for high-precision positioning of the Global Navigation Satellite System (GNSS), this study proposes a modeling method of ZTD based on the UNB3m model and Kriging spatial interpolation, in which the optimal spatial interpolation parameters are determined based on the errors corresponding to different combinations of the interpolation parameters, and the spatial distribution of the GNSS modeling stations is determined by the interpolation errors of the randomly selected GNSS stations for several times. To verify the accuracy and reliability of the proposed model, the ZTD estimates of 132,685 epochs with 1 h or 2 h temporal resolution for 28 years from 1997 to 2025 of the global network of continuously operating GNSS tracking stations are used as inputs; the ZTD results at any position and the corresponding observation moment can be obtained with the proposed model. The experimental results show that the model error is less than 30 mm in more than 85% of the observation epochs, the ZTD estimation results are less affected by the horizontal position and height of the GNSS stations than traditional models, and the ZTD interpolation error is improved by 10–40 mm compared to the GPT3 and UNB3m models at the four GNSS checking stations. Therefore, this technology can provide ZTD estimation results for single- and dual-frequency hybrid deformation monitoring, as well as dense ZTD data for Precipitable Water Vapor (PWV) inversion. Since the proposed method has the advantages of simple implementation, high accuracy, high reliability, and ease of promotion, it is expected to be fully applied in other high-precision positioning applications. Full article

This paper presents the design, simulation, and experimental validation of a dual-Circularly Polarized (CP) array antenna to be used as single element for a bistatic radar system, aimed at detecting and tracking objects in Low Earth Orbit (LEO). The antenna operates at 412 MHz in reception mode and consists of an array of 19 slotted-patch radiating elements with a cavity-based metallic superstrate, designed to support dual circular polarization. These elements are arranged in a hexagonal configuration, enabling the array structure to achieve a maximum realized gain of 17 dBi and a Side Lobe Level (SLL) below −17 dB while maintaining high polarization purity. Two identical analog feeding networks enable the precise control of phase and amplitude, allowing the independent reception of Right-Hand and Left-Hand Circularly Polarized (RHCP and LHCP) signals. Full-wave simulations and experimental measurements confirm the high performance and robustness of the system, demonstrating its suitability for integration into large-scale Space Situational Awareness (SSA) sensor networks. Full article

Access to clean cooking remains a major challenge in rural and off-grid areas where traditional fuels are costly, harmful, or scarce. Solar cooking offers a sustainable solution, but many existing systems suffer from fixed positioning and low efficiency. This study presents a low-cost, dual-axis solar tracking parabolic dish cooker designed for such regions, featuring adjustable pot holder height and portability for ease of use. The system uses an Arduino UNO, LDR sensors, and a DC gear motor to automate sun tracking, ensuring optimal alignment throughout the day. A 0.61 m parabolic dish with ≥97% reflective silver-coated mirrors concentrates sunlight to temperatures exceeding 300 °C. Performance tests in April, June, and November showed boiling times as low as 3.37 min in high-irradiance conditions (7.66 kWh/m²/day) and 6.63 min under lower-irradiance conditions (3.86 kWh/m²/day). Compared to fixed or single-axis systems, this design achieved higher thermal efficiency and reliability, even under partially cloudy skies. Built with locally available materials, the system offers an affordable, clean, and effective cooking solution that supports energy access, health, and sustainability in underserved communities. Full article

Porcine reproductive and respiratory syndrome virus remains one of the most economically significant pathogens in swine production, with PRRSV-2 being the dominant variant in the United States. While lineage classification has traditionally relied on ORF5 sequencing, recent studies suggest that this single-gene approach may overlook key evolutionary events such as recombination. In this study, we performed whole-genome sequencing and phylogenetic analysis of seven PRRSV-2 isolates collected in the U.S. between 2006 and 2024. Using reference-guided assembly, lineage assignment, and recombination detection with RDP5 and SIMplot, we identified discordant phylogenetic placements between ORF5 and whole genomes in four of the seven isolates. These discordances were explained by multiple recombination events affecting different genomic regions, particularly ORF2–ORF7. In contrast, three isolates showed phylogenetic concordance and no strong evidence of recombination. Our findings demonstrate that recombination plays a significant role in shaping PRRSV-2 evolution and highlight the limitations of ORF5-based lineage classification. Whole-genome surveillance is therefore essential to accurately track viral diversity, detect recombinant strains, and inform control strategies. This work underscores the need for a broader adoption of full-genome analysis in routine PRRSV surveillance and research. Full article

Background/Objectives: Myelofibrosis (MF) is a myeloproliferative neoplasm characterized by the replacement of healthy bone marrow (BM) with malignant and fibrotic tissue. In a healthy state, bone marrow is composed of approximately 60–70% fat cells, which are replaced as disease progresses. Proton density fat fraction (PDFF), a non-invasive and quantitative MRI metric, enables analysis of BM architecture by measuring the percentage of fat versus cells in the environment. Our objective is to investigate variance in quantitative PDFF-MRI values over time as a marker of disease progression and response to treatment. Methods: We analyzed existing data from three cohorts of mice: two groups with MF that failed to respond to therapy with approved drugs for MF (ruxolitinib, fedratinib), investigational compounds (navitoclax, balixafortide), or vehicle and monitored over time by MRI; the third group consisted of healthy controls imaged at a single time point. Using in-house MATLAB programs, we performed a voxel-wise analysis of PDFF values in lower extremity bone marrow, specifically comparing the variance of each voxel within and among mice. Results: Our findings revealed a significant difference in PDFF values between healthy and diseased BM. With progressive disease non-responsive to therapy, the expansion of hematopoietic cells in BM nearly completely replaced normal fat, as determined by a markedly reduced PDFF and notable reduction in the variance in PDFF values in bone marrow over time. Conclusions: This study validated our hypothesis that the variance in PDFF in BM decreases with disease progression, indicating pathologic expansion of hematopoietic cells. We can conclude that disease progression can be tracked by a decrease in PDFF values. Analyzing variance in PDFF may improve the assessment of disease progression in pre-clinical models and ultimately patients with MF. Full article

Topographic slope is a key parameter for characterizing landscape geomorphology. The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) offers high-resolution along-track slopes based on the ground profiles generated by dense signal photons. However, the across-track slopes are typically derived using the ground photon geolocations from the weak-beam and strong-beam pair, limiting the retrieval accuracy and losing valid results over rugged terrains. The goal of this study is to propose a new method to derive the across-track slope merely using single-track photon data of a strong beam based on the theoretical formula of the received signal pulse width. Based on the ICESat-2 photon data over the Walker Lake area, the specific purposes are to (1) extract the along-track slope and surface roughness from the signal photon data on the ground; (2) generate an elevation frequency histogram (EFH) and calculate its root mean square (RMS) width; and (3) derive the across-track slope from the RMS width of the EFH and evaluate the retrieval accuracy against the across-track slope from the ICESat-2 product and plane fitting method. The results show that the mean absolute error (MAE) obtained by our method is 11.45°, which is comparable to the ICESat-2 method (11.61°) and the plane fitting method (12.51°). Our method produces the least invalid data proportion of ~2.5%, significantly outperforming both the plane fitting method (10.29%) and the ICESat-2 method (32.32%). Specifically, when the reference across-track slope exceeds 30°, our method can consistently yield the optimal across-track slopes, where the absolute median, inter quartile range, and whisker range of the across-track slope residuals have reductions greater than 4.44°, 1.31°, and 0.10°, respectively. Overall, our method is well-suited for the across-track slope estimation over rugged terrains and can provide higher-precision, higher-resolution, and more valid across-track slopes. Full article

The vertical stiffness of railway tracks is crucial for ensuring safe and efficient rail transport. Rail-pad dynamic stiffness is a key component influencing track performance. Determining the dynamic stiffness of rail pads poses a challenge because it depends not only on the material and geometry of the rail pad but also on the testing conditions, due to the non-linear material response. To address this issue, a methodology is proposed in this paper to estimate dynamic stiffness using static stiffness measurements. This approach enables the prediction of dynamic stiffness for different situations from a single laboratory test. This study further examines whether this correlation remains valid for different types of rail pads, even when their mechanical behavior has been degraded by temperature, wear, or chemical agents. Experiments were conducted under varying temperatures and on rail pads that underwent mechanical and chemical degradation. The analysis assesses the validity of the static-to-dynamic stiffness correlation under degraded conditions and investigates the influence of each testing condition on the ability to estimate dynamic stiffness from static stiffness and operational parameters. The findings provide insights into the reliability of this predictive model and highlight the impact of degradation mechanisms on the dynamic behavior of rail pads. This research enhances the understanding of rail pad performance and offers a practical approach for evaluating dynamic stiffness. By considering all of the variables used in the analysis, the approach achieves R² values of up to 0.99, which carries significant implications for track design and maintenance. Full article

User requirements are the core driving force behind the iterative development of complex products. Their comprehensive collection, accurate interpretation, and effective integration directly affect design outcomes. However, current practices often depend heavily on single-source data and designer intuition, resulting in incomplete, biased, and fragile design decisions. Moreover, multi-source heterogeneous user requirements often exhibit inherent asymmetry and imbalance in both structure and contribution. To address these issues, this study proposes a symmetric and balanced optimization method for multi-source heterogeneous user requirements in complex product design. Multiple acquisition and analysis approaches are integrated to mitigate the limitations of single-source data by fusing complementary information and enabling balanced decision-making. Firstly, unstructured text data from online reviews are used to extract initial user requirements, and a topic analysis method is applied for modeling and clustering. Secondly, user interviews are analyzed using a fuzzy satisfaction analysis, while eye-tracking experiments capture physiological behavior to support correlation analysis between internal preferences and external behavior. Finally, a cooperative game-based model is introduced to optimize conflicts among data sources, ensuring fairness in decision-making. The method was validated using a case study of oxygen concentrators. The findings demonstrate improvements in both decision robustness and requirement representation. Full article

Background: Wearable movement sensors can measure upper limb (UL) activity, but single variables may not capture the full picture. This study aimed to replicate prior work identifying five multivariate categories of UL activity performance in people with stroke and controls and expand those findings to other UL conditions. Methods: Demographic, self-report, and wearable sensor-based UL activity performance variables were collected from 324 participants (stroke n = 49, multiple sclerosis n = 19, distal UL fracture n = 40, proximal UL pain n = 55, post-breast cancer n = 23, control n = 138). Principal component (PC) analyses (12, 9, 7, or 5 accelerometry input variables) were followed by cluster analyses and numerous assessments of model fit across multiple subsets of the total sample. Results: Two PCs explained 70–90% variance: PC1 (overall UL activity performance) and PC2 (preferred-limb use). A five-variable, five-cluster model was optimal across samples. In comparison to clusters, two PCs and individual accelerometry variables showed higher convergent validity with self-report outcomes of UL activity performance and disability. Conclusions: A five-variable, five-cluster model was replicable and generalizable. Convergent validity data suggest that UL activity performance in daily life may be better conceptualized on a continuum, rather than categorically. These findings highlight a unified, data-driven approach to tracking functional changes across UL conditions and severity of functional deficits. Full article

This study addresses the limitations of conventional filtering methods in handling irregular outliers and missing observations, which can compromise filter robustness and accuracy. We propose the Transformer-based Outlier-Robust Kalman Filter (TORKF), a hybrid data and knowledge hybrid-driven framework for stochastic discrete-time systems. Initially, this study derives the filtering formulas applicable when outliers exist in observation vectors and, based on these formulations, proposes a novel method capable of accurately identifying observation vectors containing outliers. In addition, a transformer-based prediction compensation approach is employed to compute the prediction vector compensation value in scenarios involving outliers. This method utilizes a specially designed data structure to ensure the transformer encoder fully extracts the input features. Furthermore, to address outlier-induced inaccuracy in prediction error covariance, a compensation method aggregating all prediction outcomes is proposed, leading to enhanced filtering accuracy. Aircraft tracking presents challenges from complex motion models and outlier-prone observations, making it an ideal testbed for robust filtering algorithms. TORKF demonstrates superior performance, with a 12.7% lower RMSE than state-of-the-art methods across both propeller and jet datasets, while maintaining sub-90 ms single-frame processing to meet real-time requirements. Ablation studies confirm that all three proposed methods enhance accuracy and demonstrate synergistic improvements. Full article

Background/Objectives: The development of normal fetal cardiac function, a dynamic process that has not yet been precisely documented throughout the literature, is difficult to quantify by classic echocardiography. Our aim was to analyze the function of the fetal myocardium through speckle tracking and establish reference values for global and segmental longitudinal strain for both ventricles in fetuses with a gestational age (GA) between 22 and 39 weeks. Methods: We conducted a prospective study in which 170 fetuses underwent echocardiographic evaluation and those 150 that were eligible for the study underwent offline speckle tracking analysis. Results: A mixed-design ANOVA model with Greenhouse–Geisser correction showed no significant differences in regional strain measurements among GA groups (F [2, 147] = 1.25, p = 0.289) but showed significant differences in regional strain measurements among the right ventricle (RV), left ventricle (LV), and interventricular free wall (Greenhouse–Geisser F [1.3, 195.2] = 45.70, p < 0.001, GG ε = 0.66, original df = 2, 294). The wall-by-segment interaction term of the model was statistically significant for regional strain (Greenhouse–Geisser F [2.7, 394.2] = 27.00, p < 0.001, GG ε = 0.67, original df = 4, 588), while the segment-by-gestational age group term had a tendency toward statistical significance (Greenhouse–Geisser F [3.0, 221.4] = 2.21, p = 0.088, GG ε = 0.75, original df = 4, 294). The results of Welch’s ANOVA model showed no significant difference in right-ventricle peak global longitudinal strain (pGLS) between GA groups (F [2.0, 92.2] = 0.52, p = 0.5972) and global longitudinal strain measurements (F [2.0, 89.6] = 27.00, p = 0.3733). Conclusions: The reference values for longitudinal strain, represented by the pGLS for LV, ranged from −20.79 to −8.05 for fetuses with a GA between 22 and 27 weeks, from −20.14 to −8.99 for fetuses with a GA between 28 and 33 weeks, and from −20.19 to −8.88 for fetuses with a GA between 34 and 39 weeks. For RV pGLS, the reference values were between −18.99 and −6.35, also depending on GA. Reference ranges for the large gestational groups studied can help us to recognize subtle changes in fetal cardiac function. Full article

Coastal blue carbon ecosystems (traditional types such as mangroves, salt marshes, and seagrass meadows; emerging types such as tidal flats and mariculture) play pivotal roles in capturing and storing atmospheric carbon dioxide. Reliable assessment of the spatial and temporal variation and the carbon storage potential holds immense promise for mitigating climate change. Although previous field surveys and regional assessments have improved the understanding of individual habitats, most studies remain site-specific and short-term; comprehensive, multi-decadal assessments that integrate all major coastal blue carbon systems at the national scale are still scarce for China. In this study, we integrated 30 m Landsat imagery (1992–2022), processed on Google Earth Engine with a random forest classifier; province-specific, literature-derived carbon density data with quantified uncertainty (mean ± standard deviation); and the InVEST model to track coastal China’s mangroves, salt marshes, tidal flats, and mariculture to quantify their associated carbon stocks. Then the GeoDetector was applied to distinguish the natural and anthropogenic drivers of carbon stock change. Results showed rapid and divergent land use change over the past three decades, with mariculture expanded by 44%, becoming the dominant blue carbon land use; whereas tidal flats declined by 39%, mangroves and salt marshes exhibited fluctuating upward trends. National blue carbon stock rose markedly from 74 Mt C in 1992 to 194 Mt C in 2022, with Liaoning, Shandong, and Fujian holding the largest provincial stock; Jiangsu and Guangdong showed higher increasing trends. The Normalized Difference Vegetation Index (NDVI) was the primary driver of spatial variability in carbon stock change (q = 0.63), followed by precipitation and temperature. Synergistic interactions were also detected, e.g., NDVI and precipitation, enhancing the effects beyond those of single factors, which indicates that a wetter climate may boost NDVI’s carbon sequestration. These findings highlight the urgency of strengthening ecological red lines, scaling climate-smart restoration of mangroves and salt marshes, and promoting low-impact mariculture. Our workflow and driver diagnostics provide a transferable template for blue carbon monitoring and evidence-based coastal management frameworks. Full article