Search Results (480)

The extensive use of copper in thermal and electrical systems calls for constant performance enhancement by means of innovative material treatments. The effects on the microstructural, mechanical, and electrical characteristics of copper in deep cryogenic treatment (DCT) and deep cryogenic treatment followed by heat treatment (DCT + HT) are investigated in this work. Copper samples were treated for various soaking durations ranging from 6 to 24 h. Mechanical properties such as tensile strength, hardness, and wear rate were analyzed. In the DCT-treated samples, tensile strength increased, reaching a peak of 343 MPa at 18 h, alongside increased hardness (128 HV) and a refined grain size of 9.58 µm, primarily due to elevated dislocation density and microstrain. At 18 h of soaking, DCT + HT resulted in improved structural stability, high hardness (149 HV), a fine grain size (7.42 µm), and the lowest wear rate (7.73 × 10⁻¹⁰ mm³/Nm), consistent with Hall–Petch strengthening. Electrical measurements revealed improved electron mobility (52.08 cm²/V·s) for samples soaked for 24 h in DCT + HT, attributed to increased crystallite size (39.9 nm), reduced lattice strain, and higher (111) texture intensity. SEM–EBSD analysis showed a substantial increase in low-angle grain boundaries (LAGBs) in DCT + HT-treated samples, correlating with enhanced electrical conductivity. Overall, an 18 h soaking duration was found to be optimal for both treatments. However, the strengthening mechanism in DCT + HT is influenced by grain boundary stabilization and thermal recovery and is different to DCT, which is strain-induced enhancement. Full article

Off-road path planning involves navigating vehicles through areas lacking established road networks, which is critical for emergency response in disaster events, but is limited by the complex geographical environments in natural conditions. How to model the vehicle’s off-road mobility effectively and represent environments is critical for efficient path planning in off-road environments. This paper proposed an improved A* path planning algorithm based on a Delaunay triangular NavMesh model with off-road environment representation. Firstly, a land cover off-road mobility model is constructed to determine the navigable regions by quantifying the mobility of different geographical factors. This model maps passable areas by considering factors such as slope, elevation, and vegetation density and utilizes morphological operations to minimize mapping noise. Secondly, a Delaunay triangular NavMesh model is established to represent off-road environments. This mesh leverages Delaunay triangulation’s empty circle and maximum-minimum angle properties, which accurately represent irregular obstacles without compromising computational efficiency. Finally, an improved A* path planning algorithm is developed to find the optimal off-road mobility path from a start point to an end point, and identify a path triangle chain with which to calculate the shortest path. The improved road-off path planning A* algorithm proposed in this paper, based on the Delaunay triangulation navigation mesh, uses the Euclidean distance between the midpoint of the input edge and the midpoint of the output edge as the cost function $g\left(n\right)$, and the Euclidean distance between the centroids of the current triangle and the goal as the heuristic function $h\left(n\right)$. Considering that the improved road-off path planning A* algorithm could identify a chain of path triangles for calculating the shortest path, the funnel algorithm was then introduced to transform the path planning problem into a dynamic geometric problem, iteratively approximating the optimal path by maintaining an evolving funnel region, obtaining a shortest path closer to the Euclidean shortest path. Research results indicate that the proposed algorithms yield optimal path-planning results in terms of both time and distance. The navigation mesh-based path planning algorithm saves 5~20% of path length than hexagonal and 8-directional grid algorithms used widely in previous research by using only 1~60% of the original data loading. In general, the path planning algorithm is based on a national-level navigation mesh model, validated at the national scale through four cases representing typical natural and social landscapes in China. Although the algorithms are currently constrained by the limited data accessibility reflecting real-time transportation status, these findings highlight the generalizability and efficiency of the proposed off-road path-planning algorithm, which is useful for path-planning solutions for emergency operations, wilderness adventures, and mineral exploration. Full article

Single-frequency global navigation satellite system/inertial navigation system (GNSS/INS) integration has wide application prospects in urban environments; however, correct integer ambiguity is the major challenge because of GNSS-blocked environments. In this paper, a sequential strategy of partial ambiguity resolution (PAR) of GNSS/INS for tightly coupled integration based on the robust posteriori residual, elevation angle, and azimuth in the body frame using INS aids is presented. First, the satellite is eliminated if the maximum absolute value of the robust posteriori residuals exceeds the set threshold. Otherwise, the satellites with a minimum elevation angle of less than or equal to 35° are successively eliminated. If satellites have elevation angles greater than 35°, these satellites are divided into different quadrants based on their azimuths calculated in body frame. The satellite with the maximum azimuth in each quadrant is selected as the candidate satellite, the candidate satellites are eliminated one by one, and the remaining satellites are used to calculate the position dilution of the precision (PDOP). Finally, the candidate satellite with the lowest PDOP is eliminated. Two sets of vehicle-borne data with a low-cost GNSS/INS integrated system are used to analyze the performance of the proposed algorithm. These experiments demonstrate that the proposed algorithm has the highest ambiguity fixing rates among all the designed PAR methods, and the fixing rates for these two sets of data are 99.40% and 98.74%, respectively. Additionally, among all the methods compared in this paper, the proposed algorithm demonstrates the best positioning performance in GNSS-blocked environments. Full article

The use of high-resolution satellite stereo pairs for dense image matching is a core technology for the low-cost generation of large-scale digital surface models (DSMs). However, water areas in satellite imagery often exhibit weak texture characteristics. This leads to serious issues in reconstructing water surface DSMs with traditional dense matching methods, such as significant holes and abnormal undulations. These problems significantly impact the intelligent application of satellite DSM products. To address these issues, this study innovatively proposes a water region DSM reconstruction method, boundary plane-constrained surface water stereo reconstruction (BPC-SWSR). The algorithm constructs a water surface reconstruction model with constraints on the plane’s tilt angle and boundary, combining effective ground matching data from the shoreline and the plane constraints of the water surface. This method achieves the seamless planar reconstruction of the water region, effectively solving the technical challenges of low geometric accuracy in water surface DSMs. This article conducts experiments on 10 high-resolution satellite stereo image pairs, covering three types of water bodies: river, lake, and sea. Ground truth water surface elevations were obtained through a manual tie point selection followed by forward intersection and planar fitting in water surface areas, establishing a rigorous validation framework. The DSMs generated by the proposed algorithm were compared with those generated by state-of-the-art dense matching algorithms and the industry-leading software Reconstruction Master version 6.0. The proposed algorithm achieves a mean RMSE of 2.279 m and a variance of 0.6613 m² in water surface elevation estimation, significantly outperforming existing methods with average RMSE and a variance of 229.2 m and 522.5 m², respectively. This demonstrates the algorithm’s ability to generate more accurate and smoother water surface models. Furthermore, the algorithm still achieves excellent reconstruction results when processing different types of water areas, confirming its wide applicability in real-world scenarios. Full article

Fused filament fabrication of thermoplastic composites has grown exponentially owing to its efficiency, thereby meeting numerous engineering demands. However, these materials have limitations owing to their structural vulnerability to elevated temperatures. To address this drawback, this study aims to investigate the tensile behavior of 3D-printed composites in a broad thermal domain from ambient temperature to the crystallization point. For this purpose, a commercial high-temperature-resilient polyamide carbon fiber was selected. To assess the optimal bead configuration and application range, the methodology includes tensile testing of five infill orientations across the four principal thermal domains of the polymers. The results highlight different bead arrangements under constant thermal conditions and demonstrate how temperature effects the tensile performance at similar raster angles, as further correlated with fracture mechanism analysis via scanning electron microscopy. The key findings indicate that raster orientation has a minor influence compared to temperature change. In accordance with the literature, a significantly decreased strength and an abrupt increase in plasticity is observed above the glass transition temperature. Nevertheless, the material retains one-third of its ambient tensile strength at 150 °C, demonstrating its potential for high-temperature applications. Full article

Rock slopes containing weak interlayers are highly prone to instability under the disturbance of blasting vibrations due to the influence of structural planes. To address the limitations of traditional models in predicting vibration attenuation for such slopes, this study conducted in situ blasting tests on sand–mudstone interbedded slopes from the Pinglu Canal project. Based on dimensional analysis, the Sadowsky formula was modified to incorporate both elevation difference (H/R) and soft interlayer dip angle (θ), resulting in an enhanced predictive model. Field data revealed that the proposed model significantly improved prediction accuracy, with determination coefficients (r²) increasing from 0.847 to 0.9946 in the vertical (Z) direction. Compared to traditional models, the root mean square error (RMSE) decreased by 96%, demonstrating superior capability in capturing vibration attenuation influenced by geological heterogeneity. Key findings reveal that steeper interlayer dip angles significantly accelerate PPV attenuation, particularly in the X direction. These findings provide a critical tool for optimizing blasting parameters in layered rock slopes, effectively mitigating collapse risks and enhancing construction safety. The model’s practicality was validated through its application in the Pinglu Canal project, offering a paradigm for similar engineering challenges in complex geological settings. Full article

The contact characteristics of radial tires are crucial for optimizing stress distribution, deformation, and wear. The non-uniform contact stress behavior induced by complex tread patterns remains under-explored in existing tire mechanics research. Taking the 205/50R17 radial tire as a representative case, a reverse modeling approach was employed to develop an accurate finite element model for tires incorporating intricate tread pattern features. The fidelity of the proposed tire simulation model was confirmed utilizing high-precision contour profiling techniques. The impact of diverse usage conditions and design parameters on the tire outer profile and ground contact characteristics under static and free-rolling states was analyzed. Experimental observations demonstrate that the increased inflation pressure leads to a proportional decrease in contact area. Under incremental vertical loading, the contact patch develops progressively into a saddle-shaped geometry featuring elevated shoulder regions and a recessed central zone. Increasing the belt angle compromises its hoop-stiffening function, thereby inducing elliptical contact patch geometry. Larger design diameters compromise contact length symmetry in shoulder regions. Variation in shoulder thickness at 85% of the tread width results in a significant difference in contact length between the left and right tread blocks in the rolling state. This work enables refinement strategies for both tread configurations and tire dimensional designs in industrial applications. Full article

This study explores the potential of unsupervised machine learning algorithms to identify latent cardiac risk profiles by analyzing ECG-derived parameters from two general groups: clinically healthy individuals (Norm dataset, n = 14,863) and patients hospitalized with heart failure (patients’ dataset, n = 8220). Each dataset includes 153 ECG and heart rate variability (HRV) features, including both conventional and novel diagnostic parameters obtained using a Universal Scoring System. The study aims to apply unsupervised clustering algorithms to ECG data to detect latent risk profiles related to heart failure, based on distinctive ECG features. The focus is on identifying patterns that correlate with cardiac health risks, potentially aiding in early detection and personalized care. We applied a combination of Uniform Manifold Approximation and Projection (UMAP) for dimensionality reduction and Hierarchical Density-Based Spatial Clustering (HDBSCAN) for unsupervised clustering. Models trained on one dataset were applied to the other to explore structural differences and detect latent predispositions to cardiac disorders. Both Euclidean and Manhattan distance metrics were evaluated. Features such as the QRS angle in the frontal plane, Detrended Fluctuation Analysis (DFA), High-Frequency power (HF), and others were analyzed for their ability to distinguish different patient clusters. In the Norm dataset, Euclidean distance clustering identified two main clusters, with Cluster 0 indicating a lower risk of heart failure. Key discriminative features included the “ALPHA QRS ANGLE IN THE FRONTAL PLANE” and DFA. In the patients’ dataset, three clusters emerged, with Cluster 1 identified as potentially high-risk. Manhattan distance clustering provided additional insights, highlighting features like “ST DISLOCATION” and “T AMP NORMALIZED” as significant for distinguishing between clusters. The analysis revealed distinct clusters that correspond to varying levels of heart failure risk. In the Norm dataset, two main clusters were identified, with one associated with a lower risk profile. In the patients’ dataset, a three-cluster structure emerged, with one subgroup displaying markedly elevated risk indicators such as high-frequency power (HF) and altered QRS angle values. Cross-dataset clustering confirmed consistent feature shifts between groups. These findings demonstrate the feasibility of ECG-based unsupervised clustering for early risk stratification. The results offer a non-invasive tool for personalized cardiac monitoring and merit further clinical validation. These findings emphasize the potential for clustering techniques to contribute to early heart failure detection and personalized monitoring. Future research should aim to validate these results in other populations and integrate these methods into clinical decision-making frameworks. Full article

Visible and thermal-infrared re-identification (VTI-ReID) based on aerial images is a challenging task due to the large range of elevation angles, which exacerbates the modality differences between different modalities. The substantial modality gap makes it challenging for existing methods to extract identity information from aerial images captured at wide elevation angles. This limitation significantly reduces VTI-ReID accuracy. This issue is particularly pronounced in elongated targets. To address this issue, a robust framework for extracting identity representation (RIRE) is proposed, specifically designed for VTI-ReID in aerial cross-modality images. This framework adopts a mapping method based on global representation decomposition and local representation aggregation. It effectively extracts features related to identity from aerial images and aligns the global representations of images captured from different angles within the same identity space. This approach enhances the adaptability of the VTI-ReID task to elevation angle differences. To validate the effectiveness of the proposed framework, a dataset group for elongated target VTI-ReID based on unmanned aerial vehicle (UAV)-captured data has been created. Extensive evaluations of the proposed framework on the proposed dataset group indicate that the framework significantly improves the robustness of the extracted identity information for elongated targets in aerial images, thereby enhancing the accuracy of VTI-ReID. Full article

Background and Objectives: Major depressive disorder (MDD) is a prevalent psychiatric illness increasingly recognized as a systemic condition with implications for cardiovascular diseases. Growing evidence indicates that individuals with MDD have an elevated risk of cardiovascular mortality, underscoring the need for reliable, non-invasive biomarkers to assess cardiac risk. While underlying mechanisms remain unclear, electrocardiogram (ECG)-based markers offer a promising, non-invasive means of evaluation. Among these, the electrical risk score (ERS), a composite derived from specific ECG parameters, has emerged as a predictor of adverse cardiac outcomes. This study aimed to investigate the association between ERS and MDD, and whether ERS correlates with depression severity and illness duration. Materials and Methods: In this retrospective cross-sectional study, 12-lead ECGs were evaluated to calculate the ERS based on six ECG parameters: heart rate, corrected QT interval, Tp-e interval, frontal QRS-T angle, QRS transition zone, and presence of left ventricular hypertrophy according to Sokolow–Lyon criteria. The Hamilton Depression Rating Scale (HAM-D) was utilized. Results: The study included 102 patients with MDD and 62 healthy controls. No significant differences were observed in baseline or laboratory parameters between the groups. However, heart rate, Tp-e interval, frontal QRS-T angle, and ERS were significantly higher in the depression group. ROC analysis identified ERS as the strongest predictor of depression. ERS was significantly higher in patients with severe depression compared to those with mild symptoms and showed a positive correlation with both disease duration and HAM-D score. Conclusions: Here, we show that the ECG-derived ERS is significantly elevated in patients with MDD and is associated with increased cardiac risk. ERS outperformed conventional ECG parameters in identifying individuals with depression and demonstrated positive associations with both illness duration and symptom severity. These findings suggest that ERS may serve as a practical, non-invasive biomarker for assessing cardiovascular vulnerability in this population. Full article

This study systematically investigated the effects of selective laser melting (SLM) process parameters on the forming quality and microstructure of FeCrAl oxide dispersion-strengthened (ODS) alloy. Through orthogonal experimental design, the influences of laser power (300–320 W), scanning speed (650–850 mm/s), and hatch spacing (0.05–0.07 mm) on the surface morphology and internal defects of as-built samples were analyzed. The microstructural evolution under different volumetric energy densities (VED) was also analyzed. The results indicate that hatch spacing significantly affected crack and pore formation, with minimal defects observed at 0.06 mm. Excessive laser power (320 W) or VED (318.0 J/mm³) led to elevated melt pool temperatures, causing element evaporation, grain coarsening, and <100> preferential oriented texture, thereby reducing hardness to 234 HV. The optimal parameters—laser power of 310 W, scanning speed of 650 mm/s, and hatch spacing of 0.06 mm (VED 265.0 J/mm³)—yielded the highest hardness (293 HV), fine-grained structures, and a high proportion of low-angle grain boundaries (LAGBs) with significant residual stress. This research provides a theoretical foundation for optimizing SLM processes for FeCrAl-ODS alloys. Full article

This is a research study on the high-temperature solid particle erosion behavior of basalt/carbon hybrid composites with varying ply arrangements (B₈, C₈, B₄C₄, C₄B₄, B₂C₄B₂, and C₂B₄C₂). Solid particle erosion experiments were carried out by employing garnet particles at temperatures of 25 °C, 50 °C, 80 °C, and 120 °C at impingement angles of 30° and 90°. The erosion weight loss rate differed substantially with the temperature, angle of impact, and ply arrangement. The highest erosion rates were obtained by single-component composites at 544.9 mg/g (B8, 120 °C, 30°) and 541.3 mg/g (C₈, 120 °C, 90°). In contrast, the hybrid composites were more resistant, with the lowest rate being 200.0 mg/g at an ambient temperature (25 °C, 30°) for C₄B₄. The erosion weight loss at 50 °C increased typically due to thermal softening, whereas at elevated temperatures (80 °C, 120 °C), there was some stabilization seen, reflecting the positive synergies between basalt and carbon fibers. The factorial analysis of ANOVA revealed that material type (43.17%) was the most significant factor, followed by the temperature (19.97%) and impingement angle (0.52%). SEM and profilometry analysis confirmed that hybrid arrangements lower the erosion crater depth by a great extent, affirming the improved wear resistance of balanced basalt-carbon configurations. This work demonstrates the potential applications of optimally designed hybrid composites for durability under erosive high-temperature environments. Full article

Background: The mechanical conditioning (MeCo) score is a multigene expression signature that is acquired by cancer cells in the primary breast tumor and is reflective of their responsiveness to ECM stiffness caused by tumor fibrosis. Chromatin remodeling downstream of mechanotransduction allows cancer cells to retain these acquired aggressive features even in the absence of mechanical stimulation from the primary tumor microenvironment, for instance, after dissemination through systemic circulation during metastasis. Importantly, patients who have high MeCo score tumors are at higher risk of developing metastatic breast cancer, compared to those with low MeCo scores. Moreover, circulating tumor cells (CTCs) are associated with a higher rate of metastatic dissemination, making CTC detection in the circulation of patients with breast cancer a significant prognostic biomarker for breast cancer metastasis. Beyond their enumeration per blood volume units, specific prognostic features of CTCs are not fully explored. We sought to determine whether MeCo scores increase stepwise along the metastatic cascade, from primary tumors to CTCs to distant metastatic colonization, using patient-matched biopsies. Methods: CTCs were isolated from the peripheral blood of two patient cohorts: patients with early-stage breast cancer using immunomagnetic enrichment/FACS methodology; and patients with late-stage breast cancer using the ANGLE Parsortix microfluidics system. Gene expression profiling using RNA-seq was performed on CTCs and matched primary tumors (PTs) in the early-stage cohort, and on CTCs and matched metastases (METs) for the late-stage cohorts. A quantile normalization approach was used to allow comparison across cohorts and MeCo scores were computed for all samples. The Wilcoxon matched-pairs signed rank test was performed for the comparison of MeCo scores from matching samples within each cohort; the Mann–Whitney unpaired test was used to compare MeCo scores of CTCs across cohorts. Results: In 12 pairs of patients with early-stage breast cancer, MeCo scores in CTCs were significantly higher than in their matched PTs (p = 0.026). Additionally, in 26 pairs of metastatic patient CTCs and METs, MeCo scores were significantly higher in METs compared to matched CTCs (p = 0.0004). MeCo scores of CTCs were similar between patients with early- and late-stage breast cancers, despite differing CTC isolation strategies (epitope-dependent and microfluidics size gradient). Notably, 98% of the genes in the MeCo score were present across evaluable CTC, MET, and PT samples. Conclusions: Our results show that the MeCo score is higher in CTCs than in PTs, and higher in METs compared to CTCs, in early- and late-stage breast cancer, respectively (i.e., PT < CTC < MET). Therefore, the MeCo score is progressively higher throughout the metastatic cascade in breast cancer. These findings demonstrate that mechanical conditioning from primary tumors is retained during metastatic progression, after mechanical induction by ECM stiffness is lost, as cancer cells disseminate through systemic circulation. Additionally, these findings support that cancer cells with higher MeCo scores are more competent with—and potentially selected for—metastatic progression. Importantly, these findings provide a novel feature of CTCs, mechanical conditioning (MeCo), which is associated with higher capacity for metastasis. Furthermore, since the CTC MeCo score is elevated even in early-stage breast cancer, it could provide, in addition to CTC enumeration, a potential prognostic indicator to improve metastatic risk assessment in early disease. Full article

The style and overall urban texture of historic districts embody rich social and cultural values. Therefore, how to make relevant environmental elements effectively perceived visually has become the key to protecting and displaying historic streets. Based on this, the non-subjective eye movement data and subjective impression of the subject were collected through an eye-tracking experiment and semi-structured interview. ErgoLAB was used to generate eye-tracking metrics and heat maps based on eye movement data, and ROST-CM6 software was used to generate word frequency and emotional degree data for interview text. Through comparative analysis, it is found that the subjective and objective evaluation indexes of the subjects tend to be consistent in general, but the visual behavior characteristics of different environmental elements’ types are different. The greater the variety of elements involved in visual perception, the longer the time required for participants to identify the relevant elements. The extent of element distribution also influenced differences in visual perception. Additionally, visual perceptions from partial elevation views and overall human perspective angles were largely similar, with distinctive elements attracting more interest. This study has an exploratory nature, and its findings contribute to the preservation and enhancement of the visual quality of historic districts. Full article

Highly (0002)-oriented Al_1−xSc_xN thin films with different Sc doping concentrations (x = 0, 0.2, 0.25, 0.3, and 0.43) were prepared via a magnetron sputtering system. The effects of Sc doping on the crystal structure and electrical property of the as-prepared thin films were investigated experimentally. The results of synchrotron radiation grazing-incidence wide-angle X-ray scattering (GIWAXS) and X-ray diffraction (XRD) demonstrated that the Sc³⁺ substitution for Al³⁺ induced asymmetric lattice distortion: the a-axis exhibited monotonic expansion (reaching 3.46 Å at x = 0.43) due to the larger atomic radius of Sc (~0.87 Å), while the c-axis attained a maximum value of 5.14 Å at x = 0.2 and subsequently contracted as the bond angle reduction became dominant. The dielectric constant increased to 34.67 (225% enhancement) at x = 0.43, attributed to the enhanced polarization of Sc-N bonds and interfacial charge accumulation effects. Simultaneously, the dielectric loss increased from 0.15% (x = 0) to 6.7% (x = 0.43). Leakage current studies revealed that high Sc doping (x = 0.43) elevated the leakage current density to 10⁻⁶ A/cm² under an electric field of 0.2 MV/cm, accompanied by a transition from Ohmic conduction to space-charge-limited current (SCLC) at a low electric field strength (<0.072 MV/cm). Full article