Search Results (1,498)

Traffic signs exposed to long-term outdoor conditions frequently exhibit deformation, inclination, or other forms of physical damage, highlighting the need for timely and reliable anomaly assessment to support road safety management. While point-cloud data provide accurate three-dimensional geometric information, their sparse distribution and lack of appearance cues make traffic sign extraction challenging in complex environments. High-resolution sequential video-log images captured from multiple viewpoints offer complementary advantages by providing rich color and texture information. In this study, we propose an integrated traffic sign detection and assessment framework that combines video-log images and mobile-mapping point clouds to enhance both accuracy and robustness. A dedicated YOLO-SIGN network is developed to perform precise detection and multi-view association of traffic signs across sequential images. Guided by these detections, a frustum-based point-cloud extraction strategy with seed-point density growing is introduced to efficiently isolate traffic sign panels and supporting poles. The extracted structures are then used for geometric parameterization and damage assessment, including inclination, deformation, and rotation. Experiments on 35 simulated scenes and nine real-world road scenarios demonstrate that the proposed method can reliably extract and evaluate traffic sign conditions in diverse environments. Furthermore, the YOLO-SIGN network achieves a localization precision of 91.16% and a classification mAP of 84.64%, outperforming YOLOv10s by 1.7% and 8.7%, respectively, while maintaining a reduced number of parameters. These results confirm the effectiveness and practical value of the proposed framework for large-scale traffic sign monitoring. Full article

3D printing offers the ability to fabricate lightweight structural profiles with controlled infill and geometry. This study examines the mechanical behaviour of 3D-printed polylactic acid (PLA) structures with a 10% infill density under four load conditions (10, 15, 20, and 25 N). Four designs (M1, M2, M3, and M4), representing commonly used structural profiles found in beam and column applications, were analysed using ANSYS finite element simulations. Each design was evaluated under roller and nodal boundary conditions to study deformation, stress, and strain responses. Three-point flexural tests were also carried out on all four designs, and the measured peak flexural stress and apparent flexural modulus were compared with the simulated stiffness values. Both the simulations and experimental results showed that Design M3 exhibited the highest stiffness and more consistent behaviour compared to the other designs, while Design M4 showed higher deformation and lower bending resistance. Roller supports generally reduced deformation through better load distribution, whereas nodal supports increased local stiffness in selected designs. Although the magnitude of stiffness differed between simulation and experiment, the ranking of the designs remained consistent. Overall, the study confirms that the geometry plays an important role in their load-bearing performance, and the numerical model provides a reliable tool for comparing and selecting suitable designs before fabrication. Full article

This study presents a comprehensive atomistic investigation of the structural, mechanical, and thermal properties of Pd₆₀Ir_nRh_19−n trimetallic nanoclusters adopting a truncated octahedral geometry. The compositional evolution of chemical ordering, local pressure distributions, and melting behavior was systematically analyzed using Gupta potential-based basin-hopping global optimization. The accuracy of the Gupta potential predictions was further validated for all configurations using density functional theory (DFT) calculations. The surface layer consisted solely of Pd atoms and was held constant throughout the study. Meanwhile, Ir and Rh atoms were distributed within the 19-atom core region, allowing a detailed evaluation of how variations in core composition affect the energetic and thermal stability of the clusters. The Pd₆₀Ir₆Rh₁₃ configuration exhibits the minimum value of mixing energy, corresponding to the most symmetric and energetically stable atomic arrangement. Local pressure analyses showed that Ir incorporation enhances internal compressive stress and induces tensile relaxation on the Pd surface, achieving an optimal strain balance at n = 6. Melting analyses based on caloric curves and Lindemann indices revealed a non-monotonic dependence of melting temperature on Ir content, with Ir-rich clusters displaying the highest thermal resistance and Rh-rich systems showing reduced stability. These findings clarify how Ir/Rh distribution governs the energetic, mechanical, and thermal response of Pd–Ir–Rh nanoalloys, offering a coherent atomistic framework for understanding their composition-dependent stability. Full article

The increasing power density of 2.5D and 3D chiplets imposes severe thermal constraints that have a direct impact on the performance and long-term reliability of high-performance computing systems. Stacked and laterally integrated dies, which generate hundreds of watts per package, create localized hotspots and inconsistent temperature fields, major obstacles to scalable heterogeneous integration. Research efforts have addressed these challenges by finite element and compact heat modeling, thermal interface material optimization (TIM), and advanced cooling solutions such as micro-channel liquid cooling and cold racks. While these approaches provide valuable insights, most remain case-specific, focusing on isolated packages or single design variables, and lack a general methodology for assessing thermal feasibility at an early stage. This review consolidates and critically analyzes contributions to thermal modeling at the package level, interposer thermal spreading, thermal characterization of TIMs, and the development of cooling technologies. A comparative review of published studies indicates a consistent threshold: 2.5D stacks are viable under air cooling at approximately 300 W, whereas 3D stacks require liquid or hybrid cooling in conjunction with high-performance thermal interface materials at about 350 W. The investigations identify interposer conductivity, thermal interface material thickness, and hotspot power distribution as the primary sensitivity elements. This study explores Thermal Feasibility Maps (TFMs), defined as multidimensional charts parameterized by architecture, cooling regime, and material stack. TFMs provide a systematic framework for comparing design trade-offs and support architecture cooling co-design in advanced chiplet systems. Full article

Populus euphratica Oliv. serves as a keystone species in desert riparian ecosystems. Owing to its pronounced tolerance to drought and salinity, as well as its robust reproductive capacity, it has become a pioneer species in desert oases. The xyloglucan endotransglucosylase (XET)/hydrolase (XTH) gene family plays a critical role in the remodeling of plant cell walls; however, its potential biological functions in poplar remain poorly understood. In this study, we identified the XTH gene family in P. euphratica and conducted a preliminary functional analysis. A total of 33 PeXTH genes were identified, which were unevenly distributed across the chromosomes, with the highest density observed on chromosome 6. Conserved domain analysis indicated that most members contain the typical GH16 domain associated with xyloglucan endotransglucosylase activity. Phylogenetic analysis classified them into four distinct subgroups, exhibiting evolutionary conservation with the model dicot plant of Arabidopsis thaliana. Notably, the promoter analysis revealed an abundance of ABA-responsive and stress-related cis-elements, suggesting their potential involvement in response to multiple stresses. Under drought stress, PeXTH7 (PeuTF07G00088.1) exhibited a distinct expression pattern, with transcript levels significantly increasing with persistent treatment. RT-qPCR results confirmed that PeXTH7 is highly expressed in both roots and leaves. Furthermore, subcellular localization assays demonstrated that the PeXTH7 protein localizes to the secretory pathway and the cell wall, implying a role in cell wall dynamic remodeling through the regulation of xyloglucan metabolism. The PeXTH7-overexpressing transgenic lines exhibited a significant increase in root length compared to the wild-type controls. As the first systematic analysis of the XTH gene family in P. euphratica, this study fills an important knowledge gap and provides new insights into the adaptive mechanisms of desert tree species. Full article

In deep coal seams, where nanopores (~2 nm) dominate, wettability effects, which govern molecule–wall interaction strength, critically control the methane storage, yet remain poorly understood. This work establishes, for the first time, a theoretical framework coupling the Simplified Local Density (SLD) model with wettability effects to systematically describe nanoconfined methane behavior. Key innovations include modifying the equation of state (EoS) by incorporating a molecule–wall interaction term, correlating the nanopore wall energy parameter and adsorption layer thickness with the interaction strength, and deriving wettability-dependent shifted critical properties. This approach successfully relates the local methane density distribution to the surface contact angle, bridging the knowledge gap between nanoconfined behavior and both pore size and wettability. The results show that (a) the bulk-like gas proportion in deep seams exceeds 35%, far higher than in shallow seams, indicating superior development potential; (b) the bulk-like gas increases faster with pressure than adsorbed gas, while the adsorption amount decreases by up to 46%, as the contact angle rises from 0° to 80°; (c) the modified EoS significantly impacts the bulk-like gas, reducing its amount by about 8% in 3 nm pores due to weakened intermolecular interactions. This study underscores the necessity of integrating wettability to accurately predict the nanoconfined fluid behavior, especially for deep coal seam gas. Full article

The review has explored the application of Geographic Information Systems (GIS) in evaluating road traffic crashes, stressing its role in identifying crash spatial patterns and hotspots. GIS offers a framework for integrating spatial and non-spatial data, allowing scholars and planners to visualize crash-prone areas and understand their distribution. A total of 77 research articles from the publication period of 2010–2025 were included for final reviews. A Systematic Reviews and Meta-Analyses (PRISMA) approach is followed to provide well-structured, transparent, and standardized information on articles. The intention is to assess how different GIS techniques contribute to road safety analysis and to the development of effective intervention strategies. The review focused particularly on four key GIS-based spatial analysis methods: Kernel Density Estimation (KDE), Network KDE, Moran’s I (Global and Local), and Getis-Ord Gi*. Among these, KDE and Moran’s I were the most frequently adopted techniques, covering about 31.17% and 23.38% of reviewed articles, respectively. These techniques are essential for identifying statistically significant clusters and crash concentration. Despite their promising results, the studies also reveal limitations, including inconsistent data quality, high computational demands, and limited use of variables such as road geometry characteristics. Although GIS is an effective tool for planning and analyzing road safety, these deficiencies might be addressed by future studies that advance the use of real-time spatial analytics and incorporate more diversified information. Overall, the review has reinforced the critical role of GIS in improving traffic safety through real-time data-driven interventions. Full article

The study proposes a methodology that combines digital image correlation (DIC) with cluster analysis (CA) to investigate the damage evolution and localization behavior of basalt fiber foam concrete (BFFC) under tensile loading. This method can simultaneously conduct quantitative analysis of both the process of damage accumulation and the process of damage localization. Quasi-static tensile tests were performed on specimens with different matrix densities and basalt fiber content. The full-field and full-process deformation images of the specimens were recorded by a high-resolution CCD. Cluster analysis was performed on the precise deformation data obtained from the DIC method, and damage extent factors and damage localization coefficients were defined. Statistical analysis indicates that the incorporation of basalt fibers not only effectively delays the progression of damage in foam concrete materials but also significantly enhances their initial damage threshold load and inhibits the phenomenon of damage localization in foam concrete. Compared to specimens without basalt fibers, those incorporating basalt fibers exhibited increases in the damage localization coefficients at tensile failure of 0.4, 0.33 and 0.18, respectively, under three different matrix density conditions. Therefore, the proposed DIC-CA method, in conjunction with the defined damage extent factor and damage localization coefficient, can effectively and quantitatively capture the two key dimensions of damage (accumulation extent and spatial distribution characteristics) in fiber-reinforced foam concrete under tensile loading. This provides an efficient, intuitive, quantitative analysis method for characterizing the initiation, development and localization processes of damage in similar materials. Full article

Aluminum alloys require improved surface performance to satisfy the demands of today’s aerospace, automotive, marine, and structural applications. This paper compares three key surface hardening methods: diffusion-assisted microalloying, thermomechanical deformation-based treatments, and composite/hybrid reinforcing procedures. Diffusion-assisted Zn/Mg enrichment allows for localized precipitation hardening but is limited by the native Al₂O₃ barrier, slow solute mobility, alloy-dependent solubility, and shallow hardened depths. In contrast, thermomechanical techniques such as shot peening, surface mechanical attrition treatment (SMAT), and laser shock peening produce ultrafine/nanocrystalline layers, high dislocation densities, and deep compressive residual stresses, allowing for predictable increases in hardness, fatigue resistance, and corrosion performance. Composite and hybrid reinforcement systems, such as SiC, B₄C, graphene, and graphite-based aluminum matrix composites (AMCs), use load transfer, Orowan looping, interfacial strengthening, and solid lubrication effects to enhance wear resistance and through-thickness strengthening. Comparative evaluations show that, while diffusion-assisted procedures are still labor-intensive and solute-sensitive, thermomechanical treatments are more industrially established and scalable. Composite and hybrid systems provide the best tribological and load-bearing performance but necessitate more sophisticated processing approaches. Recent corrosion studies show that interfacial chemistry, precipitate distribution, and galvanic coupling all have a significant impact on pitting and stress corrosion cracking (SCC). These findings highlight the importance of treating corrosion as a fundamental design variable in all surface hardening techniques. This work uses unified tables and drawings to provide a thorough examination of strengthening mechanisms, corrosion and fatigue behavior, hardening depth, alloy suitability, and industrial feasibility. Future research focuses on overcoming diffusion barriers, establishing next-generation gradient topologies and hybrid processing approaches, improving strength ductility corrosion trade-offs, and utilizing machine-learning-guided alloy design. This research presents the first comprehensive framework for selecting multifunctional aluminum surfaces in demanding aerospace, automotive, and marine applications by seeing composite reinforcements as supplements rather than strict alternatives to diffusion-assisted and thermomechanical approaches. Full article

This study investigates the microstructural evolution, porosity characteristics, and mechanical behavior of LPBF-manufactured Scalmalloy^®, which were investigated in the as-built conditions and after long-term exposure to direct aging of 275, 325, and 400 °C. Optical microscopy, and electron backscatter diffraction (EBSD) analyses were employed to examine the grain morphology, pore distribution, and defect characteristics. In the as-built state, the microstructure displayed the typical fish-scale melt pool morphology with columnar grains in the melt pool centers and fine equiaxed grains along their boundaries, combined with a small number of gas pores and lack-of-fusion defects. After direct aging, coarsening of grains was revealed, accompanied by partial spheroidization of pores, though the global density remained above 99.7%, ensuring structural integrity. Grain orientation analyses revealed a reduction in crystallographic texture and local misorientation after direct aging, suggesting stress relaxation and a more homogeneous microstructure. The hardness distribution reflected this transition: in the as-built state, higher hardness values were found at melt pool edges, while coarser central grains exhibited lower hardness. After direct aging, the hardness differences between these regions decreased, and the average hardness increased from (104 ± 7) HV0.025 to (170 ± 10) HV0.025 due to precipitation of Al₃(Sc,Zr) phases. Long-term aging studies confirmed the stability of mechanical performance at 325 °C, whereas aging at 400 °C induced overaging and hardness loss due to precipitate coarsening. Electrical conductivities increased monotonically at all tested temperatures from ~11.7 MS/m, highlighting the interplay between solute depletion and precipitate evolution. Full article

This study investigates the morphometric and anthropogenic controls governing the occurrence and spatial distribution of tributary–junction fans (TJFs) along the Chaudière River, Québec, Canada. Using GIS-based morphometric analysis, field validation, and multivariate statistics (PCA, CART, LDA), 142 tributary watersheds were analyzed, of which 41 display fan-shaped depositional features. Basin relief, drainage density, contributing area, and slope–area coupling emerge as the dominant predictors of TJF development, delineating an intermediate energy domain where sediment supply and transport capacity become balanced enough to allow partial geomorphic coupling at confluence nodes. CART analysis identified approximate slope and area thresholds (slope < 9°, area > 20 km²; 66% accuracy), while LDA achieved 76%, indicating that morphometry provides useful but incomplete predictive power. These moderate performances reflect the additional influence of event-scale hydrological forcing and unquantified Quaternary substrate heterogeneity typical of postglacial terrain. Beyond morphometry, anthropogenic disturbance exerts a secondary but context-dependent influence, with moderately disturbed watersheds (10–50% altered) showing higher frequencies of fans than both highly engineered (>50%) and minimally disturbed (<10%). This pattern suggests that land-use modification can locally reinforce or offset morphometric predisposition by altering sediment-routing pathways. Overall, TJFs function as localized sediment-storage buffers that may be periodically reactivated during high-magnitude floods. The combined effects of basin geometry, land-use pressures, and hydroclimatic variability explain their spatial distribution. The study provides an indicative, process-informed framework for evaluating sediment connectivity and depositional thresholds in cold-region fluvial systems, with implications for geomorphic interpretation and hazard management. Full article

Textile materials are widely used in diverse applications, yet their anisotropic structure and large deformations present major challenges in mechanical characterization. Conventional uniaxial tensile tests can quantify bulk properties but offer limited insight into local strain distributions. In this work, it was shown that a 2D Digital Image Correlation (DIC) technique captures full-field strain data in three types of woven cotton fabrics with distinct weave patterns and densities, each tested in warp and weft orientations. In controlled tensile experiments conducted per EN ISO 13934-1, DIC revealed that strain in the loading direction (EpsY) was highly orientation-dependent (p < 0.001), whereas strain perpendicular to loading (EpsX) was unaffected by orientation (p = 0.193). These findings contrast with traditional tensile data, which indicate significant orientation effects on maximum force and elongation (both p < 0.001). Compared to point-based techniques, 2D DIC provided richer information on anisotropic deformation, including the ability to detect local strain concentrations before failure. The strong interaction between fabric type and orientation indicates that each fabric exhibits distinct strain response when loaded along warp and weft directions, underscoring the importance of evaluating both orientations when designing or selecting textiles for multidirectional loading. By combining standard tensile testing with full-field optical strain measurements, a more comprehensive understanding of textile behavior emerges, enabling improved material selection, enhanced product performance, and broader applications in engineering and textile fields. Full article

This article investigates the problem of univariate and bivariate density estimation using wavelet decomposition techniques. Special attention is given to the estimation of copula functions, which capture the dependence structure between random variables independent of their marginals. We consider two distinct frameworks: the case of independent and identically distributed (i.i.d.) variables and the case where variables are dependent, allowing us to highlight the impact of the dependence structure on the performance of wavelet-based estimators. Building on this framework, we propose a novel iterative thresholding method applied to the detail coefficients of the wavelet transform. This iterative scheme aims to enhance noise reduction while preserving significant structural features of the underlying density or copula function. Numerical experiments illustrate the effectiveness of the proposed method in both univariate and bivariate settings, particularly in capturing localized features and discontinuities in the presence of varying dependence patterns. Full article

In order to characterize topological insulators it is customary to use representations of the electronic structure, such as the band structure, where the energy of electrons is represented as a function of their momenta. Topological insulators are then represented as those systems whose surface states have an odd number of crossings at the Fermi level, or, equivalently, as those systems where the spin and momentum is locked at the surface. The density of states, however, cannot in principle be used to distinguish if a material is a topological insulator because it integrates the momentum information for a given energy. In this article, we show that, despite that fact, the density of states of topological insulators show some distinctive characteristics that may even be used to predict if a certain material is of that type or not by using such quantity. We use a series of machine learning algorithms to classify first the density of states and predict then systems with similar densities of states that can lead to new topological materials. We find that, contrary to what would be expected, the densities of states of topological insulators have distinct features that allow to classify and identify these materials according to them. In particular, the DOS of topological insulators tends to exhibit sharper and more concentrated spectral features near the band edges, indicating a narrower distribution of bulk electronic states (spectral localization) rather than spatial localization of surface modes. Full article

In this study, first-principles calculations were employed to analyze the effect of Si and O doping on the electronic structure of the Fe/Zn interface, aiming to reveal the mechanism underlying the degradation of its interfacial stability. Through detailed analysis of bond population, charge density, differential charge density, as well as total density of states (TDOS) and partial density of states (PDOS), the following findings were obtained: After Si and O doping, the charge distribution at the Fe/Zn interface exhibits local aggregation or sparsity. The differential charge density shows a redistribution of charges, and the charge density in the Fe-Zn bonding region changes. In terms of density of states, the contribution of orbitals related to Fe and Zn atoms to the density of states near the Fermi level is altered. The hybridization between the orbitals of Si/O atoms and those of Fe/Zn atoms affects the electronic interaction. Comprehensive analysis indicates that the degradation of Fe/Zn interfacial stability caused by Si and O doping is mainly attributed to the following factors: it modifies the chemical bonding, induces lattice distortion which generates internal stress, enhances the inhomogeneity of charge distribution, and weakens the bonding force between Fe and Zn atoms. This research provides a theoretical basis for the performance regulation of related material systems. Full article