Search Results (4,214)

Polycrystalline copper optics are widely utilized in infrared systems due to their exceptional electrical and thermal conductivity combined with favorable machining characteristics. The grain size profoundly influences both surface quality consistency and fundamental material removal behavior during processing. This investigation employs multiscale numerical modeling to simulate nanoscale cutting processes in polycrystalline copper with controlled grain structures, coupled with experimental ultra-precision machining validation. Comprehensive analysis of stress distribution, subsurface damage formation, and cutting force evolution reveals that refined grain structures promote more homogeneous plastic deformation, resulting in superior surface finish with reduced roughness and diminished grain boundary step formation. However, the enhanced grain boundary density in fine-grained specimens necessitates increased cutting energy input. These findings establish critical process–structure–property relationships essential for advancing precision manufacturing of copper-based optical systems. Full article

Fine-grained sedimentary rocks exhibit significant textural heterogeneity, often obscured by conventional grain size analysis techniques that require sample disaggregation. We propose a non-destructive, image-based grain size characterization workflow, utilizing stitched polarized thin-section photomicrographs, k-means clustering, and watershed segmentation algorithms. Validation against laser granulometry data indicates strong methodological reliability (absolute errors ranging from −5% to 3%), especially for particle sizes greater than 0.039 mm. The methodology reveals substantial internal heterogeneity within Es3 laminated shale samples from the Shahejie Formation (Bohai Bay Basin), distinctly identifying coarser siliceous laminae (grain size >0.039 mm, Φ < 8 based on Udden-Wentworth classification) indicative of high-energy depositional environments, and finer-grained clay-rich laminae (grain size <0.039 mm, Φ > 8) representing low-energy conditions. Conversely, massive mudstones exhibit comparatively homogeneous grain size distributions. Additionally, a multifractal analysis (Multifractal method) based on the S_50bi/S_50si ratio further quantifies spatial heterogeneity and pore-structure complexity, significantly enhancing facies differentiation and reservoir characterization capabilities. This method significantly improves facies differentiation ability, provides reliable constraints for shale oil reservoir characterization, and has important reference value for the exploration and development of the Bohai Bay Basin and similar petroliferous basins. Full article

To explore the influence of double-electrode regulation technology on the solidification microstructure and properties of GCr15 bearing steel, the double-electrode insertion process was employed in this study, combined with metallographic analysis, mechanical property testing, and electron probe composition characterization. We analyzed the mechanisms of solidification microstructure evolution and mechanical property improvement, as well as the composition segregation control effect, of GCr15 steel under double-electrode regulation. The results show that the double-electrode technology significantly refines the microstructure and improves the internal quality of the ingot by optimizing the temperature field and electromagnetic field distribution in the molten pool and enhancing the internal flow of the melt. The tensile strengths in the upper and middle parts were increased by 84.6% and 29.6%, respectively, which can be attributed to the uniform distribution of carbides at the grain boundaries and the reduction of segregation. Composition analysis indicates that the macroscopic segregation index of C element was decreased under the dual-electrode process. This research provides a theoretical basis and process optimization direction for the high-quality preparation of high-carbon chromium bearing steel. Full article

This study aims to investigate the syntactic complexity in individuals with Alzheimer’s disease (AD) by conducting a comprehensive analysis that incorporates mean dependency distance (MDD), fine-grained grammatical metrics, and dependency network structures. A total of 150 adults with AD and 150 healthy controls (HC) responded in English to interview prompts based on the Cookie Theft picture description task, and the results were compared. The key findings are as follows: (1) The primary syntactic change is a strategic shift from hierarchical, clause-based constructions to linear, phrase-based ones, a direct consequence of working memory deficits designed to minimize cognitive load. (2) This shift is executed via a resource reallocation, where costly, long-distance clausal dependencies are systematically avoided in favor of a compensatory reliance on local dependencies, such as intra-phrasal modification and simple predicate structures. (3) This strategic reallocation leads to a systemic reorganization of the syntactic network, transforming it from a flexible, distributed system into a rigid, centralized one that becomes critically dependent on the over-leveraged structural role of function words to maintain basic connectivity. (4) The overall syntactic profile is the result of a functional balance governed by the principle of cognitive economy, where expressive richness and grammatical depth are sacrificed to preserve core communicative functions. These findings suggest that the syntactic signature of AD is not a random degradation of linguistic competence but a profound and systematic grammatical adaptation, where the entire linguistic system restructures itself to function under the severe constraints of diminished cognitive resources. Full article

Aluminum metal matrix composites (AMMCs) were obtained using the stir-casting method, adding 0.15, 0.25, and 0.50 in vol.% of ZrO₂. Microstructural observations made using scanning electron microscopy (SEM) indicated that oxide addition modified grain size. X-ray diffraction analyses revealed that mainly ZrAl₃ and Al₂O₃ phases had formed. Hardness evaluation indicated a maximum value of 63 HV for the zirconia-reinforced samples, representing an increase of approximately 70% compared with pure aluminum. This hardness increase was mainly attributed to the zirconia distribution in the aluminum matrix promoting lattice distortion, which promoted the inhibition of dislocation mobility. Wear tests indicated that the samples with 0.50 vol.% of ZrO₂ added presented the lowest wear rate because of the hardness they acquired. The results are discussed considering composite strengthening due to ZrO₂ addition and the thermal treatment applied (cooling rate). Full article

The concept of a genome signature broadly refers to characteristic patterns in DNA sequences that enable the identification and comparison of species or individuals, often without requiring sequence alignment. Such signatures have applications ranging from forensic identification of individuals to cancer genomics. In comparative genomics and evolutionary biology, genome signatures typically rely on statistical properties of DNA that are species-specific and carry phylogenetic information reflecting evolutionary relationships. We propose a novel genome signature based on the compositional structure of DNA, defined by the distributions of strong/weak, purine/pyrimidine, and keto/amino ratios across DNA segments identified through entropic segmentation. We observe that these ratio distributions are similar among closely related species but differ markedly between distant ones. To quantify these differences, we employ the Jensen–Shannon distance—a symmetric and robust measure of distributional dissimilarity—to define a genome-to-genome distance metric, termed Segment Compositional Distance ($\mathcal{D}$). Our results demonstrate a clear correlation between $\mathcal{D}$ and species divergence times, and also that this metric captures a strong phylogenetic signal. Our method employs a genome-wide approach rather than tracking specific mutations; thus, $\mathcal{D}$ offers a coarse-grained perspective on genome compositional evolution, contributing to the ongoing discussion surrounding the molecular clock hypothesis. Full article

Myanmar blue jadeite jade is a rare and highly prized gemstone, yet its coloration and formative mechanisms remain poorly understood. In this study, petrographic analysis, ultraviolet–visible (UV–Vis) spectroscopy, electron probe microanalysis (EPMA), and laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) were performed on a sample of Myanmar blue jadeite with small white blocks to investigate its mineral composition, trace element distribution, and coloration mechanisms. Most of the sample was found to be blue, with surrounding white areas occurring in small ball-shaped blocks. The main mineral component in both the blue and white domains was jadeite. Although both areas underwent recrystallization, their textures differed significantly. The blue areas retained primary structural features within a medium- to fine-grained texture, reflecting relatively weaker recrystallization. The white areas, however, were recrystallized into a micro-grained texture, reflecting relatively stronger recrystallization, with the superimposed effects of external stress producing a fragmented appearance. The blue jadeite had relatively higher contents of Ti, Fe, Ca, and Mg, while the white jadeite contained compositions close to those of near-end-member jadeite. It was noted that, while white jadeite may have a high Ti content, its Fe content is low. UV–Vis spectra showed a broad absorption band at 610 nm associated with Fe²⁺-Ti⁴⁺ charge transfer and a gradually increasing absorption band starting at 480 nm related to V⁴⁺. Combining the chemical composition and the characteristics of the UV–Vis spectra, we infer that the blue coloration of jadeite is attributed to Fe²⁺-Ti⁴⁺ charge transfer; i.e., the presence of both Ti and Fe in blue jadeite plays a key role in its color formation. V⁴⁺ exhibited no significant linear correlation with the development of blue coloration. Prominent oscillatory zoning was observed in the jadeite, transitioning from NaAlSi₂O₆-dominant cores to Ca-Mg-Fe-Ti-enriched rims, reflecting the trend of fluid evolution during blue jadeite crystallization. Petrographic analysis indicated that the formation of the Myanmar blue jadeite occurred in two or three stages, with the blue regions forming earlier than the white regions. The blue jadeite also underwent significant recrystallization. Our findings contribute to the understanding of the formation of blue jadeite and the diversity of colors in jadeite jade. Full article

Improving the isotropic magnetic properties of FeSi electrical steels has traditionally focused on enhancing their crystallographic texture and microstructural morphology. Strengthening the cube texture within a ferritic matrix of optimal grain size is known to reduce core losses and increase magnetic induction. However, conventional cold rolling followed by annealing remains insufficient to optimise the magnetic performance of thin FeSi strips fully. This study explores an alternative approach based on grain boundary migration driven by temperature gradients combined with deformation gradients, either across the sheet thickness or between neighbouring grains, in thin, weakly deformed non-oriented (NO) electrical steel sheets. The concept relies on deformation-induced grain growth supported by rapid heat transport to promote the preferential formation of coarse grains with favourable orientations. Experimental material consisted of vacuum-degassed FeSi steel with low silicon content. Controlled deformation was introduced by temper rolling at room temperature with 2–40% thickness reductions, followed by rapid recrystallisation annealing at 950 °C. Microstructure, texture, and residual strain distributions were analysed using inverse pole figure (IPF) maps, kernel average misorientation (KAM) maps, and orientation distribution function (ODF) sections derived from electron backscattered diffraction (EBSD) data. This combined thermomechanical treatment produced coarse-grained microstructures with an enhanced cube texture component, reducing coercivity from 162 A/m to 65 A/m. These results demonstrate that temper rolling combined with dynamic annealing can surpass the limitations of conventional processing routes for NO FeSi steels. Full article

This work presents a comprehensive study on the fabrication, microstructural evolution, and mechanical performance of hybrid aluminum matrix composites based on Al6060 alloy reinforced with ~2 wt.% TiB₂ and ~1 wt.% multi-walled carbon nanotubes (MWCNTs). The composites were produced via ultrasonically assisted stir casting followed by radial-shear rolling (RSR). The combined processing route enabled a uniform distribution of reinforcing phases and significant grain refinement in the aluminum matrix. SEM, EDS, XRD, and EBSD analyses revealed that TiB₂ particles acted as nucleation centers and grain boundary pinning agents, while MWCNTs provided a network structure that suppressed agglomeration of ceramic particles and enhanced interfacial load transfer. As a result, hybrid composites demonstrated a submicron-grained structure with reduced anisotropy. Mechanical testing confirmed that yield strength (YS) and ultimate tensile strength (UTS) increased by 67% and 38%, respectively, in the cast state compared to unreinforced Al6060, while after RSR processing, YS exceeded 115 MPa and UTS reached 164 MPa, with elongation preserved at 14%. Microhardness increased from 50.2 HV0.2 (base alloy) to 82.2 HV0.2 (hybrid composite after RSR). The combination of ultrasonic melt treatment and RSR thus provided a synergistic effect, enabling simultaneous strengthening and ductility retention. These findings highlight the potential of hybrid Al6060/TiB₂–MWCNT composites for structural applications requiring a balance of strength, ductility, and wear resistance. Full article

Non-grain conversion threatens food security in karst mountainous regions where fragmented terrain and shallow soils create unique agricultural challenges. This study examines Yunnan Province (28% karst coverage) in the Yunnan-Guizhou Plateau, where cultivated land faces distinct pressures from limited soil depth (average < 30 cm in karst areas) and poor water retention capacity. Using multi-source data (2001–2021) and an integrated Dynamic Spatial-Temporal Clustering Model (DSTCM), we quantify non-grain conversion through a clearly defined Non-Grain Conversion Index (NGCI = 0.35 × CPI + 0.25 × LUI + 0.20 × RSI + 0.20 × PSI). Results reveal the NGCI declined from 45.91 to 21.05, indicating a 54% intensification in conversion (lower values = higher conversion intensity). Spatial analysis shows significant clustering (Moran’s I = 0.57, p < 0.001), with karst areas experiencing 23% higher conversion rates than non-karst regions. Key drivers include soil fertility limitations (t = 2.35, p = 0.027), crop type transitions (t = 3.12, p = 0.047), and economic pressures (t = 2.88, p = 0.012). Model predictions (accuracy: 92.51% ± 2.3%) forecast continued intensification with NGCI reaching 9.31 by 2035 under current policies. Spatial distribution mapping reveals concentrated conversion hotspots in southeastern karst regions, with 73% of high-intensity conversion occurring in areas with >30% karst coverage. This research provides critical insights for managing cultivated land in karst landscapes facing unique geological constraints. Full article

Real-time distribution system state estimation is hampered by limited observability, frequent topology changes, and measurement errors. Neural networks can capture the nonlinear characteristics of power-grid operation through a data-driven approach that possesses important theoretical value and is promising for engineering applications. In that context, we develop a deep learning framework that leverages General Attributed Multiplex Heterogeneous Network Embedding to explicitly encode the multiplex, heterogeneous structure of distribution networks and to support inductive learning that adapts to dynamic topology. A cross-modal attention mechanism further models fine-grained interactions between input measurements and node/edge attributes, enabling the capture of nonlinear correlations essential for accurate state estimation. To ensure physical feasibility, soft power-flow residuals are incorporated into training as a physics-constrained regularization, guiding predictions toward consistency with grid operation. Extensive studies on IEEE/CIGRE 14-, 70-, and 179-bus systems show that the proposed method surpasses conventional weighted least squares and representative neural baselines in accuracy, convergence speed, and computational efficiency while exhibiting strong robustness to measurement noise and topological uncertainty. Full article

The effects of (Zn + Mg) total content (9.6–11.7 wt.%) combined with multi-directional forging (MDF) on the microstructure and properties of high-strength Al–Zn–Mg–Cu alloys were systematically investigated. Our results demonstrate that the alloy obtains significant grain refinement, which is attributed to the dynamic recrystallization in the MDF process. Specifically, Al-8.6Zn-1.55Mg-1.9Cu-0.11Zr (Zn + Mg = 10.15 wt.%) obtains the maximum recrystallization ratio (51.8%) and the weakest texture strength, and also forms the mortise and tenon nested grain structure. Increasing the total (Zn + Mg) content can achieve significant performance enhancement, which is attributed to the refinement of the η′ phase; however, a higher total (Zn + Mg) content will lead to the continuous distribution of coarse η-MgZn₂ phases formed along the grain boundary, accompanied by the broadening of precipitate-free precipitation zones (PFZs). Compared with other alloys, Al-8.6Zn-1.55Mg-1.9Cu-0.11Zr (Zn + Mg = 10.15 wt.%) maintains high strength while ensuring desirable plasticity due to its mortise and tenon nested grain structure. In addition, its desirable grain boundary precipitation behavior makes it exhibit the best corrosion resistance. These findings indicate that maintaining the total (Zn + Mg) content around 10 wt.% achieves a balance between strength and corrosion resistance, offering a theoretical foundation for the design of high-strength and corrosion-resistant Al–Zn–Mg–Cu alloys. Full article

Heavy minerals, such as Rutile, Ilmenite and Zircon, and other essential trace elements are important in modern technology development. The integration of hyperspectral imaging and artificial intelligence presents a promising approach for the accurate identification of heavy minerals, especially Rare Earth Element (REE)–bearing phases such as Monazite. This study evaluates three AI classifiers, Support Vector Machine (SVM), Neural Networks (NNs) and Convolutional Neural Networks (CNNs), for their performance in classifying ten different minerals distributed across six grain size groups ranging from 125 μm to over 300 μm. The analysis focuses on how grain size affects spectral reflectance and classification accuracy. Among the tested models, SVM consistently outperformed NN and CNN, achieving the highest precision, recall and spectral similarity, particularly within the 150–300 μm grain size range. CNN showed the lowest performance and frequently misclassified spectrally similar minerals, such as Zircon and Rutile, likely due to its 1D architecture and limited spatial representation. Monazite, notable for its strong Nd³⁺ absorption features, was accurately identified across applicable grain sizes, highlighting its reliability for REE detection. Spectral Angle Mapper (SAM) analysis confirmed that SVM and NN maintained better spectral similarity than CNN. In general, the results highlight the significant influence of grain size, spectral similarity and dataset size on classification accuracy and the overall effectiveness of AI models in hyperspectral mineral analysis. Full article

Employing commercial off-the-shelf coherent optical transmission components and methods to design a continuous variable quantum key distribution (CVQKD) system is a promising trend of achieving QKD with high security key rate (SKR) and cost-effectiveness. In this paper, we explore a CVQKD system based on the widely used polarization division multiplexed (PDM) coherent optical transmission structure and pilot-aided digital signal processing methods. A simplified pilot-aided phase noise compensation scheme based on frequency division multiplexing (FDM) is proposed, which introduces less total excess noise than classical pilot-aided schemes based on time division multiplexing (TDM). In addition, the two schemes of training symbol (TS)-aided equalization are compared to find the optimal strategy for TS insertion, where the scheme based on block insertion strategy can provide the SKR gain of around 29%, 22%, and 15% compared with the scheme based on fine-grained insertion strategy at the transmission distance of 5 km, 25 km, and 50 km, respectively. The joint optimization of pilot-aided and TS-aided methods in this work can provide a reference for achieving a CVQKD system with a high SKR and low complexity in metropolitan-scale applications. Full article

The irregular and highly non-uniform spatial distribution inherent to dynamic three-dimensional (3D) point clouds (DPCs) severely hampers the extraction of reliable temporal context, rendering inter-frame compression a formidable challenge. Inspired by two-dimensional (2D) image and video compression methods, existing approaches attempt to model the temporal dependence of DPCs through a motion estimation/motion compensation (ME/MC) framework. However, these approaches represent only preliminary applications of this framework; point consistency between adjacent frames is insufficiently explored, and temporal correlation requires further investigation. To address this limitation, we propose a hierarchical ME/MC framework that adaptively selects the granularity of the estimated motion field, thereby ensuring a fine-grained inter-frame prediction process. To further enhance motion estimation accuracy, we introduce a dual-attention-based KNN block-matching (DA-KBM) network. This network employs a bidirectional attention mechanism to more precisely measure the correlation between points, using closely correlated points to predict inter-frame motion vectors and thereby improve inter-frame prediction accuracy. Experimental results show that the proposed DPC compression method achieves a significant improvement (gain of 70%) in the BD-Rate metric on the 8iFVBv2 dataset. compared with the standardized Video-based Point Cloud Compression (V-PCC) v13 method, and a 16% gain over the state-of-the-art deep learning-based inter-mode method. Full article