Search Results (2,223)

Abrasive flow machining (AFM) is an effective finishing process for complex internal surfaces, particularly cavities, intersecting holes, and micro-channels that are difficult to access using conventional tools. However, when low-viscosity abrasive media is used (here defined, relative to conventional putty-like viscoelastic AFM carriers (with apparent viscosities of 10³–10⁵ mPa·s), as a water-based slurry with an apparent viscosity below 300 mPa·s over the operating shear-rate range), unfavorable flow conditions during the initial polishing stage can induce local over-polishing, erosion depressions, stepped patterns, and cavitation pits, resulting in non-uniform surface quality. The relationship between these flow behaviors and polishing defects remains insufficiently understood. To address this issue, this study investigates the AFM process applied to turbine blade film cooling holes through combined experimental and numerical approaches. The observed defects include erosion depressions, stepped surface patterns, and cavitation pits. The effects of abrasive injection pressure, flow velocity, hole geometry, abrasive viscosity, and particle size on defect formation are systematically examined. The results show that the initial abrasive filling level strongly affects defect distribution by altering the evolution of shear fields and void regions within the hole. Experimentally, at high Reynolds numbers (Re > 2 × 10⁴), intensified local shear and cavitation promote defect formation, while a moderate inclination angle (45–60°) and a higher aspect ratio (>8) are favorable for polishing uniformity. Complementary numerical simulations further indicate that smaller abrasive particles (<5 μm) and a moderate abrasive viscosity (~60 mPa·s) are predicted to improve polishing uniformity. This study clarifies the fluid-dynamic origin of polishing defects in film cooling holes and provides process guidance for suppressing local over-polishing, cavitation, and uneven material removal. Full article

Aiming at the problems of low permeability, strong heterogeneity, high salinity, poor injectivity of the target reservoir, and the inability of traditional water flooding to effectively improve the oil recovery rate, a low interfacial tension and salt-resistant nano-scale particle-stabilized foam flooding system (LITF-NSF) was developed to improve the recovery rate of low-permeability oil reservoirs. Through core dynamic plugging and injectivity experiments, single/dual-tube core displacement experiments, and static imbibition oil displacement experiments, the injectivity, plugging effect, imbibition effect, oil displacement mechanism, and optimal injection parameters of the LITF-NSF foam flooding system were studied from three aspects: injection pressure, slug volume, and gas–liquid ratio. The LITF-NSF foam flooding system has good injectivity in the target reservoir, with a resistance coefficient ranging from 1.086 to 15.468; the optimal injection parameters are a constant pressure difference of 5 MPa, a gas–liquid slug volume of 0.3 PV, and a gas–liquid ratio of 2:1. Under these optimal injection parameters, the subsequent water flooding recovery rate can be increased by 29.77%; it can produce a good plugging effect on the high-permeability reservoirs of the target reservoir with a permeability difference of less than 50, and can increase the comprehensive recovery rate of heterogeneous oil reservoirs by more than 10%; the static imbibition recovery rate is 4.38% higher than that of pure water without surfactant, and has a certain permeability-enhancing oil displacement effect. The LITF-NSF foam flooding system has good adaptability to the target reservoir environment and stable foam performance, which can effectively improve the subsequent water flooding recovery rate and has a good application prospect in chemical flooding for improving the recovery rate of low-permeability oil reservoirs. Full article

Intricate natural wood textures and diversified defect morphologies hinder high-precision recognition of visible surface defects on sawn lumber. Six common types of surface defects exist on sawn lumber, including dry knots, edge knots, small knots, sound knots, wavy defects, and splits. Among these defect types, edge knots, small knots, wavy defects, and splits bring great difficulties to detection due to their tiny areas, slender geometric outlines and indistinct boundaries. To accurately identify the above defects, a customized You Only Look Once version 8 medium (YOLOv8m)-based framework was developed for lumber surface inspection. First, the Cross-Stage Partial Bottleneck with Two Convolutions embedded with Efficient Channel Attention (C2f-ECA) and Space-to-Depth Convolution (SPD-Conv) are introduced into the backbone to enhance channel-wise feature representation and preserve fine spatial details during downsampling, while C2f with Deformable Convolution (C2f-DCN) is embedded in the deep feature extraction branch to improve the geometric modeling of irregular defects. Second, a C2f-DCN with Exponential Moving Average Attention (C2f-DCN-EMA) module and dynamic upsampling (DySample) are integrated in the feature-fusion stage to refine multi-scale features and reconstruct local edges. Third, Scaled Intersection over Union (SIoU) loss is used to improve bounding-box regression for defects with extreme aspect ratios. Experiments show that the proposed model achieves 91.8% mean Average Precision at IoU 0.5 (mAP@50) and 69.3% mean Average Precision across IoU thresholds of 0.5–0.95 (mAP@50-95), exceeding the YOLOv8m baseline by 1.0 and 1.5 percentage points, respectively. Full article

This study presents new morphological, mineralogical, and chemical data on fibrous erionite, a carcinogenic zeolite, discovered for the first time in the volcanic rocks of the Vulsini Volcanic District, Italy. The erionite fibers were investigated using OM, TGA, SEM-EDS, XRPD, and EDXRF techniques. They occur as inhalable aggregates composed of extremely thin, tangled crystals with very high aspect ratios, features that may enhance both inhalability and toxicological significance. Mineralogical associations with saponite indicate formation under low-temperature hydrothermal conditions (<150 °C), likely related to late- to post-magmatic fluid circulation through cavities and vesicles in the host volcanic rocks. Chemical analyses identify the zeolite as erionite-K, reflecting the potassic character of associated volcanic lithologies and highlighting the influence of host-rock and fluid chemistry on zeolite formation. Trace-element contents, particularly in As, V, Rb, and Pb, may represent an additional factor influencing fiber toxicity through synergistic effects. The occurrence of carcinogenic fibrous erionite in volcanic lithologies widely exploited for quarrying and industrial applications raises significant environmental and occupational health concerns for zeolitized volcanic terrains in central Italy. These findings highlight the need for detailed fiber characterization and mineralogical investigations to support exposure prevention and environmental risk assessment in future excavation and quarrying activities. Full article

Synchronous Reluctance Machines (SynRMs) have emerged as a strong candidate for electric vehicle (EV) traction owing to their rare-earth-free construction, robust rotor structure, and competitive efficiency relative to permanent magnet (PM) and induction machines (IMs). Their performance, however, is governed by complex electromagnetic and thermal phenomena—saliency, magnetic saturation, flux-barrier geometry, and temperature-dependent losses—that demand accurate yet computationally tractable modelling. This paper reviews the modelling and design landscape for SynRMs in EV traction, covering analytical approaches (d–q models, magnetic equivalent circuits), numerical methods (finite element analysis), and recent hybrid techniques such as the Enhanced Hybrid Subdomain Method (EHSDM). Rotor geometry optimization, including flux-barrier shaping and saliency-ratio enhancement, is examined alongside coupled magnetic–thermal analysis, an aspect typically treated in isolation in earlier surveys. The review compares the trade-offs of competing techniques across the design workflow—from initial sizing to final verification—and identifies open challenges in reducing computational cost while preserving accuracy. The synthesis is intended to guide motor designers toward modelling choices appropriate to each design stage and to highlight directions for future research in high-performance, rare-earth-free traction motors. Full article

Ni–Co γ/γ′-strengthened superalloys are key structural materials for modern energy and flow turbomachinery systems due to their exceptional high-temperature strength, creep resistance, as well as hydrogen and corrosion stability. However, operation in gaseous hydrogen environments typical of hydrogen-cooled generators, cooled gas-turbine blades, and emerging hydrogen-energy technologies can significantly affect their microstructural stability and fracture behavior. This study presents a comprehensive multiscale review of hydrogen-induced nanoscale degradation and crack initiation mechanisms in Ni–Co superalloys produced by wrought, powder metallurgy, and additive manufacturing routes. Transmission electron microscopy combined with quantitative morphometric analysis was employed to characterize the size, morphology, and spatial distribution of γ′ precipitates, revealing a dense population of coherent particles predominantly in the 40–120 nm range, governed by a log-normal distribution. Correlations between precipitate size, aspect ratio, and circularity indicate the onset of partial loss of coherency and coarsening for particles exceeding ~80 nm, creating favorable sites for hydrogen localization. The presence of TCP phases (η, σ, μ, Laves) and carbides at grain boundaries and within grains was shown to enhance microstructural heterogeneity and act as effective hydrogen traps, promoting interfacial decohesion and microcrack initiation. To support microstructural interpretation, convolutional neural network analysis with Grad-CAM visualization was applied to SEM images, enabling the identification of the structural regions most sensitive to hydrogen-assisted damage, particularly γ/γ′ interfaces and defect clusters. The results demonstrate that hydrogen-induced degradation in Ni–Co superalloys is governed by the coupled interactions among microstructure, hydrogen distribution, and local stress state. The findings provide a physically grounded basis for optimizing alloy chemistry, heat treatment, and additive manufacturing parameters, as well as for developing AI-assisted predictive models for the durability of critical components in hydrogen-energy and high-temperature power-generation systems to increase hydrogen safety. Full article

The Yanhe River Basin on the Loess Plateau suffers frequent landslides, threatening sustainable development and ecological security. This study employs correlation and multicollinearity analyses to select evaluation factors and construct an index system. The information value (IV) model determines indicator values, while the Analytic Hierarchy Process (AHP), frequency ratio (FR), and their combination, FR-AHP, establish relative weights of influencing factors, thereby constructing weighted IV models validated by Receiver Operating Characteristic (ROC) curves. Results demonstrate that: (1) slope, aspect, curvature, lithology, average annual precipitation, NDVI, distance from rivers, distance from roads, and land use type are key influencing factors; (2) compared with IV, AHP-IV, and FR-IV models, the FR-AHP-IV model achieves the highest prediction accuracy with an AUC = 0.818; and (3) areas of low, moderate, high, and very high susceptibility account for 32.01%, 40.69%, 18.49%, and 8.80%, respectively, with higher susceptibility along both banks of the middle and lower reaches. The landslide-prone hotspot in the southern Baota District aligns with actual survey conditions. The methodological framework proposed in this study can serve as a reference for basins with similar geographic settings and hazard backgrounds, yet the specific zoning results should be adapted and validated in accordance with the actual conditions of individual basins. Full article

Electromagnetic interference (EMI) shielding materials are critical for reducing EMI pollution and enhancing information security. This study presents a systematic comparison of silver-plated copper (Cu@Ag; flake-like morphology; the average particle size D50 = 20.1 μm) and silver-plated aluminium (Al@Ag; spherical morphology; D50 = 47.5 μm) fillers with distinct morphologies incorporated into silicone rubber matrices via Rheomixer blending, open-mill compounding, and peroxide vulcanisation. This work aims to elucidate how filler morphology and multilayer sandwich architecture govern shielding efficiency and related material properties. The flake-like Cu@Ag fillers demonstrated superior low-loading performance. Due to their high aspect ratio and enhanced interfacial contact, Cu@Ag composites reached a critical loading for practical EMI shielding performance at 150 phr. In contrast, spherical Al@Ag fillers required a higher loading of 200 phr to achieve the same effect. Both composites achieved EMI shielding effectiveness exceeding 90 dB at 250 phr filler loading across the X-band frequency range (8.2–12.4 GHz). Innovatively, sandwich-structured composites were fabricated by combining Cu@Ag and Al@Ag layers through co-vulcanization, achieving approximately 110 dB shielding effectiveness, which is a ~33% improvement over single-layer composites at equivalent filler loading (200 phr). Analysis of the shielding mechanisms reveals that this enhancement results from multiple electromagnetic wave interactions, including increased reflection losses at morphologically distinct layer interfaces and enhanced absorption through conductivity gradients. This work demonstrates that a rational combination of flake-like and spherical fillers with contrasting morphologies and conductivity characteristics in multilayer architectures provides a powerful strategy for developing high-performance flexible EMI shielding materials. Full article

Real-time pupil detection is a critical prerequisite for eye-tracking applications such as vestibular videonystagmography (VNG) and gaze-based interaction: deep learning methods offer high accuracy but incur substantial computational cost, while traditional thresholding methods are fast but lack robustness under complex illumination. This paper proposes the Confidence-Guided Fallback (CGF) framework, which employs four geometric factors—circularity, aspect ratio, area consistency, and convex-hull ratio—to evaluate the reliability of traditional detection results, and it invokes RITNet (a deep segmentation network finetuned on randomly cropped s-OpenEDS data) only when the confidence score falls below a threshold, thereby adaptively balancing accuracy and efficiency on a per-frame basis. RITNet is not trained on LPW; thus, LPW evaluation simultaneously constitutes cross-dataset validation. Hyperparameters (confidence weights and threshold values) are tuned on a validation set (15% of LPW subjects) and final performance is reported on an independent test set (18% of subjects), avoiding data leakage. On the validation set, the combined confidence factors achieve an AUROC of 0.961 for distinguishing high-quality frames, and the confidence distribution exhibits a bimodal structure with a valley near 0.7, providing a data-driven basis for threshold selection. On the independent test set (23,999 frames), CGF ($\tau =0.8$) detects 79.0% of frames; among detected frames, 84.0% achieve sub-5 px accuracy (median error 1.25 px) at 298 FPS on NVIDIA RTX 5090. At $\tau =0.7$, detection rate reaches 89.6% with 72.0% sub-5 px accuracy at 391 FPS. Failure-mode analysis reveals that approximately 64.3% of triggered deep-model frames represent “dilemma frames” where both traditional and deep methods fail, marking the practical ceiling of single-frame visual methods and pointing toward temporal modeling. The framework demonstrates robustness to confidence-weight perturbations (±20% weight changes yield < 1% performance variation) and exhibits strong discriminative power on high-confidence frames (93.4% accuracy within 5 px on the test set). Full article

Polymeric microneedles are introduced as a promising platform for minimally invasive drug delivery and molecular transport control. In the present study, hollow dissolving nanocomposite microneedles based on a mixture of high- and low-molecular-weight hyaluronic acid (HA) in a 40:60 ratio, including zinc oxide nanoparticles (ZnO NPs), have been created and evaluated as hydrated polymer transport matrices. Surface modification of ZnO nanoparticles using citric acid was proposed to improve dispersion by reducing agglomeration of nanoparticles in the polymer matrix. ZnO nanoparticles in concentrations ranging from 1 to 10% (w/w) were used to study the effects of the loading level of nanoparticles on the structure, mechanical response, and controlled diffusion behavior of hydrated polymer matrices. The created nanocomposites exhibited clear hollow structures with tip radius of 18–23 μm, height of 1500 μm, and aspect ratio of 5.7. Nanoscale surface organization and particle dispersion in the polymer matrix were studied by scanning electron microscope (SEM) and atomic force microscope (AFM). Low nanoparticle concentrations were favorable for maintaining high matrix homogeneity, while high concentrations resulted in increased surface roughness and nanoparticle agglomeration. Mechanical compression testing confirmed that hydrated HA/ZnO microneedles were characterized by elastic bending behavior until fracture. Diffusion experiments performed in Franz diffusion cells showed that nanoparticle concentration significantly impacted the cumulative transport and flux of molecules through the hydrated microneedle matrix. Formulations with 5% and 7% ZnO nanoparticles were characterized by a prolonged diffusion behavior attributed to ZnO-induced tortuous transport channels in the polymer matrix. In contrast, formulations with 10% ZnO nanoparticles exhibited accelerated heterogeneous transport due to ZnO-induced changes in structure and morphology. The experimental diffusion data correlated well with the Higuchi kinetic model, and anomalous transport was detected using the Korsmeyer–Peppas model, which indicated a synergistic effect of diffusion and polymer relaxation on molecular transport. As compared to coating and tip-loaded microneedle designs, the obtained HA/ZnO nanocomposite microneedles offered a simple approach for embedding Ciprofloxacin in the hydrated polymer matrix. This was achieved due to the direct creation of microneedles containing dissolved particles. Full article

To tackle the challenges in oriented object detection, such as discontinuous angle regression, low precision in discrete classification, and high complexity in probabilistic modeling, this paper proposes VODet (Vertex Offset-based Detector), a novel oriented object detection method. VODet models the geometric structure of rotated objects through a coupled vertex offset mechanism, inherently avoiding boundary discontinuity. Specifically, it first regresses the horizontal enclosing rectangle of a target from horizontal anchors, then predicts only two offset ratios to reconstruct the four vertices of the rotated bounding box. For near-horizontal critical cases, a lightweight direction classifier adaptively distinguishes horizontal boxes from rotated ones, reducing regression complexity and improving robustness. To further enhance feature representation, we design a hierarchical special attention model (HSAM) based on Swin Transformer blocks, which effectively captures both local details and global contextual dependencies. Extensive experiments on DOTA, HRSC2016, and UCAS-AOD demonstrate that VODet achieves competitive detection accuracy (80.44% mAP on DOTA, 90.42% AP on HRSC2016, and 90.11% mAP on UCAS-AOD), with particular advantages for objects of large aspect ratios and dense arrangements. Moreover, VODet achieves the highest inference speed (26.7 FPS) and the lowest computational complexity (192.3 GFLOPs) among the compared methods, demonstrating a favorable accuracy-efficiency trade-off. These results confirm the effectiveness and practical value of the proposed method. Full article

Axial-flux permanent-magnet (AFPM) machines are becoming an increasingly promising solution for electromechanical systems requiring high power density. In particular, their use is expanding to electric vehicles (EVs), the aerospace industry, and advanced industrial applications, such as renewable energy applications. Their compact design, high torque-to-mass ratio, and relatively high efficiency make AFPM machines an attractive alternative to traditional radial-flux solutions. However, their integration for widespread application remains limited due to challenges in design, manufacturing, thermal management, and control systems, which ultimately also have an economic impact. This article presents a comprehensive and systematic review of AFPM machines, covering key aspects, including topology classification, design methodologies, electromagnetic modelling, optimisation methods, materials and manufacturing processes, and advanced control strategies. A structured, multi-level classification of AFPM machines is presented, incorporating stator and rotor configurations, magnetic circuit structures, winding types, and materials, thereby providing a unified overview of existing designs. Furthermore, the article presents an in-depth analysis of the sizing equations used to calculate and estimate the parameters, approaches to electromagnetic modelling (including the finite element method and magnetic equivalent circuits), and modern optimisation methods based on artificial intelligence. Particular attention is paid to materials science and new manufacturing technologies, such as soft magnetic composites, printed circuit board stators, and additive manufacturing, as well as to thermal management solutions required for high-power-density applications. This work provides a unified reference framework for researchers and engineers and outlines future directions for the development and industrial adoption of AFPM machines. Full article

Nanoparticles of the aminopolysaccharide chitosan (ChNPs) are effective delivery platforms for biologically active substances for agrobiotechnological applications and hold great promise for solving precision problems in sustainable and efficient agriculture. This review presents an analysis of research publications during the past 20 years examining methods for producing ChNPs through ionotropic gelation using sodium tripolyphosphate for cross-linking macrochains, which are of practical interest for agriculture. Key aspects of the nanostructure formation process are analyzed, including the influence of the physicochemical characteristics of the aminopolysaccharide, the concentration and ratio of reagents, and ionic cross-linking conditions on the average size, size distribution (polydispersity), and zeta potential of nanoparticles. Particular attention is paid to several approaches proposed in the literature for determining optimal gelation conditions to obtain ChNPs with pre-specified size characteristics. Potential applications of nanostructured preparations based on these nanoparticles for agrobiochemical purposes are considered, including the encapsulation of antifungal, antiviral and antimicrobial agents, pesticides, NPK fertilizers, metal ions, plant extracts, essential oils, etc., to develop biodegradable stimulants for seed germination and plant growth, increased crop yields, and improved agricultural product quality. It is concluded that blocking the protonated amino groups of chitosan with tripolyphosphate anions is undesirable due to the reduced biological activity of the macromolecules and the nanostructured preparations obtained therefrom. An alternative approach for producing ChNPs with high biological activity with neither use of cross-linking agents nor encapsulation of agrochemicals is described. Full article

This work presents an automated vision-based measurement system designed for the precise dimensional characterization of high-aspect-ratio micro-holes, achieving a relative dimensional error of less than 1% for characterizing high-aspect-ratio damage geometries. The system integrates coaxial microscopic imaging with a precision motorized scanning stage. To ensure high-fidelity measurements in early-stage warning applications, depth is determined using a focus variation method driven by a robust data fusion strategy. By capturing a sequence of images along the Z-axis, the focal planes of the defect’s surface orifice and internal base are automatically identified using a data fusion algorithm based on a consensus evaluation of three parallel sharpness metrics (Tenengrad, Laplacian, and Brenner variants). The Z-axis scanning module, featuring encoder feedback and bi-directional compensation, achieves a repeated positioning error of ±0.5 µm. For lateral damage assessment, the system’s high magnification provides an effective sampling resolution of 0.09 µm. The equivalent diameter of the focused orifice image is calculated through a robust pipeline involving adaptive thresholding, morphological filtering, and sub-pixel ellipse fitting, which serves as a highly sensitive indicator for early-stage structural deformation. The entire process can be completed within five minutes, demonstrating a rapid, highly accurate, and localized optical inspection solution that generates high-precision dimensional data crucial for quality inspection in aerospace and precision engineering. Full article

Given the issues in natural orchard environments, such as large-scale variations of defective citrus fruits, weak texture boundaries, strong illumination changes, branch and leaf occlusion, and significant background interference, this paper constructs a lightweight detection model, RT-DETR-DCEA, based on RT-DETR-R18. This model is improved through four aspects: “fine-grained defective feature extraction—multi-scale feature fusion—up-sampling detail recovery—global feature interaction for noise suppression”. First, a Dynamic Hybrid Convolution Module (DIMB) is introduced into the backbone network, drawing on the ideas of Inception-style multi-branch depthwise convolution and MetaFormer residual mixing. It extracts local textures of various forms through square convolution, horizontal strip convolution, and vertical strip convolution, and utilizes dynamic branch weights to enhance the model’s adaptability to irregular defects such as lesions, mildew, and external damage. Second, a Content-Guided Attention Feature Fusion Network (CGAFN) is designed in the neck network, which achieves adaptive fusion of low-level detail features and high-level semantic features through channel attention, spatial attention, and pixel-level fusion weights. Next, a lightweight upsampling enhancement module called EUCB-SC is constructed, which introduces channel rearrangement and Shift spatial offset into the efficient upsampling convolutional structure to enhance the local spatial interaction capability of upsampled features with low parameter overhead. Finally, adaptive sparse self-attention is introduced into the AIFI module to form AIFI-ASSA, which suppresses irrelevant background interactions through a sparse attention branch and retains necessary contextual information through a dense attention branch. The experimental results demonstrate that on a dataset containing four categories of citrus images—healthy, diseased, moldy, and severely externally damaged—RT-DETR-DCEA achieves 92.1% Precision, 86.1% Recall, and 91.8% mAP@50, with a parameter count of 1.477 × 10⁷ and an inference speed of 81 FPS. Compared with the original RT-DETR-R18 and various YOLO series models, this method strikes a favorable balance among detection accuracy, recall capability, and model lightweightness. This paper also discusses limitations such as data scale, ratio of private data, single training result, and insufficient validation on edge devices, providing a basis for subsequent cross-regional data validation and real-world deployment testing. Full article