Search Results (4,072)

Driven by the high-end furniture industry’s demand for skin-tactile decorative boards, UV inkjet printing shows potential for wood-based surface finishing. Using primer-treated inkjet-printed melamine-faced panels, this study compared traditional UV varnish coatings with different thicknesses and UV curing intensities and 254 nm UV excimer-cured coatings with different radiant energies. Varnish thickness significantly affected surface roughness, 20° gloss, 85° gloss, and color difference, indicating a trade-off between matte tactile appearance and color fidelity. Thinner varnish coatings exhibited higher roughness and lower gloss but larger color differences, whereas thicker coatings better preserved color fidelity but resulted in higher gloss. For the UV excimer-cured system, one-way ANOVA showed significant treatment effects on acrylate conversion, water contact angle, 85° gloss, surface roughness, and abrasion mass loss. The coating prepared at an excimer radiant energy of 827.9 mJ/cm² showed the lowest 85° gloss of 5.28 GU and a pencil hardness of 3H, but also exhibited the highest abrasion mass loss in the short-cycle abrasion screening test. For both coating systems, three independently prepared specimens were tested for each processing condition. The UV varnish system was analyzed using two-way ANOVA, whereas the UV excimer-cured system was analyzed using one-way ANOVA. Friedman tests of sensory evaluation data showed significant differences among the eight selected samples for fineness, smoothness, and elasticity, with the excimer-cured coatings generally receiving higher fineness and smoothness scores than the UV varnish coatings. These results indicate that 254 nm UV excimer curing is a promising route for producing low-gloss, micro-wrinkle-induced skin-tactile surfaces on inkjet-printed melamine-faced panels, although optimization should balance tactile quality, gloss reduction, and abrasion resistance. Full article

To address surface quality defects caused by traditional mechanical polishing of gears, such as machining scratches and large surface waviness, this study proposes a novel immersed chemical-mechanical polishing (CMP) process for gear finishing. Numerical simulations were conducted in FLUENT to analyze the gear surface stress distribution and polishing fluid flow trajectories under different process conditions. The Euler–Euler method and RNG k–ε turbulence model were used to optimize process parameters and clarify the formation mechanism of ultra-smooth tooth surfaces. Experimental results for spiral bevel gears show that the proposed immersed CMP process effectively improves surface quality. The tooth profile roughness was reduced from Ra 1.531 μm to 0.509 μm, and surface scratches were significantly alleviated. These results confirm the feasibility and effectiveness of the proposed process. This study provides a reliable approach for efficient and precision polishing of complex-structured gears and extends the application of CMP technology to non-planar mechanical components. Full article

For stable operation of autonomous surface ships, real-time anomaly detection of engine conditions must be coupled with an operational framework that sustains model performance in dynamic maritime environments. This study proposes an autonomous maintenance system that combines a subsystem-level condition-based maintenance (CBM) model with a dedicated MLOps framework. The main engine is decomposed into multiple functional component units, each governed by an independent diagnostic pipeline that applies a hybrid algorithm combining an attention LSTM autoencoder with an isolation forest to capture subtle anomalies. Although this hybrid attains higher precision than conventional single models, it remains sensitive to operating environment shifts. To address this, we develop an onboard MLOps pipeline that monitors distributional shifts in real-time sensor data and executes an autonomous maintenance mechanism, retraining and redeploying models on local data when performance degradation is anticipated. A dual-monitoring rule set based on a standardized deviation score and its smoothed change rate is used to discriminate abrupt mechanical anomalies from gradual drift. Experiments on a fault simulation testbed indicate that, under data drift, the system can recover detection reliability and adapt to changing engine conditions, providing a technical basis for the self-sustaining reliability of autonomous surface ships. Full article

Coarsely gridded Land Surface Models (LSMs) often smooth over sub-grid spatial heterogeneity and non-linear surface soil moisture dynamics during extreme-precipitation events. This study introduces a clustering-based Soil Moisture Active Passive (SMAP) residual framework, evaluating the spatiotemporal discrepancies between 3 km SMAP Level 2 (SMAP-L2) retrievals and 9 km SMAP Level 4 (SMAP-L4) data-assimilation products within the Yanco study region during the extreme March 2021 floods in New South Wales, Australia. By applying k-means clustering to the residual time series, we partitioned the landscape into three distinct hydrological response patterns: a Low-Residual Baseline ($64.5\%$), a Persistent Positive Anomaly ($20.7\%$) indicative of unmodeled inundation, and a Transient Negative Anomaly ($14.8\%$) representing rapid drainage. Consequently, $35.5\%$ of the usable analysis area exhibited temporal trajectories that diverged significantly from model expectations, highlighting profound geographic heterogeneity in surface wetting and retention that cannot be captured by uniform precipitation inputs alone. Benchmarking the satellite-derived time series against the Yanco in situ network provided critical context for cross-scale variations, illustrating general agreement in overarching temporal trends despite the inherent scale mismatch. Ultimately, this approach leverages residual dynamics as a scalable spatial diagnostic, offering a robust, data-driven method to map localized flood responses that are typically obscured by broad-scale model parameters. Full article

Hot isostatic pressing (HIP) was employed to fabricate 3 mol% Y₂O₃-stabilized ZrO₂ ceramics with nearly pore-free microstructures. Zirconia ceramics containing residual pores (size: ~0.3 μm, <0.1%) exhibited a four-point bending strength of 1.11 GPa. In contrast, pore-free specimens achieved significantly higher strengths of 1.74 GPa for samples containing a small fraction of cubic grains and 2.29 GPa for specimens composed solely of the tetragonal phase. At the moment of fracture in the high-strength specimens, intense electrical discharges (visible sparks) were observed near the fracture origin. Post-fracture observations revealed that zirconia containing residual pores fractured into two pieces with relatively smooth fracture surfaces, whereas pore-free zirconia exhibited extensive fragmentation, producing highly irregular fracture surfaces. This behavior is likely associated with extensive rupture of Zr–O bonds within the crystal lattice during catastrophic fracture. These results demonstrate that the elimination of residual pores by HIP markedly enhances the attainable strength of zirconia ceramics and significantly alters their fracture behavior. Full article

The growing demand for sustainable construction materials has led to significant advancements in ultra-high-performance concrete (UHPC), with a particular focus on geopolymer-based systems as an alternative to conventional cementitious binders. This review explores the latest developments in sustainable Ultra-High-Performance Geopolymer Concrete (UHPGPC) by analysing key material composition, mechanical, durability and microstructural properties. The incorporation of ground granulated blast furnace slag (GGBFS), silica fume (SF), and fly ash (FA) has demonstrated notable improvements in compressive strength, durability, and workability. Additionally, the use of activators such as sodium silicate and sodium hydroxide optimizes geopolymerization, resulting in a denser microstructure and enhanced mechanical performance. This review highlights the critical role of fibre reinforcement in UHPGPC, where steel fibres (SFs) and hybrid fibres significantly enhance compressive and tensile strength, as well as crack resistance. The inclusion of waste materials such as rice husk ash and recycled glass promotes sustainability by reducing CO₂ emissions while maintaining structural integrity. However, higher waste-glass content may adversely affect bonding due to its smooth surface texture. The findings highlight the potential of UHPGC as a high-performance, eco-friendly alternative to traditional cement-based UHPC. By integrating industrial by-products and alternative activation techniques, UHPGPC can contribute significantly to the global shift towards sustainable and low-carbon construction materials. Full article

Single-crystal diamond (SCD) is an ideal substrate material for semiconductor devices due to its extremely wide bandgap and exceptionally high thermal conductivity. However, diamond’s extreme hardness and chemical inertness pose challenges for the fabrication of ultra-smooth surfaces. Traditional polishing processes are not only inefficient but also prone to introducing subsurface defects, which severely degrade device performance. To address the above issues, this study proposes a hybrid polishing process combining reactive ion etching (RIE) surface modification with chemical mechanical polishing (CMP), which enables low-loss atomic-level processing of SCD. The study found that RIE treatment induces lattice disorder on the diamond surface, forming a sp²-hybridized amorphous carbon-modified layer. Compared to the sp³ structure of native diamond, this modified layer has lower hardness and is easier to remove. We conducted the verification of the optimized process using high-quality single-crystalline diamond (SCD) samples with an initial surface roughness Ra of 0.68 nm. Under the optimized RIE parameters (substrate bias power: 200 W, etching time: 600 s, gas flow ratio of Ar:O₂:CF₄ = 40:50:10), the surface roughness Ra was reduced to as low as 0.35 nm after 2 h of CMP treatment. Furthermore, systematic characterization of the SCD’s as-received surface, RIE-modified surface, and CMP-treated surface was performed using Raman spectroscopy and X-ray photoelectron spectroscopy (XPS), elucidating the “etching modification–mechanical removal” polishing mechanism. Full article

As the prevalence of myopia among adolescents continues to increase, the design and fabrication of myopia control lenses have become an important research direction in modern optics. Existing myopia control lenses mostly adopt purely refractive structures, which suffer from limited design freedom, insufficient chromatic aberration suppression, and relatively large lens thickness, thereby restricting further improvement of optical performance. This paper proposes a refractive–diffractive hybrid design and fabrication method for myopia control lenses. Centered on a harmonic diffractive optical element (HDOE), an optimization model is established to balance achromatization performance and fabrication feasibility. To address the challenges of small period width, tool shadow effect, and sensitivity to machining tolerances in diffractive lenses with large-aperture and high-additional-power, harmonic design is employed to increase the period width, thereby reducing fabrication difficulty and mitigating the influence of shadowing errors on diffraction efficiency. On this basis, two lenses with different phase structures are designed: one adopts a conventional diffractive correction phase to verify the role of HDOE in achromatization and edge-thickness reduction, while the other adopts a high-degree-of-freedom smooth phase to achieve a continuous multifocal visual effect. Both lenses are fabricated by single-point diamond turning (SPDT), and the effects of surface profile and machining parameters on performance are analyzed. Simulations and measurements show that the proposed method provides stable diffraction efficiency and effective chromatic aberration correction across the design band, while reducing the edge thickness by approximately 37.85% without additional thinning of the aspheric substrate. The results indicate that the refractive–diffractive hybrid design provides a feasible design and fabrication approach for functionally more complex myopia control lenses. Full article

Aircraft taxiing speed control along predefined airport surface routes is a constrained single-aircraft longitudinal control problem involving heterogeneous route geometry, action smoothness, and terminal velocity feasibility. Existing learning-based taxiing controllers commonly use a unified policy for the whole route, which may be insufficient for handling straight-segment propulsion, curved-segment speed regulation, and action discontinuities near straight–curve transitions. This paper proposes SegCoord-Taxi, a geometry-aware segmented deep reinforcement learning framework for taxiing speed control. The route is decomposed into straight segments, curved segments, and transition boundary zones. A Straight-Segment Policy (SSP) and a Curved-Segment Policy (CSP) generate geometry-dependent base acceleration commands, a Switch Residual Adapter (SRA) provides local residual correction near transition regions, and a Route-Level Feasibility Projection (RFP) maps the coordinated action into an executable acceleration satisfying route-level feasibility constraints. Experiments on departure taxiing routes at Chengdu Tianfu International Airport (ZUTF) included baseline comparison, ablation analysis, projection diagnostics, sensitivity analysis, and a trajectory-level case study. On the evaluated ZUTF case-study routes, SegCoord-Taxi achieves the lowest final velocity on the test set, $0.336\pm 0.017 \mathrm{m}/\mathrm{s}$, compared with $0.732\pm 0.061 \mathrm{m}/\mathrm{s}$ for the unified Proximal Policy Optimization (PPO) controller and 0.586 m/s for the curvature-aware constrained optimizer. The complete framework also reduces switch action jump from $1.022\pm 0.017 \mathrm{m}/{\mathrm{s}}^2$ to $0.429\pm 0.004 \mathrm{m}/{\mathrm{s}}^2$ in the ablation study. These results indicate improved terminal feasibility and transition-region smoothness in the evaluated single-airport case-study setting under an explicit efficiency–smoothness–feasibility trade-off. Future work will extend the framework to multi-aircraft and multi-airport settings under operational uncertainty. Full article

Background: Breast clip marker movement after ultrasound-guided biopsy can negatively affect lesion re-localisation rates and surgical outcomes, underscoring the need for improved understanding of the factors influencing clip displacement. Thus, this study aimed to compare four different breast clip markers and identify risk factors for clip migration and dislocation after ultrasound-guided placement. Methods: This retrospective study included 350 patients who underwent ultrasound-guided biopsy of a newly diagnosed breast lesion with placement of one of four types of breast clips (UltraClip Dual Trigger Biodur 108 Coil Marker [UC], TUMARK Professional [TP], TUMARK Vision [TV] and HydroMARK Breast Biopsy Site Marker [HM]). Clip migration and dislocation were assessed immediately after placement and during follow-up imaging for at least 3 months. A binary logistic regression analysis was performed to identify predictors of clip dislocation including lesional, perilesional and procedural parameters. Results: Clip migration rates were 26.0%, 18.0%, 10.0% and 25.0% and clip dislocation rates were 14.0%, 20.0%, 9.0% and 38.0% for UC, TP, TV and HM, respectively. Features significantly associated with clip dislocation included predominantly fatty surrounding tissue (p = 0.046) with low perilesional shear wave velocities (p = 0.054), smooth lesion contours (p = 0.041), soft lesion strain elastography (p =0.001), low clip-to-lesion-surface distance (p = 0.002) and the use of an HM breast clip (p = 0.032). Conclusions: The type of breast clip-marker, as well as perilesional and lesional characteristics, influence the likelihood of clip dislocation. Notably, the hydrogel-coated clip (HM) exhibited the highest rate of dislocation. Full article

The fatigue life of additively manufactured GH4169 components is strongly affected by internal defects, stress concentration, and life scatter, making reliable structural assessment difficult. In this study, a probability-based fatigue life prediction framework was developed by extending the conventional surface weakest link concept to a volume-defect weakest link formulation. Fatigue tests of smooth specimens with different build orientations were first conducted to establish baseline probabilistic fatigue relationships, and both log-normal and two-parameter Weibull distributions were considered. The proposed framework was then applied to a feature specimen representing the critical region of an aero-engine exhaust frame by combining the baseline fatigue statistics with element-wise maximum principal stress and volume information extracted from finite element analysis. The results show that the log-normal distribution provided a more stable statistical description of the smooth-specimen fatigue data than the Weibull distribution. For the feature specimens tested at 11,200 N, the measured fatigue lives ranged from 25,585 to 61,989 cycles. Compared with the conventional local stress method, the weakest link framework gave a more reasonable description of the structural fatigue life distribution, and the log-normal weakest link model showed the best overall agreement with the experimental results. Full article

Industrial control systems (ICS), which manage critical infrastructure such as power grids and water treatment, are increasingly exposed to cyber threats and operational faults as their connectivity to external networks grows. AI-based anomaly detection has emerged as a key defense, yet three limitations restrict its practical deployment: (i) detected anomalies are treated uniformly without distinguishing between transient faults and intentional attacks, hindering tailored incident response; (ii) the trade-off between detection accuracy and the false-positive rate burdens experts with extensive manual triage and delays prompt action; and (iii) prevailing feature-attribution Explainable AI (XAI) techniques such as SHAP and LIME produce fragmented sensor-level explanations and fail to capture correlations among sensors in time-series data, undermining trust in model decisions. To address these gaps, this paper proposes a graph-based deep learning framework that (a) defines anomaly types in terms of the anomalous-sensor ratio measured before and after smoothing—which operationalizes the correlation-maintenance principle that faults keep coupled sensors jointly anomalous while attacks isolate them—enabling explicit separation of faults, attacks, false positives, and false negatives; (b) identifies ambiguous decisions near the detection threshold as candidate false alarms via dynamic threshold smoothing; and (c) provides correlation-aware graph visualizations for intuitive interpretation. Experiments on the Secure Water Treatment (SWaT) dataset center on this post-detection layer: built on a standard graph-based detector (F1-score 0.787 at Top-K = 10) that serves only as the substrate, the categorization separates faults from attacks, and the subsequent ambiguity analysis identifies false negatives with 83% precision and false positives with 73% precision. By separating attacks from faults and surfacing high-likelihood false alarms together with intuitive sensor-correlation explanations, the proposed approach reduces analyst workload and supports more reliable, prioritized incident response in ICS environments. Full article

The volumetric mass transfer coefficient ($k_{L}a$) governs the design, operation, and scale-up of aerobic bioprocesses, yet its dependence on reactor geometry, impeller design, operating conditions, and fluid properties limits prediction by empirical correlations. Machine learning (ML) improves accuracy but faces two barriers in bioprocess practice: selecting the best model among many candidates requires expertise, and small, highly multicollinear data make models chosen based on test error alone prone to overfitting. Using a browser-based, no-code platform, we trained 14 regression algorithms under an identical pipeline on a published $k_{L}a$ dataset, and introduced a composite objective, the generalization-penalized error (GPE), which is the test RMSE plus the absolute train–test RMSE gap. Minimizing GPE rather than test RMSE expanded the top statistically equivalent group to include not only boosting ensembles but also simpler, interpretable models, indicating that black-box models hold no clear advantage once train–test consistency is assessed. Sensitivity analysis showed that tree models produce discontinuous responses, whereas algebraic learning via elastic net (ALVEN) yields smooth surfaces. Shapley additive explanations (SHAP) and an ontology graph, interpreted by a retrieval-augmented language-model agent, identified rotational speed and gas flow rate as dominant, reproducing the established mass transfer mechanism. The framework offers a reproducible, interpretable, expertise-light route to bioprocess model selection. Full article

Accurate characterization of mining-induced surface subsidence is essential for safety assessment in mining areas; however, single monitoring techniques have inherent limitations. Spaceborne interferometric synthetic aperture radar (InSAR) provides large-area coverage but suffers from low signal-to-noise ratio in the subsidence center, whereas terrestrial laser scanning offers high accuracy but limited spatial coverage. To achieve physically consistent quantitative fusion, a multi-source subsidence fusion framework based on variational data assimilation is proposed. By constructing an objective function that incorporates a background prior, D-InSAR-derived boundary constraints, TLS observations, spatial smoothness constraints, and gradient penalty terms, multi-source data are integrated into a unified optimization framework. The results show that, compared with RTK observations, the fused subsidence field achieves an RMSE of 0.12 m and an RRMSE of 2.4% approximately. Parameter sensitivity analysis indicates that the smoothing strength has the greatest influence on fusion accuracy, whereas the observation weight and gradient penalty coefficient exhibit relatively wide stable intervals, and the background constraint has a minor effect on the results. Parameter interaction analysis further demonstrates that the coupling between smoothing strength and observation weight is the most significant. The proposed method provides a physically consistent and parameter-controllable framework for multi-source deformation data fusion in mining subsidence monitoring. Full article

The thermal stability of the shot-peened gradient microstructure in LPBF AlSi10Mg during annealing at 500 °C for up to 8 h was investigated. EBSD boundary maps were analyzed using the box-counting method to determine fractal dimension, D, as a quantitative descriptor of grain-boundary geometrical complexity. It was found that D decreased with depth from the dynamically recrystallized surface layer (0–10 µm; D = 1.73; ECD = 0.8 ± 0.2 µm) through the transition layer (10–40 µm; D = 1.56; ECD = 2.4 ± 0.7 µm) to the matrix (>40 µm; D = 1.16; ECD = 3.0 ± 0.7 µm). After 5 min, the surface network simplified (D = 1.63; ECD = 2.4 µm), whereas the transition layer exhibited increased complexity (D = 1.72; ECD = 3.7 µm), suggesting a strong contribution of near-surface particle pinning and extensive recovery/polygonization within the subsurface. The matrix showed a transient increase in D (1.16 → 1.71), associated with fragmentation of the cellular Si network. Continued annealing reduced D in the surface and transition layers to 1.50 and 1.64 after 1 h due to progressive boundary smoothing and consumption of deformation substructure. Prolonged exposure triggered sparse discontinuous recrystallization exclusively within the transition layer, producing abnormally large grains that migrated bi-directionally into both the pinned surface layer and the bulk matrix. After 8 h, the gradient microstructure collapsed and the boundary trace became disconnected, yielding an apparent exponent D ≈ 0.97 at the map scale. Full article