Search Results (469)

Natural wood possesses a hierarchically porous and anisotropic structure, which provides a foundation for functional utilization, but its flammability and hygroscopicity limit its applications. Conventional bulk impregnation modification involves introducing functional agents throughout the entire pore system. This approach can enhance performance, but inevitably leads to lumen occlusion and increased density. To address this trade-off, researchers have recently developed micronano coating strategies based on interfacial decoration rather than bulk deposition within the lumina. These strategies confine functional components to cell wall surfaces while preserving the natural porous scaffold. Two fabrication routes have been developed, namely liquid-phase methods and gas-phase methods, which differ in coating precision, penetration depth, and interfacial bonding. In flame retardancy, interfacial coatings act as physical barriers and promote chemical charring. Inorganic layers suppress oxygen diffusion and heat transfer, while phosphorus or nitrogen components catalyze cellulose dehydration. In acoustics, conformal coatings regulate pore wall roughness and acoustic impedance, enhancing viscous and thermal dissipation without blocking channels. Challenges for practical application include mass transfer limitations in large logs, conflicts between high-precision processes and industrial economics, and interfacial durability under service conditions. This narrative review summarizes fabrication strategies, flame-retardant mechanisms, and acoustic regulation principles, providing guidance for coating strategy selection and process optimization. It is noted that this review focuses on wood species with open, permeable pore structures suitable for functional modification, rather than species whose pores are occluded by heartwood extractives. Full article

Rough volatility and jump clustering are empirically important features of equity dynamics, yet their joint treatment in American-option pricing remains computationally demanding. We develop a Volterra–Hawkes stochastic-volatility model in which a regularized weakly singular fractional kernel governs both rough diffusive memory and variance-jump propagation. Regularizing the kernel at an explicit resolution scale preserves complete monotonicity and a nonnegative Bernstein representation while replacing the unresolved zero-lag jump response by a finite plateau. A positive exponential-sum approximation then yields a finite-dimensional Ornstein–Uhlenbeck Markovian lift; we identify and correct a rank-one covariance defect in the naive shared-shock simulation of the lifted factors and combine the corrected scheme with least-squares Monte Carlo valuation for American puts. We assess the model by an ablation over a two-branch nested design—rough-Heston diffusion as the common base, a price-jump Hawkes channel and a variance-jump Hawkes channel as two parallel single-channel extensions, and the full model combining both—calibrated and evaluated out of sample on short-maturity puts for five underlyings (NVDA, TSLA, META, AAPL, MSFT). Pooled across assets, the full model attains the lowest per-date vega-weighted RMSE on $77.8\%$ of out-of-sample dates and the lowest pooled error on four of five underlyings, with META the exception. The improvement is not claimed to be uniform, and the two jump channels are complementary rather than individually sufficient. The evidence in this short-maturity sample supports the presence of both channels through their baseline intensities; the identification of their self-exciting feedback is left to a longer-maturity panel. Full article

Biofilm is known to negatively affect the hydraulic performance of open channels by increasing friction and energy losses and, consequently, economic costs. This study proposes a method to accurately quantify the effect of flow reduction caused by biofouling in open channels. The approach is based on Manning’s equation, using the Monte Carlo Method for the propagation of input uncertainties. A case study concerning a trapezoidal concrete open channel of an agricultural irrigation network is presented, showing a relative expanded uncertainty (95% confidence level) of 10% for flow rates ranging from 1 m³·s⁻¹ to 50 m³·s⁻¹. Manning’s roughness coefficient was identified as the dominant contribution to the overall dispersion of flow rate values, being probabilistically modeled by a beta distribution using minimum, expected and maximum reference values. This information was used for conformity assessment of the studied open channel, considering flow reductions due to moderate and severe biofilm effects of 12.5% and 31.3%, respectively. The corresponding standard uncertainties (4.8% and 7.8%), the adopted confidence level (95%), tolerance upper limit (33%) and decision rule led to different conformity assessment outcomes. These results highlight the importance of accurate assessment of flow reduction due to biofouling for management entities. Full article

To characterize the cross-scale coupling between macroscopic deformation and microscopic rough-surface contact in metal O-rings, this study proposes a macro–micro contact analysis and leakage flow regime identification method for metal O-ring sealing interfaces. Finite element analysis was employed at the macroscopic scale to obtain the sealing contact width and pressure distribution, while microscopic rough-surface morphology was characterized using fractal theory. Based on asperity contact analysis, the equivalent leakage channel height was determined, and the Knudsen number (Kn) was introduced to identify the fluid flow regime within micro-gaps. The results show that the O-ring cross-section flattens into an elliptical shape under compression, while the contact pressure exhibits a saddle-shaped distribution, and micro-gap leakage channels remain present. As the compression ratio increased from 5% to 25%, the sealing contact width and contact pressure increased, whereas the leakage gap height decreased significantly, resulting in an increase in the Kn values from 0.00176 to 0.045, 0.07, 0.155, and 2.78. Consequently, the flow regime evolved from continuum flow through the transition regime to molecular flow. The findings reveal the cross-scale coupling mechanism between macro–micro contact behavior and leakage flow regime evolution, providing practical guidance for determining the preload level and selecting appropriate leakage models in the engineering design of metal O-ring seals. Full article

River regulation often leads to uniform conditions within the river channel, alters sediment dynamics, and contributes to the degradation of aquatic habitats. Deflectors are increasingly used as habitat-forming elements in river restoration projects. However, the interaction between hydraulic conditions, sediment inflow, deflector location, and surface roughness and their effects on the spatial extent of sediment removal remain insufficiently studied. Laboratory experiments were conducted in a flow channel using three geometrically identical deflectors arranged according to the configuration observed in the Flinta River in western Poland. The studies were conducted for the following combinations: three discharges (Q = 0.40, 0.64, and 1.70 dm³ s⁻¹), three water depths (h = 0.03, 0.06, and 0.09 m), three cumulative surrogate-sediment masses (R_um = 0.5, 1.0, and 1.5 kg), three dimensionless longitudinal positions (ξ = 0.21, 0.61, and 1.00), and two deflector roughness specifications—smooth or rough. The two-dimensional extent of the sediment-free zone was quantified based on aerial photographs using the normalized surface index A^∗. Dimensionless water depth and dimensionless discharge were the dominant factors χ > Q^∗ > R^∗ ≫ ξ, and their interaction $Q^{\ast }\times \chi$ constituted the strongest two-way effect, whereas sediment mass had a significant but secondary influence. Surface roughness did not independently affect the mean $A^{\ast }$ value but altered the spatial characteristics: longitudinal position was non-significant for smooth deflectors but became significant for rough deflectors, particularly through interactions between depth and location and between depth, location, and surface. These findings indicate that deflector roughness should not be specified as an isolated design parameter but should be selected jointly with the expected flow-depth regime and the longitudinal placement of successive structures. In practical terms, the results can support the preliminary design and positioning of habitat-forming deflectors intended to create or maintain spatially differentiated sediment-cleared zones in regulated channels, thereby contributing to more evidence-based and sustainable river restoration. Full article

In this study, pure hydroxyapatite (HAp) was successfully produced by using eggshells. The eggshells were collected locally and calcined to get CaO from them. The CaO powder was reacted with diammonium hydrogen phosphate in a mechanochemical method using attritor milling. A portion of the synthesized samples was subjected to a second calcination process at 900 °C to investigate the thermal effects on the material. The structures of the samples were investigated by scanning electron microscopy, X-ray diffraction, and infrared spectroscopy. The as-prepared HAp appeared to be nanocrystalline with low-intensity reflections, which transformed into a highly crystalline hexagonal phase after heat treatment, as revealed by XRD analysis. Quantitative analysis revealed the thermal evolution of the secondary Ca(OH)₂ phase, due to the thermal decomposition into CaO without causing HAp decomposition into tricalcium phosphates. FTIR analysis showed characteristic phosphate bands for both samples, but the calcined sample displayed sharper peaks and a clear loss of residual water and carbonates. SEM observations also highlighted the major morphological transformation. The highly aggregated as-prepared nanoparticles formed larger, well-defined grains. Notably, the calcined sample also exhibited a rough, textured surface with a macroporous network with interconnected channels. EDS analysis confirmed a Ca-P-O-rich composition, where the elevated Ca/P ratio (Ca/P = 2.28) suggested the presence of secondary calcium-rich phases. These structural, chemical, and morphological characteristics suggest that eggshell-derived HAp, with or without a second heat treatment, has high potential and may be optimized for different applications in bone tissue engineering. However, biological performance was not evaluated in this study. Full article

We demonstrate an integrated eight-wavelength high-power laser source on a 4H-silicon carbide (SiC)-based optical integration platform. Eight discrete Fabry–Perot laser diodes emitting at 445 nm, 637 nm, 789 nm, 806 nm, 846 nm, 978 nm, 1316 nm, and 1552 nm are integrated on a single SiC chip, each delivering ≥100 mW continuous-wave output power. A complete fabrication process is developed, including lift-off metallization (Ni/Ti/Pt/Au), surface hydrophilic activation bonding, and multi-step blade dicing to form SiC waveguides with a width of 500 μm and a thickness defined by the ~510 μm dicing depth, matching the output aperture of the multimode laser diodes. The resulting waveguides exhibit a facet misorientation of <1° and an approximate facet mean surface roughness of ~2 nm. The laser diodes are directly butted against the waveguide facets for edge coupling, and fixed using In₅₂Sn₄₈ solder bonding with pulse temperature control. Under controlled temperature, all eight channels operate stably with measured peak wavelengths matching the design targets. This work provides a scalable and practical solution for multi-wavelength, high-power on-chip light source integration on the SiC platform, addressing critical thermal and integration challenges for dense wavelength division multiplexing. Full article

Total internal reflection (TIR) loss is a critical bottleneck limiting light extraction in AlGaN-based deep-ultraviolet (DUV) light-emitting diodes (LEDs), primarily due to the large refractive-index contrast at the light-emitting interface. Here, pyramid-shaped dielectric metasurfaces are designed and fabricated at the sapphire/air interface of flip-chip AlGaN-based DUV LEDs using a scalable nanoimprinting process. The metasurface functions as a light outcoupling layer that modifies the interfacial momentum-matching condition and redistributes photon propagation directions. Experimental results and theoretical simulations show that metasurfaces with different feature sizes enhance light extraction through distinct mechanisms. The subwavelength pyramid nanoarray perturbs the local optical field and provides additional in-plane momentum components, facilitating the coupling of high-angle photons into radiative channels, whereas the larger pyramid void structure mainly promotes photon extraction through geometrical redirection, tilted output interfaces, and dry-etching-induced rough surface scattering. As a result, an average light output power (LOP) enhancement of over 8% is achieved for AlGaN-based DUV LEDs emitting at approximately 275 nm. This work demonstrates a low-cost, scalable, and effective strategy for enhancing the LEE of DUV LEDs, with promising potential for high-efficiency ultraviolet optoelectronic application. Full article

This study proposes a deep learning-based precipitation retrieval framework to improve precipitation retrieval using Fengyun-4B (FY-4B) multi-channel infrared brightness temperatures and cloud-top temperature (CTT). Four deep learning models are evaluated, ranging from lightweight architectures (DS-UNet, SmaAt-UNet) to more computationally intensive ones (U-Net, Attention U-Net). Unlike previous FY-4B/deep learning precipitation retrieval studies that mainly emphasize satellite-derived cloud-top features, this study explicitly evaluates the contribution of underlying-surface information within a unified retrieval framework. To quantitatively assess the impacts of underlying-surface information on retrieval performance, a digital elevation model (DEM), topographic factors (slope and surface roughness), and land use/land cover (LULC) are integrated into the framework. The impacts of surface information on precipitation retrieval were further examined through validation against the Global Precipitation Measurement (GPM) mission’s Integrated Multi-satellitE Retrievals for GPM (IMERG) product and rain-gauge observations. Results show that precipitation detection can be improved by incorporating underlying-surface information. Model architecture strongly influenced the results. Lightweight models (DS-UNet and SmaAt-UNet) significantly enhanced retrieval performance by providing clearer spatial constraints because of their greater sensitivities to discrete LULC boundaries. Deeper models (U-Net and Attention U-Net) more effectively encoded continuous topographic gradients because they benefited more from terrain-related variables. Precipitation retrieval skill may not necessarily be improved by a simple combination of DEM and LULC, probably due to thermodynamic forcing and spatial scale mismatches. By comparison, incorporating multiple factors can effectively alleviate these conflicts and improve precipitation detection. This study highlights that precipitation monitoring over complex regions can be enhanced through the proper integration of underlying-surface information. Considering both network architecture and operational objectives is therefore important for developing optimal integration strategies, which can support regional hydrological modeling and flood early-warning systems by providing more reliable input datasets. Full article

Two-dimensional electron gases (2DEGs) at complex oxide interfaces have emerged as a promising platform for next-generation oxide semiconductor devices, owing to their tunable electronic properties and rich interfacial phenomena. In this work, a LaAlO₃/SrTiO₃/LaTiO₃ (LAO/STO/LTO) trilayer heterostructure is proposed and theoretically investigated using a self-consistent Schrödinger–Poisson framework to examine the effects of multi-interface engineering on quantum confinement and carrier transport. The proposed architecture combines polar-discontinuity-driven electronic reconstruction at the LAO/STO interface with charge-transfer-induced electron accumulation at the STO/LTO interface, forming two coupled 2DEG channels within the SrTiO₃ layer. Compared with conventional single-interface oxide heterostructures, the coupled-interface configuration significantly enhances sheet carrier density and electrical conductivity, with predicted carrier densities approaching 10¹⁴ cm⁻² and gate-tunable conductivities in the range of 10³–10⁴ S cm⁻¹ under idealized operating conditions. The effects of layer thickness, gate bias, temperature, and electrostatic coupling are systematically investigated to establish practical design guidelines for optimizing carrier confinement and transport. A sensitivity analysis incorporating interface trap densities up to 2 × 10¹³ cm⁻² demonstrates that more than 60% of the ideal carrier population is retained under moderate defect concentrations, confirming the robustness of the proposed multi-interface strategy. Although the analytical model represents an upper-bound framework, its predictions are discussed in the context of experimentally relevant limitations, including interface roughness, oxygen vacancies, carrier trapping, and defect-induced scattering. Overall, the proposed LAO/STO/LTO heterostructure provides a predictive framework for engineering high-density, electrically tunable oxide 2DEGs for future nanoelectronic, terahertz, photonic, and energy-related applications. Full article

Tensegrity structures have lightweight, compliant, impact-resistant, and large-deformation characteristics, providing a deformable structural solution for mobile robots in complex environments. Inspired by earthworm peristaltic locomotion, this study proposes BAM-STR, a soft tensegrity robot driven by McKibben pneumatic artificial muscles. The robot adopts a three-layer, three-strut tensegrity structure, and the McKibben pneumatic artificial muscles are arranged at the diagonal and additional tendon positions to generate axial–radial coupled deformation under low-pressure actuation. A bio-inspired segmented peristaltic waveform control strategy is further designed. By sequentially activating and releasing the artificial muscles in the three tensegrity units, the robot generates an axially propagating deformation wave and achieves continuous forward crawling. Experimental results show that BAM-STR can achieve approximately $31\%$ axial contraction and $21\%$ radial expansion at an input pressure of $100\mathrm{kPa}$. When the control time interval is $\Delta T=1.0–1.25\mathrm{s}$, the robot reaches its maximum average crawling speed of approximately $6.5\mathrm{mm}/\mathrm{s}$. Multi-scenario experiments further show that BAM-STR can adapt to channel widths ranging from 190 to $235\mathrm{mm}$, complete continuous crawling while carrying an additional payload of $200\mathrm{g}$, and maintain forward locomotion on a rough artificial grass surface. These results indicate that BAM-STR has path-width adaptability, load-carrying crawling capability, and rough-ground adaptability. Full article

High-precision flood identification from high-resolution remote sensing images using deep learning network models is challenging. Severe cloud interference, limited receptive fields, insufficient boundary refinement and spatial detail preservation, and difficulty in accurately distinguishing water bodies from ground object shadows constrain the extraction method. Therefore, this study proposes an automatic flood information extraction method that integrates an improved Fully Convolutional Network classification and recognition model (FCN-RAM) with a rough tolerance set. First, a tolerance rough set algorithm was employed for sample data preprocessing. Subsequently, a Residual Attention Module (RAM) was introduced to optimize the U-Net architecture, dynamically adjusting the response intensity of deep features in both the channel and spatial dimensions to construct a deep learning-based FCN-RAM. Finally, comparative analyses were conducted on three high-resolution remote sensing datasets with different resolutions: Global surface water detection in Large-size very-High-resolution satellite imagery (GLH-Water), Gaofen Image Dataset (GID), and Earth Surface Water Dataset (ESWD). The results demonstrated that FCN-RAM consistently and substantially outperformed the baseline U-Net across all three datasets, achieving F1-score improvements of 10.64% (GLH-Water), 9.71% (GID), and 10.64% (ESWD), with corresponding overall accuracy gains of 9.97%, 11.15%, and 10.22%, respectively. Notably, the Intersection-over-Union (IoU) scores were elevated by 17.59% (GLH-Water), 15.66% (GID), and 13.63% (ESWD). The method also surpassed state-of-the-art models including ResNet and Water-SCNet, attaining peak overall accuracies of 98.61% (GLH-Water) and 97.37% (GID). Notably, while the proposed framework exhibits remarkable generalization across the evaluated multi-resolution benchmarks, its current validation is primarily confined to static water body delineation tasks. The model’s transferability to highly heterogeneous geographical regions with scarce training samples, as well as its extendability toward dynamic time-series flood evolution modeling, warrants further systematic investigation. The proposed method significantly improves the accuracy of waterbody information extraction, meets the requirements for high-precision information extraction from high-resolution imagery, and provides technical support for intelligent flood information extraction using high-resolution remote sensing. Full article

This study experimentally examines turbulent flow structures induced by the coupled interaction of transverse bank slope, rigid vegetation, and bed roughness heterogeneity in a compound channel. Three-dimensional velocity components were measured using Acoustic Doppler Velocimetry (ADV) in a 13 m long flume under three transverse bank slopes (0°, 10°, and 25°), both with and without submerged rigid vegetation. Quantitatively, the presence of vegetation on the sloped bank reduced local flow velocity by 40–50% due to drag caused by vegetation canopy, while the accelerating flow in the main channel reduced by 25–35%. The combined effect of a steep 25° slope and vegetation amplified the turbulent kinetic energy (TKE) by ~55% and maximum Reynolds shear stress (RSS) by 50–70% at the sand–gravel interface compared to bare-bed conditions, generating a rigorous lateral shear layer. These quantitative insights provide critical design guidance for river restoration, bank protection, and flood management. The identified interactions between bank slope and vegetation establish a predictive framework for mitigating localized scour and bank erosion while optimizing channel conveyance capacity in ecologically managed river systems. Full article

Fracturing is the dominant stimulation technique for low-porosity, low-permeability dolomite gas reservoirs, yet the lack of systematic laboratory research on multistage alternating acid etching mechanisms restricts field construction parameter optimization. Targeting the low-permeability Xixiangchi Formation dolomite reservoir in the eastern Sichuan Basin, this work develops a high-temperature, high-pressure core acid etching system coupled with 3D surface scanning. A reliable lab-to-field parameter conversion is established based on the Reynolds and Froude similarity criteria. Four-factor three-level orthogonal tests are conducted to quantify the impacts of pad fluid-to-acid viscosity ratio, total acid volume, pumping rate, and alternating injection stages on JRC-characterized wall roughness and fracture conductivity. The results show an identical factor dominance ranking for both indicators: viscosity ratio > pumping rate > injection stages > total acid volume. The optimal stimulation scheme is determined as a 50:1 viscosity ratio, 120 mL total acid volume, 12.54 mL/min laboratory pumping rate (equivalent to 8 m³/min in field operations), and 3 alternating injection stages. An elevated viscosity ratio intensifies viscous fingering, induces heterogeneous dolomite dissolution, and forms abundant irregular asperities on fracture surfaces. These self-supporting rough structures sustain stable seepage channels and markedly improve conductivity, verifying the positive roughness-conductivity correlation and revealing the core mechanism of heterogeneous etching-driven conductivity enhancement. The findings provide direct experimental support and parameter guidance for multistage alternating acid fracturing design in the Xixiangchi Formation and analogous tight dolomite reservoirs. Full article

Horizontal annular channels are widely encountered in fluid mechanics. Extensive research has explored how wall roughness alters total pressure loss in circular pipe flows. Meanwhile, existing literature has thoroughly discussed artificially enhanced rough structures (e.g., ribs, helical strips, and axial corrugations) for turbulence control and modulation. By contrast, relevant research on the inner cylinder roughness of annular channels remains insufficient. In practical engineering like petroleum drilling, inner pipes are usually treated as hydraulically smooth in theoretical and numerical models. However, machining inevitably generates tiny surface asperities on inner walls. Though this manufacturing micro-roughness is minimal in size, its true impact on flow characteristics is still unclear and requires a systematic numerical study. This study numerically explores the effects of practical inner-wall roughness on the hydrodynamic performance of annular flow. The simulation results reveal that compared with a fully smooth inner wall, surface roughness significantly rearranges the spatial distribution of major turbulent parameters (e.g., flow velocity, turbulence intensity, turbulent kinetic energy and Reynolds stress), and changes both the magnitude and position of their peak values. When the inner wall roughness is 0.4 mm, the near-wall Reynolds stress increases by 153.5%, the turbulence intensity increases by 52.4%, and the turbulent kinetic energy increases by 133.0%; the velocity peak shifts to the outer ring side, and the critical roughness is 0.06 mm. The findings confirm that considering inner tube roughness is critical for improving the prediction accuracy of both microscale turbulent behaviors and macro flow parameters in practical annular flow engineering. This work distinguishes itself from existing studies by systematically quantifying the response of full first- and second-order turbulent statistics to pipeline manufacturing micro-roughness, revealing the symmetry-breaking turbulent transport mechanism of annular flow, and quantitatively determining the critical inner-wall roughness that shifts the velocity peak to the annular geometric midpoint, which provides a targeted quantitative database for the correction of annular flow numerical simulations that ignore native micro-roughness. Full article