Search Results (2,743)

The influence of impact configuration and block shape on rockfall rebound behavior has been highlighted in numerous experimental studies. To further investigate these effects, an experimental campaign was conducted using rigid elliptical-disk blocks, allowing the simultaneous examination of block geometry and impact configuration under controlled conditions. The experimental setup was inspired by existing analytical approaches that employ elliptical geometries to describe rebound mechanics and to provide a more detailed interpretation of the parameters governing impact response. The results show that coefficients of restitution exhibit substantial scatter and do not display systematic trends with respect to the geometric aspect ratio of the elliptical disks (a/b ≈ 1.25–2.0), within the testing range of this experiment. Instead, rebound behavior is primarily controlled by impact configuration, as described by the orientation of the major axis and the impact angle. The following two distinct impact types were identified: instantaneous and rolling, associated with different responses. The experimental data were further used to assess existing analytical models, revealing limited quantitative agreement due to the idealized assumptions in their formulation. Overall, the study demonstrates that rebound response in rockfall processes is strongly configuration-dependent, emphasizing the need for modeling approaches that explicitly account for impact configuration and contact geometry. Full article

The research gap addressed in this study is the lack of a transparent and quantitative evaluation of the governing parameters influencing deflection behavior in reinforced concrete (RC) beams reinforced with glass fiber-reinforced polymer (GFRP) bars. The objective of this study is to identify and quantify the relative importance of the key parameters controlling deflection in GFRP-reinforced RC beams, which exhibit fundamentally different behavior compared to steel-reinforced beams due to the linear-elastic response of GFRP bars until rupture. To achieve this objective, the method integrates explainable artificial intelligence (XAI) techniques, including SHapley Additive exPlanations (SHAP), Pearson correlation heatmap, scatter plot analysis, and sensitivity analysis—with experimental structural data obtained from beams with three different concrete strength classes. The main contribution of this study is the quantitative ranking and interpretation of the governing parameters affecting deflection behavior through a transparent and data-driven framework. Key parameters—including elastic modulus (Ec), compressive strength (fck), creep coefficient (φ), failure moment (Mexp), effective moment of inertia (Ieff), and applied load (P)—were evaluated. The results consistently indicate that stiffness- and capacity-related parameters dominate the deflection response. Sensitivity analysis reveals that the failure moment (Mexp) is the most influential parameter, contributing approximately 23% of the total relative influence on deflection, followed by compressive strength (fck) and cracking-related parameters. Pearson correlation heatmap and scatter plot analyses further confirm strong relationships between deflection and Ec, fck, φ, and Ieff. The proposed framework improves the interpretability of deflection prediction in GFRP-reinforced RC beams and provides a transparent basis for serviceability-based structural design and performance-oriented assessment. Full article

Using advanced optical modelling, we quantify how sinusoidal corrugation and emitter dipole orientation jointly govern light extraction from OLED thin-film stacks into a glass substrate for red, green, and blue emission. Irrespective of emission colour, the corrugation aspect ratio (AR = height/period) is the dominant geometric parameter controlling extraction, with absolute period and height playing secondary roles, as periods of 600–1000 nm deliver similar gains across all colours. Extraction peaks at AR ≈ 0.2 for predominantly horizontal dipoles, AR ≈ 0.5 for vertical dipoles, and AR ≈ 0.3 for isotropic orientations. For the isotropic case, extraction improves by up to 40%, 34%, and 20% relative to flat red, green, and blue devices, respectively. Absorption analysis attributes the principal gains to suppression of surface-plasmon-polariton losses of vertical dipoles, supported by local dipole reorientation, waveguide disruption, and scattering. Because practical texturing can alter dipole orientation, optimum conditions must be re-evaluated; if orientations follow the sinusoidal profile, an AR of approximately 0.2–0.3 is favoured for isotropic to moderately horizontal orientations, whereas higher ARs benefit strongly vertical orientations. The results provide guidelines for co-optimising corrugation geometry and dipole orientation for high-efficiency OLEDs. Full article

Electromagnetic scattering from electronic platforms degrades system performance, increases radar detectability, and intensifies electromagnetic interference in modern radar and communication systems. Electromagnetic absorbing layers offer an effective approach for radar cross-section (RCS) reduction; however, existing machine-learning-based design methods rely on black-box, composition-specific models lacking physical interpretability and generalizable design rules. In this work, a physics-informed and interpretable machine learning framework is proposed for application-oriented electromagnetic absorber design in electronic systems. Physically meaningful electromagnetic descriptors related to impedance matching and attenuation are embedded into an explainable learning model to establish transparent relationships between absorber parameters and reflection-related performance. Unlike prior approaches, SHAP-based interpretability is applied to extract universal, quantitative design rules, and ML-driven inverse design is explicitly validated for electronic-system-level RCS reduction. Experimental validation confirms that the predicted designs achieve reflection-related performance with deviations below 5%, demonstrating the reliability of the proposed framework. Full article

Forest height is a key parameter for quantifying forest biomass and carbon stocks and serves as an important indicator of forest ecosystem health. The successful launch of the European Space Agency’s P-band Biomass satellite, which provides Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) data for global high-precision forest height mapping, heralds a new era in global forest carbon monitoring. However, the accuracy of forest height inversion is significantly influenced by scattering mechanisms. This study investigates the impact of dominant scattering mechanisms on forest height inversion accuracy. Four classical algorithms were selected: the polarimetric phase center height estimation method (PPC), the complex coherence phase center differencing algorithm (CCPCD), the coherence amplitude inversion method (CAI), and the hybrid inversion method using both phase and coherence information. The Freeman–Durden three-component decomposition was employed to identify the dominant scattering mechanisms. The results show that (1) at P-band, inversion model performance exhibits strong coupling with scattering mechanisms, and no single algorithm achieves global robustness; (2) the hybrid inversion method using both phase and coherence information performs better in regions dominated by surface and double-bounce scattering, whereas the coherence amplitude inversion method (CAI) yields higher accuracy in volume-scattering-dominated regions; and (3) the adaptive joint inversion strategy based on scattering mechanisms achieved a root mean square error ($\mathrm{RMSE}$) of 4.62 m and a coefficient of determination ($R^2$) of 0.76 at P-band, representing an improvement of approximately 30% over the best single-model performance ($\mathrm{RMSE}$ = 6.51 m). This approach overcomes the accuracy limitations of single models in complex global forest scenarios and provides a valuable reference for scientific forest height inversion. Full article

Extensive research and empirical evidence demonstrate that nanofluids enhance heat transfer efficiency in microchannels, but this improvement is often accompanied by increased pressure drop and particle clogging. This study aims to determine the optimal parameters for preparing stable nanofluids and to discuss the effects of different parameters on thermal and hydraulic performance. By analyzing the impact of varying ultrasonication time, particle concentration, particle size, surfactant type, and mixing ratios on stability, the most stable nanofluid was selected for evaluation of flow heat transfer and cost-effectiveness. Results indicate that a 1:1 mixed nanofluid of TiO₂ (20 nm)-SiO₂ (50 nm) exhibits optimal stability under conditions of 90 min ultrasonication, 0.20 vol% total particle concentration, and 0.15 wt% xanthan gum. At a Reynolds number of 550, this mixed nanofluid exhibits superior thermal performance. Compared with deionized water, its convective heat transfer coefficient and Nusselt number increase by 40.25% and 37.94%, respectively, while the pressure drop rises by only 17.18%. The performance evaluation criterion reaches 1.43, accompanied by a high cost–performance factor. These findings demonstrate that mixing large and small particles of TiO₂ and SiO₂ not only significantly enhances thermal performance but also positively impacts stability and hydraulic properties. A 90 min ultrasonic treatment time markedly improves stability and optimizes dynamic light scattering results. Full article

This study presents a machine learning framework for the reconstruction of fatigue life and fracture toughness in natural fiber-reinforced composites, evaluating the predictive accuracy of six regression algorithms—Random Forest, Gradient Boosting, Support Vector Machine, Neural Network, Ridge Regression, and Lasso Regression—using a controlled synthetic dataset of 600 samples generated from established Basquin fatigue and Rule of Mixtures fracture equations, incorporating stochastic noise calibrated to experimental scatter (CV = 15–50%), with log-normal noise standard deviation of 0.20 for fatigue life and Gaussian noise standard deviation of 0.15 for fracture toughness. The dataset encompasses eight natural fiber types (flax, jute, sisal, hemp, bamboo, coconut, banana, and pineapple) and five matrix systems (epoxy, polyester, PLA, vinyl ester, and polyurethane). Models were evaluated using a 70-15-15 train–validation–test split with 5-fold cross-validation and exhaustive grid search hyperparameter optimisation. Gradient Boosting achieved R² = 0.93 for fatigue life and Stacking Ensemble achieved R² = 0.87 for fracture toughness, representing 97% and 89% of their respective noise-ceiling values (theoretical maximum R² of 0.96 and 0.98 given the programmed noise levels). The ML models perform supervised function approximation—learning to reconstruct the programmed generation equations rather than discovering novel physical composite behaviour—and function as automated surrogates for the governing equations. Feature importance analysis identified engineered composite indicators, stress amplitude, and fiber length as the most influential parameters. The framework provides a reproducible ML evaluation pipeline as a methodological template for future experimental composite studies. Full article

To study the influence of freckle defects on the hot deformation behavior and the inheritance of dynamic recrystallization (DRX) structure in GH4706 alloy, the microstructures of specimens with and without freckles and the evolution laws of hot-processing parameters were compared. Hot compression experiments were conducted on a thermal simulation testing machine at 950–1150 °C, strain rates of 0.001–1 s⁻¹, and 55% deformation. Freckle-containing specimens were tested under DRX critical conditions. The flow stresses of both specimens increase with strain rate or with decreasing temperature. The power dissipation coefficient (η) and instability value (ξ) follow complex laws. Electron back-scattering diffraction (EBSD) was used to analyze DRX microstructures and nucleation mechanisms. The DRX degree of freckle-containing specimens is lower, with a larger average grain size. The DRX mechanism initiates preferentially in freckle-containing specimens, and its volume fraction changes in a complex manner. Grain coarsening occurs in freckle-containing specimens at high temperatures and low strain rates. Freckle defects lead to significant differences in the DRX mechanism of GH4706 alloy. Freckle-containing specimens exhibit both discontinuous dynamic recrystallization (DDRX) and continuous dynamic recrystallization (CDRX), whereas freckle-free specimens primarily display DDRX and second-phase particle-stimulated nucleation (PSN). The presence of MC carbides and Laves phases within freckle defects provides nucleation sites, further supporting a typical second-phase particle-stimulated nucleation mechanism. Full article

Leveraging differential interferometric techniques, ground-based synthetic aperture radar (GB-SAR) delivers highly accurate displacement measurements, typically reaching submillimeter scales. However, in practical engineering, minor platform instability induced by environmental factors gives rise to space-variant baseline errors, which affects the deformation value. In response to this issue, this paper presents a method combining Taylor expansion and singular value decomposition for estimation and compensation of the space-variant baseline error. Initially, the Gaussian Mixture Model (GMM) is employed to adaptively select high-quality Permanent Scatterers (PSs) to facilitate robust data provision for the following error parameter estimation. Subsequently, a three-dimensional multi-parameter model for the space-variant baseline error is established via Taylor expansion, followed by parameter estimation using Singular Value Decomposition (SVD). Experiments indicate that the proposed approach effectively mitigates the error phase arising from platform vibration, thereby enhancing the precision of GB-SAR deformation inversion. Full article

Nano-graphene oxide (NGO) is a nanomaterial that has been frequently used in the fields of health and bioengineering in recent years. However, its potential use in semen cryopreservation is still in the exploratory phase. In this study, Angora bucks, a breed with low resistance to cold shock, were used. Sperm was collected from five different Angora bucks, pooled, diluted with a Tris-based egg yolk diluent, and frozen with the addition of NGO at two different sizes (50 and 500 nm) and doses (10 and 50 µg/mL). Nanoparticle characterization was performed using field emission scanning electron microscopy (FE-SEM), dynamic light scattering (DLS), and Fourier-transform infrared spectroscopy (FTIR). Post-thaw sperm analyses were evaluated based on motility and kinematic parameters, mitochondrial membrane potential (MMP), plasma membrane and acrosome integrity (PMAI), and DNA fragmentation. Applying 50 nm NGO at a dose of 50 µg/mL led to statistically significant improvements in motility and PMAI (p < 0.05). The same dose of 500 nm NGO, however, only showed a statistically significant improvement in the PMAI parameter (p < 0.05). No significant differences were observed between the groups for MMP and kinematic parameters (p > 0.05). Conversely, it was found that all sizes and doses of NGO significantly protected post-thaw sperm regarding DNA integrity (p < 0.05). These findings indicate that the NGO, at a size of 50 nm and a dose of 50 µg/mL, improves the post-thaw quality of Angora buck sperm and provides a cryoprotective effect that depends on size and dose. This study provides preliminary data on the potential effects of NGO; however, comprehensive mechanistic and in vivo validation studies are required to establish the biological and clinical validity of these findings. Full article

The constant growth of IP data traffic, driven by sustained annual increases surpassing 26%, is pushing current optical transport infrastructures towards their capacity limits. Since the deployment of new fiber cables is economically demanding, ultra-wideband transmission is emerging as a promising cost-effective solution, enabled by multi-band amplifiers and transceivers spanning the entire low-loss window of standard single-mode fibers. In this scenario, an accurate modeling of the frequency-dependent fiber parameters is essential to reliably model optical signal propagation. In particular, the combined impact of attenuation variations with frequency and inter-channel stimulated Raman scattering (SRS) fundamentally shapes the power evolution of wide wavelength division multiplexing (WDM) combs and directly affects nonlinear interference (NLI) generation, as well as the amount of ASE noise. In this work, we review a set of analytical approximations, based on phenomenological approaches, for frequency-dependent attenuation and Raman scattering gain, and analyze their impact on achieving an effective balance between computational efficiency and physical fidelity. Through extensive analyses performed with the open-source software GNPy (version 2.12, Telecom Infra Project) on an optical line system exploring multi-band scenarios spanning C+L+S, C+L+E, and U-to-E transmission, we demonstrate that the proposed approximations reproduce the reference SRS power evolution and NLI profiles with root mean square errors (RMSEs) consistently below 0.03 dB, and down to the 10⁻³–10⁻² dB range for the most accurate configurations. Although the current implementation does not yet provide a direct reduction in computational time, the proposed framework lays the groundwork for future developments toward closed-form or semi-analytical solutions, enabling more efficient modeling and optimization of ultra-wideband optical transmission. Full article

Antibody–drug conjugates (ADCs) comprise a monoclonal antibody covalently bound to a cytotoxic payload by a linker. ADCs minimize off-target effects on healthy tissues, leveraging the specificity of monoclonal antibodies to deliver cytotoxic drugs to the intended tumor site. ADCs can be prone to poor behavior, including aggregation and misfolding, leading to poor efficacy, impaired pharmacokinetics, and immunogenicity. It is advantageous to understand the developability and potential liabilities of a protein candidate prior to costly in vivo studies or clinical trials. This review summarizes biophysical and structural techniques used to characterize ADCs and introduces emerging techniques aimed at accurately assessing the developability of protein candidates. Stability is commonly assayed using techniques like differential scanning calorimetry (DSC), differential scanning fluorimetry (DSF), or spectroscopic probes such as circular dichroism and intrinsic fluorescence. Drug-to-antibody ratio (DAR) is a critical parameter that can be measured using absorbance spectroscopy or chromatographic analysis. Aggregation and self-association can be probed using scattering techniques such as dynamic light scattering (DLS), static light scattering (SLS), and size exclusion chromatography–multi-angle light scattering (SEC-MALS), as well as more specialized approaches such as fluorescence correlation spectroscopy (FCS) and analytical ultracentrifugation (AUC). Mass spectrometry (MS) provides extremely valuable insight into stability, covalent modifications, and, through approaches like hydrogen–deuterium exchange (HDX-MS), structural dynamics of ADCs. Looking forward, the use of biophysical assays in ex vivo matrices and strategic use of artificial intelligence/machine learning (AI/ML) approaches are likely to advance the efficient and rapid development of ADCs and other next-generation protein therapeutics. Full article

This study proposes a method to quantify uncertainty in the scattering parameter (S-parameter) measurements when using de-embedding techniques. After calibrating the measurement setup with reference standards, de-embedding algorithms are employed to extract the intrinsic S-parameter of the device under test (DUT). This process introduces additional complexity to the uncertainty analysis. This study investigates the sources of uncertainty inherent to vector network analyzer (VNA) measurements. Subsequently, a covariance matrix-based approach is employed to propagate these uncertainties, culminating in the quantification of S-parameter uncertainty. The effectiveness of the proposed is determined by comparing the measured S-parameters of power dividers and couplers to their nominal values, considering parameters such as balance, coupling, and voltage standing wave ratio (VSWR). Additionally, an uncertainty analysis is conducted for the power divider’s S-parameters, tracing the uncertainty sources back to the calibration standards. Full article

This study proposed a standardized oilseed rape seedling-carrying potting molding method to improve the adaptability of mechanical transplanting of potting seedlings. This method aims to address the failure in seedling pick-up and transport during the mechanized transplanting of rapeseed pot seedlings, which is caused by matrix breakage and seedling damage. This study selected cylindrical oilseed rape seedling-carrying potting as the research object and investigated the relationship between the physical characteristics of seedling-carrying potting and the proportion of the composition of the matrix soil as well as the characteristics of seedling growth after planting. The optimal parameter combination of the matrix soil was obtained using Design-Expert 8.0.6 software: dry matter ratio of 4:1, compression ratio of 0.36, and moisture content of 45%. A single-factor test was conducted using a seedling-carrying potting test bed. According to the single-factor test results, the dry matter ratios (commercial substrate: clay loam mass ratios of 2:1, 3:1, and 4:1), matrix soil compression ratios (0.35, 0.40, and 0.45), and matrix soil moisture content (35%, 40%, and 45%) were selected as the factors of influence, while the drop loss rate, shear resistance, and scattering rate were used as the indicators of evaluation. The drop loss rate of seedling-carrying potting under this parameter combination was 1.5%, the shear resistance was 7.1 N, and the scattering rate was 34.9%. Validation tests were conducted on a seedling-carrying potting test bed, and the relative errors between the actual and simulated values of the drop loss rate, shear resistance, and scattering rate were 7.1%, 7.0%, and 8.4%, respectively, verifying the accuracy of the model and the optimized parameters. Comparison tests of the growth characteristics of the optimized seedling-carrying potting, hole-tray seedling, and bare seedling in field transplanting were conducted. The results displayed that root length, root diameter, root dry matter, chlorophyll content, and seedling vigor index consistently followed the same descending order: seedling-carrying potting > hole-tray seedlings > bare seedlings. Compared to hole-tray seedlings, the corresponding growth characteristics of seedling-carrying potting were 11.7%, 10%, 21.7%, 2.8%, and 27.8% higher, respectively. Compared to bare seedlings, they were 17.1%, 12.5%, 32.2%, 10.8%, and 32.7% higher, respectively. The seedling length, seedling width, plant taper angle, and dry matter mass of stem and leaves were, in descending order, greater in hole-tray seedlings, followed by seedling-carrying potting, and then bare seedlings. In comparison, the corresponding growth characteristics of seedling-carrying potting were 8.9%, 9.8%, 2.3%, and 30.6% higher than those of bare seedlings, respectively. Full article

Haze removal methods based on the estimation of scene depth ratio in the Atmospheric Scattering Model (ASM) have achieved satisfactory results. However, the ASM ignores the blur equivalent to a point spread function caused by forward scattering. This paper proposes a simplified United Scattering Transmission Model (USTM), in which both forward scattering and back scattering are taken into consideration physically. It utilizes Taylor expansion to correlate the hazy image and its second-order operator with the dehazed image. Additionally, we establish a layered decomposition mechanism of the scattering medium; by fitting the limitation expression and the image signal at infinity, the parameters related to the inherent optical properties used in the model can be obtained. When the stable transmittance estimation approaches are applied into this USTM, the scene radiance can be restored effectively. We conducted evaluation experiments on datasets including RESIDE-RTTS (Real-world Task-Driven Testing Set), Haze4K, and DenseHaze, using metrics such as PSNR, SSIM, newly visible edges and the ratio of the gradients. The results demonstrate that USTM achieves satisfactory results across multiple evaluation dimensions. Regarding the core objective fidelity metric PSNR, it achieves an optimal score of 11.87 dB, representing an approximate 3.85% improvement over the second-best method. Compared to the traditional ASM, the USTM shows an average improvement of approximately 23.5% in edge restoration capability (newly visible edges) and an average improvement of approximately 18.1% in gradient fidelity (the mean ratio of the gradients). Furthermore, compared with advanced deep learning dehazing methods, our method remains highly competitive in edge and gradient restoration metrics, and its lightweight design provides excellent efficiency and compatibility with downstream tasks. The comprehensive results show that the USTM achieves effective improvements in both physical accuracy and detail restoration performance. Full article