Search Results (171)

Experimental tests on confined concrete specimens are essential to characterize the mechanisms activated under varying degrees of confinement. Such characterization is critical for understanding how full, partial, and non-uniform wrapping configurations influence strength and ductility enhancements. This study investigates the compressive behavior of concrete cylinders (160 mm × 320 mm) confined using full, partial, and non-uniform carbon fiber-reinforced polymers (CFRP) configurations. In the first phase, all wrapping schemes were applied with equivalent quantities of CFRP, enabling a direct performance comparison under material parity. The results indicate that non-uniform confinement (NUC) achieved approximately 15% higher axial strength than full confinement (FC2) using the same amount of CFRP. In the second phase, the NUC configuration was tested with 25% less CFRP material, yet the reduction in strength was limited to about 3%, demonstrating its superior efficiency. A new predictive model was developed to estimate peak axial stress and strain in CFRP-confined concrete cylinders. Compared to existing models, the proposed model demonstrated greater predictive accuracy (R² = 0.98 for stress and 0.91 for strain) and reduced error metrics (RMSE and scatter index). ANOVA confirmed the statistical significance of the model’s predictions (p < 0.00001 for stress, p = 0.002 for strain). These findings highlight the performance advantages and material efficiency of non-uniform CFRP confinement and support the utility of the proposed model as a practical design tool for developing advanced confinement strategies in structural engineering. Full article

Phase images from gradient echo MRI sequences reflect underlying magnetic field inhomogeneities but are inherently wrapped within the range of −π to π, requiring phase unwrapping to recover the true phase. In this study, we present DIP-UP (Deep Image Prior for Unwrapping Phase), a framework designed to refine two pre-trained deep learning models for phase unwrapping: PHUnet3D and PhaseNet3D. We compared the DIP-refined models to their original versions, as well as to the conventional PRELUDE algorithm from FSL, using both simulated and in vivo brain data. Results demonstrate that DIP refinement improves unwrapping accuracy (achieving ~99%) and robustness to noise, surpassing the original networks and offering comparable performance to PRELUDE while being over three times faster. This framework shows strong potential for enhancing downstream MRI phase-based analyses. Full article

Chloride ions in zinc refining accelerate equipment corrosion and anode and cathode losses, increase lead content, and reduce zinc quality. Therefore, the removal of chloride ions has become a research priority. The existing copper slag dechlorination process has problems such as the solid film barrier leading to impeded mass transfer, product wrapping triggering active site coverage, and incomplete reactions due to insufficient reaction-driving force, leading to low utilization of copper slag, poor dechlorination efficiency, and long reaction times. To address these issues, a new method of deep dechlorination based on the reduction of Cu²⁺ by liquid-phase mass transfer is proposed in this paper. The process utilizes ascorbic acid as a reducing agent, establishes a homogeneous aqueous phase reaction system, breaks through the solid membrane barrier, and avoids the encapsulation of the product layer, achieving efficient dechlorination. The enol structure of ascorbic acid promotes rapid dechlorination through proton-coupled electron transfer (PCET). Thermodynamic calculations show that compared to the current copper slag dechlorination process, this method increases the reaction-driving force by 18.6%, reduces the Gibbs free energy (ΔG^θ) by 59.3%, and increases the equilibrium constant by 6.7 × 10⁹ times, making the reaction more complete and achieving a higher degree of purification. The experimental results show that under optimized conditions, the chloride ion concentration in the solution decreases from 1 g/L to 0.0917 g/L within 20 min, with a removal rate of 90.8%. The main precipitate is CuCl. This process provides a more efficient solution to the chloride ion contamination problem in the hydrometallurgical zinc refining process. Full article

Implied by the terminologies “Harang Reversal” and “Harang Discontinuity”, there are two significant features of the Harang region: (i) the reversal of auroral electrojets along with the underlying plasma convection flow and electric (E) fields, and (ii) the discontinuity between the electrojets/convection flows/E-fields. Even the earliest studies reported the discontinuity observed in the meridional E-field. Conversely, some of the previous studies state that convection flow and E-field reversals do not involve any physical discontinuity. We investigate these two features (i–ii) observed in five topside-ionosphere Harang scenarios. Each scenario occurred during a sequence of events that led to the onset of the substorm expansion phase, when the Harang region was newly formed. Our results show (1) the newly formed Harang region between the dusk and dawn convection cells, where one convection cell wraps around the other, (2) the zonal drift- and E-field reversals, (3) the discontinuity between the dusk and dawn convection flows and also between the reversing E-field components, and (4) the Earthward electromagnetic energy deposition locally minimizing or diminishing within the discontinuity and peaking within the reversing zonal drift and E-fields. Thus, the observed convection flow and E-field reversals involved the development of discontinuity. Full article

Phase wrapping is a common phenomenon in optical full-field imaging or measurement systems. It arises from large phase retardations and results in wrapped-phase maps that contain essential information about surface roughness and topology. However, these maps are often degraded by noise, such as speckle and Gaussian, which reduces the measurement accuracy and complicates phase reconstruction. Denoising such data is a fundamental problem in computer vision and plays a critical role in biomedical imaging modalities like Full-Field Optical Interferometry. In this paper, we propose WPD-Net (Wrapped-Phase Denoising Network), a lightweight deep learning-based neural network specifically designed to restore phase images corrupted by high noise levels. The network architecture integrates a shallow feature extraction module, a series of Residual Dense Attention Blocks (RDABs), and a dense feature fusion module. The RDABs incorporate attention mechanisms that help the network focus on critical features and suppress irrelevant noise, especially in high-frequency or complex regions. Additionally, WPD-Net employs a growth-rate-based feature expansion strategy to enhance multi-scale feature representation and improve phase continuity. We evaluate the model’s performance on both synthetic and experimentally acquired datasets and compare it with other state-of-the-art deep learning-based denoising methods. The results demonstrate that WPD-Net achieves superior noise suppression while preserving fine structural details even with mixed speckle and Gaussian noises. The proposed method is expected to enable fast image processing, allowing unwrapped biomedical images to be retrieved in real time. Full article

As a candidate material for turbine blades in aerospace engines, Nb-Si-based alloys have attracted significant research attention due to their high melting point and low density. However, their poor high-temperature oxidation resistance limits practical applications. Different alloying elements, including Ti, Mo, and Hf, were added to Nb-Si-based alloys to study the microstructural evolution of alloys. Additionally, the oxidation behavior and the oxidation kinetics of different alloys, as well as the morphology and microstructure of oxide scale and interior alloys at 1523 K from 1 h to 20 h were analyzed systematically. The current findings indicated that the Mo element is more conducive to promoting the formation of high-temperature precipitates of β-Nb₅Si₃ than the Ti and Hf elements. Inversely, the Ti element tends to cause the transition from high-temperature-phase β-Nb₅Si₃ to low-temperature-phase α-Nb₅Si₃, while the Hf element improves the appearance of the γ-Nb₅Si₃ phase but inhibits the other phases and refines the primary Nbss effectively. Noteworthily, compared with the oxidation weight gain of different alloys, Nb-16Si-20Ti-5Mo-3Hf-2Al-2Cr alloy has excellent high-temperature oxidation resistance, in which the oxidation products are TiNb₂O₇, Nb₂O₅, SiO₂, TiO₂, and HfO₂. It can be determined that in the oxidation process, the Ti element will preferentially form an oxide film of TiO₂, thereby wrapping around the matrix phases, protecting the matrix, and improving the antioxidant capacity, while the Hf element can form an infinite solid solution with the matrix and consume the small number of oxygen atoms entering the matrix, so as to achieve the effect of improving the oxidation resistance. Full article

Phase demodulation is the core of fringe projection profilometry systems. However, current U-Net-based phase demodulation approaches demonstrate deficiencies in global context propagation, adversely affecting wrapped phase extraction precision. To this end, this paper proposes a deep-learning-based model for single-shot wrapped phase extraction, named the full-scale connection and attention enhancement network (SEC-UNet3+). The network mitigates the limitations of the traditional U-Net architecture by introducing cross-layer full-scale connection and a feature integration module in the decoder, enabling efficient interaction between shallow detail features and deep semantic features. Unlike the skip connection strategy within the same-layer in U-Net, cross-layer full-scale connection can enhance the feature utilization. Additionally, a skip connection is embedded between the feature mapping layer and the output transformation layer in the squeeze and excitation module, preventing information loss during the feature calibration process. Compared to the U-Net model, the proposed method achieves an approximately 5% to 15% reduction in both the mean squared error and mean absolute error for phase extraction. The experimental results confirm that SEC-UNet3+ outperforms traditional Fourier transform and mainstream U-Net-based approaches in phase demodulation accuracy, proving particularly effective for single-shot wrapped phase retrieval in dynamic scenarios. Full article

The surface magnetic field of three-core cables is essential for estimating phase currents and locating the single-phase grounding faults. However, the double-layer steel tape-wrapped structure will shield the magnetic field, affecting the measurement of the surface magnetic field of the three-core cable. Disregarding the shielding effect of the steel tape during the measurement of the surface magnetic field of the cable leads to an erroneous phase current obtained from the inversion of the observed magnetic field. To measure the surface magnetic field and invert it to obtain three-phase currents accurately, a model to evaluate the shielding effect of the steel tape is proposed, and a differential evolutionary algorithm method is proposed to invert it to obtain the three-phase currents. The results indicate that the difference between the analytical and numerical solutions is below 5%. As the thickness of steel tape increases, the shielding coefficient also increases. The differential evolution algorithm can accurately estimate the three-phase currents. The differential evolutionary algorithm measured the current amplitude of phases A, B, and C to 354.62 A, 354.33 A, and 323.46 A, respectively. Additionally, the algorithm measured the current phase magnitudes to be −8.21°, −128.01°, and 111.87°. The maximum value of the amplitude error of the three-phase current is 4.83%, and the maximum value of the phase error is 8.01°. Full article

Aiming at the problems of low utilization rate of spodumene resources and serious environmental pollution, our team proposes a clean process to produce manganese-silicon alloy for lithium enrichment by carbothermal reduction of spodumene. In this process, the melting point and viscosity of the slag phase are very high, which affects the slag discharge and slag–metal separation. Therefore, this experiment considers the addition of CaO as a slagging agent based on the previous process and tests and analyzes the slag phase under different CaO contents. When the CaO content is 30%, the slag phase is mainly Ca₂Al₂SiO₇; the reduction rate of lithium is 99.02%; the direct yield of the alloy is 89.12%; and the melting point of the slag is 1260 °C. It can melt and wrap the alloy before removing the alloy, which has heat preservation and oxidation resistance. The viscosity of the slag at 1360 °C is 0.11 Pa·s, which is within the optimum viscosity range of the slag in actual industrial production. Experiments show that the addition of CaO is beneficial to the removal of lithium and the separation of slag and metal, which lays a good foundation for the industrialization development of the previous process and improves the economic benefits of the whole process. Full article

Fringe projection profilometry (FPP) is a measurement technique widely used in the field of 3D reconstruction. However, it faces issues of phase shift and reduced fringe modulation depth when measuring translucent objects, leading to decreased measurement accuracy. To reduce the impact of surface scattering effects on the wrapped phase during actual measurement, we propose a single-frame phase retrieval method named GAN-PhaseNet to improve the subsequent measurement accuracy for translucent objects. The network primarily relies on a generative adversarial network framework, with significant enhancements implemented in the generator network, including integrating the U-net++ architecture, Resnet101 as the backbone network for feature extraction, and a multilevel attention module for fully utilizing the high-level features of the source image. The results of the ablation and comparison experiment show that the proposed method has superior phase retrieval results, not only achieving the accuracy of the conventional method on objects with no scattering effect and a slight scattering effect but also obtaining the lowest errors on objects with severe scattering effects when compared with other phase retrieval convolution neural networks (CDLP, Unet-Phase, and DCFPP). Under varying noise levels and fringe frequencies, the proposed method demonstrates excellent robustness and generalization capabilities. It can be applied to computational imaging techniques in the fringe projection field, introducing new ideas for the measurement of translucent objects. Full article

A drawback of fringe projection profilometry (FPP) is that it is still a challenge to perform efficient and accurate high-resolution absolute phase recovery with only a single measurement. This paper proposes a single-model self-recovering fringe projection absolute phase recovery method based on deep learning. The built Fringe Prediction Self-Recovering network converts a single fringe image acquired by a camera into four single mode self-recovering fringe images. A self-recovering algorithm is adopted to obtain wrapped phases and fringe grades, realizing high-resolution absolute phase recovery from only a single shot. Low-cost and efficient dataset preparation is realized by the constructed virtual measurement system. The fringe prediction network showed good robustness and generalization ability in experiments with multiple scenarios using different lighting conditions in both virtual and physical measurement systems. The absolute phase recovered MAE in the real physical measurement system was controlled to be 0.015 rad, and the reconstructed point cloud fitting RMSE was 0.02 mm. It was experimentally verified that the proposed method can achieve efficient and accurate absolute phase recovery under complex ambient lighting conditions. Compared with the existing methods, the method in this paper does not need the assistance of additional modes to process the high-resolution fringe images directly. Combining the deep learning technique with the self-recovering algorithm simplified the complex process of phase retrieval and phase unwrapping, and the proposed method is simpler and more efficient, which provides a reference for the fast, lightweight, and online detection of FPP. Full article

The TiB₂ film exhibits exceptional hardness and chemical stability due to its unique crystal structure and robust covalent bonds, but it also demonstrates high brittleness and poor toughness, which restricts its practical applications in engineering. By appropriately incorporating metal dopants, the toughness of the ceramic matrix can be enhanced without compromising its inherent hardness. In this study, TiB₂ films with different nickel contents (0–32.22 at.%) were fabricated through radio frequency magnetron sputtering. The microstructure, chemical composition, phase structure, and mechanical properties were analyzed using scanning electron microscopy, transmission electron microscopy, X-ray diffraction, X-ray photoelectron spectroscopy and nanoindentation tester. The pure TiB₂ film exhibited (0001) and (0002) peaks; however, the addition of nickel resulted in broadening of the (0001) peak and disappearance of the (0002) peak, and no crystalline nickel or other nickel-containing phases could be identified. It was found that the incorporation of nickel refines the grain structure of titanium diboride, with nickel present in an amorphous form at the boundaries of titanium diboride, thereby forming a wrapped structure. The enrichment of nickel at the grain boundary becomes more pronounced as the nickel content is further increased, which hinders the growth of TiB₂ grains, resulting in the thinning of columnar crystals and formation of nanocrystalline in the film, and the coating hardness remains above 20 GPa, when the nickel content is less than 10.83 at.%. With the increase in nickel content, titanium diboride exhibited a tendency to form an amorphous structure, while nickel became increasingly enriched at the boundaries, and the coating hardness and elastic modulus decreased. The wrapped microstructure could absorb the energy generated by compressive shear stress through plastic deformation, which should be beneficial to improve the toughness of the coatings. The addition of nickel enhanced the adhesion between the film and substrate while reducing the friction coefficient of the film. Specifically, when the nickel content reached 4.26 at.%, a notable enhancement in both nanohardness and toughness was observed for nanocomposite films. Full article

Iodine, being an important resource, must be recovered and reused. Iodine is not only attracted to the hydrophobic silicone membrane but also easily vaporized. In this study, we explored the use of five types of silicone hollow fiber membrane modules (SFMMs) for separating iodine in the gaseous phase. In the SFMM, iodine gas and the recovery solution (sodium sulfite and sodium carbonate at a concentration of 10 mM each) were flowed outside and inside the silicone hollow fiber, respectively, in a co-current-flow manner. At an iodine gas flow rate of 0.2 L/min (8.4 × 10⁻³ mmol-I₂/L), the capture efficiency of iodine into the SFMM was approximately 100% for all five SFMMs. With increasing feed gas flow rates, the capture efficiency of iodine decreased, reducing to approximately 50% at 0.8 L/min. However, the recovery efficiency of iodine in the recovery solution was 60–30% at 0.2–0.8 L/min. This decrease in capture efficiency with increasing flow rates was because iodine could not spread and diffuse successfully in the SFMM, resulting in a low recovery efficiency of iodine. Thus, we next improved the structure of the SFMM by placing a perforated pipe in the center of the module. The perforated pipe effectively directs the iodine feed gas from the holes in the pipe to the hollow fiber membrane bundle wrapped around the pipe. With the improved SFMM, the capture efficiency markedly increased to approximately 100% in the range of the flow rates tested in our experiments. The recovery efficiency also increased to ≥70%. These data illustrate the potential application of the improved SFMM for recovering iodine in the gaseous phase. Full article

In this paper, we demonstrate a blazed phase grating to achieve tunable beam steering and propose a novel algorithm to reduce the stripe noise in wrapped phase. To control the diffraction angle to steer light to the desired direction, an electrically tunable transmission-type beam deflector based on liquid crystals is introduced, and electric fields are applied to the patterned indium tin oxide electrodes to change its phase retardation. Two different 2π phase-wrapping methods are applied to obtain various diffraction angles within the minimum cell-gap, and the method of equal interval of phase achieves a worthwhile diffraction efficiency compared to the methods based on equal interval of diffraction angle. The proposed method is able to completely eliminate the stripe noise in all steering angles that helps to improve the diffraction efficiency. Full article

Fourier ptychographic microscopy (FPM) has recently emerged as an important non-invasive imaging technique which is capable of simultaneously achieving high resolution, wide field of view, and quantitative phase imaging. However, FPM still faces challenges in the image reconstruction due to factors such as noise, optical aberration, and phase wrapping. In this work, we propose a semi-supervised Fourier ptychographic transformer network (SFPT) for improved image reconstruction, which employs a two-stage training approach to enhance the image quality. First, self-supervised learning guided by low-resolution amplitudes and Zernike modes is utilized to recover pupil function. Second, a supervised learning framework with augmented training datasets is applied to further refine reconstruction quality. Moreover, the unwrapped phase is recovered by adjusting the phase distribution range in the augmented training datasets. The effectiveness of the proposed method is validated by using both the simulation and experimental data. This deep-learning-based method has potential applications for imaging thicker biology samples. Full article