Soft biomaterials have found widespread applications across the biomedical field; however, single-component soft biomaterials suffer from a limited tunable range of mechanical properties. To address this critical limitation, this study fabricated sinusoidal microlattice scaffolds via melt electrowriting technology and embedded them into soft biomaterials for mechanical reinforcement. A theoretical design framework was constructed for sinusoidal microlattice scaffolds to forecast the relationship between the mechanical behaviors of the materials and three key geometric parameters: amplitude, wavelength, and fiber diameter. Additionally, a lag error trajectory compensation strategy was developed to ensure the high-precision fabrication of the scaffolds. To validate the theoretical model, experimental tests and finite element simulations were conducted on sinusoidal microlattices with three distinct topological structures (triangular, orthogonal, and rectangular), revealing excellent agreement between the theoretical predictions, experimental results, and simulation outcomes. Mechanical characterizations of lattice-hydrogel composites confirmed that sinusoidal microlattice scaffolds exert a remarkable reinforcing effect on soft biological matrices. Specifically, the scaffolds significantly enhanced the ultimate stress and failure strain of hydrogels, while maintaining excellent interfacial compatibility between the scaffold and the matrix. Numerical results further demonstrated that modulating the geometric parameters enables broad-range regulation of the scaffold's mechanical properties, including elastic modulus (ranging from several kPa to several MPa), extensibility (with a maximum strain of 226%), and Poisson's ratio (varying from 0.46 to 1.75). This study provides a novel theoretical approach and technical support for predicting the mechanical properties of microlattice structures and rationally designing mechanically reinforced soft biomaterials, thereby holding great significance for advancing biomedical fields.

Vibration monitoring of glass substrate transfer systems is crucial for ensuring the stable operation of Flat Panel Display (FPD) manufacturing equipment. Fabrication defects in the functional nanofiber membrane of flexible vibration sensors can significantly degrade sensing performance and lead to inaccurate monitoring results. To address the challenge of achieving an effective balance between detection accuracy and inference efficiency in such defect-dense scenarios characterized by large variations in defect scale, this paper proposes a novel defect detection model, termed MA-YOLO. The proposed model incorporates four key architectural enhancements: the Multi-level Bidirectional Feature Aggregation Network (MLBAN), the Multi-Receptive Field Adaptive Fusion Module (MRAF), the Morphology-Adaptive Feature Extraction Module (MA-C2f), and the Interactive Dynamic Decoupling Head (IDDH). These components collaboratively improve defect feature extraction, multi-scale feature fusion, and localization performance while maintaining a lightweight architecture and high inference speed. Experimental results on a self-constructed defect dataset demonstrate that MA-YOLO achieves a mean Average Precision (mAP@0.5) of 91.9%, which is a 3.1 percentage point improvement over the baseline model. Moreover, with only 9.15 million parameters and an inference speed of 119.05 FPS, the proposed model exhibits superior overall performance compared with several mainstream and state-of-the-art object detection methods.

Whispering gallery mode (WGM) microresonators have shown great potential for precise displacement measurement due to their compact size, ultrahigh sensitivity, and rapid response. However, traditional WGM-based displacement sensors are susceptible to environmental noise interference, resulting in reduced accuracy and too long signal demodulation time. To address these limitations, this article proposes a multimodal displacement sensing method for surface nanoscale axial photonics (SNAPs) resonators based on deep learning (DL) techniques. A 1-D convolutional neural network (1D-CNN) is used to extract features from the full spectrum, which significantly improves the noise immunity and sensing accuracy while avoiding the time-consuming spectral preprocessing. Experimental results show that the average prediction error is as low as 0.05 mu m and the maximum error does not exceed 1.4 mu m when using the 1D-CNN network for displacement measurements. This work provides an effective solution for fast, highly accurate and robust displacement sensing.

Gas-assisted coaxial electrospinning (GACES) is a simple and general method for the mass preparation of coaxial nanofiber membranes, which has great industrial potential. However, in the manufacturing process, due to the bending instability of the jet in the electric field and the pulling effect of the gas flow field, the deposition uniformity of the fiber is still a big problem. Through finite element simulation analysis of the flow field in the manufacturing process and the construction of the jet mechanics model after adding the flow field, the influence mechanism of coaxial auxiliary flow on the fiber deposition area and its uniformity was successfully revealed in this research. Finally, the deposition area and thickness uniformity of coaxial fibers are increased by 3 times (the deposition area: 19.63 cm2 → 78.50 cm2) and 2.34 times (the standard variance: 3 μm2 → 10 μm2) by gas-assisted coaxial electrospinning. At the same time, the coaxial auxiliary gas flow also reduces the coaxial fiber diameter by 36.9% (the average fiber diameter: 241 nm ± 5 nm → 152 nm ± 23 nm) and the distribution range by 66% (the standard variance: 1.5 × 102 nm2 → 51 nm2). This research provides a reliable idea and experimental basis for homogeneous preparation of coaxial nanofiber membranes.

Electrohydrodynamic (EHD) technology is renowned for its significant advantages in high resolution and micro-nanoscale printing, demonstrating an immense potential in the development of micro-nano devices. During the printing process, it is inevitably influenced by different interferences, which result in printing errors that influence its printing precision. This article summarizes several research topics on printing errors of EHD printing technology, involving the sources, and correction of different types of printing errors. First, the induced factors of printing errors are summarized in details, which are used to categorize the error correction methods. Then, the existing correction methods are comprehensively summarized and analyzed according to the types of printing errors. Finally, the conclusions are provided, involving some potential research topics.

The total variation(TV)minimization algorithm is widely applied for handling sparse or noisy projection data in computed tomography(CT),to enable high-precision CT image reconstruction.However,traditional TV regularization terms suffer from issues of isotropy and single directionality,which limit improvements in the quality of reconstructed images.To address this problem,this paper proposes a sparse angle CT image reconstruction algorithm based on multi-directional total variation.This method incorporates information from multiple directions and adjusts the regularization factor,to better preserve the structural characteristics of the reconstructed image.Experiments were conducted using the Shepp-Logan phantom model and gear CT projection data,with peak signal-to-noise ratio,root mean-square error,and structural similarity used as evaluation criteria for reconstruction image quality.The results were compared with those of three other traditional reconstruction algorithms.The experimental results show that the images reconstructed by the proposed algorithm are closer to the original images and superior in detail preservation compared to those of the other three algorithms.

Five-axis precision dispensing machines are employed for semiconductor packaging. The dispensing accuracy is significantly affected by multiple geometric errors among the five axes. This paper proposes a vision-based measurement (VBM) system for identifying geometric errors and calibrating kinematics. The VBM system is also employed to complete the detection of the workpiece. A kinematic model of the machine was established using a local product-of-exponential formulation of screw theory. A geometric error identification algorithm was designed. Eight position-independent geometric errors (PIGEs) and position-dependent geometric errors (PDGEs) were involved. The system of overdetermined equations was solved. Combining the singular value decomposition and regularization, eight PIGEs in the A and C axes were identified. Comprehensive error measurement results verified the proposed approach. The VBM system measured a mean spatial position error of approximately 59.9 μm and a mean orientation error of about 160 arcsec for the end-effector, reflecting the geometric error level of the prototype machine. The proposed approach provides a feasible and automated calibration solution for five-axis precision dispensing machines.

We propose and experimentally demonstrate a robust and efficient coupling method for whispering-gallery-mode (WGM) microresonators using a non-adiabatic tapered fiber. An ultrashort fiber with a total length of ∼1mm and a minimum waist diameter of ∼5µm was fabricated through a two-step process combining arc discharge and oxyhydrogen flame heating. The fiber excites cladding modes and achieves coupling efficiencies of up to 92%, while offering improved mechanical robustness compared with conventional adiabatic tapered fibers, which are typically 20–30 mm long with fragile micron-scale waists. Experiments show that the proposed fiber excites both Lorentzian and Fano resonances and exhibits high sensitivity to axial displacement. Efficient coupling was further demonstrated with SNAP, bottle, and sausage resonators, confirming the universality of this approach. The compact structure, low fabrication cost, and enhanced robustness make the proposed fiber a promising candidate for the development of miniaturized, high-sensitivity, and multifunctional WGM optical sensors.

To improve the measurement precision of the optical encoder working at various speeds, an adaptive high-precision measurement framework is designed based on deep reinforcement learning (DRL), cascading a compensation module, and a positioning module and a decoding module. To the best of our knowledge, this is the first attempt to introduce the DRL into the measurement via the optical encoder working at various speeds. To this end, an improved deep Q-learning network (DQN) is designed to construct the compensation module. Specifically, to construct the state space, a mapping rule is designed to convert high-dimensional grating patterns into 1-D angle variables. Adaptive actions and adaptive reward are designed to improve the compensation ability of the DQN model. The compensated grating patterns successively pass through the positioning module and the decoding module for high-precision measurement. Experimental results indicate that the designed measurement method is superior to exiting methods for precise measurement of the optical encoder at various speeds, especially for high-speed measurement.

We propose and demonstrate an absolute displacement sensor based on a surface nanoscale axial photonics (SNAP) microresonator and similarity matching method. To obtain a direct readout of the absolute displacement from the spectrum, we calculate the correlation coefficient between the detected spectrum and the spectrum in the reference spectrum library. The feasibility of this method is first verified by simulation, and further, the influence of signal-to-noise ratio (SNR), sampling points, coupling parameters, and resonant wavelengths on displacement sensing accuracy is studied. Simulation results show that this method is highly resistant to noise interference and low sampling points. Importantly, we experimentally implemented SNAP's absolute displacement sensing with an error of +/- 2 mu m after optimizing the reference library. This work lays the foundation for the development of high-performance probe absolute position sensors based on the whispering gallery mode (WGM) resonators.

As the global facial mask market continues to grow, consumers have put forward higher requirements for the functionality and ingredients of mask products. Ordinary facial masks mostly use ordinary non-woven fabrics as the mask base fabric and are used with essence. Preservatives are generally added. At the same time, they are susceptible to the influence of the external environment and are easily oxidized, causing the mask to deteriorate and cause skin allergic reactions. In addition, traditional facial masks have problems such as poor fit with the skin, poor breathability, insufficient absorption of nutrient solutions, and easy dripping. The high specific surface area and high porosity of a nanofiber mask prepared by electrospinning technology are beneficial to the skin’s absorption of nutrients, and it has good fit with the skin and strong breathability. A unique advantage of this nanofiber mask is that it uses spray. After the mask is sprayed with water or essence, the water-soluble polymer within it can be quickly dissolved, saving a lot of time. Nanofiber facial mask products can effectively solve consumer pain points and are conducive to the high-end development of facial masks. Therefore, this article combines needleless electrospinning technology to develop a new solid-state, preservative-free, quick-dissolving nanofiber facial mask that can be prepared on a large scale. Based on needleless electrospinning technology, this article deeply explores the process parameters and their influencing mechanisms for preparing nanofiber, quick-dissolving facial masks to achieve the stable preparation of nanofiber facial masks with the best morphology; a comprehensive analysis of the structure and influence of nanofiber facial masks from micro and macro perspectives demonstrates their performance and allows evaluation of them. The experimental results show that the mask morphology is optimal under the process conditions of using a spinning liquid of 20% collagen peptide solution, a spinning voltage of 30 kV, a collection distance of 19 cm, and a liquid supply speed of 130 mL/h.

To improve the accuracy of absolute grating ruler positioning and decoding, a positioning and decoding method based on end-to-end deep learning framework is proposed. Attention modules were integrated to improve the positioning of symbol edges of UNet++ , and a channel edge information extraction network was designed to achieve regression prediction of channel images to position information. To reduce cumulative errors, a loss function was designed based on the characteristics of absolute grating ruler images, which integrates the symbol edge-positioning network and the code channel edge information extraction network into an end-to-end network framework, thereby constructing a code channelpositioning module. A pseudo-random code decoding method was designed based on the center pixel of the code path to achieve absolute grating size path decoding. The experimental results demonstrate that the proposed positioning decoding method can improve the measurement accuracy of absolute grating rulers, within a 95% confidence interval ( - 0.206, 0.243) mu m, the root mean square error is 0.265 mu m, superior to existing absolute grating ruler positioning and decoding methods.

Multi-needle electrospinning is an efficient method for producing nanofiber membranes. However, fluctuations in the fluid flow rate during the process affect membrane quality and cause instability, an issue that remains unresolved. To address this, a multi-stage flow runner spinneret needs to be developed for large-scale nanofiber membrane production. This paper uses COMSOL finite element software to simulate polymer flow in the spinneret runner. From this, the velocity field distribution and velocity instability coefficient were obtained, providing theoretical guidance for optimal spinneret design. In addition, response surface analysis (RSM) was used to experimentally explore the process parameters, and then residual probability plots were used for reliability verification to evaluate the effect of each process parameter on fiber diameter. These process parameters can guide the controlled production of nanofibers during multi-needle electrospinning.

The current network for the dual-task grinding wheel defect semantic segmentation lacks high-precision lightweight designs, making it challenging to balance lightweighting and segmentation accuracy, thus severely limiting its practical application in grinding wheel production lines. Additionally, recent approaches for addressing the natural class imbalance in defect segmentation fail to leverage the inexhaustible unannotated raw data on the production line, posing huge data wastage. Targeting these two issues, firstly, by discovering the similarity between Coordinate Attention (CA) and ASPP, this study has introduced a novel lightweight CA-ASP module to the DeeplabV3+, which is 45.3% smaller in parameter size and 53.2% lower in FLOPs compared to the ASPP, while achieving better segmentation precision. Secondly, we have innovatively leveraged the Masked Autoencoder (MAE) to address imbalance. By developing a new Hybrid MAE and applying it to self-supervised pretraining on tremendous unannotated data, we have significantly uplifted the network’s semantic understanding on the minority classes, which leads to further rises in both the overall accuracy and accuracy of the minorities without additional computational growth. Lastly, transfer learning has been deployed to fully utilize the highly related dual tasks. Experimental results demonstrate that the proposed methods with a real-time latency of 9.512 ms obtain a superior segmentation accuracy on the mIoU score over the compared real-time state-of-the-art methods, excelling in managing the imbalance and ensuring stability on the complicated scenes across the dual tasks.

Coenhancement of optical transmission and the Faraday effect are always the goals to pursue for photoelectric devices, while the current research almost concentrated on the properties based on gratings and photonic crystal structures. Nanopillars (NPs) are structures with a relatively simple preparation that have been widely used in many fields, where the Faraday effect has been rarely studied. In this work, a Faraday rotator in the wavelength range of 500-800 nm is proposed based on bismuth-iron garnet (BIG) with magnetic plasmonic NPs structure, in which the hemispherical shaped silver covered on top and silica used as the substrate for enhancement effect. Through this rotator, two peak values for both Faraday rotation angle and transmittance can be achieved, no matter what the wavelength changes are. The transmittance, Faraday rotation angle, and figure of merit (FOM) can be controlled by tuning the thickness and lattice constant of the BIG NPs, the radius of the silver hemisphere, and the external magnetic field. Furthermore, the physics mechanism of the large Faraday effect and extraordinary optical transmission is explained by employing the electric field distribution diagrams. Finally, the optimized FOM simply can be raised to 0.9, achieving well overall performance in the visible light range. The results have certain values for achieving simpler and better performance of magneto-optical devices under visible wavelength range, such as optical isolators and magneto-optical sensors.

Single-pixel imaging techniques have been extensively studied because of the low requirements for the detector resolution and the low equipment cost. However, a large amount of noise and ringing commonly occur in the reconstructed images via previous single-pixel imaging methods at ultra-low sampling rates, which limits the real applications. In this paper, a novel Fourier single-pixel imaging method is proposed to reconstruct high-quality single-pixel imaging images at ultra-low sampling rates, which involves the stages of Fourier single-pixel imaging, block matching, upgraded weight nuclear norm minimization, and aggregation. To decrease the computational burden while important image details, block matching and aggregation are employed for the blocks divided from the low-quality image reconstructed by Fourier single-pixel imaging. To adapt the proposed upgraded weight nuclear norm minimization to different contaminated images, an adaptive scheme for the iterative regularization parameter is proposed to update the traditional weight nuclear norm minimization. Experimental results indicate that the proposed Fourier single-pixel imaging method based on upgraded weight nuclear norm minimization exhibits the capacity to reliably and effectively reconstruct high-quality images for single-pixel imaging at low sampling rates, even at the sampling rate of 1 %. Experimental results also demonstrate that the proposed method performs better image reconstruction than some existing single-pixel imaging.

As a high-precision (HP) position sensor, the optical encoder is the core component of modern electromechanical machines. During measurement, it is inevitably influenced by different disturbances, which result in measurement errors that influence its measurement precision. This article summarizes several research topics on measurement errors of optical encoders, involving sources, control, and compensation of different types of measurement errors. First, the sources of measurement errors are summarized in detail, which is beneficial for purposeful designs of error control and compensation methods. Then, a large number of error control methods are surveyed and analyzed. After the survey, we find out that error control brings some additional costs, such as time costs and economical costs. Also, error compensation methods involving multierror compensation and hardware-based error compensation are summarized corresponding to different error sources. Finally, some potential research topics are provided.

The active ripple canceller used in multiphase buck converters (MBCs) always guarantees a ripple-free output current, regardless of the number of phases and duty cycle values, and can be adopted to minimize the output ripple, thus prolonging the lifespan of its electrolytic output capacitors. However, conventional ripple cancellers typically require a central controller, which compromises system flexibility and extensibility. In this article, a novel ripple-free decentralized control unit is proposed with only one nonintrusive Rogowski coil sensor required. A ripple attenuator module with the configurable feature is obtained by introducing the proposed control unit, which can be applied to attenuate the output current ripples of MBCs while retaining its remote sense and output voltage trim features. Moreover, the proposed module is robust and can be easily implemented with analog circuits. Based on extensive steady- and transient-state analysis, detailed parameter selection guidelines are carried out to confirm the design procedure. The technique is experimentally validated on a 300-kHz, 12-/1.2-V /40-A three-phase interleaved buck converter. The results demonstrated a 90.3% reduction in output current ripple and a 34.8% reduction in output voltage ripple.