Bing Li

Background/Objective: Hematoxylin and eosin (H&E) staining remains the routine reference in histopathology because it provides essential structural information for tissue evaluation. However, conventional H&E staining involves multiple reagent-based and operator-dependent procedures that may introduce variability in staining appearance. This study aimed to develop and preliminarily evaluate a technical proof-of-concept framework for generating H&E-like images from dual-modal autofluorescence microscopy data using a CycleGAN-based virtual staining approach. Methods: Human liver tissue sections were imaged using dual-modal autofluorescence, including DAPI-based nuclear fluorescence and endogenous tissue autofluorescence. A CycleGAN-based virtual staining framework with structural and perceptual constraints was developed to generate H&E-like images, followed by stain normalization to improve color consistency. The generated images were preliminarily evaluated by means of visual comparison, image-feature analysis, feature-space visualization, and nuclear counting. Results: The proposed framework generated H&E-like images with encouraging overall visual resemblance to conventional H&E images. Preliminary image-feature analysis suggested partial similarity between virtual and real H&E images, while feature-space visualization indicated that detectable differences remained. Nuclear counting on 168 images showed broadly consistent nuclear distribution between dual-modal autofluorescence and virtual H&E images, with minor discrepancies mainly related to thresholding artifacts. Some fine nuclear and chromatin-level details remained insufficiently reproduced in the current virtual H&E images. Conclusions: This study presents preliminary feasibility evidence for a technical proof-of-concept framework that translates dual-modal autofluorescence images into H&E-like images. In this small liver-tissue dataset, the generated images demonstrated encouraging overall H&E-like appearance and approximate nuclear localization. Further studies with larger and more diverse datasets, external validation, expert pathological assessment, diagnostic concordance analysis, and systematic workflow evaluation are warranted to assess robustness, diagnostic relevance, and potential utility in digital pathology workflows.

To address the challenges of insufficient accuracy and poor versatility faced by conventional interferometers in measuring high-order aspheric surfaces, this paper proposes an optimized annular sub-aperture stitching method based on a reverse global planning strategy. The method initiates sub-aperture division from the edge region with the highest asphericity, aiming to maximize the effective measurement range of each individual sub-aperture and setting the minimization of the number of sub-apertures (N) as the core optimization objective. The algorithm features a concise structure, high stability, and ease of automation, enabling a significant extension of the interferometer's dynamic measurement range. Simulation studies demonstrate that the method effectively reduces the required number of sub-apertures, thereby suppressing the propagation and accumulation of mechanical positioning errors during the stitching process. Furthermore, by integrating this method with pinhole point diffraction interferometry (PPDI), a measurement system combining high accuracy and large measurement range is constructed. Experimental measurements of a convex high-order aspheric surface yield stitched results that are in excellent agreement with full-aperture measurements, with the peak-to-valley (PV) deviation of only 2.66 nm and the root-mean-square (RMS) deviation of only 0.44 nm. A detailed uncertainty analysis of the system confirms the reliability and reproducibility of the measurement results. This study not only provides an efficient method for the precision measurement of aspheric surfaces with large deviations but also offers a new technical pathway for enhancing the measurement capability and automation level of interferometers.

Precision measurement of aspherical mirrors is critical for high-end optical systems, where surface accuracy directly impacts performance. However, there remain challenges in this task: 1) measurement precision in large scope; 2) larger ranges and flexibility in complex surface test; 3) environmental robustness in applications; 4) efficiency and accuracy in data processing. This study introduces a novel Multi-Wavelength Point Diffraction Interferometry (MPDI) method, significantly enhancing measurement flexibility and accuracy. Through the diffraction, high-accuracy wavefront (λ/10^-4) is produced to perform relative measurement to aspherical mirrors. By integrating a triple-wavelength laser module and utilizing the beat frequency concept, the system generates at least nine equivalent wavelengths (245.8 nm to 3334.2 nm), enabling efficient testing of mirrors with both large surface deviations and high-resolution requirements. A key innovation is the random two-step phase retrieval method, which improves measurement efficiency by nearly 60% compared to conventional four-step methods, while minimizing the effects of environmental disturbances. The proposed system, equipped with machine vision alignment, ensures precise optical path alignment and enhances wavefront quality. Experimental results show a 5.3-fold increase in measurement range and RMS errors as low as 0.6∙10^-3λ (λ = 633 nm), with stitching-free testing. This MPDI method offers substantial improvements in measurement range, robustness, and efficiency, making it a valuable tool for precision aspherical mirror metrology.

The complex surface characteristics and misalignment of the geometric and optical axes of off-axis aspherical surfaces pose major challenges in the work to develop a measurement technique that is accurate, effective, and simple. Phase measurement deflectometry (PMD) is a more convenient and cost-effective method than interferometry for measuring specular objects. Nevertheless, monocular PMD necessitates complicated processes or costly additional equipment to resolve height-slope problems, whereas binocular PMD requires more time-consuming matching calculations. In this article, a novel iterative strategy based on monocular PMD technology with an automatically updated seed point during the iterative process is proposed, which only requires an approximate estimation about the height of one point (either the lowest, middle, or highest) on the measured off-axis aspheric component. The lowest, middle, or highest point of the reconstructed surface is used as the target of the auto-updated seed point in the iterative process, and the surface’s overall height information is then updated until the difference between two neighboring surface profiles is less than a certain threshold. Finally, the off-axis aspherical surface result is sent out. The proposed monocular PMD method is more straightforward, cost-effective, and simple, requiring only basic and low-cost tools to estimate the initial height of the measured component without the need to obtain the seed point’s 3-D coordinate. An experiment was performed to evaluate the feasibility and accuracy of the proposed method with the results of high-precision contact measurements as benchmarks. The results also indicated that even the uncertainty in the estimated initial height using common Vernier calipers has little effect on the measured surface shape, since it only leads to a small offset in the whole surface location.

As a crucial component of air gauging sensors in intelligent CNC machine tools, air-electronic converters directly influence measurement accuracy. Nevertheless, conventional designs suffer from performance limitations due to undesirable dynamic/temperature characteristics in pressure conversion elements and accuracy loss during analog-to-digital (A/D) conversion. Aiming at this critical issue, a quasi-digital output microelectromechanical systems (MEMS) resonator-based air-electronic converter is presented and characterized in this paper. The converter employs a gas conversion element to transform slot width/displacement into chamber pressure, utilizing a bichamber structure (feeding and measuring chambers) to suppress air supply fluctuations. A differential resonant pressure-sensing unit, incorporating two quartz double-ended tuning fork (DETF) resonators, is proposed to convert chamber pressure to frequency signals while compensating for temperature-induced errors. Characterization results demonstrate the linearity of ≤ 0.5% full scale, an actual resolution of 0.04 μm, and enhanced stability through the combined output of four DETF resonators. An expanded uncertainty of 0.059 μm in displacement measurements confirms the device’s repeatability and reliability. This work advances the performance of air-electronic converters by enabling direct quasi-digital output and improved stability, offering a promising solution for high-precision air gauging sensors.

It is necessary to study the influence mechanism of blade thickness on the photoacoustic signals since material thickness will affect defect detection precision by affecting types, velocity and frequency of photoacoustic signals. In this paper, the influence mechanism of blade thickness on sound waves is obtained by setting defects with different depths in different thicknesses areas of blades. This mechanism instructs the imaging detection and characterization of defects with different depths in different thicknesses. Firstly, by combining with the dispersion curves, we found that there are more anti-symmetric A ₀ modes in thin area. Secondly, different types of sound waves lead to different velocities of sound waves. The sound velocity in the thin area of the blades is 2747.58 m/s, and the sound velocity in the thick area is 2912.22 m/s. Further, compared with the thick area, the thin area has more peaks and more complex high-frequency components, but the frequency range is basically the same. Finally, the detection and characterization of blade defects are carried out by combining imaging inspection and influence mechanism. The research results can not only promote the basic research on the influence of thickness on photoacoustic signals, but also expand the application of laser ultrasound in blade defect detection.

Micro-hole measurement methods struggle to produce the intended results because of poor in-situ measurement circumstances. The back-pressure air gauge, which is self-clean and unaffected by complicated environments, can be equipped with a micro-probe to obtain desired results, but the limited application of the sensor is caused by its expensive cost and challenging processing requirements. In this paper, an air gauging sensor without a micro-probe was presented as a low-cost and simple structure solution to the above issue and the static characteristics were optimized in several methods accounting for different micro-hole diameters. Theoretical and experimental static characteristics were obtained using the steady-state model and experimental data. Then, the effects of crucial parameters on static characteristics, including the feeding pressure and the inlet nozzle diameter, were analyzed under various settings. A novel performance improvement method of the sensor was presented by comparing and analyzing the P_x = f ( d ₁ ) and P_x = f ( d ₁ ² ) characteristics. The results indicated that a smaller inlet nozzle and appropriately higher feeding pressure helped improve the sensitivity, reduce the starting diameter and enlarge the length of the linear area. The novel method of extending the measurement range by combining P_x = f ( d ₁ ) with P_x = f ( d ₁ ² ) has a larger measurement range and a smaller starting diameter than sacrificing 1 % nonlinear error. It is possible to use the nonlinear area to measure micro-holes much smaller than the starting diameter. These new findings make contributions to the wide application of the air gauging sensor in micro-hole measurements.

This paper presents an advanced methodology for defect prediction in radiographic images, predicated on a refined particle swarm optimization (PSO) algorithm with an emphasis on fluctuation sensitivity. Conventional PSO models with stable velocity are often beleaguered with challenges in precisely pinpointing defect regions in radiographic images, attributable to the lack of a defect-centric approach and the propensity for premature convergence. The proposed fluctuation-sensitive particle swarm optimization (FS-PSO) model, distinguished by an approximate 40% increase in particle entrapment within defect areas and an expedited convergence rate, necessitates a maximal additional time consumption of only 2.28%. The model, also characterized by reduced chaotic swarm movement, enhances efficiency through the modulation of movement intensity concomitant with the escalation in swarm size. The FS-PSO algorithm’s performance was rigorously evaluated via a series of simulations and practical blade experiments. The empirical findings evince that the FS-PSO model substantially outperforms the conventional stable velocity model, particularly in terms of shape retention in defect extraction.