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In this manuscript, we propose a novel framework of computational ghost imaging, i.e., ghost imaging using deep learning (GIDL). With a set of images reconstructed using traditional GI and the corresponding ground-truth counterparts, a deep neural network was trained so that it can learn the sensing model and increase the quality image reconstruction. Moreover, detailed comparisons between the image reconstructed using deep learning and compressive sensing shows that the proposed GIDL has a much better performance in extremely low sampling rate. Numerical simulations and optical experiments were carried out for the demonstration of the proposed GIDL.

Objective: We aimed to construct the ferritin autophagy regulatory network and illustrate its mechanism in ferroptosis, TME immunity and malignant phenotypes of ovarian cancer. Methods: First, we used Western blot assays and immunohistochemistry to detect the pathway expression in ovarian cancer samples (C-MYC, NCOA4). Then, we performed RIP and FISH analysis to verify the targeted binding of these factors after which we constructed ovarian cancer cell models and detected pathway regulator expression (NCOA4). Co-localization and Western blot assays were used to detect ferritin autophagy in different experimental groups. We selected corresponding kits to assess ROS contents in ovarian cancer cells. MMP was measured using flow cytometry and mitochondrial morphology was observed through TEM. Then, we chose Clone, EdU and Transwell to evaluate the proliferation and invasion abilities of ovarian cancer cells. We used Western blot assays to measure the DAMP content in ovarian cancer cell supernatants. Finally, we constructed tumor bearing models to study the effect of the C-MYC pathway on ovarian cancer tumorigenesis and TME immune infiltration in in vivo conditions. Results: Through pathway expression detection, we confirmed that C-MYC was obviously up-regulated and NCOA4 was obviously down-regulated in ovarian cancer samples, while their expression levels were closely related to the malignancy degree of ovarian cancer. RIP, FISH and cell model detection revealed that C-MYC could down-regulate NCOA4 expression through directly targeted binding with its mRNA. Ferritin autophagy and ferroptosis detection showed that C-MYC could inhibit ferroptosis through NCOA4-mediated ferritin autophagy, thus reducing ROS and inhibiting mitophagy in ovarian cancer cells. Cell function tests showed that C-MYC could promote the proliferation and invasion of ovarian cancer cells through the NCOA4 axis. The Western blot assay revealed that C-MYC could reduce HMGB1 release in ovarian cancer cells through the NCOA4 axis. In vivo experiments showed that C-MYC could promote tumorigenesis and immune evasion in ovarian cancer cells through inhibiting HMGB1 release induced by NCOA4-mediated ferroptosis. Conclusion: According to these results, we concluded that C-MYC could down-regulate NCOA4 expression through directly targeted binding, thus inhibiting ferroptosis and promoting malignant phenotype/immune evasion in ovarian cancer cells through inhibiting ferritin autophagy.

With the wideband beamforming approaches, the synthetic aperture radar (SAR) could achieve high azimuth resolution and wide swath. However, the performance of conventional adaptive wideband time-domain beamforming is severely affected as the received signal snapshots are insufficient for adaptive approaches. In this paper, a wideband beamformer using convolutional neural network (CNN) method, namely, frequency constraint wideband beamforming prediction network (WBPNet), is proposed to obtain a satisfactory performance in the circumstances of scanty snapshots. The proposed WBPNet successfully estimates the direction of arrival of interference with scanty snapshots and obtains the optimal weights with effectively null for the interference by utilizing the uniqueness of CNN to extract potential nonlinear features of input information. Meanwhile, the novel beamformer has an undistorted response to the wideband signal of interest. Compared with the conventional time-domain wideband beamforming algorithm, the proposed method can fast obtain adaptive weights because of using few snapshots. Moreover, the proposed WBPNet has a satisfactory performance on wideband beamforming with low computational complexity because it avoids the inverse operation of covariance matrix. Simulation results show the meliority and feasibility of the proposed approach.

It is sparse and inconclusive that research on the subject whether the fatigue life of the structure will be reduced by shot peening strengthening before shot peen forming (S + F), and this study investigates accordingly. First, the crack growth rate test of the machine-processing plate and shot peening strengthening before shot peen forming plate demonstrate that both plates’ final crack growth rate and length are similar. However, the test shows the “fluctuation phenomenon” of crack growth rate and the “intersection phenomenon” in the Paris curve. This study is based on a self-developed simulation plugin for crack growth paths. The results verify that “fluctuation” causes the differential distribution of the overall stress intensity factor in the strengthened (4.5% increase compared to machine-processing) and formed (9.8% decrease compared to machine-processing) crater areas of the shot peening strengthening before shot peen forming plate. Comparing to the full coverage strengthening area, the forming area (only 30% coverage) in the early stage of growth as well as the gain amplitude of the residual stress in the late stage of growth gradually decrease and tend to be the same as that of the machine-processing, as validated by the “intersection phenomenon”.

Eigenmode decomposition of the light field at the output end of optical fibers can provide fundamental insights into the nature of electromagnetic-wave propagation through the fibers. Here we present a fast and complete modal decomposition technique for step-index optical fibers. The proposed technique employs digital holography to measure the light field at the output end of the multimode optical fiber, and utilizes the modal orthonormal property of the basis modes to calculate the modal coefficients of each mode. Optical experiments were carried out to demonstrate the proposed decomposition technique, showing that this approach is fast, accurate and cost-effective.

Background High-grade glioma has a poor prognosis, and GSCs can have pivotal roles in glioma pathology. This study investigated GSC exosome-containing circRNA mechanisms affecting the malignant progression of glioma. Methods In this study, we identified differentially expressed circRNAs and constructed a circRNA-miRNA-mRNA regulatory network through circRNA sequencing/bioinformatics analysis. Then, we identified circRNAs that were upregulated in GSC23 cells and employed them as downstream targets in subsequent investigations. Such investigations included downstream target knockout to assess any influence on A172 cell proliferation, invasion, migration and apoptosis. In addition, in vivo investigations using tumor-bearing animals evaluated the in vivo influences of the selected targets. Results This study identified circ-Serpine2/miR-124-3p/KIF20A as a regulatory pathway in glioma. Our in vitro analysis confirmed that circ-Serpine2 could upregulate KIF20A by sponging miR-124-3p, consequently promoting A172 cell proliferation, migration and invasion. Such a signaling channel could also inhibit glioma cell apoptosis. Additionally, our research indicated that circ-Serpine2 inhibited glioma apoptosis and promoted in vivo tumor progression. Conclusion Circ-Serpine2 exacerbated the malignant progression of glioma mediated by the miR-124-3p/KIF20A nexus, thus providing novel predictive/prognostic biomarkers and drug targets against glioma.

In recent years, steel surface defect detection based on machine vision has attracted significant attention and has emerged as a research hotspot. However, several challenges remain. In practical industrial scenarios, deep learning-based detection methods often involve high computational complexity, which limits their applicability for real-time defect monitoring. Moreover, due to the complex and noisy background of steel surfaces, conventional deep learning networks frequently suffer from the loss of critical defect features during the feature extraction process. To address these challenges, this paper proposes a novel latent-space attention multi-scale YOLOv10n model (LAM-YOLOv10n). First, a lightweight ghost module is integrated to significantly reduce the model’s parameter count and computational cost. Second, a spatial multi-scale attention (SMA) module is designed to enhance the extraction of discriminative features related to steel surface defects. Finally, a multi-branch feature fusion network (MFFN) is introduced to improve the effectiveness of multi-scale feature aggregation, thereby enhancing the model’s detection performance for various defect types. Experimental results demonstrate that the proposed LAM-YOLOv10n model achieves a 3.47% improvement in precision compared with the baseline YOLOv10n network, outperforming several state-of-the-art object detection models in both accuracy and efficiency. These findings indicate the effectiveness and practicality of the proposed method for real-time steel surface defect detection in complex industrial environments.

Polarization information is essential for material identification, stress mapping, biological imaging, and robust vision under strong illumination, yet conventional approaches rely on external polarization optics and active biasing, which are bulky, alignment-sensitive, and power-hungry. A more desirable route is to encode polarization at the pixel level and read it out at zero bias, enabling compact, low-noise, and polarization imaging. Low-symmetry layered semiconductors provide persistent in-plane anisotropy as a materials basis for polarization selectivity. Here, we construct an eight-terminal radial ‘star-shaped’ Au/ReS2 metal-semiconductor junction array pixel that operates in a genuine photovoltaic mode under zero external bias based on the photothermoelectric effect. Based on this, electrical summation of phase-matched multi-junction channels increases the signal amplitude approximately linearly without sacrificing the two-lobed modulation depth, achieving ‘gain by stacking’ without external amplification. The device exhibits millisecond-scale transient response and robust cycling stability and, as a minimal pixel unit, realizes polarization-resolved imaging and pattern recognition. Treating linear combinations of channels as operators in the polarization domain, these results provide a general pixel-level foundation for compact, zero-bias, and scalable polarization cameras and on-pixel computational sensing.

Despite the fruitful achievements in the development of hydrogen production catalysts with record-breaking performances, there is still a lack of durable catalysts that could work under large current densities (>1000 mA cm −2 ). Here, we investigated the catalytic behaviors of Sr 2 RuO 4 bulk single crystals. This crystal has demonstrated remarkable activities under the current density of 1000 mA cm −2 , which require overpotentials of 182 and 278 mV in 0.5 M H 2 SO 4 and 1 M KOH electrolytes, respectively. These materials are stable for 56 days of continuous testing at a high current density of above 1000 mA cm −2 and then under operating temperatures of 70 °C. The in-situ formation of ferromagnetic Ru clusters at the crystal surface is observed, endowing the single-crystal catalyst with low charge transfer resistance and high wettability for rapid gas bubble removal. These experiments exemplify the potential of designing HER catalysts that work under industrial-scale current density. The development of robust catalysts that could work under industrial-scale current densities is a challenge for hydrogen production. Here, the authors report an in-situ activation method to produce ferromagnetic Ru clusters that can catalyze the hydrogen evolution reaction at high current densities.

This article focuses on optimizing the evaporation and atomization performance of the evaporator tube in the combustion chamber of a microturbine engine, and examines its impact on engine thrust. Given that the evaporator tube design is crucial for enhancing combustion efficiency as a key component of the combustion chamber, this study introduces an innovative evaporator tube structure that incorporates biomimetic design principles through theoretical exploration. The proposed structure adds specifically sized grooves to the inner wall of traditional evaporation tubes to improve the evaporation and atomization processes of fuel droplets. The effectiveness of this design is demonstrated by comparing and analyzing the differences in evaporation and atomization characteristics between traditional and biomimetic evaporation tubes. As the incoming air temperature increases, the diameter of fuel droplets decreases, while the evaporation rate significantly rises. Moreover, when the biomimetic tube’s inner wall grooves are precisely designed with a width of 0.5 mm, a depth of 0.6 mm, and a length of 1.5 mm, the atomization effect of fuel droplets reaches its optimal state, significantly improving the evaporation rate compared to traditional evaporation tubes.