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A properly strained graphene monolayer or bilayer is expected to harbour periodic pseudo-magnetic fields with high symmetry, yet to date, a convincing demonstration of such pseudo-magnetic fields has been lacking, especially for bilayer graphene. Here, we report a definitive experimental proof for the existence of large-area, periodic pseudo-magnetic fields, as manifested by vortex lattices in commensurability with the moiré patterns of low-angle twisted bilayer graphene. The pseudo-magnetic fields are strong enough to confine the massive Dirac electrons into circularly localized pseudo-Landau levels, as observed by scanning tunneling microscopy/spectroscopy, and also corroborated by tight-binding calculations. We further demonstrate that the geometry, amplitude, and periodicity of the pseudo-magnetic fields can be fine-tuned by both the rotation angle and heterostrain. Collectively, the present study substantially enriches twisted bilayer graphene as a powerful enabling platform for exploration of new and exotic physical phenomena, including quantum valley Hall effects and quantum anomalous Hall effects. Precisely strained graphene layers can enable observation of periodic pseudo-magnetic fields with high symmetry. Here, the authors report experimental tuning of large area periodic pseudo-magnetic fields within twisted bilayer graphene and massive Dirac electrons having circularly localized pseudo-Landau levels.

Dry gas seals are widely used in the petrochemical industry for shaft end sealing of compressors, pumps and other equipment involving flammable, explosive, toxic and harmful media. To address the challenge of accurately identifying the fault states of dry gas seals under strong noise interference, this paper proposes a Multi-scale Attention 1D Residual Network (MA1D-ResNet) model based on sample augmentation. First, a dry gas seal acoustic emission (AE) test rig was built to collect non-stationary AE signals. The training dataset was expanded to five times its original size through data segmentation and Gaussian noise injection, significantly enhancing the model’s generalization capability in the data-input domain and training process. Then, the proposed model incorporates a Multi-scale Dual Attention Module (MDAM) into the ResNet18 architecture: it employs 1D convolutions to process temporal signals directly, avoiding feature loss, and integrates MDAM after the first convolutional layer and the Stage1 layer to strengthen fault feature extraction. Finally, experimental results demonstrate that the proposed model achieves an average accuracy of 99.8571% in classifying seven fault states (significantly outperforming five comparative models including CNN, ResNet, and ResNet-CBAM), with 100% recognition rate for five of the fault categories. The proposed model exhibits outstanding noise robustness, maintaining an accuracy of 92.43% under strong noise conditions of −6 dB. This study provides a highly robust solution for the intelligent fault diagnosis of dry gas seals in complex noise environments.

Background Cardiac tumors in children are rare and usually have no obvious clinical symptoms. However, a small number of children may experience serious conditions such as arrhythmia, heart obstruction, and even death. When severe arrhythmia cannot be controlled by conservative treatment, surgical intervention is needed. Case presentation A 20-day-old male neonate, born full-term via cesarean section, was admitted to the emergency department with complaints of jaundice for 16 days and a rapid heart rate detected for one day. The heart rate was recorded at 280 beats per minute. An electrocardiogram (ECG) initially suggested supraventricular tachycardia, later progressing to ventricular tachycardia. A bedside echocardiogram indicated an intracardiac mass. Conservative treatment failed to restore normal heart rhythm, then the patient underwent emergency surgery with tumor resection under general anesthesia and cardiopulmonary bypass. Post-surgery, ventilator-assisted breathing was administered, along with inotropic support, diuretics, anti-infective therapy, and fluid management. the heart rate and rhythm returned to normal. Postoperative pathology revealed the presence of a cardiac rhabdomyoma, and follow-up was arranged post-discharge. Conclusion Cardiac tumors in children are relatively rare, mostly benign, and have a good prognosis. But for some emergency situations or heart tumors that cause adverse effects, timely and effective intervention is needed to avoid adverse consequences.

When a space target malfunctions and is no longer controlled by its attitude control system, it usually tumbles in orbit and exhibits a slow spinning state. Accurately estimating the on-orbit attitude of spinning space targets is of vital importance for ensuring the operation of space assets. Moreover, it plays a significant role in tasks such as reentry observation and collision avoidance. Currently, most existing methods estimate the attitude of space targets by using a single inverse synthetic aperture radar (ISAR) for long-term observation. However, this approach not only requires a long observation time but also fails to estimate the attitude of spinning targets. To address these limitations, this paper proposes a novel approach for estimating the attitude of spinning space targets, which utilizes the joint observations of a multiple-station ISAR. Specifically, the proposed method fully exploits the projection principle of ISAR imaging and uses an ISAR high-resolution network (ISAR-HRNet) to automatically extract the projection features of typical components of the target. Then, the analytical expressions for the target’s instantaneous attitude and spin vector under the multi-station observation imaging projection model are derived. Based on the extracted features of the typical components, the lengths, orientations, and spin vectors of the space target are determined. Importantly, the proposed method can achieve the attitude estimation of the spinning space targets within a single observation period, without the need for manual intervention or prior information about the target’s three-dimensional (3D) model. Additionally, the analytical method for solving the spin vector offers high efficiency and accuracy. Finally, the effectiveness of the proposed attitude estimation algorithm is verified by experiments on simulated data, and the performance of the ISAR-HRNet is also tested in the key point extraction experiments using measured data.

This research paper introduces an innovative technique for measuring displacement using auxetic tubular structure (ATS). The proposed displacement measurement method is based on tubular structures with a negative Poisson’s ratio. It capitalizes on the underlying principle that the elastic deformation-induced change in transmittance of the ATS can be translated into a corresponding modification in the output current of the solar cell. This method allows for the conversion of the variation in light transmission into a corresponding variation in output voltage. The construction of the ATS can be achieved through 3D-printing technology, enhancing the accessibility of displacement measurement and design flexibility. The experimental results demonstrate that the proposed measurement method exhibits a linear error of less than 8% without any subsequent signal processing and achieves a sensitivity of 0.011 V/mm without signal amplification. Furthermore, experimental results also show that the proposed method has good repeatability and can maintain a high level of reliability and sensitivity when using different measurement devices. This confirms the effectiveness and feasibility of the proposed method, showing a favorable linear relationship between the input and output of the measurement system with an acceptable sensitivity, repeatability, and reliability.

Background 3D printing technology has become a research hotspot in the field of scientific research because of its personalized customization, maneuverability and the ability to achieve multiple material fabrications. The focus of this study is to use 3D printing technology to customize personalized poly L-lactic acid (PLLA) porous screws in orthopedic plants and to explore its effect on tendon-bone healing after anterior cruciate ligament (ACL) reconstruction. Methods Preparation of PLLA porous screws with good orthogonal pore structure by 3D printer. The hydroxyapatite (HA) was adsorbed on porous screws by electrostatic layer-by-layer self-assembly (ELSA) technology, and PLLA-HA porous screws were prepared. The surface and spatial morphology of the modified screws were observed by scanning electron microscopy (SEM). The porosity of porous screw was measured by liquid displacement method. Thirty New Zealand male white rabbits were divided into two groups according to simple randomization. Autologous tendon was used for right ACL reconstruction, and porous screws were inserted into the femoral tunnel to fix the transplanted tendon. PLLA group was fixed with porous screws, PLLA-HA group was fixed with HA modified porous screws. At 6 weeks and 12 weeks after surgery, 5 animals in each group were sacrificed randomly for histological examination. The remaining 5 animals in each group underwent Micro-CT and biomechanical tests. Results The pores of PLLA porous screws prepared by 3D printer were uniformly distributed and connected with each other, which meet the experimental requirements. HA was evenly distributed in the porous screw by ELSA technique. Histology showed that compared with PLLA group, mature bone trabeculae were integrated with grafted tendons in PLLA-HA group. Micro-CT showed that the bone formation index of PLLA-HA group was better than that of PLLA group. The new bone was uniformly distributed in the bone tunnel along the screw channel. Biomechanical experiments showed that the failure load and stiffness of PLLA-HA group were significantly higher than those of PLLA group. Conclusions The 3D printed PLLA porous screw modified by HA can not only fix the grafted tendons, but also increase the inductivity of bone, promote bone growth in the bone tunnel and promote bone integration at the tendon-bone interface. The PLLA-HA porous screw is likely to be used in clinic in the future.

Space targets move in orbit at a very high speed, so in order to obtain high-quality imaging, high-speed motion compensation (HSMC) and translational motion compensation (TMC) are required. HSMC and TMC are usually adjacent, and the residual error of HSMC will reduce the accuracy of TMC. At the same time, under the condition of low signal-to-noise ratio (SNR), the accuracy of HSMC and TMC will also decrease, which brings challenges to high-quality ISAR imaging. Therefore, this paper proposes a joint ISAR motion compensation algorithm based on entropy minimization under low-SNR conditions. Firstly, the motion of the space target is analyzed, and the echo signal model is obtained. Then, the motion of the space target is modeled as a high-order polynomial, and a parameterized joint compensation model of high-speed motion and translational motion is established. Finally, taking the image entropy after joint motion compensation as the objective function, the red-tailed hawk–Nelder–Mead (RTH-NM) algorithm is used to estimate the target motion parameters, and the joint compensation is carried out. The experimental results of simulation data and real data verify the effectiveness and robustness of the proposed algorithm.

In order to explore the influence of storage temperature and time on the quality of vacuum-packed water caltrop shell (WCS), this study investigated the changes in the quality of vacuum-packed WCS under different storage temperature (3 ± 1 °C, 5 ± 1 °C and 7 ± 1 °C) and time. The quality-related parameters of WCS, including sensory quality, moisture content, texture characteristics and microstructure, were examined. The results showed that at the storage temperature of 5 ± 1 °C, vacuum-packaged WCS could maintain high sensory quality within 21 days, while at 3 ± 1 °C and 7 ± 1 °C, the samples showed low sensory quality at 21 days and 14 days, respectively. For the same storage time, storage at 5 ± 1 °C resulted in the least significant decrease in elastic modulus and compressive strength of the samples. Among the three storage temperatures, storage at 7 ± 1 °C led to the most obvious change in pore structure, followed by storage at 3 ± 1 °C and then 5 ± 1 °C. The variance analysis suggested that storage time has significant effects on all the tested parameters, while storage temperature has significant effects on the sensory quality and texture characteristics of the samples but shows no significant effect on the moisture content. These findings provide a theoretical reference for the packaging and storage of WCS and the development of water caltrop sheller.

With the help of specific emitter identification (SEI), the control efficiency of the satellite communication systems can be effectively improved by discriminating the individual satellite. In recent years, deep learning has been introduced into SEI to enhance identification performance because of its powerful classification capability. However, classical real-valued neural networks exhibit some limitations in extracting the radio frequency fingerprint (RFF) features from complex signals, limiting the improvement of identification accuracy. Thus, we proposed a complex-valued conditional adversarial generative network (CC-GAN) which can directly deal with complex signals. Through adversarial learning between the generator and the discriminator, the generator implements direct mapping from the dynamic noisy signals to the noise-free signals. In addition, an auxiliary classifier is introduced into the discriminator to make the discriminator able to label the sample, which effectively compress the proposed model. The experimental results for a signal dataset collected in a real environment demonstrated that the proposed model is superior to the traditional denoising methods in denoising performance, which effectively improves the identification accuracy under dynamic noises. Furthermore, the proposed model outperforms other deep learning models in terms of identification performance under various SNRs, which can effectively improve the robustness and adaptability of the SEI system for communication satellites in dynamic noisy environments.

Background Clear cell renal cell carcinoma (ccRCC) is a highly invasive and metastatic subtype of kidney malignancy and is correlated with metabolic reprogramming for adaptation to the tumor microenvironment comprising infiltrated immune cells and immunomodulatory molecules. The role of immune cells in the tumor microenvironment (TME) and their association with abnormal fatty acids metabolism in ccRCC remains poorly understood. Method RNA-seq and clinical data of KIRC from The Cancer Genome Atlas (TCGA) and E-MTAB-1980 from the ArrayExpress dataset. The Nivolumab group and Everolimus group of the CheckMate 025 study, the Atezolizumab arm of IMmotion150 and the Atezolizumab plus Bevacizumab group of IMmotion151 cohort were obtained for subsequent analysis. After differential expression genes identification, the signature was constructed through univariate Cox proportional hazard regression and simultaneously the least absolute shrinkage and selection operator (Lasso) analysis and the predictive performance of our signature was assessed by using receiver operating characteristic (ROC), Kaplan–Meier (KM) survival analysis, nomogram, drug sensitivity analysis, immunotherapeutic effect analysis and enrichment analysis. Immunohistochemistry (IHC), qPCR and western blot were performed to measure related mRNA or protein expression. Biological features were evaluated by wound healing, cell migration and invasion assays and colony formation test and analyzed using coculture assay and flow cytometry. Results Twenty fatty acids metabolism-related mRNA signatures were constructed in TCGA and possessed a strong predictive performance demonstrated through time-dependent ROC and KM survival analysis. Notably, the high-risk group exhibited an impaired response to anti-PD-1/PD-L1 (Programmed death-1 receptor/Programmed death-1 receptor-ligand) therapy compared to the low-risk group. The overall levels of the immune score were higher in the high-risk group. Additionally, drug sensitivity analysis observed that the model could effectively predict efficacy and sensitivity to chemotherapy. Enrichment analysis revealed that the IL6-JAK-STAT3 signaling pathway was a major pathway. IL4I1 could promote ccRCC cells’ malignant features through JAK1/STAT3 signaling pathway and M2-like macrophage polarization. Conclusion The study elucidates that targeting fatty acids metabolism can affect the therapeutic effect of PD-1/PD-L1 in TME and related signal pathways. The model can effectively predict the response to several treatment options, underscoring its potential clinical utility.