In recent years, traditional metal antennas based on the interaction between free electrons and electromagnetic fields have become more and more limited in terms of bandwidth expansion, integration, and sensitivity. Antenna technology based on Rydberg atoms has received extensive attention. As highly excited state atoms, Rydberg atoms, when used as sensing receivers for electromagnetic waves, can achieve high sensitivity detection without being limited by Johnson-Nyquist noise, thereby avoiding the size effects of traditional metal antennas to achieve ultra-wideband frequency response. By utilizing all-optical signal readout methods, the system’s resilience to electromagnetic damage can be effectively improved. Thanks to these advantages, in recent years, electromagnetic wave measurement techniques based on Rydberg atom antennas have demonstrated performance surpassing traditional metal antennas in various aspects, and are expected to have a disruptive impact and applications in modern radar and wireless communication technologies. This article primarily reviews the detection principles of Rydberg atoms and the research progress from DC to terahertz ultra-wideband operating spectrum detection, comprehensively outlines the current domestic and international status of Rydberg atom antennas for continuous frequency measurement, and outlines their future development trends.

To tackle the issue of missing traffic data in urban road network, this paper proposes a road network traffic data imputation method based on the spatial-temporal matrix factorization (namely STMF). Based on the spatiotemporal properties of the road network, this study first processes the multidimensional traffic data into a 2D matrix, then decomposes it into a spatial feature matrix and a temporal feature matrix and reconstructs the incomplete traffic data matrix by a low-rank approximation to achieve the basic repair of the missing data. Then graph Laplacian (GL) and gated recurrent network (GRN) are used as spatial and temporal regularizers respectively, to further mine spatial topology information and temporal dependencies of the urban road network and enhance the accuracy of matrix reconstruction and missing value imputation. Finally, the Los Angeles Traffic Speed dataset (Metr-LA) and Guangzhou Traffic dataset (Guangzhou-D) are used to compare the performance of STMF model with benchmark models such as GAIN, BGCP, BTMF, LRTC-TNN and HaLRTC. The experimental results show that, compared with the benchmark model, the proposed STMF model based on temporal matrix decomposition can better adapt to different missing scenarios and different missing rates, and the performance of missing data repair is more robust.

A fault localization method based on sniffing strategy slime mould algorithm is studied to address the issues of complex process and slow diagnostic speed in fault localization in AC/DC hybrid microgrids. This method utilizes a sniffing strategy consisting of the Daubechies wavelet transform and node topology optimization to perform data processing. Specifically, PowerFactory is used to build a medium and low voltage AC/DC hybrid microgrid model, and set faults on the AC and DC transmission lines to extract corresponding voltage and current fault signals separately. Secondly, the fault signal is decomposed and reconstructed using the Daubechies wavelet transform to obtain its information Shannon entropy, which is then combined to form an offset value that can characterize the fault. Then, through node topology optimization, the fault offset values of nodes and branches in the hybrid microgrid are fitted. Finally, the proposed strategy is simulated and validated using the optimized slime mould algorithm in Matlab. Simulation results show that the proposed sniffing strategy technology improves the accuracy by 21% and the convergence speed by 35.9% at the expense of a small increase in the time required for each iteration, achieving the purpose of accurately and quickly locating hybrid microgrid faults.

Compared with dissipative multi-scroll chaotic systems, conservative multi-scroll chaotic systems exhibit potential application prospects in image encryption and secure communication, rendering the research significantly meaningful. Based on the principle of Hamiltonian energy conservation, a four-dimensional conservative chaotic system is constructed in this peper. By introducing piecewise functions, the equilibrium points of the system are expanded, resulting in a multi-scroll chaotic system with adjustable widths along the x and y directions. By adjusting the system parameters and initial values, multi-scroll-like attractors with different energy levels and scroll numbers are generated. Notably, as the number of scrolls increases, the Lyapunov exponent of the system also exhibits a gradually increasing trend. Through NIST testing, the potential value of the system in applications such as random signal generators and image encryption is verified. Experimental results show that the SE value of the system reaches 0.63, fully demonstrating its practicality and importance in the field of secure communication.

The catenary is a transmission line that provides power for the train, and foreign objects such as plastic bags attached to the catenary will cause potential safety hazards to the train operation. In order to solve the problems of low efficiency and high labor cost of manual inspection, a YOLO-CDBW model for catenary foreign body detection based on YOLOv7 is proposed. Firstly, in the feature extraction stage, a feature extraction module using residual bottleneck structure and depth separation convolutional layer is constructed to avoid the problem of small target feature loss caused by the increase of network depth and reduce the amount of network computation. Finally, the WIoU loss function is used to optimize the model and focus on the ordinary mass anchor frame through the dynamic focusing mechanism to improve the prediction accuracy. Experimental results show that, the average mAP_0.5 of the YOLO-CDBW model reaches 87.1% and the detection speed FPS reaches 66.5 frame/s, which are 5.0 and 10.8 percentage points higher than those of the YOLOv7 model, respectively, meeting the needs of catenary foreign body detection.

Noise is inevitably present in datasets due to labeling subjectivity, image blurring, and other factors, making expression recognition more challenging. Existing facial expression recognition methods typically address noisy labels by partially overfitting to them. In this paper, a novel Noise-Resistant Dual Constraint Network (NDC-Net) is proposed to automatically suppress noisy samples. NDC-Net primarily consists of two constraint mechanisms: Class Activation mapping attention Consistency (CAC) and Channel and Spatial feature Consistency (CSC). CAC is used to make the model focus on locally important feature information and reduces the overfitting to noisy labels, while CSC is used to ensure that the model emphasizes task-relevant information from both channels and spatial dimensions during feature extraction, ignoring irrelevant information, and reducing reliance on noisy labels. Additionally, to enhance the performance of NDC-Net, input samples are augmented with strategies such as rotation and scaling. NDC-Net achieves recognition performances of 86.57%, 88.22%, and 59.78% under 30% label noise for RAF-DB, FERPlus, and AffectNet datasets, respectively. These results significantly outperform the state-of-the-art noisy labeling methods, such as EAC, NCCTFER. Moreover, NDC-Net also shows strong generalisation capability on general classification datasets such as CIFAR100 and Tiny-ImageNet.

In order to address the challenges of complex background textures, and limited hardware resources in fabric defect detection, a lightweight fabric defect detection method based on improved YOLOv8n (GSL-YOLOv8n) is proposed. Firstly, to reduce the parameter count and complexity of the YOLOv8n model, a C2f Ghost module is constructed based on the Ghost idea and utilized to replace the regular convolutions (Conv) in the YOLOv8n network structure. Secondly, a parameter-free attention mechanism, SimAM, is embedded at the end of the backbone network to remove redundant background, enhance semantic information of small targets, and improve global information, enhancing the network’s feature extraction capability. Finally, a lightweight shared convolutional detection head (LSCDH) is designed to scale the features using a Scale layer, minimizing accuracy loss while ensuring model lightweightness. Compared with the original YOLOv8n model, the improved algorithm GSL-YOLOv8n achieves an average precision improvement of 0.60%, reaching 98.29%, and the detection speed FPS remains basically the same . The model size, computational complexity, and parameter count are reduced by 66.7%, 58.0%, and 67.4% respectively, meeting the application requirements of fabric defect detection in the textile industry.

In response to the high computational complexity, low detection accuracy, and the issues of missed detection and false alarms in the current strip steel surface defect detection algorithm, a lightweight strip steel surface defect detection model, HSED-YOLO, is proposed. Initially, the original YOLOv8n backbone network is replaced with the improved HGNetV2, reducing the redundancy in feature map computation and thereby decreasing the number of the model’s parameters. Subsequently, to further reduce the model’s complexity, a Slim-Neck structured design is introduced into the model’s bottleneck network structure. Concurrently, an EMA attention mechanism is introduced during the feature fusion stage to enhance the model’s feature extraction capability. To further improve the model’s detection accuracy, a DIoU loss function is designed. Extensive experiments are conducted on the strip steel defect dataset. The number of improved model’s parameters and computational load are 2.1×10⁶ and 6.1×10⁹ FLOPs, respectively, which are only 70% and 75.3% of those of the baseline model. Moreover, the average accuracy is improved by 2% compared with the baseline model. These results demonstrate the effectiveness of the improved network.

To address the issue of poor identification resulting from the complex geological background and diverse symbol representations in plane geological drawings, a geological structure recognition model, named MsAttenEfficientNet, is proposed based on transfer learning and the Channel Prior Convolution Attention (CPCA) mechanism. In this model, EfficientNet is utilized as the backbone network architecture, and the Squeeze and Excitation Net (SENet) in the MBConv feature extraction module of EfficientNet are replaced by the CPCA module. This replacement enables the dynamic allocation of channel and spatial attention weights, allowing the model to more accurately capture important regions and spatial structures in the images. Subsequently, improvements are made to the top prediction module by introducing the Swish activation function and Dropout layers to enhance the model’s generalization performance. Finally, the Adam optimization algorithm is employed to improve the convergence speed of the network, and transfer learning is utilized to achieve feature parameter sharing. Experimental results, obtained from training and testing on the geological structure dataset GeoStr18, demonstrate that the MsAttenEfficientNet model achieves a precision of 96.92%, a recall of 96.89%, and an F₁ score of 96.90% in geological structure recognition, outperforming mainstream classification models such as ResNet50, ShuffleNetV2, and DenseNet121, thus effectively applicable for geological structure recognition.

Aiming at the problem that U-Net and its improved network have insufficient feature characterisation ability due to ignoring the interactions between multi-level features in jump links, a building extraction method based on hierarchical scale interactions is proposed for U-Net remote sensing images. Firstly, a hierarchical scale interaction module is designed in the jump link of U-Net network to achieve the interaction enhancement of multilevel features and improve the characterisation ability of features. Then a multi-scale feature extraction module is proposed by improving the null-space pyramid pooling module and applying it to the highest level features to enhance the ability of the network to extract multi-scale features. Finally, self-calibrating convolution is introduced into the decoding process to promote better fusion of shallow and deep features. The method of this paper is compared with six remote sensing image building extraction methods on two publicly available building extraction datasets, WHU and Inria. The experimental results show that the IoU of the proposed method is 91.26% and 79.23%, respectively, which are better than the comparison methods.

Incorporating social relationships into recommender systems can effectively improve recommendation quality. However, real-life interactions among users are sparse and complex. The effective utilization of social information is a key issue. The impact of high-order friends is not fully explored by existing social recommendation models, and the strength of relationships between users and the impact of different kinds of relationships on users are ignored, leading to sub-optimal recommendation performance. To address these issues, Multi-level attention networks and Hierarchical Contrastive Learning for social recommendation (MHCL) is proposed. Specifically, the user-level hypergraph is first constructed based on different relationships between users to expand the perceptual scope of node aggregation and deepen the depth of the model. Then, a multilevel attention network is designed to better capture the relationships and importance between user interaction data, where the influence of friends on the user and the degree of inter-item relatedness are captured by the view-level self-attention mechanism, and the influence of different kinds of relationships on the user is adaptively adjusted by the channel-level attention. Meanwhile, hierarchical contrast learning is introduced to augment the data, including the first level of contrast learning between and across views and the second level of contrast learning for high-order relationships, to capture the subtle gaps and high-level abstract features of the data in multiple dimensions. Finally, the proposed model is evaluated on four publicly available benchmark datasets, and the effectiveness and reasonableness of MHCL are validated by the evaluation results. Social denoising will be the focus of future research to improve recommendation systems based on hypergraph neural networks.

Taking inspiration from the leader follower strategy, a dung beetle optimization algorithm based on neighborhood search strategy is proposed to address the problems of slow convergence speed, easy falling into local optima, and weak global exploration ability in the optimization algorithm. Firstly, introducing Singer mapping to initialize the population improves the quality of initial solutions and enhances the convergence speed of the algorithm; Secondly, a neighborhood search strategy is proposed to enhance population diversity, break away from local convergence, and improve the local development ability of the algorithm; Finally, an elite pool perturbation strategy is designed to expand the search range, enhance the algorithm’s global exploration and local optimization capabilities, and improve the algorithm’s solving efficiency and accuracy. In order to verify the effectiveness of the proposed algorithm, a series of experiments are designed in this paper to verify its performance. The results indicate that the algorithm has significantly improved optimization accuracy and convergence speed. The algorithm is applied to the three-dimensional path planning problem of unmanned aerial vehicles, and the experimental results show that the algorithm demonstrates effectiveness and efficiency in dealing with practical application problems.

This paper studies the leader-following consensus problem of nonlinear multi-agent systems under deception attacks of replay attacks, with Markov switching topologies. The Bernoulli random variable is introduced to describe the random occurrence of deception attacks or replay attacks on the system. The communication topologies of multi-agent system are randomly changed due to cyber-attacks, which are modeled as Markov switching topology. Based on stability theory, graph theory, and matrix theory, the sufficient conditions for the consensus of the system are obtained by random analysis method, Lyapunov method, infinitesimal algorithm and so on. The correctness of the results and the effectiveness of the methods are verified by numerical example.

Spatial autoregressive models are widely used for correlation analysis of spatial data, setting the spatial autoregression coefficient as a global constant to model the homogeneous features of spatial autocorrelation, but cannot be used to analyze the locally heterogeneous spatial autocorrelation features within the study area. In this paper, a class of heterogeneous spatial autoregressive varying coefficient models is studied, and the local constant maximum likelihood and local linear maximum likelihood estimation methods of the heterogeneous spatial autoregressive varying coefficient models are proposed by setting the spatial autocorrelation regression coefficient in the models as varying coefficient function that changes with geographical location to realize the modeling of local heterogeneous features and nonstationary regression relations of spatial autocorrelation at the same time. Through numerical simulation, the results show that the local linear maximum likelihood estimation and local constant maximum likelihood estimation methods have consistency and effectiveness under finite samples, and the model and estimation method proposed in this paper have good performance. The proposed models and methods are used to analyze hand-foot-mouth disease (HFMD) incidence and influencing factors based on data within the territory of China in 2018. It is found that the local spatial autocorrelation of each province shows a trend of being higher in the west and lower in the central eastern regions. The extent of the impact of each influencing factor on the incidence of HFMD also varies with spatial location.

This article is dedicated to establishing fully decoupled and unconditionally energy-stable numerical algorithms for the incompressible magnetohydrodynamics (MHD) equations with variable density. The overall idea is as follows: Firstly, two first-order semi-discrete numerical algorithms are developed based on the Gauge-Uzawa method in both convective and conserved forms. Since both algorithms successfully decouple all coupled terms, only the linearized subproblems need to be solved at the discrete level, which significantly improve computational efficiency. Secondly, it is demonstrated that both algorithms are unconditionally energy-stable, and the finite element fully discrete algorithm in convective form is also unconditionally energy-stable. Finally, numerical experiments confirm the accuracy and effectiveness of the decoupled schemes.

This study comprehensively applies network pharmacology, bioinformatics, molecular docking, molecular dynamics, transcriptomics, and in vitro experiments to explore the effects and mechanisms of an indole sophoridine derivative containing ester group on the migration and invasion of liver cancer cells. The potential targets of this derivative were retrieved by using PharmMapper, Super-PRED, Swisstarget, and Targetnet databases. Liver cancer metastasis related genes were determined through differential expression analysis. The anti-liver cancer metastasis targets of this derivative were identified by taking intersection between predicted targets of this derivative and liver cancer metastasis related genes. GO/KEGG enrichment analysis was conducted to predict the potential signaling pathways of this derivative. Core targets of this derivative were identified via using protein-protein interaction analysis. Subsequently, molecular docking and molecular dynamics were involved to reveal the potential interaction between this derivative could core target. Furthermore, transcriptome sequencing was performed to obtain differentially expressed genes caused by this derivative. Finally, western blot assays were conducted to validate the effects of this derivative on MAPK pathway. The results showed that this derivative could significantly inhibit the migration and invasion of liver cancer cells. A total of 47 potential liver cancer metastasis related targets were predicted for this derivative, including 5 core targets: STAT3, PPARG, MAP2K1, CDK4, AKT3. The MAPK pathway was significantly enriched in the analysis of predicted genes and differentially expressed genes caused by this derivative. Molecular docking and molecular dynamics simulations also showed that this derivative had a high binding affinity with the core target MAP2K1 (a key node of the MAPK pathway). The results of western blot showed that under this derivative treatment, the core member of the MAPK pathway, p-Erk1/2 protein expression, was downregulated. Conclusion: this indole sophorodine derivative inhibits the migration and invasion of liver cancer cells by reducing the activity of the MAPK/ERK pathway.

The study aimed to explore the antitumor effects of Fufang Luxiancao Keli(FLK) on liver cancer and its underlying mechanisms. A total of 57 chemical components were identified using UPLC-QTOF-MS technology. Employing network pharmacology tools and molecular docking techniques, coupled with GO and KEGG analyses, it was revealed that the drug contains 37 active ingredients with 338 intersecting targets related to liver cancer. The core targets include PIK3CA, PIK3CB, PIK3CD, PIK3R1, and EGFR, which were involved in biological processes such as protein phosphorylation, cell migration, activation, and proliferation. KEGG analysis suggested that FLK may inhibit the growth of liver cancer cells by affecting key pathways such as the cancer pathway and the PI3K-Akt signaling pathway. In vitro cellular experiments confirmed the results of network pharmacology, demonstrating that FLK effectively inhibits the proliferation, cloning, and migration of liver cancer cells, and induces apoptosis and cell cycle arrest. The findings indicated that FLK may exert its antitumor effects by regulating signaling pathways such as PI3K-Akt.

To analyze the characteristic flavonoids in Ginkgo biloba extract (GBE) and its injections, the HPLC fingerprint and liquid chromatography-mass spectrometry (LC-MS) were used to determine the flavonoid components. Additionally, a HPLC quantitative detection method for determining the total flavonoids content was established. Eight flavonoids including rutin, isoquercetin, kaempferol-3-O-rutinoside, narcissus, astragalin, quercetin, kaempferol and isorhamnetin occupied the high content in GBE and injections, which served as the characteristic flavonoid components for further research. The HPLC quantitative detection was performed using a C18 column, acetonitrile-0.2% phosphoric acid as gradient elution, flow rate of 1.0 mL/min, detection wavelength of 360 nm, and column temperature of 30 ℃. Eight flavonoids demonstrated good linear relationship (r > 0.999) when their concentration ranged from 6.25 to 75.00, 1.00 to 12.00, 1.75 to 21.00, 1.75 to 21.00, 1.25 to 15.00, 1.25 to 15.00, 0.50 to 6.00 and 0.50 to 6.00 mg/L, respectively. The precision, repeatability, stability, and recovery rate RSD were less than 2%. The total flavonoid content in three GBEs were 61.37,76.23,140.55 mg/g, respectively. Also, four injections gave the results of 246.0, 305.2, 284.5, 250.8 mg/L. This study identifies eight characteristic flavonoids in GBE and its injections and establishes a quantitative detection method, which will provide an important reference for the quality monitoring of GBE preparations.

The secondary metabolites of Cladosporium cladosporioides GXIMD 00533 from Guangxi Beihai Zhulin Salt Field, were studied by using high salinity medium. The fungal extract was separated and purified by silica gel, reversed phase ODS column chromatography and semi-preparative high performance liquid chromatography (HPLC) to obtain 16 compounds. Their structures were elucidated by NMR spectral data and identified as four lactone compounds: iso-cladospolide B (1), cladospolide B (2), pandangolide 1 (3), thiocladospolide F (4), two sterol compounds: cladosporisteroid B (5) and (22E,24R)-3β,5α,9α-trihydroxyergosta-7,22-dien-6-one (6), seven diketopiperazine: brevianamide F (7), N-acetyl-L-phenylalanine (8), cyclo (L-Pro-L-Leu) (9), cyclo (L-Ala-L-Leu) (10), cyclo (L-Val-L-Ala) (11), cyclo (L-Gly-L-Ile) (12), cyclo (L-Leu-L-Gly) (13), one amino acid: L-tryptophan (14), and two fatty acids: cladosporacid B (15) and (4R,5S,11R,2E)-4,5,11-trihydroxy-2-dodecenoate (16). The antioxidant, antibacterial, α-Glucosidase inhibitory, cytotoxic activities, anti-barnala adhesion and acetylcholinesterase inhibitory activities of all compounds were evaluated. Compounds 6 and 14 had significant iron ion reduction and antioxidant capacity. Compound 6 exhibited inhibitory activities on α-glucosidase with IC₅₀ of 2.662 μmol·L^-1; Compound 4 had significant anti-barnacle adhesion activity, and the anti-adhesion rate was 100%. Some of the compounds showed certain acetylcholinesterase activity, and the inhibitory rate of compound 5 reached 54.98%.

Erythropalum scandens Blume is a leafy vegetable and emerging medicinal plant widely distributed in Guangxi, China. However, the product development of the title plant is limited due to the insufficient investigations on its secondary metabolites. To uncover the bioactive components of E. scandens, herein, the results of a systematic phytochemical investigation on E. scandens were reported, which were the isolation and structural characterization of 11 compounds belonging to different structural types. All the compounds were reported from the genus Erythropalum for the first time. Moreover, all the isolated compounds showed moderate to good anti-inflammatory activity against LPS-stimulated RAW264.7 cells, with acetanilide being the most potent example, which possessed better potency compared with the clinical anti-inflammatory drug indomethacin.