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Road surface defect detection is essential for intelligent transportation, autonomous driving safety, and preventive road maintenance. However, accurately identifying potholes and protrusions under complex illumination, irregular morphology, and multi-scale variations remains challenging, especially for lightweight models deployed on edge devices. This paper proposes RDL-YOLO, an efficient and lightweight detection framework built upon the YOLO11n architecture. The model integrates the C3k2_RVB and C3k2_REVB modules to enhance multi-scale feature extraction and improve robustness under low-light conditions. A shared detection head LSCD_LQE is introduced to jointly optimize classification and localization accuracy by incorporating learnable quality estimation. The DySample module enhances adaptive feature upsampling and spatial detail restoration, while DWConv reduces computational overhead to achieve real-time inference on resource-constrained platforms. Experimental results on a road-defect dataset demonstrate that RDL-YOLO achieves notable performance gains. Precision increases by 0.63 percent, recall increases by 4.24 percent, the F1-score improves by 1.82 percent, and mAP at 0.5 improves by 1.76 percent compared with the baseline YOLO11n model. Meanwhile, computational complexity is significantly reduced, with GFLOPs decreasing by 44 percent and parameter count decreasing by 25 percent. Cross-dataset generalization tests further verify the robustness and adaptability of the proposed model in diverse road conditions. The results confirm that RDL-YOLO provides an effective and lightweight solution for real-time road defect detection and intelligent vehicle hazard avoidance.

The application of IoT (Internet of Things) technology to the health monitoring of expansion joints is of great importance in enhancing the efficiency of bridge expansion joint maintenance. In this study, a low-power, high-efficiency, end-to-cloud coordinated monitoring system analyzes acoustic signals to identify faults in bridge expansion joints. To address the issue of scarce authentic data related to bridge expansion joint failures, an expansion joint damage simulation data collection platform is established for well-annotated datasets. Based on this, a progressive two-level classifier mechanism is proposed, combining template matching based on AMPD (Automatic Peak Detection) and deep learning algorithms based on VMD (Variational Mode Decomposition), denoising, and utilizing edge and cloud computing power efficiently. The simulation-based datasets were used to test the two-level algorithm, with the first-level edge-end template matching algorithm achieving fault detection rates of 93.3% and the second-level cloud-based deep learning algorithm achieving classification accuracy of 98.4%. The proposed system in this paper has demonstrated efficient performance in monitoring the health of expansion joints, according to the aforementioned results.

Severe influenza A virus infection causes high mortality and morbidity worldwide due to delayed antiviral treatment and inducing overwhelming immune responses, which contribute to immunopathological lung injury. Sirolimus, an inhibitor of mammalian target of rapamycin (mTOR), was effective in improving clinical outcomes in patients with severe H1N1 infection; however, the mechanisms by which it attenuates acute lung injury have not been elucidated. Here, delayed oseltamivir treatment was used to mimic clinical settings on lethal influenza A (H1N1) pdm09 virus (pH1N1) infection mice model. We revealed that delayed oseltamivir plus sirolimus treatment protects mice against lethal pH1N1 infection by attenuating severe lung damage. Mechanistically, the combined treatment reduced viral titer and pH1N1-induced mTOR activation. Subsequently, it suppressed the NOD-like receptor family pyrin domain containing 3 (NLRP3) inflammasome-mediated secretion of interleukin (IL)-1β and IL-18. It was noted that decreased NLRP3 inflammasome activation was associated with inhibited nuclear factor (NF)-κB activation, reduced reactive oxygen species production and increased autophagy. Additionally, the combined treatment reduced the expression of other proinflammatory cytokines and chemokines, and decreased inflammatory cell infiltration in lung tissue and bronchioalveolar lavage fluid. Consistently, it inhibited the mTOR-NF-κB-NLRP3 inflammasome-IL-1β axis in a lung epithelial cell line. These results demonstrated that combined treatment with sirolimus and oseltamivir attenuates pH1N1-induced severe lung injury, which is correlated with suppressed mTOR-NLRP3-IL-1β axis and reduced viral titer. Therefore, treatment with sirolimus as an adjuvant along with oseltamivir may be a promising immunomodulatory strategy for managing severe influenza.

Natural killer (NK) cells are critical innate lymphocytes that can directly kill target cells without prior immunization. NK cell activation is controlled by the balance of multiple germline-encoded activating and inhibitory receptors. NK cells are a heterogeneous and plastic population displaying a broad spectrum of functional states (resting, activating, memory, repressed, and exhausted). In this review, we present an overview of the epigenetic regulation of NK cell-mediated antitumor immunity, including DNA methylation, histone modification, transcription factor changes, and microRNA expression. NK cell-based immunotherapy has been recognized as a promising strategy to treat cancer. Since epigenetic alterations are reversible and druggable, these studies will help identify new ways to enhance NK cell-mediated antitumor cytotoxicity by targeting intrinsic epigenetic regulators alone or in combination with other strategies.

Electric vehicles can reduce the dependence on limited resources such as oil, which is conducive to the development of clean energy. An accurate battery state of health (SOH) is beneficial for the safety of electric vehicles. A multi-feature and Convolutional Neural Network–Bidirectional Long Short-Term Memory–Multi-head Attention (CNN-BiLSTM-MHA)-based lithium-ion battery SOH estimation method is proposed in this paper. First, the voltage, energy, and temperature data of the battery in the constant current charging phase are measured. Then, based on the voltage and energy data, the incremental energy analysis (IEA) is performed to calculate the incremental energy (IE) curve. The IE curve features including IE, peak value, average value, and standard deviation are extracted and combined with the thermal features of the battery to form a complete multi-feature sequence. A CNN-BiLSTM-MHA model is set up to map the features to the battery SOH. Experiments were conducted using batteries with different charging currents, and the results showed that even if the nonlinearity of battery SOH degradation is significant, this method can still achieve a fast and accurate estimation of the battery SOH. The Mean Absolute Error (MAE) is 0.1982%, 0.1873%, 0.1652%, and 0.1968%, and the Root-Mean-Square Error (RMSE) is 0.2921%, 0.2997%, 0.2130%, and 0.2625%, respectively. The average Coefficient of Determination (R2) is above 96%. Compared to the BiLSTM model, the training time is reduced by an average of about 36%.

Blades are widely used in the engines of aerospace vehicles, fans of near-space aerostat, and other equipment, and they are the key to completing energy conversion and pressure adjustment of the capsule. Blade tip timing (BTT) is the most cost-efficient approach for the monitoring of blades. The reliability and validity of BTT is mainly investigated through numerical simulation and experimental verification. However, not all researchers are able to carry out the expensive and time-consuming task of rotating the blade test bench and its monitoring systems. Therefore, a good and easily understood simulator is necessary. In this paper, an effective BTT simulation model that is capable of considering various uncertainties such as installation errors, probe accuracy, sampling clock frequency, speed fluctuations, and mistuning is presented. A blade multi-harmonic vibration model is also presented, which is not only easy to implement but also simplifies the solution of dynamic equations. Also, the simulation results show that the proposed model is accurate and consistent with the experimental results. This will help researchers to achieve an improved understanding of BTT and form the basis for conducting research in related areas in a short period of time.

In open environments, lightweight pedestrian and vehicle detection models deployed on edge platforms of Unmanned Ground Vehicles (UGVs) often struggle to balance detection accuracy and inference efficiency when facing complex backgrounds, distant small targets, and occluded objects. To address this, we propose a lightweight object detection model based on YOLO11n, named UGV-Net. This model enhances feature interaction and global context modeling capabilities by introducing the C3k2_PS module, employs Dysample dynamic upsampling to achieve content-aware feature reconstruction, and designs an LSDECD detection head to reduce multi-scale prediction redundancy and computational overhead, thereby balancing detection accuracy and inference efficiency. Compared with the baseline model YOLO11n, UGV-Net improves F1 score, mAP50, and mAP50:95 by 2.19%, 2.26%, and 2.10%, respectively, on the KITTI dataset, while reducing GFLOPs from 6.3 to 4.9 and the number of parameters from 2.58M to 2.41M. Similarly, on the SODA10M and FLIR datasets, the F1 score improves by 1.71% and 1.41%, and mAP50 improves by 1.67% and 2.39%, respectively, demonstrating excellent detection accuracy and generalization ability. Furthermore, experiments on the Jetson Orin Nano platform verify that UGV-Net achieves robust real-time detection performance, making it an efficient, reliable, and lightweight solution for UGV perception in open environments.

The dissemination of countermeasures is diffusely recognized as one of the most valid strategies of containing computer virus diffusion. In order to better understand the impacts of countermeasure and removable storage media on viral spread, this paper addresses a dynamical model, which incorporates nonlinear countermeasure probability and infected removable storage media. Theoretical analysis reveals that the unique (viral) equilibrium of the model is globally asymptotically stable. This main result is also illustrated by some numerical experiments. Additionally, the numerical experiments of different countermeasure probabilities are conducted.

Granulomatous lobular mastitis (GLM) is a refractory chronic inflammatory breast disease characterized by granuloma formation and recurrent abscesses, yet its molecular pathogenesis remains poorly understood. To address this knowledge gap, we aimed to systematically compare the immune microenvironment between GLM and healthy breast tissues, reveal disease-associated cellular subpopulations, and characterize key dysregulated genes and pathways driving GLM pathogenesis. We performed single-cell RNA sequencing (scRNA-seq) on breast tissue samples from 3 patients with GLM and 3 healthy controls. The sequencing data were subjected to cell clustering, cell abundance comparison, and differential gene analysis to assess immune microenvironment differences. We performed macrophage subtyping and revealed differentially expressed genes. Using GO/KEGG analysis, we characterized signaling pathway disparities in M1 macrophages to investigate potential pathogenic mechanisms. 11 major cell types were detected through scRNA-seq. In GLM tissues, immune cell infiltration was significantly increased (  < 0.05), with macrophages and neutrophils showing predominant infiltration. Macrophages were further classified into M1, M2a, M2b, and M2c subtypes, with M1 being the most predominant. In M1 macrophages, we observed marked upregulation of: (CD64), and (core NADPH oxidase components), (TRAIL). Cytokine signaling and phagocytosis-related pathways were significantly enriched in M1 macrophages. To our knowledge, this is the first scRNA-seq study of GLM, identifying 11 major cellular populations and implicating macrophages-especially M1 subtype-as central to disease immunopathology. We report dysregulated expression of CD64, NADPH oxidase components, and TRAIL, prompting the hypothesis that phagocytic function may be impaired and nominating this axis as a potential therapeutic target.

Background This study aims to investigate the effects of water soluble particulate matter (WSPM) on the viability and protein expression profile of human lung adenocarcinoma cell A549 in the Bayou Obo rare earth mining area, and explore the influence of WSPM on the A549 cell cycle. Results It was found that WSPM can inhibit the viability of A549 cells and induce cell arrest in the G2/M phase. Compared with controls, exposure to WSPM10 and WSPM2.5 induced 134 and 116 proteins to be differentially expressed in A549 cells, respectively. In addition, 33 and 31 differentially expressed proteins were further confirmed, and was consistent with the proteomic analysis. The most prominent enrichment in ribosome-associated proteins were presented. When RPL6 , RPL13 , or RPL18A gene expression was inhibited, A549 cells were arrested in the G1 phase, affecting the expression of Cyclin D1 , p21 , RB1 , Cyclin A2 , Cyclin B1 , CDC25A , CDK2 , CHEK2 and E 2 F 1 . Furthermore, the La 3+ , Ce 3+ , Nd 3+ and F - in WSPM also inhibited the viability of A549 cells. After 24 h of exposure to 2 mM of NaF, A549 cells were also arrested in the G2/M phase, while the other three compounds did not have this effect. These four compounds affected the cell cycle regulatory factors in A549 cells, mainly focusing on effecting the expression of CDK2 , CDK4 , RB1 , ATM , TP53 and MDM2 genes. These results are consistent with the those from WSPM exposure. Conclusions These results revealed that WSPM from rare earth mines decreased the viability of A549 cells, and induced cell cycle G2/M phase arrest, and even apoptosis, which may be independent of the NF-κB/MYD88 pathway, and be perceived by the TLR4 receptor. The dysfunction of the cell cycle is correlated to the down-expression of ribosomal proteins (RPs). However, it is not the direct reason for the A549 cell arrest in the G2/M phase. La 3+ , Ce 3+ , and F - are probably the main toxic substances in WSPM, and may be regulate the A549 cell cycle by affecting the expression of genes, such as MDM2, RB1, ATM, TP53, E 2 F 1 , CDK2 and CDK4 . These results indicate the importance for further research into the relationship between APM and lung cancer.