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Explore how technology museums can integrate interactive garden culture to promote sustainability and environmental protection. Pay special attention to how virtual reality technology integrates with garden culture to create a unique exhibition experience that interacts with nature. In addition, conduct in-depth research on how interactive garden culture can enhance the participation and learning experience of museum visitors. One of the focuses of research is how to enhance the effectiveness of interactive garden culture through multi-sensory experiences and emotional interactions in museum exhibitions. Finally, we will explore how digital gardens can promote cultural interaction and participatory design to enrich the content and experience of museum exhibitions.

In recent years, the realm of deep learning has witnessed significant advancements, particularly in object detection algorithms. However, the unique challenges posed by remote sensing images, such as complex backgrounds, diverse target sizes, dense target distribution, and overlapping or obscuring targets, demand specialized solutions. Addressing these challenges, we introduce a novel lightweight object detection algorithm based on Yolov5s to enhance detection performance while ensuring rapid processing and broad applicability. Our primary contributions include: firstly, we implemented a new Lightweight Asymmetric Detection Head (LADH-Head), replacing the original detection head in the Yolov5s model. Secondly, we introduce a new C3CA module, incorporating the Coordinate Attention mechanism, strengthening the network’s capability to extract precise location information. Thirdly, we proposed a new backbone network, replacing the C3 module in the Yolov5s backbone with a FasterConv module, enhancing the network’s feature extraction capabilities. Additionally, we introduced a Content-aware Feature Reassembly (content-aware reassembly of features) (CARAFE) module to reassemble semantic similar feature points effectively, enhancing the network’s detection capabilities and reducing the model parameters. Finally, we introduced a novel XIoU loss function, aiming to improve the model’s convergence speed and robustness during training. Experimental results on widely used remote sensing image datasets such as DIOR, DOTA, and SIMD demonstrate the effectiveness of our proposed model. Compared to the original Yolov5s algorithm, we achieved a mean average precision (mAP) increase of 3.3%, 6.7%, and 3.2%, respectively. These findings underscore the superior performance of our proposed model in remote sensing image object detection, offering an efficient, lightweight solution for remote sensing applications.

Breast cancer (BC) is the most common malignant tumor in women worldwide, which seriously threatens women’s physical and mental health. In recent years, photodynamic therapy (PDT) has shown significant advantages in cancer treatment. PDT involves activating photosensitizers with appropriate wavelengths of light, producing transient levels of reactive oxygen species (ROS). Compared with free photosensitizers, the use of nanoparticles in PDT shows great advantages in terms of solubility, early degradation, and biodistribution, as well as more effective intercellular penetration and targeted cancer cell uptake. Under the current circumstances, researchers have made promising efforts to develop nanocarrier photosensitizers. Reasonably designed photosensitizer (PS) nanoparticles can be achieved through non-covalent (self-aggregation, interfacial deposition, interfacial polymerization or core-shell embedding and physical adsorption) or covalent (chemical immobilization or coupling) processes and accumulate in certain tumors through passive and/or active targeting. These PS loading methods provide chemical and physical stability to the PS payload. Among nanoparticles, metal nanoparticles have the advantages of high stability, adjustable size, optical properties, and easy surface functionalization, making them more biocompatible in biological applications. In this review, we summarize the current development and application status of photodynamic therapy for breast cancer, especially the latest developments in the application of metal nanocarriers in breast cancer PDT, and highlight some of the recent synergistic therapies, hopefully providing an accessible overview of the current knowledge that may act as a basis for new ideas or systematic evaluations of already promising results.

Perovskite, as a promising class of photodetection material, demonstrates considerable potential in replacing conventional bulk light-detection materials such as silicon, III–V, or II–VI compound semiconductors and has been widely applied in various special light detection. Relying solely on the intrinsic photoelectric properties of perovskite gradually fails to meet the evolving requirements attributed to the escalating demand for low-cost, lightweight, flexible, and highly integrated photodetection. Direct manipulation of electrons and photons with differentiation of local electronic field through predesigned optical nanostructures is a promising strategy to reinforce the detectivity. This review provides a concise overview of the optical manipulation strategy in perovskite photodetector through various optical nanostructures, such as isolated metallic nanoparticles and continuous metallic gratings. Furthermore, the special light detection techniques involving more intricate nanostructure designs have been summarized and discussed. Reviewing these optical manipulation strategies could be beneficial to the next design of perovskite photodetector with high performance and special light recognition.

Autophagy is responsible for maintaining cellular balance and ensuring survival. Autophagy plays a crucial role in the development of diseases, particularly human cancers, with actions that can either promote survival or induce cell death. However, brain tumors contribute to high levels of both mortality and morbidity globally, with resistance to treatments being acquired due to genetic mutations and dysregulation of molecular mechanisms, among other factors. Hence, having knowledge of the role of molecular processes in the advancement of brain tumors is enlightening, and the current review specifically examines the role of autophagy. The discussion would focus on the molecular pathways that control autophagy in brain tumors, and its dual role as a tumor suppressor and a supporter of tumor survival. Autophagy can control the advancement of different types of brain tumors like glioblastoma, glioma, and ependymoma, demonstrating its potential for treatment. Autophagy mechanisms can influence metastasis and drug resistance in glioblastoma, and there is a complex interplay between autophagy and cellular responses to stress like hypoxia and starvation. Autophagy can inhibit the growth of brain tumors by promoting apoptosis. Hence, focusing on autophagy could offer fresh perspectives on creating successful treatments. Highlights Autophagy has a dual function in cancer acting as pro-survival or pro-death mechanism. Brain tumors are among malignant cancers with high mortality and morbidity worldwide. Autophagy can interact with other cell death pathways such as apoptosis in brain tumors. Autophagy can regulate progression of various brain tumors including glioma, glioblastoma and ependymoma, among others. Autophagy can control metastasis and drug resistance in brain tumors.

Artificial intelligence (AI) has received much attention in the domain of railway traffic planning and management (TPM) from academia and industries. While many promising applications have been reported, there remains a lack of detailed review of the many AI models/algorithms and their uses and adaptations in rail TPM. To fill this gap, this systematic literature review conducts, reports, and synthesizes the state-of-the-art of AI applied in railway TPM from four perspectives, i.e., the intersection between AI research fields (e.g., expert systems, data mining, and adversarial search) and rail TPM, the intersection between AI techniques (e.g., evolutionary computing and machine learning) and rail TPM, the intersection between AI applications (e.g., operations research, scheduling, and planning) and rail TPM, and the intersection between AI related disciplines (e.g., big data analytics and digital twins) and rail TPM. The study evaluates 95 research papers published during 1970–2022. Accordingly, a comprehensive synthesis of each intersection between AI and rail TPM is presented, and the practical roadmap for application of AI in rail TPM is proposed. Furthermore, the study identifies the research gaps and areas that need more investigation. The contribution helps researchers and practitioners to get a better understanding of the status quo of research stream, research development trends, and challenges for further related study.

We propose a method based on Synchrosqueezing Transform (SST) for vibration event analysis and identification in Phase Sensitive Optical Time-Domain Reflectometry (Φ-OTDR) systems. SST has high time-frequency resolution and phase information, which can distinguish and enhance different vibration events. We use six tap events with different intensities and six other events as experimental data and test the effect of attenuation. We use Visual Geometry Group (VGG), Vision Transformer (ViT), and Residual Network (ResNet) as deep classifiers for the SST transformed data. The results show that our method outperforms the methods based on Continuous Wavelet Transform (CWT) and Short-Time Fourier Transform (STFT), while ResNet is the best classifier. Our method can achieve high recognition rate under different signal strengths, event types, and attenuation levels, which shows its value for Φ-OTDR system.

Underground coal mining leads to serious surface deformation, which negatively affects the physical properties of soils. Soil particle size distribution (PSD) is one of the most basic soil physical characteristic that influences other important properties, such as soil hydraulics and thermodynamics. Understanding the spatial variability of the soil PSD in subsided land can provide targeted guidance for land reclamation. In this study, we conducted a quantitative study on the spatial variability of the soil PSD in the Pingshuo mining area on the Loess plateau, Shanxi Province in China, and explored the effects of subsidence and reclamation on the soil PSD. A plot experiment, including one unmined plot, one subsided plot, and one reclaimed plot, was performed in Anjialing No.3 underground coal mine in the, Pingshuo mining area. Four multi-fractal parameters of the soil PSD—D(0), D(1), Δα(q), and Δf(α)—were analyzed at the three sample sites. The joint multi-fractal method was carried out to analyze the spatial correlation of the soil PSD to further reveal the impacts of coal mining subsidence and land reclamation on the soil PSD. The multi-fractal method can reflect the local non-uniformity and heterogeneity of the soil PSD, while the joint multi-fractal approach can illustrate the correlation of the soil PSD between different soil depths. The range and spatial variability of the soil PSD increased due to coal mining subsidence and the impact of subsidence on the spatial disturbance of the surface soil PSD was greater than that of the deeper layers. The spatial correlation of clay in subsided land (21.00–34.34%) was larger than those of unmined land (12.36–16.37%) and reclaimed land (15.08–19.50%), and the degree of correlation was lower in 0–20 cm (21.68%) and 20–40 cm than in 40–60 and 60–80 cm soil layers (34.34%). Whereas, for silt and sand, the correlation was smaller. Land reclamation decreased the spatial variability of the soil PSD, which was near that of the unmined land after reclamation.

This paper proposes a dual-parameter strain/displacement safety monitoring technology for railway tunnel lining structures. An integrated monitoring system with FBG (Fiber Bragg grating) and VDM (video displacement meter) components was used to monitor both the strain and deformation of the tunnel cross-section. Initially, a comprehensive experimental study was carried out using FBG strain sensors with temperature-compensated grating. The temperature-compensated grating was used to further improve the monitoring accuracy. The data show that the stability and accuracy were better than the traditional electronic strain sensor. Secondly, high-precision and multipoint monitoring of railway tunnel lining deformation was achieved by using VDM technology. Three months of case study results taken from the Gansu Railway Tunnel in China demonstrated a tunnel cross-section strain accuracy for microstrain and crown deformation at the submillimeter level, respectively. The technology provides a new high-precision way to monitor the condition of tunnel lining structures.

Multimodal perception for student-state monitoring is difficult to deploy in rural classrooms because sensors are noisy and computing resources are highly constrained. This work targets these challenges by enabling noise-resilient, multimodal, real-time student-state recognition on low-cost edge devices. We propose LightEdu-Net, a sensor-noise-adaptive Transformer-based multimodal network that integrates visual, physiological, and environmental signals in a unified lightweight architecture. The model incorporates three key components: a sensor noise adaptive module (SNAM) to suppress degraded sensor inputs, a cross-modal attention fusion module (CMAF) to capture complementary temporal dependencies across modalities, and an edge-aware knowledge distillation module (EAKD) to transfer knowledge from high-capacity teachers to an embedded-friendly student network. We construct a multimodal behavioral dataset from several rural schools and formulate student-state recognition as a multimodal classification task with explicit evaluation of noise robustness and edge deployability. Experiments show that LightEdu-Net achieves 92.4% accuracy with an F1-score of 91.4%, outperforming representative lightweight CNN and Transformer baselines. Under a noise level of 0.3, accuracy drops by only 1.1%, indicating strong robustness to sensor degradation. Deployment experiments further show that the model operates in real time on Jetson Nano with a latency of 42.8 ms (23.4 FPS) and maintains stable high accuracy on Raspberry Pi 4B and Intel NUC platforms. Beyond technical performance, the proposed system provides a low-cost and quantifiable mechanism for capturing fine-grained learning process indicators, offering new data support for educational economics studies on instructional efficiency and resource allocation in underdeveloped regions.