Magnetic iron oxide nanoparticles are relatively advanced nanomaterials, and are widely used in biology, physics and medicine, especially as contrast agents for magnetic resonance imaging. Characterization of the properties of magnetic nanoparticles plays an important role in the application of magnetic particles. As a contrast agent, the relaxation rate directly affects image enhancement. We characterized a series of monodispersed magnetic nanoparticles using different methods and measured their relaxation rates using a 0.47 T low-field Nuclear Magnetic Resonance instrument. Generally speaking, the properties of magnetic nanoparticles are closely related to their particle sizes; however, neither longitudinal relaxation rate $r_1$ nor transverse relaxation rate $r_2$ changes monotonously with the particle size $d$ . Therefore, size can affect the magnetism of magnetic nanoparticles, but it is not the only factor. Then, we defined the relaxation rates $r_i^{\prime }$ (i = 1 or 2) using the induced magnetization of magnetic nanoparticles, and found that the correlation relationship between $r_1^{\prime }$ relaxation rate and $r_1$ relaxation rate is slightly worse, with a correlation coefficient of $R^2$ = 0.8939, while the correlation relationship between $r_2^{\prime }$ relaxation rate and $r_2$ relaxation rate is very obvious, with a correlation coefficient of $R^2$ = 0.9983. The main reason is that $r_2$ relaxation rate is related to the magnetic field inhomogeneity, produced by magnetic nanoparticles; however $r_1$ relaxation rate is mainly a result of the direct interaction of hydrogen nucleus in water molecules and the metal ions in magnetic nanoparticles to shorten the $T_1$ relaxation time, so it is not directly related to magnetic field inhomogeneity.

In response to the challenges posed by complex background textures and limited hardware resources in fabric defect detection, this study proposes a lightweight fabric defect detection algorithm based on an improved GSL-YOLOv8n model. Firstly, to reduce the parameter count and complexity of the YOLOv8n network, the GhostNet concept is used to construct the C2fGhost module, replacing the conventional convolution layers in the YOLOv8n structure with Ghost convolutions. Secondly, the SimAM parameter-free attention mechanism is embedded at the end of the backbone network to eliminate redundant background, enhance semantic information for small targets, and improve the network’s feature extraction capability. Lastly, a lightweight shared convolution detection head is designed, employing the scale layer to adjust features, ensuring the lightweight nature of the model while minimizing precision loss. Compared to the original YOLOv8n model, the improved GSL-YOLOv8n algorithm increases the mAP@0.5 by 0.60% to 98.29% and reduces model size, computational load, and parameter count by 66.7%, 58.0%, and 67.4%, respectively, meeting the application requirements for fabric defect detection in textile industry production.

Unmanned aerial vehicle (UAV) forest fire detection is vital for forest safety. However, early-stage UAV fire scenarios often involve small targets, weak smoke signals, and strict onboard resource constraints, which pose significant challenges to existing detectors. To improve the speed and accuracy of UAV forest fire detection, this paper proposes a lightweight fire detection algorithm, AHE-YOLO, specifically designed for UAVs. The proposed method adopts a coordinated lightweight design to improve feature preservation and cross-scale representation under limited computational budgets. Specifically, the Adaptive Downsampling (ADown) module preserves shallow fire-related cues during spatial reduction, improving sensitivity to small flame and smoke targets. The high-level screening-feature fusion pyramid network (HS-FPN) introduces cross-scale attention to promote more discriminative multi-level feature interaction while reducing redundant computation. Furthermore, the Efficient Mobile Inverted Bottleneck Convolution (EMBC) module is employed to improve receptive-field efficiency and feature selectivity under lightweight constraints, further enhancing detection accuracy and inference speed. Finally, the performance of AHE-YOLO is comprehensively evaluated through ablation and comparative experiments on the same dataset. The final experimental results show that YOLO-AHE achieves a mean average precision (mAP) of 94.8% while reducing model parameters by 39.7%, decreasing FLOPs by 27.0%, and shrinking the model size by 36.4%. In addition, its inference speed improves by 16.5%. Beyond detection performance, the proposed framework supports sustainable forest monitoring by enabling early fire warning with reduced computational and energy demands, showing strong potential for real-time deployment on resource-constrained UAV and edge platforms.

This study focuses on the spatial magnetic field distribution of magnetic fluids, an extraordinary class of fluids composed of magnetic nanoparticles (MNPs), employing the Monte Carlo method to simulate the microstructure of magnetic fluids under an external magnetic field. On that basis, a model was established through magnetic dipole theory to delve into the spatial magnetic field distribution of magnetic fluids. The findings reveal that the application of a magnetic field leads to the formation of chain-like structures within the magnetic fluids, resulting in inhomogeneous spatial magnetic field distribution. The size and concentration of MNPs are crucial determinants that significantly affect the microstructure of magnetic fluid and its spatial magnetic field distribution. Furthermore, environmental conditions such as external magnetic field strength or temperature can also regulate the positions of MNPs within magnetic fluids and the spatial magnetic field distribution of the magnetic fluids. These observations enrich the comprehension of the fundamental mechanisms of magnetic fluids and their response to diverse factors, advancing the growing comprehension on the characteristics and applications of these remarkable magnetic fluids.

For non-linear systems (NLSs), the state estimation problem is an essential and important problem. This paper deals with the nonlinear state estimation problems in nonlinear and non-Gaussian systems. Recently, the Bayesian filter designer based on the Bayesian principle has been widely applied to the state estimation problem in NLSs. However, we assume that the state estimation models are nonlinear and non-Gaussian, applying traditional, typical nonlinear filtering methods, and there is no precise result for the system state estimation problem. Therefore, the larger the estimation error, the lower the estimation accuracy. To perfect the imperfections, a projection filtering method (PFM) based on the Bayesian estimation approach is applied to estimate the state. First, this paper constructs its projection symmetric interval to select the basis function. Second, the prior probability density of NLSs can be projected into the basis function space, and the prior probability density solution can be solved by using the Fokker–Planck Equation (FPE). According to the Bayes formula, the proposed estimator utilizes the basis function in projected space to iteratively calculate the posterior probability density; thus, it avoids calculating the partial differential equation. By taking two illustrative examples, it is also compared with the traditional UKF and PF algorithm, and the numerical experiment results show the feasibility and effectiveness of the novel nonlinear state estimation filter algorithm.