Consumer data in e-commerce platforms relies heavily on Internet of Things (IoT) devices, which bring forth numerous security threats. As an emerging proactive defense technology, IoT network security situation prediction has the capability to forecast the overall future network security conditions. However, the original network security situation sequences exhibit nonlinear and unstable characteristics, which diminish the direct predictive accuracy. In this paper, we propose a prediction model based on decomposition-fusion. Specifically, we propose a novel approach to compute situation values by integrating three key factors: IoT attack factors, IoT attack probabilities, and IoT threat factors. Then, we decompose the original sequence into more stable subsequences using Variational Mode Decomposition (VMD), and construct a Convolutional Neural Network (CNN)-Bidirectional Long Short-Term Memory (BiLSTM)-Attention architecture to predict these subsequences. Finally, we utilize BiLSTM to fuse the results from each subsequence calculation, generating the ultimate prediction. Experimental results underscore the significant advantages of this method in terms of stability and forecasting precision, with a fitting degree of 0.99. This method provides a more comprehensive security defense system for e-commerce platforms and IoT applications, thereby enhancing the overall security of consumer data. Furthermore, it presents a novel solution for the field of network security.

With the continuous development of autonomous driving, real-time target detection has become increasingly critical in autonomous driving systems. However, traditional target detection algorithms usually require huge computational resources, limiting their application on embedded autonomous driving platforms. To address this challenge, a lightweight single-stage target detection algorithm is proposed YOLOdrive. The inverted residual structure, linear bottleneck layer, and depth-separable convolution in MobileNetv2 are utilized to improve the YOLOv8 backbone network, while the spatial channel reconstructed convolution is used to improve the C2f module of YOLOv8, and the convolution of YOLOv8 neck and detection head is replaced by the depth-separable convolution. Experimental results verify that the average accuracy of YOLOdrive algorithm on MS COCO2017 dataset and VOC2007 dataset is improved compared with the baseline model YOLOv8n. The amount of model parameters has been reduced by more than 50%, and the amount of computation in the model has been reduced by more than 70%. The algorithm drastically reduces the amount of parameters and computational complexity of the network, improves the operational efficiency, saves the storage space of the network, and maintains a high detection performance.

The influence of the evolution rule of basicity (0.6∼2.4) on the mineral composition and microstructure of sinter is studied by using a polarizing microscope, and the comprehensive application analysis of the drum index, vertical sintering speed, and yield of sinter shows that, over the course of an increase in basicity (0.6∼1.0), the mineral structure changed from the original porphyritic‐granular structure to a porphyritic structure. At the same time, there was no calcium ferrite phase in the bonding phase at a basicity of less than 1.0; therefore, the downward trend of the three indicators is obvious. When the basicity was further increased to approximately 1.6, the main structure of the mineral phase changed from a corrosion structure to an interweaving corrosion structure. Because of the existence of a porphyritic‐granular structure, the structure of the mineral phase was extremely inhomogeneous and most complex near the basicity of 1.6; although a small amount of calcium ferrite displayed an acicular structure, the drum index appeared to show a very low value. With an increase in basicity to 2.0, the mineral phase structure was dominated by an interweaving corrosion structure with a uniform framework, and the content of calcium ferrite reached the highest value. Moreover, a clear acicular structure developed, and the drum index also increased to the highest value. At a basicity of more than 2.0, a mineral structure began to appear and the corrosion, porphyritic‐granular structure, and the drum index also showed a slightly declining trend. Therefore, in the actual production process, basicity should be avoided as far as possible at around 1.0 and 1.6 and it should be controlled at around 2.0. At the same time, based on the mineral facies data set of this paper, the convolutional neural network is used to carry out a simple prediction model experiment on the basicity corresponding to the mineral facies photos, and the effect is quite good, which provides a new idea and method for the follow‐up study of mineral facies.

Real-time object detection plays a critical role in advancing autonomous driving technologies. To meet the demands for real-time performance, a lightweight model is essential to reduce parameter size, simplify complexity, and enhance detection speed. This paper presents YOLO-FSE, a compact vehicle detection model built upon the YOLOv5 framework. In YOLO-FSE, the C3 module is substituted with the C3faster module from the FasterNet lightweight architecture. The backbone network is augmented with the Shuffle Attention module, which improves feature fusion by separating spatial and channel attention mechanisms. Additionally, the original loss function is replaced by EIoU loss, which directly penalizes width and height predictions, thereby improving the model’s generalization ability. During experimentation, it was assumed that the number of test images was sufficiently large and clear, with PyTorch serving as the primary framework for model development and training. Experimental results show that, on the UA-DETRAC and BIT-Vehicle datasets, the mean average precision (mAP) achieved 98.9% and 97.6%, respectively. Computational complexity (FLOPs) was reduced by 12.65%, while the average frame rate (Fps) increased by 46.13%. Overall, the enhanced model exhibits substantial improvements in both speed and performance, demonstrating its potential for deployment in autonomous driving applications.

A rational approach to predict passenger car sales and analyze the current state of the passenger car sales market can contribute to the healthy development of the automotive industry. The number of features used to describe passenger car sales in real life is too redundant. The features of human empirical filtering and combination cause loss of time. In addition, explicit features are too homogeneous, which need to be complemented by implicit features. The fixed-parameter weights in the trained model cannot provide some estimates for unknown uncertainties. Therefore, in this work, we propose a DeepFM-based nonparametric model (DFMNP) for predicting passenger car sales in sustainable way. We use a big data platform to provide data for the DFMNP model. The DFMNP model uses feature engineering to expand the number of explicit features, a multilayer neural network to extract implicit features, and a Bayesian neural network to replace the neural network with fixed weights for inferring predictive values. In addition, a factorization machine is used in the prediction function to take into account the cross information of implicit features. The combination of the above improvement points can be used to improve the model’s expressive and predictive power for unknown data. Its prediction performance on two real passenger car sales datasets is as follows, RMSE values of 0.0825 and 0.116, and MAE values of 0.0482 and 0.0595. The above experimental results verify the superiority of the method proposed in this work.

With the depletion of high-grade iron ore resources, the efficient utilization of low-grade iron ore has become a critical demand in the steel industry. Due to its uniform particle size and chemical composition, pelletized iron ore significantly enhances both the utilization rate of iron ore and the efficiency of metallurgical processes. This paper presents a deep learning model based on ResUNet, which integrates three-dimensional CT images obtained through industrial computed tomography (ICT) to precisely segment hematite, liquid phase, and porosity. By incorporating residual connections and batch normalization, the model enhances both robustness and segmentation accuracy, achieving F1 scores of 98.37%, 95.10%, and 83.87% for the hematite, pores, and liquid phase, respectively, on the test set. Through 3D reconstruction and quantitative analysis, the volume fractions and fractal dimensions of each component were computed, revealing the impact of the spatial distribution of different components on the physical properties of the pellets. Systematic evaluation of model robustness demonstrated varying sensitivity to different CT artifacts, with the strongest resistance to beam hardening and highest sensitivity to Gaussian noise. Multi-scale resolution analysis revealed that segmentation quality and fractal dimension estimates exhibit phase-dependent responses to resolution changes, with the liquid phase being the most sensitive. Despite these dependencies, the relative complexity relationships among phases remained consistent across resolutions, supporting the reliability of our qualitative conclusions. The study demonstrates that the deep learning-based image segmentation method effectively captures microstructural details, reduces human error, and enhances automation, providing a scientific foundation for optimizing pellet quality and improving metallurgical performance. It holds considerable potential for industrial applications.