To address the issues of modeling the relationships between multiple local region objects in images and enhancing local region features, as well as mapping global image semantics to global text semantics and local region image semantics to local text semantics, a novel image captioning method based on CLIP and integrating local feature enhancement and multi-scale semantic guidance is proposed. The model employs ViT as the global visual encoder, Faster R-CNN as the local region visual encoder, BERT as the text encoder, and GPT-2 as the text decoder. By constructing a KNN graph of local image features, the model models the relationships between local region objects and then enhances the local region features using a graph attention network. Additionally, a multi-scale semantic guidance method is utilized to calculate the global and local semantic weights, thereby improving the accuracy of scene description and attribute detail description generated by the GPT-2 decoder. Evaluated on MSCOCO and Flickr30k datasets, the model achieves a significant improvement in the core metric CIDEr over established strong baselines, with 4.7% higher CIDEr than OFA on MSCOCO, and 16.6% higher CIDEr than Unified VLP on Flickr30k. Ablation studies and qualitative analysis validate the effectiveness of each proposed module.

In traditional Wireless Sensor Network routing protocols, data collected through timed interval sensing tends to have high temporal redundancy, which leads to unnecessary energy drain. To alleviate this problem and enable sensor networks to save energy to some extent, a practical solution is to utilize prediction-based data fusion methods. To this end, this paper first proposes a Low Energy Adaptive Clustering Hierarchy-Energy-Kopt-N algorithm, an optimization algorithm explicitly designed to address the cluster-head election phase of the Low Energy Adaptive Clustering Hierarchy protocol. Then, a data collection model using data prediction techniques – the Grey Data Prediction Model is formatted. Combining these improvements, a new data fusion algorithm that relies on data prediction, Grey-Clusters-Leach (GCL), is proposed. Simulation experiments demonstrate that the network energy drain of the GCL algorithm is reduced by 35%, 21.5%, and 20%, and the network operation critical period life is extended by 35%, 22%, and 5% compared to the LEACH, LEACH-E, EQDC and SEP algorithms, respectively. GCL can effectively manage the size and number of clusters and reduce the number of packet transmissions by 20% through data prediction.

This study proposes a novel virus propagation model designed explicitly for SCADA(supervisory Control and Data Acquisition) industrial networks. It addresses a critical limitation in existing models applied to the Internet and Industrial Internet of Things (IIoT)—their failure to account for inter-node information exchange processes. The model is inspired by the phenomenon that “immune” nodes in real-world and biological systems inhibit the spread of viruses by exchanging information. This model incorporates isolation strategies to curb virus transmission, considering the uncertainty of vulnerable device behavior. Central to this research are the assumptions of a nonlinear infection rate and dual delays, which better mirror the real-world conditions of industrial control networks. This approach diverges significantly from prior studies that relied on bilinear infection rate assumptions. This study constructed an SMIQR model through theoretical derivation and experimental validation. The model enables nodes to autonomously enhance their defenses after receiving risk information while accounting for the impact of inter-node information exchange. Experiments based on real-world data demonstrated the model’s effectiveness in simulating virus propagation and evaluating defense strategies, overcoming the limitations of traditional bilinear infection rate assumptions. Comparative experiments show that the SMIQR model significantly reduces the number of infected nodes in SCADA industrial networks, demonstrating its superior effectiveness in curbing virus spread. Furthermore, the research proposed dynamic isolation tactics that balance industrial operational continuity, providing SCADA industrial networks with a theoretical framework (incorporating nonlinear infection rates and dual delay characteristics) and practical defense solutions to curb malware spread without disrupting production.

As a crucial renewable energy source, solar PV power generation drives environmental protection and energy transformation. However, existing forecasting models struggle to accurately capture the complex dynamics of photovoltaic (PV) power, primarily due to monolithic modeling paradigms and inadequate representation of temporal information. To address these challenges, this paper proposes a novel hybrid model that leverages data decomposition and frequency-stratified prediction. The model employs the advanced ICEEMDAN algorithm to address complex non-stationarity. Additionally, it introduces a frequency-stratified heterogeneous network for precise component-wise modeling and integrates Improved Relative Positional Encoding (IRPE) to accurately capture temporal dependencies. To comprehensively evaluate model performance, this study employs quantile regression to generate probabilistic prediction intervals, using the median output as the baseline for point predictions. The model’s performance is validated through ablation experiments and comparisons of single-step and multi-step predictions with recent benchmark models. The results indicate that the model excels under the MIMO strategy, achieving normalized nMAE values of 0.1142 and 0.1490 for 120-minute and 2880-minute forecasts on the DKASC and Solar I datasets, respectively, surpassing recent baseline models by 14.6% and 8.1%. Furthermore, the model’s statistical stability and robustness are confirmed through 30 independent Wilcoxon signed-rank tests, as well as an uncertainty analysis conducted under various weather conditions. In summary, the model’s high accuracy and stability provide robust support for power plant operations and planning.

Zero-trust security and federated learning have emerged as promising paradigms for edge computing, yet existing solutions struggle to balance security, privacy, and performance requirements effectively. This paper presents ZT-IoTrust, a zero-trust framework that integrates device-specific trust evaluation with quantum-resistant security mechanisms for secure IoT access control. The framework incorporates several key innovations: quantum-resistant cryptographic protocols based on lattice problems for long-term security, a dynamic federated trust evaluation system that continuously assesses individual IoT device behaviors, and an adaptive access control architecture that implements continuous verification principles while maintaining efficiency for resource-constrained environments. Experimental evaluation on CICIDS2017 and KDD Cup 1999 datasets demonstrates effectiveness across network-layer security metrics, achieving a 92.5% attack detection rate with 1.2% false positives and 0.5% privacy leakage. The device-specific trust evaluation mechanism achieves 93.0% accuracy within 12 federation rounds while maintaining 98.8% reliability under high concurrent loads. Performance analysis shows robust scalability, with response times remaining under 125 ms and throughput reaching 1250 requests per second as the system scales from 5 to 20 nodes. These results establish ZT-IoTrust as a practical solution for implementing zero-trust security in IoT environments, effectively balancing continuous verification with system performance requirements.

In view of the insufficiency of the text encoder using CLIP and the insufficiency of the interaction between the two towers using CLIP, a CLIP-based video description model RAMSG is proposed, which combines retrieval augmentation with multi-scale semantic guidance. Firstly, RAMSG uses the visual and text encoder module of CLIP to achieve cross-modal retrieval and extract relevant text as a supervisory signal. Then, the intrinsic order of semantic words is obtained by a semantic detector and ranker. Finally, the global and local semantic guidance of the multi-scale semantic guidance module is used to improve the video description generation effect of the decoder module. Experimental results on the video description datasets MSR-VTT and VATEX show that the RAMSG model has a significant improvement in several performance indicators compared with other work, and the additional text semantics obtained through videotext matching tasks greatly improve the performance of the model.