Accurate detection of aero-engine blades is critical for aviation safety and maintenance. While industrial endoscopes enable efficient inspection of complex engine interiors, existing systems struggle with blade detection due to geometric variations, lighting changes, reflections, and occlusions. This paper proposes a novel spatial-temporal transformer model that enhances small blade detection via multi-scale feature extraction and improves edge robustness with an enhanced deformable DETR module, addressing defects like erosion or fractures. For video sequences, a unified spatial-temporal representation resolves counting errors (e.g., omissions/duplicates). Experiments on a custom inspection platform demonstrate superior accuracy and reliability over state-of-the-art methods.

Industrial Control Systems (ICSs) are the core of industrial production. Wireless technology, with its flexibility and adaptability, is catalyzing a transformative shift from traditional ICS to the advanced Industrial Wireless Control Systems (IWCSs). However, the openness of wireless media, high dynamics of the environment, and resource scarcity present unprecedented security challenges of high security defense costs and low detection inaccuracy for IWCS. State-of-the-art methods primarily treat ICS as a typical cyber-physical system, which focuses on security issues from the cyber and control domains, rather than the physical domain. As a result, they are unable to fully address the high dynamics of wireless channels and unknown attacks, ultimately failing to meet the stringent security requirements of industrial systems. To this end, this paper proposes a physical-domain whitelist as the final line of security defense leveraging the finite nature of the physical behavior space in industrial production systems. Moreover, a holistic cross-domain security-safety architecture is introduced, drawing inspiration from the integrated cyber-control-physical collaboration. In the proposed architecture, the top-down inherent security-safety defense and bottom-up risk backtracking form a close loop, which not only prevents unknown attacks but also facilitates rapid localization and response to attacks. In the experiment, the composite AGV scheduling control has been developed to verify the effectiveness of the architecture. Ultimately, the potential challenges of the cross-domain architecture for IWCS safety-security defense have been summarized.

The clinical adoption of small interfering RNAs (siRNAs) has prompted the development of various computational strategies for siRNA design, from traditional data analysis to advanced machine learning techniques. However, previous studies have inadequately considered the full complexity of the siRNA silencing mechanism, neglecting critical elements such as siRNA positioning on mRNA, RNA base-pairing probabilities, and RNA-AGO2 interactions, thereby limiting the insight and accuracy of existing models. Here, we introduce siRNADiscovery, a Graph Neural Network (GNN) framework that leverages both non-empirical and empirical rule-based features of siRNA and mRNA to effectively capture the complex dynamics of gene silencing. On multiple internal datasets, siRNADiscovery achieves state-of-the-art performance. Significantly, siRNADiscovery also outperforms existing methodologies in in vitro studies and on an externally validated dataset. Additionally, we develop a new data-splitting methodology that addresses the data leakage issue, a frequently overlooked problem in previous studies, ensuring the robustness and stability of our model under various experimental settings. Through rigorous testing, siRNADiscovery has demonstrated remarkable predictive accuracy and robustness, making significant contributions to the field of gene silencing. Furthermore, our approach to redefining data-splitting standards aims to set new benchmarks for future research in the domain of predictive biological modeling for siRNA.