Industrial Anomaly Detection (IAD) plays a critical role in quality control and manufacturing efficiency across various industries. Recent advancements in Large Vision-Language Models (LVLMs) have introduced new paradigms for addressing the challenges in IAD, overcoming limitations of traditional approaches. This survey provides a comprehensive review of the integration of LVLMs with IAD, analyzing their evolution, methodologies, and applications. We systematically categorize existing approaches into three main frameworks: traditional anomaly detection, zero-shot methods, and LVLM-based solutions. We examine how these models leverage multimodal capabilities to enhance anomaly detection, reasoning, and explanation in industrial settings. Furthermore, we compare the performance of various methods across standard benchmarks, discuss current challenges, and highlight promising future research directions. Our findings indicate that LVLM-based approaches offer significant advantages in terms of flexibility, interpretability, and generalization capabilities, particularly in scenarios with limited anomaly samples and complex industrial environments.

Magnetic Resonance Imaging(MRI) has a wide range of applications in the medical field, such as diagnosis, treatment, pathological research,etc.However,due to hardware limitations, obtaining high-quality MR images is often challeng ing in clinical practice. Therefore, reconstructing high-quality MRimages through partial images has important significance in medical research. Existing super-resolution methods usually use multi-contrast MRI to reconstruct MR images. However, existing methods usually use single-scale MR images for reconstruction and do not combine with the specificity of MR images. To address this issue, we propose a multi-scale feature transfer network(SFSR) based on spatial and frequency domains, which comprises four components, including the shallow feature extrac tor, and the Multi-Scale Frequency Attention Block(MFAB), and the Multi-Scale Spatial Attention Block(MSAB), and the Multi Scale Fusion Block(MSFB).Firstly, we utilize the shallow feature extractor to extract features at three scales from both the target and reference images. These features are then separately fed into the Multi-Scale Frequency Attention Block and the Multi-Scale Spatial Attention Block to align the features. Finally, the Multi Scale Fusion Block are employed to fuse the aligned features across different scales.Extensive experiments on IXI and FastMRI datasets show that SFSR achieves the most competitive results over state-of-the-art approaches.

The Hybrid Flow-Shop Scheduling Problem (HFSP) is a pivotal challenge in the intelligent transformation of manufacturing, where efficient solutions are critical for enhancing production efficiency and resource utilization. However, traditional swarm intelligence algorithms for solving HFSP often exhibit weaknesses in global search capabilities, susceptibility to local optima, and insufficient convergence efficiency to meet practical scheduling demands. Furthermore, complex constraints in real industrial scenarios—such as dynamic variations in manual operation times and split orders—have not been adequately modeled, resulting in a significant gap between theoretical research and engineering applications. To address these limitations, this study conducts research from both theoretical optimization and engineering adaptation perspectives.For the classic HFSP with the objective of minimizing makespan, an Improved Subtraction-Average-Based Optimizer (ISABO) is proposed. This algorithm integrates a simulated annealing perturbation mechanism and a subgroup learning strategy. The simulated annealing perturbation enhances global search capabilities by probabilistically accepting inferior solutions during iterations, thereby avoiding local optima. Concurrently, the subgroup learning strategy guides partial populations toward optimal solutions, improving solution quality. The research follows a progression from "basic problem optimization" to "real-world scenario adaptation." Experiments are conducted using production data from a paint roller manufacturing plant, with three widely-used HFSP algorithms—Genetic Algorithm (GA), Whale Optimization Algorithm (WOA), and Grey Wolf Optimizer (GWO)—selected as benchmarks. For the classic HFSP, ISABO achieves shorter makespan than all baseline algorithms in 100% of test instances and demonstrates the highest stability (measured by standard deviation) in 61.11% of cases .

Searchable encryption (SE) enables secure retrieval over encrypted data, balancing privacy and usability in cloud storage. This paper reviews SE's evolution, contrasting symmetric (SSE) and asymmetric (ASE/PEKS) models, and summarizes breakthroughs in dynamic data support, multi-keyword search, and leakage resilience. Current challenges include efficiency in complex queries, dynamic scenario adaptability, and quantum threats. Future directions focus on lightweight protocols, post-quantum designs (e.g., lattice-based cryptography), and integration with blockchain/homomorphic encryption to advance privacy-preserving frameworks. This work provides critical insights for SE's development in the post-quantum era.