To address the issues of inaccurate positioning, weak feature extraction capability, and poor cross-domain adaptability in the detection of surface defects of steel materials, this paper proposes an improved YOLOv11-EMD algorithm and integrates a multi-stage transfer learning framework to achieve high-precision, robust, and low-cost industrial defect detection. Specifically, the InnerEIoU loss function is introduced to improve the accuracy of bounding box regression, the multi-scale dilated attention (MSDA) module is integrated to enhance the multi-scale feature fusion capability, and the Cross-Stage Partial Network with 3 Convolutions and Kernel size 2 Dynamic Convolution (C3k2_DynamicConv) module is embedded to improve the expression of and adaptability to complex defects. To address the problem of performance degradation when the model migrates between different data domains, a multi-stage transfer learning framework is constructed, combining source domain pre-training and target domain fine-tuning strategies to improve the model’s generalization ability in scenarios with changing data distributions. On the comprehensive dataset constructed of NEU-DET and Severstal steel defect images, YOLOv11-EMD achieved a precision of 0.942, a recall of 0.868, and an mAP@50 of 0.949, which are 3.5%, 0.8%, and 1.6% higher than the original model, respectively. On the cross-scenario mixed dataset composed of NEU-DET and GC10-DET data, the mAP@50 was 0.799, outperforming mainstream detection algorithms. The multi-stage transfer strategy can shorten the training time by 3.2% and increase the mAP by 8.8% while maintaining accuracy. The proposed method improves the defect detection accuracy, has good generalization and engineering application potential, and is suitable for automated quality inspection tasks in diverse industrial scenarios.

Protein secondary structure prediction (PSSP) plays a crucial role in resolving protein functions and properties. Significant progress has been made in this field in recent years, and the use of a variety of protein-related features, including amino acid sequences, position-specific score matrices (PSSM), amino acid properties, and secondary structure trend factors, to improve prediction accuracy is an important technical route for it. However, a comprehensive evaluation of the impact of these factor features in secondary structure prediction is lacking in the current work. This study quantitatively analyzes the impact of several major factors on secondary structure prediction models using a more explanatory four-class machine learning approach. The applicability of each factor in the different types of methods, the extent to which the different methods work on each factor, and the evaluation of the effect of multi-factor combinations are explored in detail. Through experiments and analyses, it was found that PSSM performs best in methods with strong high-dimensional features and complex feature extraction capabilities, while amino acid sequences, although performing poorly overall, perform relatively well in methods with strong linear processing capabilities. Also, the combination of amino acid properties and trend factors significantly improved the prediction performance. This study provides empirical evidence for future researchers to optimize multi-factor feature combinations and apply them to protein secondary structure prediction models, which is beneficial in further optimizing the use of these factors to enhance the performance of protein secondary structure prediction models.

Phase change memory (PCM) and DRAM-based hybrid main memory have emerged as one of the most promising technology for the future embedded systems. Previous research works have focused on page migration strategies to fully exploit the advantages of DRAM and PCM to achieve higher performance and energy efficiency. However, the ability to predict the pages’ future behavior in these strategies could be further improved and most strategies do not consider how to reduce the DRAM energy consumption to further decrease the hybrid system energy. In this article, we propose a novel dynamic hardware migration strategy named periodical frequency hot-region aware page replacement algorithm (PFHA), which utilizes the relation of periodical read and write access frequencies to predict the hotness of a certain page. Meanwhile, PFHA innovatively initiates write hot page self-migration within DRAM to establish a concentrate DRAM write hot region to reduce the DRAM energy. Experiment results confirm that the proposed technique is able to make most of the write hot pages absorbed by DRAM. Meanwhile, it decreases the PCM average access time to a great extent and gets preferable energy consumption.

At present, the detection of steel surface defects is still challenging, because there are some problems in steel products, such as complex background and noise interference, making it difficult to accurately detect complex small targets and great changes in defects at different scales, which directly affects product quality and endangers life safety. To solve the above problems, this paper proposes a steel surface defect detection network based on global attention perception and cross-layer interactive fusion, named GCF-Net. Firstly, this paper proposes an Interactive Feature Extraction Network (IFE-Net), which uses a local modeling feature extraction module to enhance the extraction of local detail features and uses a global attention perception module to capture the global contextual information in the image, thus improving the detection of complex background and noise defects. Secondly, this paper proposes a Cross-Layer Interactive Fusion Network (CIF-Net), which makes up for the fine-grained information lost during the gradual refinement of features through the fusion of adjacent layers, fully integrates shallow and deep features, and at the same time enhances the interaction between different scales by cross-layer fusion, thus improving the recognition ability of defect targets of different scales. Thirdly, the Interactive Fusion Module (IFM) is proposed, which can adjust the importance of each mosaic feature by attention to make efficient use of all feature information and improve the detection of complex background defects. Finally, in order to solve the problems of difficult positioning and inaccurate detection of small targets, this paper aims to strengthen the sensitive loss Q_IOU of small targets and improve the perception of complex small targets in steel defects. Compared with the baseline model, mAP@.5 is improved by 7.0%, 4.4%, and 2.5% on the NEU-DET, PCB, and Steel datasets, respectively, and it is better than all of the comparison models.

Background: Protein secondary structure prediction (PSSP) is a critical task in computational biology, pivotal for understanding protein function and advancing medical diagnostics. Recently, approaches that integrate multiple amino acid sequence features have gained significant attention in PSSP research. Objectives: We aim to automatically extract additional features represented by evolutionary information from a large number of sequences while simultaneously incorporating positional information for more comprehensive sequence features. Additionally, we consider the interdependence between secondary structures during the prediction stage. Methods: To this end, we propose a deep neural network model, ILMCNet, which utilizes a language model and Conditional Random Field (CRF). Protein language models (PLMs) pre-trained on sequences from multiple large databases can provide sequence features that incorporate evolutionary information. ILMCNet uses positional encoding to ensure that the input features include positional information. To better utilize these features, we propose a hybrid network architecture that employs a Transformer Encoder to enhance features and integrates a feature extraction module combining a Convolutional Neural Network (CNN) with a Bidirectional Long Short-Term Memory Network (BiLSTM). This design enables deep extraction of localized features while capturing global bidirectional information. In the prediction stage, ILMCNet employs CRF to capture the interdependencies between secondary structures. Results: Experimental results on benchmark datasets such as CB513, TS115, NEW364, CASP11, and CASP12 demonstrate that the prediction performance of our method surpasses that of comparable approaches. Conclusions: This study proposes a new approach to PSSP research and is expected to play an important role in other protein-related research fields, such as protein tertiary structure prediction.