Network security has always been a hot topic as security and reliability are vital to software and hardware. Network intrusion detection system (NIDS) is an effective solution to the identification of attacks in computer and communication systems. A necessary condition for high-quality intrusion detection is the gathering of useful and precise intrusion information. Machine learning, particularly deep learning, has achieved a lot of success in various fields of industry and academic due to its good ability of feature representation and extraction. In this paper, deep learning methods are integrated into the NIDS. The intrusion activity is regarded as a time-series event and a bidirectional gated recurrent unit (GRU) based network intrusion detection model with hierarchical attention mechanism is presented. The influence of different lengths of previous traffic on the performance is then studied. Some experiments are performed on the dataset UNSW-NB15, in which the proposed hierarchical attention model achieves satisfactory detection accuracy of more than 98.76% and a false alarm rate (FAR) of lower than 1.2%. An attention probability map to reflect the importance of features is then visualized using the attention mechanism. The visualization ability assists in providing an understanding of the varied importance of the same features for different traffic classes and to determine feature selection in the future.

With the rapid development of vision sensing, artificial intelligence, and robotics technology, one of the challenges we face is installing more advanced vision sensors on welding robots to achieve intelligent welding manufacturing and obtain high-quality welding components. Depth perception is one of the bottlenecks in the development of welding sensors. This review provides an assessment of active and passive sensing methods for depth perception and classifies and elaborates on the depth perception mechanisms based on monocular vision, binocular vision, and multi-view vision. It explores the principles and means of using deep learning for depth perception in robotic welding processes. Further, the application of welding robot visual perception in different industrial scenarios is summarized. Finally, the problems and countermeasures of welding robot visual perception technology are analyzed, and developments for the future are proposed. This review has analyzed a total of 2662 articles and cited 152 as references. The potential future research topics are suggested to include deep learning for object detection and recognition, transfer deep learning for welding robot adaptation, developing multi-modal sensor fusion, integrating models and hardware, and performing a comprehensive requirement analysis and system evaluation in collaboration with welding experts to design a multi-modal sensor fusion architecture.

To resolve real-time the traditional water quality detection method, a novel system of remote water quality measuring and monitoring based on wireless sensor network (WSN) and Code Division Multiple Access (CDMA) technology is proposed. The WSNs can monitor the targets and water quality information of the waters through the cooperation of a large amount of sensors. The optimization of node distribution is studied with the intention of reducing the energy consumption and ensuring the effective information acquisition in wireless sensor network. The functions of remote detection and real-time monitoring of natural water are implemented through the CDMA wireless data transmission. This system has a simple architecture, and isn't confined by the geographical position. The experimental results show that this system can run stably, and its operation is convenient.

During steel production, various defects often appear on the surface of the steel, such as cracks, pores, scars, and inclusions. These defects may seriously decrease steel quality or performance, so how to timely and accurately detect defects has great technical significance. This paper proposes a lightweight model based on multi-branch dilated convolution aggregation and multi-domain perception detection head, DAssd-Net, for steel surface defect detection. First, a multi-branch Dilated Convolution Aggregation Module (DCAM) is proposed as a feature learning structure for the feature augmentation networks. Second, to better capture spatial (location) information and to suppress channel redundancy, we propose a Dilated Convolution and Channel Attention Fusion Module (DCM) and Dilated Convolution and Spatial Attention Fusion Module (DSM) as feature enhancement modules for the regression and classification tasks in the detection head. Third, through experiments and heat map visualization analysis, we have used DAssd-Net to improve the receptive field of the model while paying attention to the target spatial location and redundant channel feature suppression. DAssd-Net is shown to achieve 81.97% mAP accuracy on the NEU-DET dataset, while the model size is only 18.7 MB. Compared with the latest YOLOv8 model, the mAP increased by 4.69%, and the model size was reduced by 23.9 MB, which has the advantage of being lightweight.

The emergence of antibiotic‐resistant bacteria poses a significant challenge to the prompt and appropriate treatment of pathogenic bacteria infections, such as acute bacterial skin and skin‐structure infections (ABSSSI), especially in the presence of biofilms. Bacterial biofilms are naturally resistant to antibiotics and the human immune system, making biofilm‐based infections extremely difficult to treat. Therefore, developing new antibacterial therapies targeting biofilms is crucial. Aggregation‐induced emission luminogens with fluorescence in the second near‐infrared window (NIR‐II AIEgens), which can be activated by a near‐infrared laser to generate heat, offer an effective and precise photothermal therapy (PTT) approach for treating deep‐tissue bacterial infections. However, the presence of biofilms impedes the entry of photosensitizers into the infected area, requiring higher drug doses and increasing the risk of PTT. Herein, we developed a biocompatible AIEgen‐based biohybrid nano formulation that incorporates the BPBBT (NIR‐II AIEgen) and antibiofilm α‐amylase into a red blood cell (RBC) membrane‐derived nanovesicle carrier for a PTT/biofilm degradation combination therapy. The synergistic effect of this new formulation enhances both the photothermal capability of BPBBT and the biofilm degradation compared to traditional individual treatments. The new combination therapy demonstrated significant improvement in treating severe Staphylococcus aureus infections caused by biofilms in vitro and in vivo, presenting a promising alternative to traditional antibiotic therapy.

In order to address the challenges of a low detection accuracy, missed detections, and false detections in marine precious biological target detection within complex marine environments, this paper presents a novel residual attention module called R-AM. This module is integrated into the backbone network of the YOLOv10 model to improve the model’s focus on the detailed features of biological targets during feature extraction. Additionally, the introduction of a bidirectional feature pyramid with adaptive feature fusion in the neck network enhances the integration of semantic information from deep layers, and localization cues from shallow layers improve the model’s ability to distinguish targets from their environments. The experimental data showed that the improved YOLOv10 model achieved 92.89% at mAP@0.5, increasing by 1.31% compared to the original YOLOv10 model. Additionally, the mAP@0.5:0.95 was 77.13%, indicating a 3.71% improvement over the original YOLOv10 model. When compared to the Faster R-CNN, SSD, RetinaNet, YOLOv6, and YOLOv7 models, the enhanced model exhibited increases of 1.5%, 1.7%, 4.06%, 4.7%, and 1.42% in mAP@0.5, respectively. This demonstrates a high detection accuracy and robust stability in complex seabed environments, providing valuable technical support for the scientific management of marine resources in underwater ranches.