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Multifunctional electroactive bio-adhesive for robustly-integrated wound therapy and postoperative wound-status warning and assessment

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Nano Research 2024年第5期17卷 4359-4370页

作者： Ouyang Yue Xuechuan Wang Mengdi Hou Siwei Sun Manhui Zheng xiaoliang Zou Zhongxue Bai Chunlin Liu xinhua Liu College of Bioresources Chemical and Materials Engineering Shaanxi University of Science&TechnologyXi’an 710021China College of Pharmacy Shaanxi University of Chinese MedicineXi’an 710021China Institute of Basic and Translational Medicine Xi’an Medical UniversityXi’an 710021China School of Mechanical Engineering Chengdu UniversityChengdu 610000China

Wound abnormalities such as secondary wound laceration and inflammation are common postoperative health hazards during clinical *** continuous treatment,healing induction,and real-time visualization of wound status and complications,including wound re-tearing,inflammation,and morphology,are key focal points for comprehensive ***,an on-demand quadruple energy dissipative strategy was proposed for the nanoengineering of a physically and chemically synergistic double-layer gelatin-based bio-adhesive(DLGel)by combining a multi-network adhesive layer and a versatile electroactive energy dissipative layer based on contrivable interlocking micro-pillar arrays and crosslinked polymer *** subtly multiple energy dissipation designs enable DLGel with robust adhesive strength to omnipotently wet and dynamic tissue,providing a basis for reliable wound *** achieves comprehensive wound-healing induction through electrical stimulation and possesses reversible underwater light/thermal adhesion,excellent hemostatic performance,outstanding antimicrobial properties,and self-repair ***,a novel deep-learning strategy is creatively established to respond to mechanical deformation due to wound *** strategy translates biological information into visual graphics,providing real-time early warning and assessment of postoperative wound-abnormality/-morphology,such as laceration,inflammation,and ***,DLGel and its associated signal collection and processing protocol enable the integration of reliable wound closure,wound healing,and real-time postoperative wound-status warning and assessment within the unobservable and undetectable“black box”regions in a context of non-clinical comprehensive therapy.

关键词： electroactive bio-adhesive quadruple energy dissipation comprehensive wound therapy postoperative wound-status warning and assessment

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Honeycomb-like biochar framework coupled with Fe_(3)O_(4)/FeS nanoparticles as efficient heterogeneous Fenton catalyst for phenol degradation

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Journal of Environmental sciences 2024年第2期136卷 390-399页

作者： Aihua Cheng Yi He xiaohe Liu Chi He Department of Environmental Engineering College of Geology and EnvironmentXi’an University of Science and TechnologyXi’an 710054China State Key Laboratory of Multiphase Flow in Power Engineering School of Energy and Power EngineeringXi’an Jiaotong UniversityXi’an 710049China National Engineering Laboratory for VOCs Pollution Control Material and Technology University of Chinese Academy of SciencesBeijing 101408China

Achieving an efficient and stable heterogeneous Fenton reaction over a wide pH range is of great significance for wastewater ***,a pollen-derived biochar catalyst with a unique honeycomb-like structure,coupled with the dispersion of magnetic Fe_(3)O_(4)/FeS(Fe/S)nanoparticles,was synthesized by simple impregnation precursor,followed by *** prepared Fe/S-biochar catalyst demonstrated outstanding phenol degradation efficiency across a wide pH range,with 98%of which eliminated even under neutral conditions(pH 7.0).The high catalytic activity was due to the multilevel porous structure of pollenderived biochar provided enough active sites and allowed for better electron transfer,then increases oxidation ability to promote the ***,the acid microenvironment formed by SO_(4)^(2-)group from Fe/S composite extended the pH range for Fenton reaction,and S^(2-)facilitated the conversion of≡Fe^(3+)to≡Fe^(2+),resulting in remarkable degradation ***,biochar can effectively promote cycling stability by limiting Fe *** work may provide a general strategy for designing 3D framework biochar-based Fe/S catalysts with excellent performance for heterogeneous Fenton reactions.

关键词： Biomass Biochar Fenton reaction Fe_(3)O_(4)/FeS(Fe/S) Phenol degradation

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Improved PointNet++ Algorithm for Feature Point Recognition in Dual-Arm Robotic Grasping and Unfolding of Leather

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Journal of the American Leather Chemists Association 2024年第12期119卷 539-547页

作者： Huan, Yuan Ren, Gongehang Xu, Ruoxuan Jin, Guang Sun, Jiangong College of Mechanical & Electrical Engineering Shaanxi University of Science & Technology Shaanxi Xi'an710021 China

With the rapid advancement of robotic technology, traditional manufacturing industries are encountering unprecedented opportunities and challenges. The leather industry, as a typical representative, urgently requires automation. However, due to the irregulär edges, softness, and variable shapes of leather, automation faces significant technical challenges. Tö address this issue, this study proposes a high-precision grasping point recognition model based on an improved PointNet++ algorithm. By incorporating the Shuffie Attention mechanism, the improved PointNet++ algorithm significantly enhances the capture of key features, improving the segmentation aecuraey and Overall Performance of the leather grasping areas. Experimental results demonstrate that the improved model exhibits superior Performance in leather recognition and grasping experiments, achieving a recognition success rate of 96% and a grasping success rate of 89.6%. These results fully validate the effectiveness and practicality of the improved model in handling leather, providing reliable technical support for automated leather processing. © 2024 American Leather Chemists Association. All rights reserved.

关键词： Leather

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Underwater Biological Target Detection Algorithm and Research Based on YOLOv7 Algorithm

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第6期51卷 594-601页

作者： Zhuang, Hongwei Liu, Weisheng School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan China College of Computer Science and Software Engineering University of Science and Technology Liaoning CO Anshan114051 China

Underwater target detection is an important method for detecting marine organisms. However, due to the image occlusion of underwater targets, blurred water quality, poor lighting conditions, small targets, and complex backgrounds, the detection of underwater biological targets has posed significant challenges. In the intricate underwater environment, the conventional feature extraction method has a few drawbacks, including imprecise feature extraction, sluggish detection speed, and inadequate robustness. Consequently, an underwater target detection method based on the enhanced You Only Look Once 7 (YOLOv7) is proposed in this study. The network architecture is reconstructed, and the Deformable Convolutional Network (DCN) modules replace some 3×3 convolutional blocks in the ELAN structure to offset sampling points and reduce background interference. Skip connections and 1× 1 convolutional architecture are added to the DCN module to improve the model’s perception of image details. In addition, Contextual Transformer 3 (COT3) is also incorporated to improve visual performance. Finally, to improve the detection efficiency of small objects, the CIoU loss function is finally replaced by the Normalized Wasserstein Distance (NWD) algorithm. The mAP of DCCN-YOLOv7 on the URPC dataset is 80.4%, according to the experimental results, 2.8% higher than the YOLOv7 network model that is used as a baseline. Furthermore, in contrast to the original YOLOv7 algorithm, the detection speed and accuracy are higher, making it more appropriate for target recognition underwater. © (2024), (International Association of Engineers). All rights reserved.

关键词： Feature extraction

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Prediction of railway track irregularity based on TCN-BiLSTMs-Attention model 24

Prediction of railway track irregularity based on TCN-BiLSTM...

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3rd International Conference on Signal Processing, computer Networks and Communications, SPCNC 2024

作者： Wu, Chen Lu, xiaofeng Wang, Yiyun Pang, Tiantian School of Computer Science and Engineering Xi’an University of Technology Shaanxi Xi’an China

ISBN: (纸本)9798400710834

Track irregularities can significantly reduce the comfort and safety of train operation. If the development trend of track irregularities can be predicted, the railway management department can issue early warnings to ensure the safe and smooth operation of trains. The sequence data associated with track irregularities exhibit complex nonlinear and non-stationary characteristics, and predictions based solely on the track quality index (TQI) do not take into account the influence of other factors. To address this issue, this paper proposes a model based on TCN-BiLSTMs-Attention for predicting the development trends of track irregularities. First, the Time Convolutional Network (TCN) is employed to extract features from the preprocessed multivariate input data of track irregularities, so as to capture the impact of various indicators on TQI values and provide richer inputs for subsequent model stages. The data is then fed into a multi-layer bidirectional long short-term memory network (BiLSTM) for training. The stacked BiLSTM layers utilize the non-linear changes and long-term dependencies of TQI sequence data that LSTM networks can learn, as well as the bidirectional LSTM, to simultaneously process past and future information. This increases the depth of the model, further improving its learning ability. Subsequently, the attention mechanism module is used for information extraction, enhancing the model’s sensitivity to key information and its ability to capture long-distance dependencies. Finally, the prediction results are output through a fully connected layer. Comparative experiments with multiple models have shown that the proposed TCN-BiLSTMs-Attention model yields more accurate predictions and has strong robustness. © 2024 Copyright held by the owner/author(s).

关键词： Railroads

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Multi-Task Chinese Speech Recognition Method Based on the Squeezeformer Model

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2025年第1期52卷 23-31页

作者： Guo, Ying Wang, Li School of Computer Science and software Engineering University of Science and Technology Liaoning Anshan114051 China College of Computer Science and Technology Liaoning Anshan114051 China

End-to-end training has emerged as a prominent trend in speech recognition, with Conformer models effectively integrating Transformer and CNN architectures. However, their complexity and high computational cost pose deployment challenges. To address these issues, we propose a multi-task Chinese speech recognition method based on the Squeezeformer model. We replace the FMCF structure in Conformer with an MF/CF structure, leveraging the convolutional module as a local Multi- Head Attention (MHA) module to enhance efficiency. Multilevel down-sampling and up-sampling using a time-series U-Net further reduce computational costs. By eliminating redundant LayerNorm layers and employing depthwise separable convolutions, we streamline the model, reduce parameters, and lower deployment costs. An Adaptor Layer is integrated into the MHSA module to mitigate the vanishing gradient problem, and a ScaleVar Layer is added to enhance flexibility. Additionally, the RealFormer module is introduced on the decoding side to improve context understanding. Combining Connectionist Temporal Classification (CTC) with attention-based encoding and decoding models for multi-task learning improves performance and accuracy. Experimental results show that the proposed method reduces the parameters on AISHELL-1 dataset by 16% and reduces the character error rate to 5.50%. At the same time, it also shows good performance on AISHELL-2 dataset. © (2025), (International Association of Engineers). All rights reserved.

关键词： Speech recognition

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EVADE: Targeted Adversarial False Data Injection Attacks for State Estimation in Smart Grid

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable Computing 2024年第3期10卷 534-546页

作者： Tian, Jiwei Shen, Chao Wang, Buhong Ren, Chao xia, xiaofang Dong, Runze Cheng, Tianhao Ministry of Education Key Laboratory for Intelligent Networks and Network Security School of Cyber Science and Engineering Xi'an Jiaotong University Xi'an China Air Traffic Control and Navigation College Air Force Engineering University Xi'an China Information and Navigation College Air Force Engineering University Xi'an China School of Electrical and Electronic Engineering Nanyang Technological University Singapore School of Computer Science and Technology Xidian University Xi'an China

Although conventional false data injection attacks can circumvent the detection of bad data detection (BDD) in sustainable power grid cyber physical systems, they are easily detected by well-trained deep learning-based detectors. Still, state estimation models with deep leaning-based detectors are not secure due to the vulnerabilities and fragility of deep learning models. Using the related laws of conventional false data injection attacks and adversarial sample attacks, this paper proposes the targEted adVersarial fAlse Data injEction (EVADE) strategy to explore targeted adversarial false data injection attacks for state estimation in Smart Grid. The proposed EVADE attack strategy selects key state variables based on adversarial saliency maps to improve the attack efficiency and perturbs as few state variables as possible to reduce the attack cost. In this way, the EVADE attack strategy can bypass the detection of BDD and neural attack detection (NAD) methods (that is, maintaining deep stealthy) with a high success rate and achieve the attack target simultaneously. Experimental results demonstrate the effectiveness of the proposed strategy, posing serious and pressing concerns for sustainable cyber physical power system security. © 2016 IEEE.

关键词： Cyber attacks

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Why are the Qinling Mountains Significant to China?

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Journal of Earth science 2025年第1期36卷 357-363页

作者： xing Chen Yanjun Shen Qingyi Mu Panpan Xu Fenghao Duan Jianbing Peng College of Geology and Environment Xi'an University of Science and TechnologyXi'an 710054China Department of Geological Engineering and Geomatics Chang'an UniversityXi'an 710054China State Key Laboratory of Loess Science Xi'an 710054China School of Water and Environmental Chang'an UniversityXi'an 710054China

0 INTRODUCTION The Qinling Mountains(QM),located at the geographical center of China,serve as a crucial dividing line between the northern and southern and play a vital role as an ecological security barrier,significantly influencing the country's geological,geographical,and ecological landscape(Jia et al.,2023;Peng et al.,2023).The QM stretch from the White Stone Mountain in the northern part of Lintan County,Gansu Province,to the Funiu Mountains in Henan Province in the east.

关键词： dividing Mountains landscape

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Adaptive coupled-sliding-variable-based finite-time control of composite formation for multi-robot systems

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science China(Information sciences) 2024年第10期67卷 244-260页

作者： xinru MA Hengyu LI Jun LIU Yueying WANG Shaorong xiE Jun LUO School of Mechatronic Engineering and Automation Shanghai University School of Mathematics and Computer Application Technology Jining University School of Computer Science Shanghai University College of Mechanical and Vehicle Engineering Chongqing University

This paper focuses on the finite-time control(FTC) of the composite formation consensus(CFC)problems for multi-robot systems(MRSs). The CFC problems are firstly proposed for MRSs under the complex network topology of cooperative or cooperative-competitive networks. Regarding the problems of FTC and CFC on multiple Lagrange systems(MLSs), coupled sliding variables are introduced to deal with the robustness and consistent convergence. Then, the adaptive finite-time protocols are given based on the displacement approaches. With the premised FTC, tender-tracking methods are further developed for the problems of tracking information disparity. Stability analyses of those MLSs mentioned above are clarified with Lyapunov candidates considering the coupled sliding vectors, which provide new verification for tender-tracking systems. Under the given coupled-sliding-variable-based finite-time protocols, MLSs distributively adjust the local formation error to achieve global CFC and perform uniform convergence in time-varying tracking. Finally, simulation experiments are conducted while providing practical solutions for the theoretical results.

关键词： composite formation time-vary tracking information disparity tender-tracking finite-time control nonlinear robots complex networks symmetry consensus

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An Intelligent Optimization Method of Reinforcing Bar Cutting for Construction Site

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computer Modeling in engineering & sciences 2023年第1期134卷 637-655页

作者： Zhaoxi Ma Qin Zhao Tianyou Cang Zongjian Li Yiyun Zhu xinhong Hei Department of Civil Engineering and Architecture Xi’an University of TechnologyXi’an710048China State Key Laboratory of Rail Transit Engineering Informatization(FSDI) Xi’an710043China Department of Computer Science and Engineering Xi’an University of TechnologyXi’an710048China

To meet the requirements of specifications,intelligent optimization of steel bar blanking can improve resource utilization and promote the intelligent development of sustainable *** one of the most important building materials in construction engineering,reinforcing bars(rebar)account for more than 30%of the cost in civil engineering.A significant amount of cutting waste is generated during the construction *** cutting waste increases construction costs and generates a considerable amount of CO_(2)*** study aimed to develop an optimization algorithm for steel bar blanking that can be used in the intelligent optimization of steel bar engineering to realize sustainable *** the proposed algorithm,the integer linear programming algorithm was applied to solve the *** was combined with the statistical method,a greedy strategy was introduced,and a method for determining the dynamic critical threshold was developed to ensure the accuracy of large-scale data *** proposed algorithm was verified through a case study;the results confirmed that the rebar loss rate of the proposed method was reduced by 9.124%compared with that of traditional distributed processing of steel bars,reducing CO_(2)emissions and saving construction *** the scale of a project increases,the calculation quality of the optimization algorithmfor steel bar blanking proposed also increases,while maintaining high calculation *** the results of this study are applied in practice,they can be used as a sustainable foundation for building informatization and intelligent development.

关键词： Building construction rebar work cutting stock problem optimization algorithm integer linear programming

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