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Construction of flaxseed polyphenol nanolipid emulsions as edible coatings and their application in shelf life extension of spiced beef

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Food Chemistry: X 2025年 27卷

作者： Ma, Yan Wang, Nan Liu, Xiaoyong Ma, Xiangjie Meng, Shaohua Zhao, Jiansheng Chen, Jingjing Song, Lianjun Qiao, Mingwu Huang, Xianqing College of Food Science and Technology Henan Agricultural University Henan Engineering Technology Research Center of Food Processing and Circulation Safety Control Zhengzhou450002 China Henan Shuanghui Investment Development Co. Ltd./Henan Intelligent Meat Segmentation and Biotransformation Engineering Research Center Luohe462005 China

Flaxseed polyphenols exhibited antibacterial and antioxidant properties;however, their poor solubility and stability limited their application in food products. Incorporation into nano-emulsions improved their dissolution and encapsulation. This study evaluated the stability of flaxseed polyphenol nano-emulsions, formulated with chitosan, lactoferrin, glyceryl monocaprate, and flaxseed gum (FP-CTS-FG, FP-LF-FG,FP-GM-FG), over 28 days of storage, heating at 60/80/100 °C, and freeze-thaw cycles at −20 °C. The initial zeta potentialof these three emulsions are all greater than 30 mV, and the particle sizes are less than 500 nm. Minimal changes in particle size, zeta potential, and phase behavior, along with reduced surface tension and droplet aggregation, indicated good stability. When used as a coating for spiced beef, the emulsion reduced moisture loss, inhibited microbial growth, and delayed lipid oxidation and quality deterioration. Overall, our findings provided new insights and avenues for the application of flaxseed polyphenols in the food industry. © 2025

关键词： Beef

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Spatiotemporal deformation analysis of glaciers and surrounding landscapes in the Shishapangma region using InSAR techniques

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Journal of Mountain Science 2025年第1期22卷 210-229页

作者： LIANG Wenli LIU Rui JI Qin KOU Pinglang Chongqing Key Laboratory of GIS Application School of Geography and TourismChongqing Normal UniversityChongqing 401331China National Earth System Science Data Center Beijing 100000China Chongqing Engineering Research Center of Spatial Big Data Intelligent Technology Chongqing University of Posts and TelecommunicationsChongqing 400065China Key Laboratory of Tourism Multisource Data Perception and Decision Ministry of Culture and Tourism(TMDPDMCT)Chongqing University of Posts and TelecommunicationsChongqing 400065China

Glacier dynamics in the Himalayan midlatitudes,particularly in regions like the Shishapangma,are not yet fully understood,especially the localized topographic and climatic impacts on glacier *** study analyzes the spatiotemporal characteristics of glacier surface deformation in the Shishapangma region using the Small Baseline Subset(SBAS)Interferometric Synthetic Aperture Radar(In SAR)*** analysis reveals an average deformation rate of-4.02±17.65 mm/yr across the entire study area,with glacier regions exhibiting significantly higher rates of uplift(16.87±13.20 mm/yr)and subsidence(20.11±14.55 mm/yr)compared to non-glacier *** identifies significant surface lowering on the mountain flanks and localized uplift in certain catchments,emphasizing the higher deformation rates in glacial areas compared to non-glacial *** found a strong positive correlation between temperature and cumulative deformation(correlation coefficient of 0.63),particularly in glacier areas(0.82).The research highlights the role of temperature as the primary driver of glacier wastage,particularly at lower elevations,with strong correlations found between temperature and cumulative *** also indicates the complex interactions between topographic features,notably,slope gradient,which shows a positive correlation with subsidence rates,especially for slopes below 35°.South-,southwest-,and west-facing slopes exhibit significant uplift,while north-,northeast-,and east-facing slopes predominantly ***,we identified transition zones between debris-covered glaciers and clean ice as areas of most intense deformation,with average rates exceeding 30 mm/yr,highlighting these as potential high-risk zones for *** study comprehensively analyzes the deformation characteristics in both glacier and non-glacier areas in the Shishapangma region,revealing the complex interplay of topographic,climatic,and hydrological factors influencing glacier dynamic

关键词： Glacier melting SBAS-InSAR Topographic factors Temperature change Middle Himalayas

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TPE for JPEG images with Dynamic M-ary Decomposition and Adaptive Threshold Constraints

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Ma, Yakun Chai, Xiuli Long, Guoqiang Gan, Zhihua Zhang, Yushu Henan University School of Artificial Intelligence Henan Engineering Research Center for Industrial Internet of Things Zhengzhou450046 China Henan University School of Software Intelligent Data Processing Engineering Research Center of Henan Province Institute of Intelligent Network System Kaifeng475004 China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing211106 China

Traditional JPEG image encryption that prioritizes solely confidentiality fails to account for the pressing usability requirements of cloud-based environments, thus boosting the boom in thumbnail-preserving encryption (TPE) to balance image privacy and usability. However, existing TPE schemes for JPEG images face numerous challenges, such as insufficient security, inability to achieve lossless decryption, and high file extension. To address these challenges, we propose a TPE scheme based on dynamic M-ary decomposition and adaptive threshold constraints (TPE-MDTC). First, the valid ranges of quantized DC coefficients for JPEG images are determined. Then, a sum-preserving encryption method for quantized DC coefficients with compliance threshold constraints is designed using the bit-plane permutation to preserve thumbnails with high accuracy. Next, the introduction of dynamic M-ary decomposition effectively changes bit statistical characteristics preserved by bit-plane permutation, enhancing the ciphertext security. Finally, a quantized AC encryption method with RV (Run/Value) pair global permutation is proposed, effectively modifying the unit block features, thereby significantly improving the security and attack resistance of encrypted images. Experimental results show that the proposed TPE-MDTC scheme can reconstruct the original JPEG images without loss, and the generated ciphertext images exhibit significant advantages over previous schemes regarding file extension and security. © 1991-2012 IEEE.

关键词： Ciphertext

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Multi-view Transformer-Based Network for Prerequisite Learning in Concept Graphs 23rd

Multi-view Transformer-Based Network for Prerequisite Learn...

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23rd International Semantic Web Conference, ISWC 2024

作者： Wang, Zhichun Shao, Yifeng Peng, Boci Li, Bangui Li, Yun Wang, Qianren Li, Nijun School of Artificial Intelligence Beijing Normal University Beijing China Engineering Research Center of Intelligent Technology and Educational Application Ministry of Education Beijing China School of Intelligence Science and Technology Peking University Beijing China School of Computer Science and Engineering Beihang University Beijing China Advanced Cognitive AI Lab Shanghai Huawei Technologies Shanghai China

ISBN: (纸本)9783031778438

Prerequisite learning is the task of identifying prerequisite relations among concepts, which is important for many AI-based educational applications. Previous approaches explore different kinds of learning resources to obtain useful features for predicting prerequisites. Early approaches use handcrafted features while recent approaches use neural networks to encode information of concepts. To further improve the results of prerequisite learning, we build concept graphs to include comprehensive information about concepts from open data and learning resources, and propose a Multi-view Transformer-based Network (MTN) to encode multi-view features of concepts in the graph. Multi-view features are fused to make accurate predictions of concept prerequisites. We evaluate our approach on four public datasets and compare it with recently published approaches. The results show that our approach can achieve state-of-the-art results in the task of prerequisite learning. The source code and data are available at https://***/kg-bnu/MTN. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive Learning

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SPDGrNet: A Lightweight and Efficient Image Classification Network for Zea mays Diseases

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Journal of Crop Health 2025年第3期77卷 1-12页

作者： Xu, Laixiang Cao, Bingxu Su, Jing feng Zhang, Fengyue Song, Hongyun Zhao, Junmin School of Computer and Data Science Research Center of Smart City and Big Data Engineering of Henan Province Henan University of Urban Construction Pingdingshan China School of Information Engineering Luohe Vocational Technology College Luohe China Institute of Technology University of Technology Luohe Luohe China School of Computer and Data Science Innovation Laboratory of Smart Traffic and Big Data Development of Hainan Province Henan University of Urban Construction Pingdingshan China School of Computer and Data Science Henan University of Urban Construction Pingdingshan China School of Computer and Data Science Research Center of Smart City and Big Data Engineering of Henan Province Research Center of Intelligent Campus Application Engineering of Henan Province Innovation Laboratory of Smart Traffic and Big Data Development of Hainan Province Henan University of Urban Construction Pingdingshan China

Controlling diseases on the foliage of cultivated plants has a major impact on crop yield and quality. Leaf symptoms and environmental information are the basis of crop disease and pest identification. Due to the complexity and diversity of disease leaf symptoms and environmental information, the accuracy of crop disease detection methods has decreased. Aiming at low disease identification efficiency and difficult feature modeling, we propose a new deep learning algorithm and obtain ideal results. We used improved convolutional neural networks (CNNs) SPDNet and GrNet (SPDGrNet) to perform Zea mays disease identification. The proposed research and analysis focus on four categories of Zea mays symptoms: healthy, leaf blight, southern leaf blight and grey spot. The experimental results and analysis confirm that the proposed approach outperforms other excellent plant disease recognition algorithms and achieves an overall classification accuracy of 98.96%. When compared to AlexNet, VGG-16, Inception-V4, ShuffleNet-V3, and Efficient-B7, the proposed model achieves accuracy improvements of 27.78%, 19.05%, 14.59%, 10.28%, and 7.06%, respectively, with corresponding accuracies of 71.18%, 79.91%, 84.37%, 88.68%, and 91.9%. The proposed model has outstanding generalization ability in open-source databases, such as the PlantifyDr, Plant Pathology, and New Plant Diseases Dataset. The suggested strategy can provide ideas for transplanting models into mobile disease detection equipment. This is very helpful in the intelligent development of agricultural systems and crop leaf disease analysis.

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Current Trends and Future Prospects of Large-Scale Foundation Model in K-12 Education

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Frontiers of Digital Education 2025年第2期2卷 22-22页

作者： Qiannan Zhu Mei Wang Ting Zhang Hua Huang 1School of Artificial Intelligence Beijing Normal University Beijing 100875 China 2Engineering Research Center of Intelligent Technology and Educational Application Ministry of Education Beijing 100816 China

The rapid advancement of artificial intelligence has significantly impacted education, with large-scale foundation models (LFMs) emerging as transformative tools. While LFMs have demonstrated exceptional performance across diverse domains, their integration into K-12 education remains in its early stages, requiring alignment with pedagogical principles, cognitive development, and curriculum standards. This paper provides a comprehensive technological review of LFM applications in K-12 education, examining current workflows, challenges, and future opportunities. We explore how LFMs facilitate personalized learning, teacher–student collaboration, and automated assessment while highlighting critical issues such as motivation, engagement, and age-appropriate instructional strategies. By analyzing global developments, this study offers valuable insights for educators seeking to optimize AI-driven teaching methods and for students leveraging AI for self-directed learning. Our findings aim to inform future research and drive innovation in educational AI, ensuring the effective and ethical integration of LFMs into the evolving K-12 educational landscape.

关键词： large-scale foundation models (LFMs) multimodal large language model large language model K-12 education

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A Unified Prompt-aware Framework for Personalized Search and Explanation Generation

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ACM Transactions on Information Systems 2025年第3期43卷 1-26页

作者： Haobo Zhang Qiannan Zhu Zhicheng Dou Gaoling School of Artificial Intelligence Renmin University of China Beijing China School of Artificial Intelligence Beijing Normal University Beijing China and Engineering Research Center of Intelligent Technology and Educational Application MOE China

Product search is crucial for users to find and purchase products they need. Personalized product search, which models users’ search intent and provides tailored results, has become a prominent research problem in industry and academia. Recent studies often leverage knowledge graphs (KGs) to improve search performance and generate explanations for search results. However, existing KG-based methods treat search and explanation tasks separately and explore paths in KGs as explanations, creating a gap between search results and generated explanations. Also, path-formed explanations in KGs are not flexible enough to build correlations with the user’s current query. To address these challenges, we propose P-PEG, a unified prompt-aware framework for personalized product search and explanation generation. P-PEG leverages a pre-trained language model (PLM) and search signal to enhance the generation of user-understandable explanations. We introduce a prompt learning technique and design prompt generators for search and explanation generation tasks based on a fixed PLM. By incorporating search results in explanation-based prompts, we bridge the gap between search results and explanations, facilitating better interaction. Additionally, we utilize the user’s current query, historical search log, and KGs to personalize the explanations and inject task knowledge into PLM. Experimental results show that P-PEG outperforms existing methods in the explanation generation task of the three datasets and the search task of the Electronics dataset, and achieves comparable performance in the search task of the Cellphones & Accessories and CD & Vinyl datasets.

关键词： Explanation generation Product search Prompt learning

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Out-of-Distribution Fault Diagnosis of Industrial Cyber-Physical Systems Based on Orthogonal Anchor Clustering With Adaptive Balance

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2025年 3卷 48-60页

作者： Liu, Ruonan Hu, Puyuan Zhao, Siheng Sun, Zhijian Han, Te Pang, Zhibo Zhang, Weidong Shanghai Jiao Tong University Department of Automation Shanghai200240 China National Key Laboratory of Marine Engine Science and Technology Shanghai201108 China Crossocean of Suzhou Technology Suzhou215121 China Beijing Institute of Technology Center for Energy and Environmental Policy Research School of Management Beijing100081 China ABB Corporate Research Sweden Department of Automation Technology Vasteras722 26 Sweden KTH Royal Institute of Technology Department of Intelligent Systems Stockholm114 28 Sweden Henan University Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Kaifeng475001 China Shanghai Jiaotong University Department of Automation Shanghai200240 China

Given the critical role of rotating machinery in industrial cyber-physical systems (ICPS), ensuring their reliable operation is essential for the stability and safety of ICPS. Deep neural networks have demonstrated competitive results for intelligent fault diagnosis, which are usually trained via the historical data of all fault modes. However, in real engineering, it is usually difficult to collect samples and exhaust all failures during the training stage. As a result, out-of-distribution fault diagnosis (OOD-FD) becomes a more realistic problem that requires the methods to not only accurately diagnose the known faults, but also effectively recognize unknown ones. Therefore, a novel orthogonal anchor clustering with focal attention (FA-OAC) is proposed in this paper for OOD-FD. Firstly, an orthogonal anchor clustering (OAC) algorithm is proposed to fix the class center of each fault mode orthogonally and distinguish the known and unknown faults at the class level. Then, because the identifiability of different fault modes changes a lot in OOD-FD, the focal attention mechanism is applied to dynamically adjust the attention to different fault modes according to the distance loss of OAC, thus addressing the identifiability imbalance problem. To verify the effectiveness of the proposed method, different OOD-FD tasks are designed based on two rotating machinery datasets. The experimental results and comparison with state-of-the-art fault diagnosis methods have demonstrated that the proposed method has improved the OOD-FD performance greatly and therefore provides an effective tool in real engineering. © 2023 IEEE.

关键词： Rotating machinery

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Low-cost spatially variable polarizers via polarization holography: retraction

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Optics Letters 2025年第4期50卷 1252-1252页

作者： Qi, Peiliang Yuan, Xinyi Hu, Po Li, Xinzhong School of Physics and Engineering Henan University of Science and Technology Henan Luoyang471000 China Information Photonics Research Center College of Photonic and Electronic Engineering Fujian Normal University Fujian Fuzhou350117 China Henan Provincial Key Laboratory of Intelligent Lighting Huanghuai University Henan Zhumadian463000 China

In the referenced article [1], the authors have identified significant errors in the data and experimental procedures presented. Based on a thorough review of the relevant literature, the authors found that the angular quantum numbers of vector vortex beams must be determined through interference measurements using both right-handed circularly polarized light and left-handed circularly polarized light. When applying this method to test the vector vortex beams produced by the fabricated spatially variable polarizers, the angular quantum numbers did not align with theoretical predictions. The observed inconsistencies suggest potential flaws in either the fabrication process of the spatially variable polarizers or the theoretical model used to describe its behavior. These findings significantly undermine the reliability of the presented results and conclusions. The authors deeply regret that these issues were not identified prior to submission and sincerely apologize for any inconvenience caused. The original article was published 18 November 2024 and was retracted on xx. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Circular polarization

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A few-shot learning model for renewable energy transmission fault diagnosis in tunnels

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Electric Power Systems research 2025年 247卷

作者： Zhang, Zhuofan Cai, Dongsheng Ukwuoma, Chiagoziem C. Huang, Qi College of Energy Chengdu University of Technology Chengdu610059 China College of Nuclear Technology and Automation Engineering Chengdu University of Technology Chengdu610059 China Sichuan Engineering Technology Research Center for Industrial Internet Intelligent Monitoring and Application Chengdu University of Technology Chengdu610059 China

The buried high-voltage direct current (HVDC) transmission technology has been successfully applied in domestic projects, addressing challenges such as extreme cold, seismic activity, and high altitudes. However, existing fault diagnosis methods face two main issues: insufficient fault data samples and difficulty in diagnosing multiple or coupled faults. To address these, this paper proposes and optimizes a Siamese neural network-based fault diagnosis model using limited data. The study simulates six fault types, including coupled fault scenarios. Compared to other models, the proposed model achieves an accuracy of over 90 %, while CNN and deep belief network models achieve 75 % and 60 %, respectively. In coupled fault scenarios, the proposed model maintains an accuracy of 93 %, demonstrating its effectiveness. © 2025

关键词： HVDC power transmission

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