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Identification of phenolanine in rape bee pollen and its protective effect and mechanism on alcoholic liver injury

Food science and Human Wellness 2025年第2期14卷 484-496页

作者： Cheng Lü Mingjuan Xu Xinbiao Yang Yanlun Feng Yun Zhu Qun Lu Chengming Wang Rui Liu College of Food Science and Technology Huazhong Agricultural UniversityWuhan 430070China Wuhan Engineering Research Center of Bee Products on Quality and Safety Control Wuhan 430070China Key Laboratory of Environment Correlative Dietology(Huazhong Agricultural University) Ministry of EducationWuhan 430070China Key Laboratory of Urban Agriculture in Central China Ministry of Agriculture and Rural AffairsWuhan 430070China

Rape bee pollen has attracted increasing interests for its excellent protective effect against chemicalinduced liver injury owing to its abundant *** study aims to analyze the types and contents of phenolamides(seldom concerned)in rape bee pollen and their protective mechanism on alcoholic liver disease(ALD).Different from the previous finding that flavonoids are dominant polyphenols in bee pollen polyphenolic extract,our results demonstrated that there are only three flavonoids but 24 phenolamides in the as-prepared rape bee pollen phenolic extract(PPE).In addition,PPE was found to significantly improve the viability(from 54.9%to 84.1%,89.2%,and 94.0%)of alcohol-induced AML12 cells and alleviate alcoholinduced cell apoptosis(from 28.5%to 22.89%,22.0%,and 17.4%).To dissect the underlying mechanism for the protective effect of PPE against ALD,the molecular pathway was identified by RNA-Seq *** data revealed that PPE may protect against ALD by decreasing inflammation,cholesterol,and fatty acid synthesis(P<0.05).The National Institute on Alcohol Abuse and Alcoholism(NIAAA)model was used to further evaluate the hepatoprotective effect of PPE in vivo,and the results validated that PPE could alleviate liver injury and hepatic steatosis(from 22.7%to 11.5%and 10.9%)induced by *** the dominant polyphenols in PPE,phenolamides can be a class of valuable polyphenolic compounds in bee pollen with the potential to alleviate ALD.

关键词： Rape bee pollen Phenolamide Alcoholic liver disease(ALD) AML12s National Institute on Alcohol Abuse and Alcoholism(NIAAA) Transcriptome

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Object Detection in Occluded Environments Based on Adaptive Optimal Label Assignment

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IEEE Sensors Journal 2025年

作者： Dong, Yongxiang Lian, Jie Wang, Dong Dalian University of Technology Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education School of Control Science and Engineering Dalian116024 China

The occluded environment leads to a low success rate of object detection, owing to label assignment errors and imbalances for occluded objects during neural network training process. To address this problem, an adaptive optimal label assignment algorithm is proposed, which employs a global optimal assignment strategy to minimize global loss by label assignment. It assigns an appropriate number of anchors to each ground-truth, to improve the detection success rate of occluded objects. Furthermore, a focal angle loss function is proposed based on the angle loss between anchors and ground-truth to achieve smooth convergence, and intersection over union loss weights are added to solve the problem of an uneven number of high-quality and low-quality anchors. Adaptive optimal label assignment and focal angle loss are integrated to YOLOv10-X, termed as YOLOv10-X-FAO. Experiments demonstrate that YOLOv10-X-FAO enhances performance over the baseline and other latest detection models. Furthermore, ablation experiments confirm the effectiveness of adaptive optimal label assignment and focal angle loss function in enhancing the detection performance of occluded targets for both anchor-based and anchor-free detectors. © 2001-2012 IEEE.

关键词： Deep learning

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A Deep Transferable Convolution Neural Network for Unsupervised Cross-Domain Diagnostics of Rolling Bearings in industrial Internet of Things

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IEEE Internet of Things Journal 2025年第11期12卷 17316-17332页

作者： Hou, Yaochun Mi, Junpeng Lu, Junjie Wu, Peng Yang, Shuai Huang, Bin Huang, Wenjun Wu, Dazhuan Zhejiang University College of Energy Engineering Hangzhou310027 China Massachusetts Institute of Technology Department of Mechanical Engineering CambridgeMA02139 United States Zhejiang University College of Control Science and Engineering Hangzhou310027 China Ningbo Key Laboratory of Advanced Seal Ningbo315000 China NingboTech University Ningbo315000 China Zhejiang University Ocean College Zhoushan316021 China

In recent years, deep learning-based intelligent diagnosis approaches have showcased broad application prospects in the field of rolling bearing fault identification in industrial Internet of Things (IIoT). Notwithstanding, the implementation effect of most existing deep diagnostic architectures can be severely restrained when there are distinct distribution discrepancies between the source and target domain, which could mainly be attributed to dissimilar working conditions, various fault severity levels, different machine structures, nonidentical complex noise interferences and so on. Aiming at ameliorating the effectiveness of unsupervised cross-domain fault diagnosis of rolling bearings, a deep transferable convolution neural network (DTCNN) based upon cooperative domain alignment (CDA) and partial-contractive layers is proposed in this paper. On the one hand, the CDA in DTCNN features the conjoint match of both marginal and conditional distributions of the first and second order statistical characteristics of data to enhance domain adaptation. On the other hand, the partial-contractive layers can prompt DTCNN to learn internal factors and capture more robust domain-invariant features through penalizing the Frobenius norm of the Jacobian matrix of hidden activations with respect to the inputs. Furthermore, a modified network learning strategy is leveraged to facilitate the efficient adjustment of weights and biases at different training stages, which is beneficial for convergence during the network optimization process. Experiments on different transfer tasks tangibly authenticate the ascendancy of the proposed DTCNN in cross-domain diagnostics of rolling element bearings, compared with other contrastive methods. The results reveal that the proposed DTCNN model not only attains high-precision unsupervised transfer diagnosis of rolling bearings across a range of circumstances, but also exhibits superior noise resistance and computational efficiency, underscoring i

关键词： Convolution

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Combining intrinsic and sliding-induced polarizations for multistates in two-dimensional ferroelectrics

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Physical Review B 2025年第8期111卷 L081407-L081407页

作者： Chuhan Tang Zhiqiang Tian Tao Ouyang Anlian Pan Mingxing Chen Key Laboratory for Matter Microstructure and Function of Hunan Province Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education School of Physics and Electronics Hunan Normal University Changsha 410081 China Hunan Key Laboratory for Micro-Nano Energy Materials and Device and School of Physics and Optoelectronics Xiangtan University Hunan Xiangtan 411105 China Key Laboratory for Micro-Nano Physics and Technology of Hunan Province College of Materials Science and Engineering Hunan University Changsha 410082 China State Key Laboratory of Powder Metallurgy Central South University Changsha 410083 China

Going beyond the bistability paradigm of the charge polarization in ferroelectrics is highly desired for ferroelectric (FE) memory devices toward ultrahigh-density information storage. Here, we propose to build multistates by combining the intrinsic and sliding-induced polarizations. The physics is that there is at least one order of magnitude difference in the energy barriers between these two types of polarizations, which leads to a significant difference in the electric fields for reversing the polarization. This difference, along with the symmetry breaking, allows for a unique flipping mechanism involving layer-by-layer sliding followed by layer-by-layer flipping during the transformation of the multistates. As a result, six and ten switchable states can be achieved for the 1T″ bilayers and trilayers, respectively. We further illustrate the concept in H-stacking bilayers and trilayers of 1T″ transition-metal dichalcogenides by first-principles calculations. Our study provides a route to design polarization multistates for developing next-generation memory devices.

关键词： Ferroelectricity

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FPGA-based Real-Time High-resolution Image Processing for rocket on-board processing 17

FPGA-based Real-Time High-resolution Image Processing for ro...

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17th IEEE International Conference on Computer Research and Development, ICCRD 2025

作者： Zhou, Hui Wang, Xiaofeng Xie, Yujia Cao, Yongxiang Tang, Yu Huo, Yongchun Wen, Huan Zhao, Xiongbo Li, Chaoran National Key Laboratory of Science and Technology on Aerospace Intelligent Control Beijing Aerospace Automatic Control Institute Beijing China Beihang University Computer Science and Engineering Beijing China Beihang University National Superior College for Engineers Beijing China

ISBN: (纸本)9798331531881

The structure of the rocket-borne model is inherently complex, with processed images exhibiting high resolution and generating substantial amounts of data and calculations. Achieving robust real-Time computing on an embedded platform poses significant challenges due to strictly limited resources, power consumption constraints, and size limitations. Our review of rocket-borne applications reveals considerable variability in the design resources of different devices, indicating a need for expanded design approaches. Upon evaluating existing methods, we identified two primary drawbacks. First, certain operators within the high-resolution target detection model are difficult to parallelize, resulting in significant inference delays that hinder the ability to meet task requirements. Although existing methods have been extended, there remains significant potential for performance enhancement in core scheduling for poor acceleration. This paper proposes an optimized architecture for the target detection algorithm accelerator designed for high-resolution images, along with a novel highly parallel data pre-processing and post-processing module implemented on FPGA to address these issues. Compared to the ARM implementation, this architecture demonstrates an improved performance of 24.64x. Furthermore, to ensure flexible application across various rocket launch scenarios, we introduce an optimization structure for convolution, pooling, and fusion operators and a multi-core expansion optimization method. This approach yields a 1.29x improvement in computing unit utilization compared to state-of-The-Art multi-core scaling efforts. Finally, we assessed the accelerator architecture across multiple FPGA platforms, achieving a peak processing element utilization rate of 99.71% for a single core and layer. The overall computing efficiency, excluding the first layer, exceeded 90%. The peak computing power for the four cores reached 1638.4 GOPS, and the end-To-end computation time for

关键词： Image enhancement

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A robust temporal multivariable fault detection method for blast furnace: Robust temporal convolution detection network

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Information sciences 2025年 716卷

作者： Zhu, Xiongzhuo Yang, Chunjie Lou, Siwei Yang, Yuelin State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou310027 China

As a complex industrial process, the blast furnace ironmaking process (BFIP) often suffers from faults due to changes in raw materials, variables’ setpoints, reaction conditions, etc. However, because of the nonlinearity, dynamics, and mixture of normal and abnormal outliers in BFIP, the existing methods always meet difficulties in practical application. Therefore, this paper proposes a robust temporal convolution detection network (RTCDN) for BFIP fault detection. The basic TCDN network is constructed by stacking the residual blocks of the temporal convolution network (TCN). The residual block consists of the 1-D dilated causal convolution, which is performed on the time scale of variables, giving the network the ability to extract time-scale information. Since TCDN consists of convolutional networks, it balances the extraction of temporal information and speed compared to RNN-type methods. Furthermore, a robust solution is proposed to overcome the interference of abnormal outliers. The robust solution exploits that TCDN can't reconstruct abnormal outliers and decomposes the training dataset into the clean part and abnormal outliers. In this work, the proximal approach to optimizing the sparse outlier matrix is also improved to achieve complete separation of abnormal outliers. Finally, TCDN is applied to three BF fault datasets and performs better than the conventional temporal detection methods. RTCDN has also been proven to have good outlier separation capability, maintaining satisfactory detection performance when the proportion of abnormal outliers is less than 20%. © 2025 Elsevier Inc.

关键词： Time measurement

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A review of water-based suppressants for coal dust suppression

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International Journal of Coal science and technology 2025年第1期12卷 1-18页

作者： Han, Fangwei Liu, Mei Hu, Fuhong Niu, Guirui Xue, Diandian Zhao, Yue Hu, Shengyong Wang, Hetang College of Safety Science and Engineering Liaoning Technical University Liaoning Province Huludao125105 China Key Laboratory of Mine Thermodynamic Disasters and Control Ministry of Education Liaoning Technical University Liaoning Province Huludao125105 China College of Safety and Emergency Management Engineering Taiyuan University of Technology Taiyuan030024 China School of Safety Engineering China University of Mining and Technology Jiangsu Xuzhou221116 China

The large amount of harmful particles in coal dust not only pollutes the production environment, affects the production efficiency and resource utilization of enterprises, but also poses a risk to human health. Effectively controlling coal dust is of great significance to clean production. Water-based dust suppressants are extensively employed to mitigate coal dust. This paper provides a comprehensive review of the water-based dust suppression materials for coal dust control. According to the difference of mechanism, the dust suppressants are divided into wetting type, hygroscopic coalescence type, cohesive agglomeration type, and composite type. The evaluation methods for dust suppressants key properties such as wettability, permeability, moisture absorption and water retention, and consolidation are summarized. The review results show that coal dust suppressants are no longer limited to a single dust suppression function. For example, it is necessary to develop multi-functional coal suppressants to meet the needs for synergistic suppression both coal dust and coal spontaneous combustion. Driven by the concept of green, low-carbon and sustainable development, attention should be paid to the development of bio-based environmentally friendly coal dust suppressants. In addition, the evaluation method system for the key performance of water-based dust suppressants should also be improved, and further research is necessary. © The Author(s) 2025.

关键词： Coal dust

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Integrated online identification of aerodynamic and thrust parameters for air-breathing aircraft 3

Integrated online identification of aerodynamic and thrust p...

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2024 3rd International Conference on Aerospace and control engineering, ICoACE 2024

作者： Jiang, Zhenyu Li, Xiaodong Liu, Shuang College of Aerospace Science and Engineering National University of Defense Technology Hunan Changsha410073 China Hunan Provincial Key Laboratory of Aerospace Cross-Domain Flight Vehicle System and Control Technology Changsha410073 China

A method for integrated online identification of aerodynamic and thrust parameters has been proposed. Firstly, a motion model of an aircraft powered by a solid rocket ramjet engine was established in the longitudinal plane. Secondly, polynomial models of aerodynamic and thrust coefficients were established for the horizontal flight segment. Then, an integrated online identification simulation of aerodynamic and thrust parameters was carried out using the unscented Kalman filter method. The results indicate that the proposed method has high identification accuracy and efficiency. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Kalman filters

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Semi-Cervixseg: A Multi-Stage Training Strategy for Semi-Supervised Cervical Segmentation

Semi-Cervixseg: A Multi-Stage Training Strategy for Semi-Sup...

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IEEE International Symposium on Biomedical Imaging

作者： Juntao Jiang Yali Bi Chunlin Zhou Yong Liu Jiangning Zhang College of Control Science and Engineering Zhejiang University College of Computer and Information Science Southwest University State Key Laboratory of Industrial Control Technology Zhejiang University Youtu Lab Tencent

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Image segmentation plays a critical role in computer-aided diagnosis and treatment planning for cervical cancer. Obtaining a large number of labeled images for supervised cervical segmentation is often labor-intensive and time-consuming. In this paper, we propose a multi-stage semi-supervised learning framework (Semi-CervixSeg) to address the cervical seg-mentation task in ultrasound images for Fetal Ultrasound Grand Challenge: Semi-Supervised Cervical Segmentation in ISBI 2025. Specifically, in the initial stage, we utilize unlabeled data through a multi-view random augmentation strategy, using consistency constraints and a contrastive learning method. Subsequently, a progressive multi-stage training strategy is adopted to generate and optimize pseudo-labels, further improving segmentation results. Experimental re-sults demonstrate that the proposed method significantly im-proves segmentation performance compared with supervised methods. As a technical report for the challenge, this paper elaborates on our methodology and experimental findings in detail. The code can be accessed at h t t p s: / / g i t hub. com/juntaoJianggavin/Semi-CervixSeg.

关键词： Training Image segmentation Ultrasonic imaging Codes Contrastive learning Semisupervised learning Writing Data models Planning Computer aided diagnosis

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Distributed Multi-Target Tracking with Overlapping Sensor Fields of View Using Jump-Markov Probability Hypothesis Density

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IEEE Transactions on Aerospace and Electronic Systems 2025年

作者： Lian, Lei Lian, Jie Wang, Dong Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of Education China Dalian University of Technology School of Control Science and Engineering Dalian116024 China

This paper addresses the challenge of information fusion in multi-sensor multi-target tracking (MTT) systems, focusing on the limitations of the arithmetic average (AA) fusion process. Through an in-depth analysis of information content (IC) changes during complete information fusion and AA fusion, we reveal how inaccuracies in correlation estimates or partial sensor coverage can lead to information loss and suboptimal estimates. We propose a fusion framework designed for the Jump-Markov probability hypothesis density (JMPHD) filter to overcome these issues, incorporating the measurement information cross-correlation (MIC) method. This approach enhances target position detection efficiency in an overlapping sensor field of view (FoV) by pre-fusing measurement data. In the process of MIC fusion, to improve efficiency, the method uses detected targets from a single local sensor as the index for matching, rather than simply aggregating all neighboring sensor data. This approach significantly enhances clustering speed. Simulations validate the effectiveness of our proposed framework in scenarios with multi-target non-linear movements and overlapping sensor FoV. © 1965-2011 IEEE.

关键词： Information fusion

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