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Experimental and Modeling Study on Void Fraction using Thermal Distribution Sensor in Horizontal Annular Flow

IEEE Sensors Journal 2025年第10期25卷 17640-17649页

作者： Zhao, Ning Chen, Xiangyi Sun, Mingcong Song, Yajing Fang, Lide Li, Zhibin Hebei University College of Quality and Technical Supervision Hebei Baoding071000 China National and Local Joint Engineering Research Center of Metrology Instrument and System Hebei Baoding071002 China Chengdu University of Information Technology School of Software Engineering Chengdu610225 China Sichuan University of Arts and Science Dazhou Key Laboratory of Government Data Security Sichuan Dazhou635000 China Chinese Academy of Sciences Xinjiang Technical Institute of Physics & Chemistry Urumqi830011 China

The void fraction is an important parameter to characterize the characteristics of annular flow, which is an important precondition for calculating the flow velocity, average density, pressure gradient and analyzing the flow status. Based on the principle of convective heat transfer, the effects of the void fraction α and the liquid Froude number Frl on the heat transfer coefficient h of annular flow are analyzed. The thermal distribution sensor of constant power type is designed in this paper. Air-water annular flow experiments have been conducted in a stainless-steel tubular test section having an inside diameter 17.0 mm on the high-precision small-diameter two-phase flow experimental device of hebei University. The 75 sets of temperature T at four angles (0°, 90°, 180°, 270°) of pipe circumference and entrainment E under different flow conditions were collected. Utilizing the S. L. Smith correlation, the void fraction are calculated. By introducing dimensionless heat transfer coefficient h+, the prediction correlation of void fraction α which could acceptably handle the data is proposed. laboratory results indicate that the relative error of 99% the data are within ± 3% error band, the mean absolute percentage error (MAPE) of the correlation is 0.63%, and the root mean square error (RMSE) is 0.76%. © 2001-2012 IEEE.

关键词： Void fraction

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Interactions between nanoplastics and Tetrahymena thermophila: Low toxicity vs. potential biodegradation

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Chemosphere 2025年 373卷 144166页

作者： Li, Aiyun Yuan, Fengyu Li, Lianshan Gu, Jihai Zhang, Yuming Li, Fengchao Tang, Ting Liu, Fengsong The Key Laboratory of Zoological Systematics and Application College of Life Sciences Hebei University Baoding071002 China Hebei Basic Science Center for Biotic Interaction Hebei University Baoding071002 China

Nanoplastics (NPs) are prevalent throughout the environment and have raised growing environmental concerns. Although numerous studies have examined the toxicological aspects of NPs, few have investigated their environmental fate and behavior when affected by organisms other than bacteria or fungi. Planktonic ciliates are essential components of aquatic ecosystems and play important roles in decomposing organic matter and transferring energy from the microbial food web to higher trophic levels. To investigate the interplay between NPs and the ciliate Tetrahymena thermophila, we executed a sequence of feeding experiments utilizing 50 nm polystyrene nanoplastics (PS-NPs). In the presence of sufficient nutrition, exposure to PS-NPs (even at concentrations up to 500 mg/L) did not significantly inhibit growth in Tetrahymena thermophila, indicating only a mild toxic effect of PS-NPs. When ingested by T. thermophila, the PS-NPs are repackaged into aggregates with lysosomal components in the food vacuole and finally expelled as compacted "fecal pellets". This process modifies the physical attributes of PS-NPs, including their hydrophilicity, aggregability, and buoyancy, influencing their transportation, retention, deposition dynamics, and ultimately their bioavailability within the environment. A total of 73 proteins were identified from the fecal pellets, containing various hydrolases. Gel permeation chromatography (GPC), Fourier transform infrared (FTIR), and thermogravimetric analysis (TGA) were used to identify changes in molecular weights, functional groups, and thermal stabilities of PS-NP residues in fecal pellets. The results verified the degradation of PS-NPs during the passage through the T. thermophila cell. © 2025 Elsevier Ltd

关键词： Gel permeation chromatography

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Convolution Type of Metaplectic Cohen's Distribution Time-Frequency Analysis Theory, Method and Technology

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IEEE Transactions on Information Theory 2025年第3期71卷 2292-2314页

作者： Cui, Manjun Zhang, Zhichao Han, Jie Chen, Yunjie Cao, Chunzheng Nanjing University of Information Science and Technology School of Mathematics and Statistics Center for Applied Mathematics of Jiangsu Province Jiangsu International Joint Laboratory on System Modeling and Data Analysis Nanjing210044 China Nanjing University of Information Science and Technology School of Mathematics and Statistics Nanjing210044 China Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China Hainan Normal University Key Laboratory of Computational Science and Application of Hainan Province Haikou571158 China Nanjing University of Information Science and Technology School of Remote Sensing and Geomatics Engineering Nanjing210044 China

The conventional Cohen's distribution can't meet the requirement of additive noises jamming signals high-performance denoising under the condition of low signal-to-noise ratio, it is necessary to integrate the metaplectic transform for non-stationary signal fractional domain time-frequency analysis. In this paper, we embark on blending time-frequency operators and coordinate operator fractionizations to formulate the definition of the joint fractionizations metaplectic Wigner distribution (JFMWD), based on which we integrate the generalized metaplectic convolution to address the unified representation issue of the convolution type of metaplectic Cohen's distribution (CMCD), whose special cases and essential properties are also derived. We blend Wiener filter principle and fractional domain filter mechanism of the metaplectic transform to design the least-squares adaptive filter method in the JFMWD domain, giving birth to the least-squares adaptive filter-based CMCD whose kernel function can be adjusted with the input signal automatically to achieve the minimum mean-square error (MSE) denoising in Wigner distribution domain. We discuss the optimal symplectic matrices selection strategy of the proposed adaptive CMCD through the minimum MSE minimization modeling and solving. Some examples are also carried out to demonstrate that the proposed filtering method outperforms some state-of-the-arts including the classical Gaussian filter, the Cohen's distribution filtering methods with fixed kernel functions, the classical Wiener filter, the adaptive generalized linear canonical convolution-based filtering method, the adaptive Cohen's distribution filtering method, the adaptive JFMWD-based Cohen's distribution filtering method, and the adaptive generalized metaplectic convolution-based Cohen's distribution filtering method in noise suppression. © 1963-2012 IEEE.

关键词： Adaptive filtering

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Extraction and Area Calculation of Chicken Comb and Wattle Based on YOLO v7 and Optimized U-Net Network

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Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery 2025年第4期56卷 415-426页

作者： Yang, Duanli Shen, Hongshuo Chen, Hui Gao, Yuan College of Information Science and Technology Hebei Agricultural University Baoding071001 China Hebei Key Laboratory of Agricultural Big Data Baoding071001 China College of Animal Science and Technology Hebei Agricultural University Baoding071001 China Key Laboratory of Broiler and Layer Facilities Engineering Ministry of Agriculture and Rural Affairs Baoding071001 China

Traditional manual measurement of poultry comb and wattle areas poses contact-induced stress risks, zoonotic disease transmission hazards, and substantial measurement errors. A non-contact measurement system integrating YOLO v7 with an improved U - Net architecture was proposed. Three key innovations were presented: a dual-stage detection framework utilizing YOLO v7 for head pose screening and ROI extraction, effectively eliminating off-angle image interference;a novel CoT - UNet model incorporating Contextual Transformer blocks into U - Net's encoder for dynamic-static context fusion, combined with DyC - UP module employing dynamically adjustable convolution kernels to enhance irregular edge detection;a pixel-area conversion algorithm achieving precise spatial mapping through calibration coefficients. Experimental results demonstrated significant improvements;the enhanced CoT-UNet outperformed baseline models by 4. 77 percentage points (comb) and 8. 75 percentage points (wattle) in loll, along with 5.31 percentage points and 5.06 percentage points precision gains respectively. Absolute measurement errors for comb (0. 62 ∼ 3. 50 cm2) and wattle (0. 10 ∼ 2. 93 cm2) showed marked superiority over manual methods (3.58 ∼ 7.27 cm2). Multi-scenario validation revealed stable relative errors of 2.41% ∼ 13.62% for combs and 1.00% ∼ 29.21% for wattles across varied postures (three types), angles (two positions), and distances (two levels). This automated system enabled stress-free poultry biometric measurement, providing reliable technical support for intelligent breeding selection. © 2025 Chinese Society of Agricultural Machinery. All rights reserved.

关键词： Semantic Segmentation

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EPRFL:An Efficient Privacy-Preserving and Robust Federated Learning Scheme for Fog Computing

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China Communications 2025年第4期22卷 202-222页

作者： Ke Zhijie Xie Yong Syed Hamad Shirazi Li Haifeng School of Computer Technology and Application Qinghai UniversityXining 810016China School of Computer and Information Science Qinghai Institute of TechnologyXining 810016China Guangdong Key Laboratory of Blockchain Security Guangzhou UniversityGuangzhou 510006China Department of Information Technology Hazara UniversityBaffa 21110Pakistan Qinghai Provincial Key Laboratory of Big Data in Finance and Artificial Intelligence Application Technology Xining 810016China

Federated learning(FL)is a distributed machine learning paradigm that excels at preserving data privacy when using data from multiple *** combined with Fog Computing,FL offers enhanced capabilities for machine learning applications in the Internet of Things(IoT).However,implementing FL across large-scale distributed fog networks presents significant challenges in maintaining privacy,preventing collusion attacks,and ensuring robust data *** address these challenges,we propose an Efficient Privacy-preserving and Robust Federated Learning(EPRFL)scheme for fog computing ***,we first propose an efficient secure aggregation strategy based on the improved threshold homomorphic encryption algorithm,which is not only resistant to model inference and collusion attacks,but also robust to fog node ***,we design a dynamic gradient filtering method based on cosine similarity to further reduce the communication *** minimize training delays,we develop a dynamic task scheduling strategy based on comprehensive *** analysis demonstrates that EPRFL offers robust security and low *** experimental results indicate that EPRFL outperforms similar strategies in terms of privacy preserving,model performance,and resource efficiency.

关键词： federated learning fog computing internet of things privacy-preserving robustness

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Five-user quantum virtual local area network with an AlGaAs entangled photon source

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science China(Physics,Mechanics & Astronomy) 2025年第3期68卷 61-73页

作者： Cheng Qian Hongkai Tian Xu Jing Yichen Liu Zhe Chen Huiran Luo Yuan Du Xiaodong Zheng Tangsheng Chen Yuechan Kong Hua-Lei Yin Dong Jiang Bin Niu Liangliang Lu Key Laboratory of State Manipulation and Advanced Materials in Provincial Universities School of Physical Science and TechnologyNanjing Normal UniversityNanjing210023China School of Internet Anhui UniversityHefei230039China National Key Laboratory of Solid-State Microwave Devices and Circuits Nanjing Chip Valley Industrial Technology InstituteNanjing Electronic Devices InstituteNanjing210016China Research Center for Quantum Optics and Quantum Communication School of ScienceQingdao University of TechnologyQingdao266520China Department of Physics and Beijing Key Laboratory of Opto-electronic Functional Materials and Micro-nano Devices Key Laboratory of Quantum State Construction and Manipulation(Ministry of Education)Renmin University of ChinaBeijing100872China Beijing Academy of Quantum Information Sciences Beijing100193China National Engineering Research Center of Agro-Ecological Big Data Analysis and Application Anhui UniversityHefei230601China National Laboratory of Solid-State Microstructures and School of Physics Nanjing UniversityNanjing210093China Hefei National Laboratory Hefei230088China

The promotion of quantum network applications demands the scalable connection of quantum *** is preferable to set up multiple logical networks coexisting on a single physical network infrastructure to accommodate a larger number of *** we present a quantum virtual network architecture that offers this level of scalability,without being constrained to a fixed physical-layer network relying solely on passive multiplexing *** architecture can be understood as arising from the superposition of a fully connected entanglement distribution network and port-based virtual local area network,which group multiusers by access *** terms of hardware,we leverage a semiconductor chip with a high figure-of-merit modal overlap to directly generate high-quality polarization entanglement,and a streamlined polarization analysis module,which requires only one single-photon detector for each end *** experimentally perform the BBM92 QKD protocol on the five-user quantum virtual network and demonstrate voice and image encryption on a campus area *** results may provide insights into the realization of large-scale quantum networks with integrated and cost-efficient photonic architecture.

关键词： quantum network quantum entanglement distribution integrated photonics image and voice encryption

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Dynamic updating approximations of local multigranulation neighborhood covering rough sets

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Soft Computing 2025年第6期29卷 2531-2550页

作者： Shi, Qi Zhang, Yan-Lan School of Computer Science Minnan Normal University Fujian Zhangzhou363000 China Key Laboratory of Data Science and Intelligence Application Fujian Province University Fujian Zhangzhou363000 China

The lower and upper approximations in rough set theory are important for dealing with uncertain knowledge. The covering rough set is an important part of the rough set theory and is suitable for dealing with numerical data. With the development of information technology, the information is constantly updated and changed in various information systems, and it is crucial to efficiently obtain the lower and upper approximations in dynamic environments. In this paper, the matrix-based methods for updating the approximate operators of local multigranulation neighborhood covering rough sets when objects are added or deleted are mainly investigated. Firstly, the local multigranulation neighborhood covering rough set model is introduced. Then, we analyze the relationships between the prior matrices and the updating matrices when objects change. Meanwhile, the dynamic updating processes for lower and upper approximations are proposed. The time complexity analyses of algorithms theoretically prove the efficiency of dynamic algorithms compared with static ones. To illustrate the effectiveness of the proposed dynamic algorithms, six datasets from the UCI are employed for comparative experiments. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Knowledge based systems

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Relativistic effects in the strong and electromagnetic decays of the meson

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The European Physical Journal C 2025年第3期85卷 1-7页

作者： Jia, Man Li, Wei Pei, Su-Yan Du, Xin-Yao Ning, Guo-Zhu Wang, Guo-Li Department of Physics Hebei University Baoding China Hebei Key Laboratory of High-precision Computation and Application of Quantum Field Theory Hebei University Baoding China Hebei Research Center of the Basic Discipline for Computational Physics Hebei University Baoding China College of Science Hebei Agriculture University Baoding China

In this paper, we solve the complete Salpeter equation and use the obtained relativistic wave function to calculate the strong and radiative electromagnetic decays of the $${D^*}$$ meson. We obtain the results $$\Gamma (D^{*}(2007)^{0}\rightarrow D^{0}\pi ^{0})=34.6~\textrm{keV}$$ and $$\Gamma (D^{*}(2007)^{0}\rightarrow D^{0}\gamma )=19.4~\textrm{keV}$$ , and the estimated full width is $$\Gamma (D^{*}(2007)^{0})=54.0~\textrm{keV}$$ . The focus of this study is on the relativistic corrections. In our method, the wave function of the D meson is not a pure S-wave, but includes both a non-relativistic S-wave and a relativistic P-wave, while the wave function of the $$D^*$$ meson includes a non-relativistic S-wave as well as both relativistic P-wave and D-wave. Therefore, in this case, the decay $${D^{*}\rightarrow {D}\gamma }$$ is not a non-relativistic M1 transition, but rather an $$M1+E2+M3+E4$$ decay. We find that in a strong decay $$D^{*}\rightarrow {D}{\pi }$$ , the non-relativistic contribution is dominant, while in an electromagnetic decay $${D^{*}\rightarrow {D}\gamma }$$ , the relativistic correction is dominant.

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Customized priors for enhanced multispectral and hyperspectral image sharpening

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Information Fusion 2025年 122卷

作者： Zhang, Junkang Zhang, Haoyang Wang, Tingting Fang, Faming Zhang, Guixu Xu, Ming School of Computer Science and Technology East China Normal University Shanghai200062 China Key Laboratory of Advanced Theory and Application in Statistics and Data Science Shanghai China

In recent years, deep unfolding (DU) models have gained significant traction in multi-source image fusion applications, including remote sensing pansharpening and the integration of multispectral and hyperspectral images, owing to their enhanced interpretability. However, despite embedding physical degradation models, these DU models still rely on implicit learning for their priors, which can impede interpretability. In this paper, we present a novel image fusion model based on maximum a posterior (MAP) framework. This model enhances interpretability and fusion performance by incorporating two innovative prior modules. The first module employs codebook to capture the spectral traits of the target image, subsequently integrating these insights within our DU framework. The second module specializes in uncovering the intrinsic correlations between multi-source images, ensuring that the fusion process effectively leverages the rich, diverse information encoded within each modality. Specifically, we have effectively combined the self-similarity and cross-modal similarity paradigms into a unified cross image prior module. To counteract the redundancy present in images, we have integrated an agent mechanism that significantly simplifies the computational load of our prior module. Quantitative and qualitative experiments on multiple benchmark datasets demonstrate that the proposed method achieves more robustness and high-quality results than other state-of-the-art sharpening methods. © 2025 Elsevier B.V.

关键词： Miocene

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Archive-based multiple feature construction method using adaptive genetic programming

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Memetic Computing 2025年第2期17卷 1-16页

作者： Jia, Kaixuan Zhang, Fan Gao, Xiaoying Ma, Jianbin College of Information Science and Technology Hebei Agricultural University Baoding China College of Information Science and Technology Hebei Key Laboratory of Agricultural Big Data Hebei Agricultural University Baoding China School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand

The quality of features is an important factor that affects the classification performance of machine learning algorithms. Feature construction based on Genetic Programming (GP) can automatically create more discriminative features, sometimes greatly improving classification performance. However, constructing a single feature or a small number of features may make the linkage information between labels and features insufficient, resulting in poor classification performance, so we introduce a multi-feature construction method. In addition, premature convergence of the GP may also affect classification performance. This paper proposes an archive-based multiple feature construction method which uses elite archive strategy to preserve and select effective constructed features, and employs an adaptive strategy for GP to adjust the crossover and mutation probabilities based on fitness values. Experiments on ten datasets show that our proposed archive-based multiple feature construction method without using adaptive GP can significantly improve the classification performance compared with traditional single feature construction method, and the classification performance can be maintained or further improved by adding the adaptive strategy. The comparisons with four state-of-the-art techniques show that our proposed method can significantly achieve better classification performance.

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