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Nonlinear Seebeck and Peltier effects in a Majorana nanowire coupled to leads

Chinese Physics B 2024年第7期33卷 547-552页

作者：迟锋刘佳付振国刘黎明易子川 School of Electronic and Information Engineering UESTC of ChinaZhongshan InstituteZhongshan 528400China School of Science Inner Mongolia University of Science and TechnologyBaotou 014010China Institute of Applied Physics and Computational Mathematics Beijing 100088China

We theoretically study nonlinear thermoelectric transport through a topological superconductor nanowire hosting Majorana bound states(MBSs) at its two ends, a system named as Majorana nanowire(MNW). We consider that the MNW is coupled to the left and right normal metallic leads subjected to either bias voltage or temperature gradient. We focus our attention on the sign change of nonlinear Seebeck and Peltier coefficients induced by mechanisms related to the MBSs, by which the possible existence of MBSs might be proved. Our results show that for a fixed temperature difference between the two leads, the sign of the nonlinear Seebeck coefficient(thermopower) can be reversed by changing the overlap amplitude between the MBSs or the system equilibrium temperature, which are similar to the cases in linear response regime. By optimizing the MBS–MBS interaction amplitude and system equilibrium temperature, we find that the temperature difference may also induce sign change of the nonlinear thermopower. For zero temperature difference and finite bias voltage, both the sign and magnitude of nonlinear Peltier coefficient can be adjusted by changing the bias voltage or overlap amplitude between the MBSs. In the presence of both bias voltage and temperature difference, we show that the electrical current at zero Fermi level and the states induced by overlap between the MBSs keep unchanged, regardless of the amplitude of temperature difference. We also find that the direction of the heat current driven by bias voltage may be changed by weak temperature difference.

关键词： quantum dot nonlinear Seebeck coefficient Peltier coefficient Majorana bound states sign change

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Detecting the symptoms of COVID-19 during pandemic environment using smart spectacle thermal images and deep capsule networks

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Multimedia Tools and Applications 2025年第8期84卷 4475-4501页

作者： Dwarakanath, B. Pandimurugan, V. Mohandas, R. Sambath, M. Baiju, B.V. Chinnasamy, A. SRM Institute of Science and Technology Ramapuram Chennai India Networking and Communications School of Computing SRMIST kattankulathur Chennai India Computational Intelligence School of Computing SRMIST kattankulathur Chennai India Computer Science and Engineering Hindustan Institute of Technology and Science Chennai India School of Computer Science and Engineering Vellore Institute of Technology Vellore India Department of Data Science and Business Systems School of Computing SRMIST kattankulathur Chennai India

The pandemic creates a more complicated providence of medical assistance and diagnosis procedures. In the world, Covid-19, Severe Acute Respiratory Syndrome Coronavirus-2 (SARS Cov-2), and plague are widely known pandemic disease desperations. Due to the recent COVID-19 pandemic tragedies, various medical diagnosis models and intelligent computing solutions are proposed for medical applications. In this era of computer-based medical environment, conventional clinical solutions are surpassed by many Machine Learning and Deep Learning-based COVID-19 diagnosis models. Anyhow, many existing models are developing lab-based diagnosis environments. Notably, the Gated Recurrent Unit-based Respiratory Data Analysis (GRU-RE), Intelligent Unmanned Aerial Vehicle-based Covid Data Analysis (Thermal Images) (I-UVAC), and Convolutional Neural Network-based Computer Tomography Image Analysis (CNN-CT) are enriched with lightweight image data analysis techniques for obtaining mass pandemic data at real-time conditions. However, the existing models directly deal with bulk images (thermal data and respiratory data) to diagnose the symptoms of COVID-19. Against these works, the proposed spectacle thermal image data analysis model creates an easy and effective way of disease diagnosis deployment strategies. Particularly, the mass detection of disease symptoms needs a more lightweight equipment setup. In this proposed model, each patient's thermal data is collected via the spectacles of medical staff, and the data are analyzed with the help of a complex set of capsule network functions. Comparatively, the conventional capsule network functions are enriched in this proposed model using adequate sampling and data reduction solutions. In this way, the proposed model works effectively for mass thermal data diagnosis applications. In the experimental platform, the proposed and existing models are analyzed in various dimensions (metrics). The comparative results obtained in the experiments just

关键词： COVID-19

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Flow instability of supercritical hydrocarbon fuel in a multi-channel cooling system

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Chinese Journal of Aeronautics 2024年第2期37卷 283-295页

作者： Yichao JIN Kun WU Xuejun FAN Institute of Applied Physics and Computational Mathematics Beijing 100094China Institute of Mechanics Chinese Academy of SciencesBeijing 100190China School of Engineering Science University of Chinese Academy of SciencesBeijing 100049China

Thermally-induced flow instabilities are a critical issue in multi-channel regenerative cooling *** particular,the interactions between Density-Wave Oscillations(DWO)and Flow Maldistribution(FMD)can result in complex and disastrous instability *** study investigates the instability behaviors of hydrocarbon fluid in a four-channel system with a constant heat flux ratio using both frequency-and time-domain *** the heat flux increases,the in-tube flow sequentially destabilizes in each channel and converges to new equilibrium states,leading to the emergence of FMD *** also causes the system eigenvalue to change repeatedly from negative to positive rather than increasing ***,the system eigenvalues are between those of the two most unstable channels,indicating that the stability behavior of the entire system is dictated by the most unstable *** FMD occurs,flow oscillations are activated in channels with weak stability,and the in-tube flow is observed to evolve into various flow patterns,including stable flow,self-sustained oscillation,oscillation divergence,quasiperiodic oscillation,and oscillation *** novel instability mode of oscillation excursion involves a spontaneous transition of operating *** oscillates from an equilibrium state and then stabilizes at a new operational state after oscillation-induced ***,the newfound stable state may also be only temporary,with the in-tube flow regressing to the initial state,resulting in quasi-periodic oscillation.

关键词： Cooling Supercritical fluids Hydrocarbons Flow instability Flow maldistribution Density-wave oscillations

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Image Fusion Based on NSCT and Sparse Representation for Remote Sensing Data

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Computer Systems science & engineering 2023年第9期46卷 3439-3455页

作者： N.A.Lawrance T.S.Shiny Angel Department of Computational Intelligence School of ComputingCollege of Engineering and TechnologySRM Institute of Science and TechnologyChengalpattu603203India

The practice of integrating images from two or more sensors collected from the same area or object is known as image *** goal is to extract more spatial and spectral information from the resulting fused image than from the component *** images must be fused to improve the spatial and spectral quality of both panchromatic and multispectral *** study provides a novel picture fusion technique that employs L0 smoothening Filter,Non-subsampled Contour let Transform(NSCT)and Sparse Representation(SR)followed by the Max absolute rule(MAR).The fusion approach is as follows:first,the multispectral and panchromatic images are divided into lower and higher frequency components using the L0 smoothing *** comes the fusion process,which uses an approach that combines NSCT and SR to fuse low frequency ***,the Max-absolute fusion rule is used to merge high frequency ***,the final image is obtained through the disintegration of fused low and high frequency *** terms of correlation coefficient,Entropy,spatial frequency,and fusion mutual information,our method outperforms other methods in terms of image quality enhancement and visual evaluation.

关键词： Remote sensing multispectral image pan chromatic image L0 smoothening filter non-sub sampled contourlet transform sparse representation

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NVERSE CONDUCTIVITY PROBLEM WITH INTERNAL DATA

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Journal of computational mathematics 2023年第3期41卷 482-500页

作者： Faouzi Triki Tao Yin Laboratoire Jean Kuntzmann UMR CNRS 5224Université Grenoble-Alpes700 Avenue Centrale38401 Saint-Martin-d’HèresFrance Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems ScienceChinese Academy of ScienceBeijing 100190China

This paper concerns the reconstruction of a scalar coefficient of a second-order elliptic equation in divergence form posed on a bounded domain from internal *** problem finds applications in multi-wave imaging,greedy methods to approximate parameter-dependent elliptic problems,and image treatment with partial differential *** first show that the inverse problem for smooth coefficients can be rewritten as a linear transport *** that the coefficient is known near the boundary,we study the well-posedness of associated transport equation as well as its numerical resolution using discontinuous Galerkin *** propose a regularized transport equation that allow us to derive rigorous convergence rates of the numerical method in terms of the order of the polynomial approximation as well as the regularization *** finally provide numerical examples for the inversion assuming a lower regularity of the coefficient,and using synthetic data.

关键词： Inverse problems Multi-wave imaging Static transport equation Internal data Diffusion coeffcient Stability estimates Regularization

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Cervical Cancer Risk Prediction using SMOTE and Explainable AI

Cervical Cancer Risk Prediction using SMOTE and Explainable ...

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2025 International Conference on Pervasive computational Technologies, ICPCT 2025

作者： Agarwal, Priyansh Chaudhary, Annanya Sarvanan, T.R. Srm Institute of Science and Technology School of Computing Department of Computational Intelligence India Srm Institute of Science and Technology Faculty of Engineering & Tech. Department of Computational Intelligence India

ISBN: (纸本)9798331508685

Cervical cancer, accounting for 7.9% of cancers in women globally, is a critical health challenge, particularly due to its asymptomatic nature in early stages. This study proposes a machine learning-driven framework to predict cervical cancer risk using clinical data, addressing class imbalance through SMOTE and optimizing model performance via GridSearchCV. By evaluating five classifiers - K-Nearest Neighbors, Random Forest, Support Vector Classifier, Logistic Regression, and Decision Trees - this research identifies the Decision Tree model as the most robust, achieving superior precision, recall, and accuracy metrics. Additionally, Explainable AI techniques such as SHAP and LIME were employed to enhance interpretability, offering clinicians actionable insights into key risk factors driving predictions. The results demonstrate the effectiveness of machine learning in facilitating early detection and personalized treatment strategies for cervical cancer, thereby advancing its integration into clinical applications. © 2025 IEEE.

关键词： Lung cancer

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An anti-aliasing filtering of quantum images in spatial domain using a pyramid structure

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Chinese Physics B 2024年第5期33卷 223-237页

作者：吴凯周日贵罗佳 College of Information Engineering Shanghai Maritime UniversityShanghai 201306China Research Center of Intelligent Information Processing and Quantum Intelligent Computing Shanghai 201306China School of Mathematics and Computational Science Shangrao Normal UniversityShangrao 334001China

As a part of quantum image processing,quantum image filtering is a crucial technology in the development of quantum ***-pass filtering can effectively achieve anti-aliasing effects on ***,most quantum image filterings are based on classical domains and grayscale images,and there are relatively fewer studies on anti-aliasing in the quantum *** paper proposes a scheme for anti-aliasing filtering based on quantum grayscale and color image scaling in the spatial *** achieves the effect of anti-aliasing filtering on quantum images during the scaling ***,we use the novel enhanced quantum representation(NEQR)and the improved quantum representation of color images(INCQI)to represent classical *** aliasing phenomena are more pronounced when images are scaled down,this paper focuses only on the anti-aliasing effects in the case of ***,we perform anti-aliasing filtering on the quantum representation of the original image and then use bilinear interpolation to scale down the image,achieving the anti-aliasing *** constructed pyramid model is then used to select an appropriate image for upscaling to the original image ***,the complexity of the circuit is *** to the images experiencing aliasing effects solely due to scaling,applying anti-aliasing filtering to the images results in smoother and clearer ***,the anti-aliasing filtering allows for manual intervention to select the desired level of image smoothness.

关键词： quantum color image processing anti-aliasing filtering algorithm quantum multiplier pyramid model

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Optimal weight support vector regression ensemble with cluster-based subsampling for electricity price forecasting

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Journal of Intelligent and Fuzzy Systems 2024年第1期1卷 1-16页

作者： Li, Yuerong Zhang, Yuhua Che, Jinxing School of Science Nanchang Institute of Technology Jiangxi Nanchang China Key Laboratory of Engineering Mathematics and Advanced Computing Nanchang Institute of Technology Jiangxi Nanchang China

Accurate prediction of short-term electricity price is the key to obtain economic benefit and also an important index of power system planning and management. Support vector regression (SVR) based ensemble works have gained remarkable achievements in terms of high accuracy and steady performance, but they are highly dependent on data representativeness and have a high computational complexity O (k ∗ N 3) of data samples and parameter selection. To further improve the data representativeness and reduce its computational complexity, this paper develops a new approach to forecast electricity price via optimal weighted ensemble. In the model, the cluster-based subsampling algorithm is proposed to categorize the inputs being seasonally decomposed into several groups, and representative data are drawn from each group in a certain proportion to ensure that each subset trained with SVR has the same representativeness and features. Moreover, the optimal weighted combination method is presented to assign weights to the sub-SVRs to obtain the optimal support vector regression ensemble model (OWSSVRE). The experimental results show that the improved support vector regression ensemble model with the same features and representativeness of the subset has better performance in electricity price forecasting. As a result, it is suitable to support decision making in the energy and other sectors. © 2024 - IOS Press. All rights reserved.

关键词： K-means clustering

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Solving Time Dependent Fokker-Planck Equations via Temporal Normalizing Flow

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Communications in computational Physics 2022年第7期32卷 401-423页

作者： Xiaodong Feng Li Zeng Tao Zhou LSEC Institute of Computational Mathematics and Scientific/Engineering ComputingAMSSChinese Academy of SciencesBeijingChina

In this work,we propose an adaptive learning approach based on temporal normalizing flows for solving time-dependent Fokker-Planck(TFP)*** is well known that solutions of such equations are probability density functions,and thus our approach relies on modelling the target solutions with the temporal normalizing *** temporal normalizing flow is then trained based on the TFP loss function,without requiring any labeled *** a machine learning scheme,the proposed approach is mesh-free and can be easily applied to high dimensional *** present a variety of test problems to show the effectiveness of the learning approach.

关键词： Temporal normalizing flow Fokker-Planck equations adaptive density approximation

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Attention-Enhanced and Knowledge-Fused Dual Item Representations Network for Recommendation

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Tsinghua science and Technology 2025年第2期30卷 585-599页

作者： Qiang Hua Jiachao Zhou Feng Zhang Chunru Dong Dachuan Xu College of Mathematics and Information Science Hebei UniversityBaoding 071002China Beijing Institute for Scientific and Engineering Computing Beijing University of TechnologyBeijing 100124China

Integrating Knowledge Graphs(KGs)into recommendation systems as supplementary information has become a prevalent *** leveraging the semantic relationships between entities in KGs,recommendation systems can better comprehend user *** to the unique structure of KGs,methods based on Graph Neural Networks(GNNs)have emerged as the current technical ***,existing GNN-based methods struggle to(1)filter out noisy information in real-world KGs,and(2)differentiate the item representations obtained from the knowledge graph and bipartite *** this paper,we introduce a novel model called Attention-enhanced and Knowledge-fused Dual item representations Network for recommendation(namely AKDN)that employs attention and gated mechanisms to guide aggregation on both knowledge graphs and bipartite *** particular,we firstly design an attention mechanism to determine the weight of each edge in the information aggregation on KGs,which reduces the influence of noisy information on the items and enables us to obtain more accurate and robust representations of the ***,we exploit a gated aggregation mechanism to differentiate collaborative signals and knowledge information,and leverage dual item representations to fuse them together for better capturing user behavior *** conduct extensive experiments on two public datasets which demonstrate the superior performance of our AKDN over state-of-the-art methods,like Knowledge Graph Attention Network(KGAT)and Knowledge Graph-based Intent Network(KGIN).

关键词： Recommender System(RS) Knowledge Graph(KG) Graph Neural Networks(GNN) attention mechanism

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