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  • Open Access

    ARTICLE

    Radiation Effect on Heat Transfer Analysis of MHD Flow of Upper Convected Maxwell Fluid between a Porous and a Moving Plate

    P. Pai Nityanand, B. Devaki, G. Bhat Pareekshith, V. S. Sampath Kumar*

    Frontiers in Heat and Mass Transfer, Vol.22, No.2, pp. 655-673, 2024, DOI:10.32604/fhmt.2024.050237

    Abstract The study in this manuscript aims to analyse the impact of thermal radiation on the two-dimensional magnetohydrodynamic flow of upper convected Maxwell (UCM) fluid between parallel plates. The lower plate is porous and stationary, while the top plate is impermeable and moving. The equations that describe the flow are transformed into non-linear ordinary differential equations with boundary conditions by employing similarity transformations. The Homotopy Perturbation Method (HPM) is then employed to approach the obtained non-linear ordinary differential equations and get an approximate analytical solution. The analysis includes plotting the velocity profile for different Reynolds number… More >

  • Open Access

    ARTICLE

    A HEVC Video Steganalysis Method Using the Optimality of Motion Vector Prediction

    Jun Li1,2, Minqing Zhang1,2,*, Ke Niu1, Yingnan Zhang1, Xiaoyuan Yang1,2

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2085-2103, 2024, DOI:10.32604/cmc.2024.048095

    Abstract Among steganalysis techniques, detection against MV (motion vector) domain-based video steganography in the HEVC (High Efficiency Video Coding) standard remains a challenging issue. For the purpose of improving the detection performance, this paper proposes a steganalysis method that can perfectly detect MV-based steganography in HEVC. Firstly, we define the local optimality of MVP (Motion Vector Prediction) based on the technology of AMVP (Advanced Motion Vector Prediction). Secondly, we analyze that in HEVC video, message embedding either using MVP index or MVD (Motion Vector Difference) may destroy the above optimality of MVP. And then, we define More >

  • Open Access

    ARTICLE

    A Deep Learning Framework for Mass-Forming Chronic Pancreatitis and Pancreatic Ductal Adenocarcinoma Classification Based on Magnetic Resonance Imaging

    Luda Chen1, Kuangzhu Bao2, Ying Chen2, Jingang Hao2,*, Jianfeng He1,3,*

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 409-427, 2024, DOI:10.32604/cmc.2024.048507

    Abstract Pancreatic diseases, including mass-forming chronic pancreatitis (MFCP) and pancreatic ductal adenocarcinoma (PDAC), present with similar imaging features, leading to diagnostic complexities. Deep Learning (DL) methods have been shown to perform well on diagnostic tasks. Existing DL pancreatic lesion diagnosis studies based on Magnetic Resonance Imaging (MRI) utilize the prior information to guide models to focus on the lesion region. However, over-reliance on prior information may ignore the background information that is helpful for diagnosis. This study verifies the diagnostic significance of the background information using a clinical dataset. Consequently, the Prior Difference Guidance Network (PDGNet)… More >

  • Open Access

    ARTICLE

    TSCND: Temporal Subsequence-Based Convolutional Network with Difference for Time Series Forecasting

    Haoran Huang, Weiting Chen*, Zheming Fan

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3665-3681, 2024, DOI:10.32604/cmc.2024.048008

    Abstract Time series forecasting plays an important role in various fields, such as energy, finance, transport, and weather. Temporal convolutional networks (TCNs) based on dilated causal convolution have been widely used in time series forecasting. However, two problems weaken the performance of TCNs. One is that in dilated casual convolution, causal convolution leads to the receptive fields of outputs being concentrated in the earlier part of the input sequence, whereas the recent input information will be severely lost. The other is that the distribution shift problem in time series has not been adequately solved. To address… More >

  • Open Access

    ARTICLE

    Steady Natural Convection from a Vertical Hot Plate with Variable Radiation

    Dewi Puspitasari1, Diah Kusuma Pratiwi1, Pramadhony Amran2, Kaprawi Sahim1,*

    Frontiers in Heat and Mass Transfer, Vol.22, No.1, pp. 305-315, 2024, DOI:10.32604/fhmt.2023.041882

    Abstract The natural convection from a vertical hot plate with radiation and constant flux is studied numerically to know the velocity and temperature distribution characteristics over a vertical hot plate. The governing equations of the natural convection in two-dimension are solved with the implicit finite difference method, whereas the discretized equations are solved with the iterative relaxation method. The results show that the velocity and the temperature increase along the vertical wall. The influence of the radiation parameter in the boundary layer is significant in increasing the velocity and temperature profiles. The velocity profiles increase with More >

  • Open Access

    ARTICLE

    ON UNCERTAINTY AND LOCAL SENSITIVITY ANALYSIS FOR STEADY-STATE CONJUGATE HEAT TRANSFER PROBLEMS PART 1: EMISSIVITY, FLUID TEMPERATURE, AND CONDUCTANCE

    Christian Rauch*

    Frontiers in Heat and Mass Transfer, Vol.2, No.3, pp. 1-8, 2011, DOI:10.5098/hmt.v2.3.3006

    Abstract In recent years, significant effort has been placed into developing automated multi-physics simulation. The exchange of boundary conditions has lead to more realistic as well as more complex simulations with usually slower convergence rate when the coupling is being performed between two different codes. In this paper the equations of local sensitivities for element centered steady-state combined convection, conduction, and thermal radiation problems are being derived. A numerical analysis on the stability of the solution matrix is being conducted. Partial uncertainties and the relative importance of the heat transfer modes are investigated by their uncertainty More >

  • Open Access

    ARTICLE

    Fault Diagnosis Method of Rolling Bearing Based on ESGMD-CC and AFSA-ELM

    Jiajie He1,2, Fuzheng Liu3, Xiangyi Geng3, Xifeng Liang1, Faye Zhang3,*, Mingshun Jiang3

    Structural Durability & Health Monitoring, Vol.18, No.1, pp. 37-54, 2024, DOI:10.32604/sdhm.2023.029428

    Abstract Incomplete fault signal characteristics and ease of noise contamination are issues with the current rolling bearing early fault diagnostic methods, making it challenging to ensure the fault diagnosis accuracy and reliability. A novel approach integrating enhanced Symplectic geometry mode decomposition with cosine difference limitation and calculus operator (ESGMD-CC) and artificial fish swarm algorithm (AFSA) optimized extreme learning machine (ELM) is proposed in this paper to enhance the extraction capability of fault features and thus improve the accuracy of fault diagnosis. Firstly, SGMD decomposes the raw vibration signal into multiple Symplectic geometry components (SGCs). Secondly, the More >

  • Open Access

    ARTICLE

    Gender Differences in the Incidence and Related Factors of Low Social Support among Adolescents with Subthreshold Depression

    Yi Shi, Fangfang Shangguan*, Jing Xiao*

    International Journal of Mental Health Promotion, Vol.25, No.12, pp. 1257-1263, 2023, DOI:10.32604/ijmhp.2023.030516

    Abstract Background: Social support is related to depression, but the gender differences and related factors that contribute to low social support among adolescents with subthreshold depression remain to be elucidated. This study explores the relationship between social support and depression, in addition to the gender difference in the incidence of low social support among adolescents with subthreshold depression and its related factors. Methods: A total of 371 Chinese adolescents with subthreshold depression were recruited. All subjects were rated on the Social Support Scale for Adolescents, the Response Style Scale, the Self-Perception Profile for Children, the Individualism-collectivism… More >

  • Open Access

    ARTICLE

    Fuzzy Difference Equations in Diagnoses of Glaucoma from Retinal Images Using Deep Learning

    D. Dorathy Prema Kavitha1, L. Francis Raj1, Sandeep Kautish2,#, Abdulaziz S. Almazyad3, Karam M. Sallam4, Ali Wagdy Mohamed5,6,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 801-816, 2024, DOI:10.32604/cmes.2023.030902

    Abstract The intuitive fuzzy set has found important application in decision-making and machine learning. To enrich and utilize the intuitive fuzzy set, this study designed and developed a deep neural network-based glaucoma eye detection using fuzzy difference equations in the domain where the retinal images converge. Retinal image detections are categorized as normal eye recognition, suspected glaucomatous eye recognition, and glaucomatous eye recognition. Fuzzy degrees associated with weighted values are calculated to determine the level of concentration between the fuzzy partition and the retinal images. The proposed model was used to diagnose glaucoma using retinal images… More >

  • Open Access

    ARTICLE

    Multi-Branch Fault Line Location Method Based on Time Difference Matrix Fitting

    Hua Leng1, Silin He2, Jian Qiu3, Feng Liu4,*, Xinfei Huang4, Jiran Zhu2

    Energy Engineering, Vol.121, No.1, pp. 77-94, 2024, DOI:10.32604/ee.2023.028340

    Abstract The distribution network exhibits complex structural characteristics, which makes fault localization a challenging task. Especially when a branch of the multi-branch distribution network fails, the traditional multi-branch fault location algorithm makes it difficult to meet the demands of high-precision fault localization in the multi-branch distribution network system. In this paper, the multi-branch mainline is decomposed into single branch lines, transforming the complex multi-branch fault location problem into a double-ended fault location problem. Based on the different transmission characteristics of the fault-traveling wave in fault lines and non-fault lines, the endpoint reference time difference matrix S… More >

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