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  • Open Access

    ARTICLE

    A Novel Deep Learning-Based Model for Classification of Wheat Gene Expression

    Amr Ismail1, Walid Hamdy1,2, Aya M. Al-Zoghby3, Wael A. Awad3, Ahmed Ismail Ebada3, Yunyoung Nam4, Byeong-Gwon Kang4,*, Mohamed Abouhawwash5,6

    Computer Systems Science and Engineering, Vol.48, No.2, pp. 273-285, 2024, DOI:10.32604/csse.2023.038192

    Abstract Deep learning (DL) plays a critical role in processing and converting data into knowledge and decisions. DL technologies have been applied in a variety of applications, including image, video, and genome sequence analysis. In deep learning the most widely utilized architecture is Convolutional Neural Networks (CNN) are taught discriminatory traits in a supervised environment. In comparison to other classic neural networks, CNN makes use of a limited number of artificial neurons, therefore it is ideal for the recognition and processing of wheat gene sequences. Wheat is an essential crop of cereals for people around the world. Wheat Genotypes identification has… More >

  • Open Access

    ARTICLE

    HEAT GENERATION EFFECTS ON NATURAL CONVECTION IN POROUS CAVITY WITH DIFFERENT WALLS TEMPERATURE

    Majid Tahmasebi Kohyania, Behzad Ghasemia, Ahmad Pasandideh Fardb,*

    Frontiers in Heat and Mass Transfer, Vol.3, No.2, pp. 1-6, 2012, DOI:10.5098/hmt.v3.2.3008

    Abstract Natural convection heat transfer in a square cavity with a porous medium subjected to a uniform energy generation per unit volume is studied numerically in this paper. Temperature of the vertical walls is not equal but it is constant . There are two effective parameters in this condition that appear in the nondimensionalized equations and they are functions of temperature difference between hot and cold walls and energy generation in the porous medium. Nondimensionalized governing equations are obtained based on the Darcy model. a control volume approach is used for solving these equations. The effects of the variation of two… More >

  • Open Access

    ARTICLE

    THE EXPERIMENTAL AND NUMERICAL INVESTIGATION OF THE SOLIDIFICATION OF A POROUS CERAMIC CASTING

    Frantisek Kavickaa,*, Jana Dobrovskab, Karel Stranskya, Bohumil Sekaninaa, Josef Stetinaa

    Frontiers in Heat and Mass Transfer, Vol.3, No.2, pp. 1-9, 2012, DOI:10.5098/hmt.v3.2.3002

    Abstract Corundo-baddeleyit material (CBM) – EUCOR – is a heat- and wear-resistant material even at extreme temperatures. This article introduces an original numerical model of solidification and cooling of this material in a non-metallic mold. The second, cooperating model of chemical heterogeneity and its application on EUCOR samples prove that the applied method of measuring the chemical heterogeneity provides the detailed quantitative information on the material structure and makes it possible to analyze the solidification process. The verification of both numerical models was conducted on a real cast 350 x 200 x 400 mm block. More >

  • Open Access

    ARTICLE

    NUMERICAL SOLUTIONS FOR A NANOFLUID PAST OVER A STRETCHING CIRCULAR CYLINDER WITH NON-UNIFORM HEAT SOURCE

    A. Rasekha,*, D.D. Ganjib, S. Tavakolib

    Frontiers in Heat and Mass Transfer, Vol.3, No.4, pp. 1-6, 2012, DOI:10.5098/hmt.v3.4.3003

    Abstract The present paper deals with the analysis of boundary layer flow and heat transfer of a nanofluid over a stretching circular cylinder in the presence of non-uniform heat source/sink. The governing system of partial differential equations is converted to ordinary differential equations by using similarity transformations, which are then solved numerically using the Runge–Kutta–Fehlberg method with shooting technique. The solutions for the temperature and nanoparticle concentration distributions depend on six parameters, Prandtl number Pr, Lewis number Le, the Brownian motion parameter Nb, the thermophoresis parameter Nt, and non-uniform heat generation/absorption parameters A*, B*. Numerical results are presented both in tabular… More >

  • Open Access

    ARTICLE

    Research on the Generation Mechanism and Suppression Method of Aerodynamic Noise in Expansion Cavity Based on Hybrid Method

    Haitao Liu1,2,*, Jiaming Wang1, Xiuliang Zhang1, Yanji Jiang2, Qian Xiao1

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 2747-2772, 2024, DOI:10.32604/cmes.2024.047129

    Abstract The expansion chamber serves as the primary silencing structure within the exhaust pipeline. However, it can also act as a sound-emitting structure when subjected to airflow. This article presents a hybrid method for numerically simulating and analyzing the unsteady flow and aerodynamic noise in an expansion chamber under the influence of airflow. A fluid simulation model is established, utilizing the Large Eddy Simulation (LES) method to calculate the unsteady flow within the expansion chamber. The simulation results effectively capture the development and changes of the unsteady flow and vorticity inside the cavity, exhibiting a high level of consistency with experimental… More > Graphic Abstract

    Research on the Generation Mechanism and Suppression Method of Aerodynamic Noise in Expansion Cavity Based on Hybrid Method

  • Open Access

    ARTICLE

    Generative Multi-Modal Mutual Enhancement Video Semantic Communications

    Yuanle Chen1, Haobo Wang1, Chunyu Liu1, Linyi Wang2, Jiaxin Liu1, Wei Wu1,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 2985-3009, 2024, DOI:10.32604/cmes.2023.046837

    Abstract Recently, there have been significant advancements in the study of semantic communication in single-modal scenarios. However, the ability to process information in multi-modal environments remains limited. Inspired by the research and applications of natural language processing across different modalities, our goal is to accurately extract frame-level semantic information from videos and ultimately transmit high-quality videos. Specifically, we propose a deep learning-based Multi-Modal Mutual Enhancement Video Semantic Communication system, called M3E-VSC. Built upon a Vector Quantized Generative Adversarial Network (VQGAN), our system aims to leverage mutual enhancement among different modalities by using text as the main carrier of transmission. With it,… More >

  • Open Access

    ARTICLE

    A Robust Framework for Multimodal Sentiment Analysis with Noisy Labels Generated from Distributed Data Annotation

    Kai Jiang, Bin Cao*, Jing Fan

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 2965-2984, 2024, DOI:10.32604/cmes.2023.046348

    Abstract Multimodal sentiment analysis utilizes multimodal data such as text, facial expressions and voice to detect people’s attitudes. With the advent of distributed data collection and annotation, we can easily obtain and share such multimodal data. However, due to professional discrepancies among annotators and lax quality control, noisy labels might be introduced. Recent research suggests that deep neural networks (DNNs) will overfit noisy labels, leading to the poor performance of the DNNs. To address this challenging problem, we present a Multimodal Robust Meta Learning framework (MRML) for multimodal sentiment analysis to resist noisy labels and correlate distinct modalities simultaneously. Specifically, we… More >

  • Open Access

    ARTICLE

    Sub-Homogeneous Peridynamic Model for Fracture and Failure Analysis of Roadway Surrounding Rock

    Shijun Zhao1, Qing Zhang2, Yusong Miao1, Weizhao Zhang3, Xinbo Zhao1, Wei Xu1,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3167-3187, 2024, DOI:10.32604/cmes.2023.045015

    Abstract The surrounding rock of roadways exhibits intricate characteristics of discontinuity and heterogeneity. To address these complexities, this study employs non-local Peridynamics (PD) theory and reconstructs the kernel function to represent accurately the spatial decline of long-range force. Additionally, modifications to the traditional bond-based PD model are made. By considering the micro-structure of coal-rock materials within a uniform discrete model, heterogeneity characterized by bond random pre-breaking is introduced. This approach facilitates the proposal of a novel model capable of handling the random distribution characteristics of material heterogeneity, rendering the PD model suitable for analyzing the deformation and failure of heterogeneous layered… More >

  • Open Access

    ARTICLE

    Optimizing Household Wastes (Rice, Vegetables, and Fruit) as an Environmentally Friendly Electricity Generator

    Deni Ainur Rokhim1,2, Isma Yanti Vitarisma1, Sumari Sumari1,*, Yudhi Utomo1, Muhammad Roy Asrori1

    Journal of Renewable Materials, Vol.12, No.2, pp. 275-284, 2024, DOI:10.32604/jrm.2023.043419

    Abstract The high consumption of electricity and issues related to fossil energy have triggered an increase in energy prices and the scarcity of fossil resources. Consequently, many researchers are seeking alternative energy sources. One potential technology, the Microbial Fuel Cell (MFC) based on rice, vegetable, and fruit wastes, can convert chemical energy into electrical energy. This study aims to determine the potency of rice, vegetable, and fruit waste assisted by Cu/Mg electrodes as a generator of electricity. The method used was a laboratory experiment, including the following steps: electrode preparation, waste sample preparation, incubation of the waste samples, construction of a… More >

  • Open Access

    ARTICLE

    Enhancing Image Description Generation through Deep Reinforcement Learning: Fusing Multiple Visual Features and Reward Mechanisms

    Yan Li, Qiyuan Wang*, Kaidi Jia

    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2469-2489, 2024, DOI:10.32604/cmc.2024.047822

    Abstract Image description task is the intersection of computer vision and natural language processing, and it has important prospects, including helping computers understand images and obtaining information for the visually impaired. This study presents an innovative approach employing deep reinforcement learning to enhance the accuracy of natural language descriptions of images. Our method focuses on refining the reward function in deep reinforcement learning, facilitating the generation of precise descriptions by aligning visual and textual features more closely. Our approach comprises three key architectures. Firstly, it utilizes Residual Network 101 (ResNet-101) and Faster Region-based Convolutional Neural Network (Faster R-CNN) to extract average… More >

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