R. Madan Kumar¹, R. Srinivasa Raju², F. Mebarek-Oudina^3,*, M. Anil Kumar⁴, V. K. Narla²

Frontiers in Heat and Mass Transfer, Vol.22, No.1, pp. 15-34, 2024, DOI:10.32604/fhmt.2024.048045

Abstract The primary aim of this research endeavor is to examine the characteristics of magnetohydrodynamic Williamson nanofluid flow past a nonlinear stretching surface that is immersed in a permeable medium. In the current analysis, the impacts of Soret and Dufour (cross-diffusion effects) have been attentively taken into consideration. Using appropriate similarity variable transformations, the governing nonlinear partial differential equations were altered into nonlinear ordinary differential equations and then solved numerically using the Runge Kutta Fehlberg-45 method along with the shooting technique. Numerical simulations were then perceived to show the consequence of various physical parameters on the plots of velocity, temperature, and… More > Graphic Abstract

Morteza Eslamian^*

Frontiers in Heat and Mass Transfer, Vol.2, No.4, pp. 1-20, 2011, DOI:10.5098/hmt.v2.4.3001

Abstract Recent advances in thermodiffusion (Soret effect) in binary and higher multicomponent liquid mixtures are reviewed. The mixtures studied include the hydrocarbon, associating, molten metal and semiconductor, polymer, and DNA mixtures. The emphasis is placed on the theoretical works, particularly models based on the nonequilibrium thermodynamics, although other approaches such as the statistical, kinetic and hydrodynamic approaches are discussed as well. For each mixture, the major theoretical and experimental works are discussed and the research trends and challenges are addressed. Some of the challenges include a need for combining various methods to develop a comprehensive theoretical model or at least to… More >

Zheng Miao^a, Ya-Ling He^a,*, Tian-Shou Zhao^b, Wen-Quan Tao^a

Frontiers in Heat and Mass Transfer, Vol.2, No.1, pp. 1-10, 2011, DOI:10.5098/hmt.v2.1.3001

Abstract A non-isothermal two-phase mass transport model is developed in this paper to investigate the heat generation and transport phenomena in a direct methanol fuel cell with anisotropic gas diffusion layers (GDLs). Thermal contact resistances at the GDL/CL (catalyst layer) and GDL/Rib interfaces, and the deformation of GDLs are considered together with the inherent anisotropy of the GDL. Latent heat effects due to condensation/evaporation of water and methanol between liquid and gas phases are also taken into account. Formulation of the two-phase mass transport across the membrane electrode assembly (MEA) is mainly based on the classical multiphase flow theory in the… More >

Irfan Haider¹, Muhammad Attique Khan^1,*, Muhammad Nazir¹, Taerang Kim², Jae-Hyuk Cha²

Computer Systems Science and Engineering, Vol.48, No.2, pp. 529-554, 2024, DOI:10.32604/csse.2023.042080

Abstract Fruit infections have an impact on both the yield and the quality of the crop. As a result, an automated recognition system for fruit leaf diseases is important. In artificial intelligence (AI) applications, especially in agriculture, deep learning shows promising disease detection and classification results. The recent AI-based techniques have a few challenges for fruit disease recognition, such as low-resolution images, small datasets for learning models, and irrelevant feature extraction. This work proposed a new fruit leaf leaf leaf disease recognition framework using deep learning features and improved pathfinder optimization. Three fruit types have been employed in this work for… More >

Z. Aouachria^a,*, F. Rouichi^a, D. Haddad^b

Frontiers in Heat and Mass Transfer, Vol.3, No.2, pp. 1-6, 2012, DOI:10.5098/hmt.v3.2.3004

Abstract Natural convection flow past a vertical porous plate in a porous medium is studied numerically, by taking into account the Dufour and Soret effects. The similarity equations of the problem considered are obtained by using usual similarity technique. This system of ordinary differential equations, which are solved numerically by using the Nachtsheim -Swigerst hooting iteration technique together with a sixth order Runge-Kutta integrations scheme. The results show that Soret and Dufour effects do not appreciably influence the velocity, temperature and concentration fields, but rather only tend to increase the mass and energy flux due to the added contributions. More >

Rui Wang¹, Yao Zhou^3,*, Guangchun Luo¹, Peng Chen², Dezhong Peng^3,4

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3011-3027, 2024, DOI:10.32604/cmes.2023.047065

Abstract Time series anomaly detection is crucial in various industrial applications to identify unusual behaviors within the time series data. Due to the challenges associated with annotating anomaly events, time series reconstruction has become a prevalent approach for unsupervised anomaly detection. However, effectively learning representations and achieving accurate detection results remain challenging due to the intricate temporal patterns and dependencies in real-world time series. In this paper, we propose a cross-dimension attentive feature fusion network for time series anomaly detection, referred to as CAFFN. Specifically, a series and feature mixing block is introduced to learn representations in 1D space. Additionally, a… More >

Kuosheng Jiang¹, Chengrui Han¹, Yasheng Chang^2,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3329-3352, 2024, DOI:10.32604/cmes.2024.046729

Abstract Angular contact ball bearings have been widely used in machine tool spindles, and the bearing preload plays an important role in the performance of the spindle. In order to solve the problems of the traditional optimal preload prediction method limited by actual conditions and uncertainties, a roller bearing preload test method based on the improved D-S evidence theory multi-sensor fusion method was proposed. First, a novel controllable preload system is proposed and evaluated. Subsequently, multiple sensors are employed to collect data on the bearing parameters during preload application. Finally, a multisensor fusion algorithm is used to make predictions, and a… More >

Weiya Shi^1,*, Shaowen Zhang², Shiqiang Zhang²

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3209-3231, 2024, DOI:10.32604/cmes.2023.044863

Abstract In recent years, there has been extensive research on object detection methods applied to optical remote sensing images utilizing convolutional neural networks. Despite these efforts, the detection of small objects in remote sensing remains a formidable challenge. The deep network structure will bring about the loss of object features, resulting in the loss of object features and the near elimination of some subtle features associated with small objects in deep layers. Additionally, the features of small objects are susceptible to interference from background features contained within the image, leading to a decline in detection accuracy. Moreover, the sensitivity of small… More >

Yuchun Li^1,4, Mengxing Huang^1,*, Yu Zhang², Zhiming Bai³

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1649-1668, 2024, DOI:10.32604/cmc.2023.046883

Abstract The precise and automatic segmentation of prostate magnetic resonance imaging (MRI) images is vital for assisting doctors in diagnosing prostate diseases. In recent years, many advanced methods have been applied to prostate segmentation, but due to the variability caused by prostate diseases, automatic segmentation of the prostate presents significant challenges. In this paper, we propose an attention-guided multi-scale feature fusion network (AGMSF-Net) to segment prostate MRI images. We propose an attention mechanism for extracting multi-scale features, and introduce a 3D transformer module to enhance global feature representation by adding it during the transition phase from encoder to decoder. In the… More >

Bin Luo^1,2,3, Liangguo Chen^1,2,3, Shuhua Ruan^1,2,3,*, Yonggang Luo^2,3,*

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1731-1754, 2024, DOI:10.32604/cmc.2023.045739

Abstract Considering the stealthiness and persistence of Advanced Persistent Threats (APTs), system audit logs are leveraged in recent studies to construct system entity interaction provenance graphs to unveil threats in a host. Rule-based provenance graph APT detection approaches require elaborate rules and cannot detect unknown attacks, and existing learning-based approaches are limited by the lack of available APT attack samples or generally only perform graph-level anomaly detection, which requires lots of manual efforts to locate attack entities. This paper proposes an APT-exploited process detection approach called ThreatSniffer, which constructs the benign provenance graph from attack-free audit logs, fits normal system entity… More >