P. Bala Anki Reddy^a,*, S. Suneetha^b

Frontiers in Heat and Mass Transfer, Vol.8, pp. 1-11, 2017, DOI:10.5098/hmt.8.24

Abstract We report on a mathematical model for analyzing the effects of homogeneous-heterogeneous chemical reaction and slip velocity on the MHD stagnation point flow of electrically conducting micropolar fluid over a stretching/shrinking surface embedded in a porous medium. The governing boundary layer coupled partial differential equations are transformed into a system of non-linear ordinary differential equations, which are solved numerically using the MATLAB bvp4c solver. The effects of physical and fluid parameters such as the stretching parameter, micropolar parameter, permeability parameter, strength of homogeneous and heterogeneous reaction parameter on the velocity and concentration are analyzed, and these results are presented through… More >

P. Bala Anki Reddy^a,*, S. Suneetha^b

Frontiers in Heat and Mass Transfer, Vol.10, pp. 1-9, 2018, DOI:10.5098/hmt.10.7

Abstract This work concentrates on the effects of homogeneous-heterogeneous chemical reactions on MHD boundary layer flow of Casson fluid over a stretching surface. Cattaneo-Christov heat flux model is considered instead of classical Fourier’s law to explore the heat transfer phenomena. Appropriate similarity transformations are used to convert the governing partial differential equations into a system of coupled non-linear differential equations. The resulting coupled non-linear differential equations are solved numerically by using the fourth order Runge-Kutta method with shooting technique. The impact of significant parameters on velocity, temperature, concentration, skin friction coefficient and the Nusselt number are presented graphically and in tabular… More >

Kuihua Huang¹, Rui Li², Wenyin Gong^2,*, Weiwei Bian³, Rui Wang¹

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2077-2101, 2023, DOI:10.32604/iasc.2023.040215

Abstract This work aims to resolve the distributed heterogeneous permutation flow shop scheduling problem (DHPFSP) with minimizing makespan and total energy consumption (TEC). To solve this NP-hard problem, this work proposed a competitive and cooperative-based strength Pareto evolutionary algorithm (CCSPEA) which contains the following features: 1) An initialization based on three heuristic rules is developed to generate a population with great diversity and convergence. 2) A comprehensive metric combining convergence and diversity metrics is used to better represent the heuristic information of a solution. 3) A competitive selection is designed which divides the population into a winner and a loser swarms… More >

Shuang Cao^a,*, Hui Yang^a, Luxing Zhao^a, Tao Wang^a, Jian Xie^b,†

Frontiers in Heat and Mass Transfer, Vol.17, pp. 1-16, 2021, DOI:10.5098/hmt.17.17

Abstract Flow boiling heat transfer is an effective way to fulfill the energy transfer. The wettability of boiling surface influences the liquid spreading ability and the growth, departure, and release frequency of bubbles, which determines the heat transfer performance. According to the wettability and combination forms, boiling surface are classified into weak wetting surface, strong wetting surface, and heterogeneous wetting surface. Fabricating by physical, chemical method or coating the original surface with a layer of low surface energy, the weak wetting surface has more effective activation point and nucleation center density to improve heat transfer performance at low heat flux. The… More >

Yang Zhao¹, Jingmin An¹, Hao Li¹, Saru Kumari^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 555-575, 2023, DOI:10.32604/cmes.2023.026808

Abstract The vehicular sensor network (VSN) is an important part of intelligent transportation, which is used for real-time detection and operation control of vehicles and real-time transmission of data and information. In the environment of VSN, massive private data generated by vehicles are transmitted in open channels and used by other vehicle users, so it is crucial to maintain high transmission efficiency and high confidentiality of data. To deal with this problem, in this paper, we propose a heterogeneous fault-tolerant aggregate signcryption scheme with an equality test (HFTAS-ET). The scheme combines fault-tolerant and aggregate signcryption, which not only makes up for… More > Graphic Abstract

Sufen Zhao^1,2, Rong Peng^1,*, Po Hu², Liansheng Tan²

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 83-130, 2023, DOI:10.32604/cmes.2023.024781

Abstract Real-world complex networks are inherently heterogeneous; they have different types of nodes, attributes, and relationships. In recent years, various methods have been proposed to automatically learn how to encode the structural and semantic information contained in heterogeneous information networks (HINs) into low-dimensional embeddings; this task is called heterogeneous network embedding (HNE). Efficient HNE techniques can benefit various HIN-based machine learning tasks such as node classification, recommender systems, and information retrieval. Here, we provide a comprehensive survey of key advancements in the area of HNE. First, we define an encoder-decoder-based HNE model taxonomy. Then, we systematically overview, compare, and summarize various… More > Graphic Abstract

Lelisa Adeba Jilcha¹, Deuk-Hun Kim², Julian Jang-Jaccard³, Jin Kwak^4,*

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3261-3284, 2023, DOI:10.32604/csse.2023.037615

Abstract Contemporary attackers, mainly motivated by financial gain, consistently devise sophisticated penetration techniques to access important information or data. The growing use of Internet of Things (IoT) technology in the contemporary convergence environment to connect to corporate networks and cloud-based applications only worsens this situation, as it facilitates multiple new attack vectors to emerge effortlessly. As such, existing intrusion detection systems suffer from performance degradation mainly because of insufficient considerations and poorly modeled detection systems. To address this problem, we designed a blended threat detection approach, considering the possible impact and dimensionality of new attack surfaces due to the aforementioned convergence.… More >

S. Vijayashaarathi^1,*, S. NithyaKalyani²

Computer Systems Science and Engineering, Vol.46, No.3, pp. 2881-2897, 2023, DOI:10.32604/csse.2023.036864

Abstract In recent years, real-time video streaming has grown in popularity. The growing popularity of the Internet of Things (IoT) and other wireless heterogeneous networks mandates that network resources be carefully apportioned among versatile users in order to achieve the best Quality of Experience (QoE) and performance objectives. Most researchers focused on Forward Error Correction (FEC) techniques when attempting to strike a balance between QoE and performance. However, as network capacity increases, the performance degrades, impacting the live visual experience. Recently, Deep Learning (DL) algorithms have been successfully integrated with FEC to stream videos across multiple heterogeneous networks. But these algorithms… More >

Dong Qing^1,*, Qi Song¹, Shuangyun Huang², Gening Xu¹

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2345-2370, 2023, DOI:10.32604/cmes.2023.026552

Abstract For the randomness of crane working load leading to the decrease of load spectrum prediction accuracy with time, an adaptive TSSA-HKRVM model for crane load spectrum regression prediction is proposed. The heterogeneous kernel relevance vector machine model (HKRVM) with comprehensive expression ability is established using the complementary advantages of various kernel functions. The combination strategy consisting of refraction reverse learning, golden sine, and Cauchy mutation + logistic chaotic perturbation is introduced to form a multi-strategy improved sparrow algorithm (TSSA), thus optimizing the relevant parameters of HKRVM. The adaptive updating mechanism of the heterogeneous kernel RVM model under the multi-strategy improved… More >

Wu-Chun Chung¹, Yung-Chin Chang¹, Ching-Hsien Hsu^2,3, Chih-Hung Chang⁴, Che-Lun Hung^4,5,*

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 351-371, 2023, DOI:10.32604/cmc.2023.035720

Abstract Federated learning is an emerging machine learning technique that enables clients to collaboratively train a deep learning model without uploading raw data to the aggregation server. Each client may be equipped with different computing resources for model training. The client equipped with a lower computing capability requires more time for model training, resulting in a prolonged training time in federated learning. Moreover, it may fail to train the entire model because of the out-of-memory issue. This study aims to tackle these problems and propose the federated feature concatenate (FedFC) method for federated learning considering heterogeneous clients. FedFC leverages the model… More >