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

Frontiers in Heat and Mass Transfer, Vol.17, No.1, 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 >

Hong Li^1,*, Jianjun Li¹, Guohui Li¹, Rong Gao², Lingyu Yan²

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1687-1709, 2023, DOI:10.32604/cmc.2023.035239

Abstract Expert Recommendation (ER) aims to identify domain experts with high expertise and willingness to provide answers to questions in Community Question Answering (CQA) web services. How to model questions and users in the heterogeneous content network is critical to this task. Most traditional methods focus on modeling questions and users based on the textual content left in the community while ignoring the structural properties of heterogeneous CQA networks and always suffering from textual data sparsity issues. Recent approaches take advantage of structural proximities between nodes and attempt to fuse the textual content of nodes for modeling. However, they often fail… More >

Xin Huang^1,*, Yunpeng Jiang¹, Daowu Huang¹, Xianke He¹, Xianguo Zhang², Ping Guo³

FDMP-Fluid Dynamics & Materials Processing, Vol.19, No.6, pp. 1679-1691, 2023, DOI:10.32604/fdmp.2023.025312

Abstract

The seepage mechanism plays a crucial role in low-permeability gas reservoirs. Compared with conventional gas reservoirs, low-permeability sandstone gas reservoirs are characterized by low porosity, low permeability, strong heterogeneity, and high water saturation. Moreover, their percolation mechanisms are more complex. The present work describes a series of experiments conducted considering low-permeability sandstone cores under pressure-depletion conditions (from the Xihu Depression in the East China Sea Basin). It is shown that the threshold pressure gradient of a low-permeability gas reservoir in thick layers is positively correlated with water saturation and negatively correlated with permeability and porosity. The reservoir stress sensitivity is… More >

Zhao Yu^1,2,*

FDMP-Fluid Dynamics & Materials Processing, Vol.19, No.6, pp. 1521-1534, 2023, DOI:10.32604/fdmp.2023.023296

Abstract Oil saturation is a critical parameter when designing oil field development plans. This study focuses on the change of oil saturation during water flooding. Particularly, a meter-level artificial model is used to conduct relevant experiments on the basis of similarity principles and taking into account the layer geological characteristics of the reservoir. The displacement experiment’s total recovery rate is 41.35%. The changes in the remaining oil saturation at a millimeter-scale are examined using medical spiral computer tomography principles. In all experimental stages, regions exists where the oil saturation decline is more than 10.0%. The shrinkage percentage is 20.70% in the… More >