Umer Nauman¹, Yuhong Zhang², Zhihui Li³, Tong Zhen^1,3,*

Intelligent Automation & Soft Computing, Vol.39, No.3, pp. 477-510, 2024, DOI:10.32604/iasc.2024.050726

Abstract Cloud computing has gained significant recognition due to its ability to provide a broad range of online services and applications. Nevertheless, existing commercial cloud computing models demonstrate an appropriate design by concentrating computational assets, such as preservation and server infrastructure, in a limited number of large-scale worldwide data facilities. Optimizing the deployment of virtual machines (VMs) is crucial in this scenario to ensure system dependability, performance, and minimal latency. A significant barrier in the present scenario is the load distribution, particularly when striving for improved energy consumption in a hypothetical grid computing framework. This design… More >

Aneel Manan¹, Pu Zhang^1,*, Shoaib Ahmad², Jawad Ahmad²

Journal of Polymer Materials, Vol.41, No.1, pp. 15-44, 2024, DOI:10.32604/jpm.2024.053859

Abstract Fiber-reinforced polymer (FRP) bars are gaining popularity as an alternative to steel reinforcement due to their advantages such as corrosion resistance and high strength-to-weight ratio. However, FRP has a lower modulus of elasticity compared to steel. Therefore, special attention is required in structural design to address deflection related issues and ensure ductile failure. This research explores the use of machine learning algorithms such as gene expression programming (GEP) to develop a simple and effective equation for predicting the elastic modulus of hybrid fiber-reinforced polymer (HFPR) bars. A comprehensive database of 125 experimental results of HFPR… More >

Zineb Fergani¹, Zakaria Triki¹, Rabah Menasri¹, Hichem Tahraoui^1,2,*, Meriem Zamouche³, Mohammed Kebir⁴, Jie Zhang⁵, Abdeltif Amrane^6,*

Frontiers in Heat and Mass Transfer, Vol.22, No.2, pp. 455-473, 2024, DOI:10.32604/fhmt.2024.050332

Abstract Seawater desalination stands as an increasingly indispensable solution to address global water scarcity issues. This study conducts a thorough exergoenvironmental analysis of a multi-effect distillation with thermal vapor compression (MED-TVC) system, a highly promising desalination technology. The MED-TVC system presents an energy-efficient approach to desalination by harnessing waste heat sources and incorporating thermal vapor compression. The primary objective of this research is to assess the system’s thermodynamic efficiency and environmental impact, considering both energy and exergy aspects. The investigation delves into the intricacies of energy and exergy losses within the MED-TVC process, providing a holistic… More >

Ming Yan¹, Caijiang Lu^2,*, Pan Shi^1,*, Meiling Zhang³, Jiawei Zhang¹, Liang Wang¹

Frontiers in Heat and Mass Transfer, Vol.22, No.2, pp. 615-631, 2024, DOI:10.32604/fhmt.2024.048333

Abstract In response to escalating challenges in energy conservation and emission reduction, this study delves into the complexities of heat transfer in two-phase flows and adjustments to combustion processes within coal-fired boilers. Utilizing a fusion of hybrid modeling and automation technologies, we develop soft measurement models for key combustion parameters, such as the net calorific value of coal, flue gas oxygen content, and fly ash carbon content, within the Distributed Control System (DCS). Validated with performance test data, these models exhibit controlled root mean square error (RMSE) and maximum absolute error (MAXE) values, both within the… More > Graphic Abstract

Nguyen Huu Loc Khuu^1,2,3, Thuy Duy Truong^1,2,3, Quoc Dien Le^1,2,3, Tran Thanh Cong Vu^1,2,3, Hoa Binh Tran^1,2,3, Tuong Quan Vo^1,2,3,*

Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 255-279, 2024, DOI:10.32604/iasc.2024.043854

Abstract Optimizing Flow Path Design (FPD) is a popular research area in transportation system design, but its application to Overhead Transportation Systems (OTSs) has been limited. This study focuses on optimizing a double-spine flow path design for OTSs with 10 stations by minimizing the total travel distance for both loaded and empty flows. We employ transportation methods, specifically the North-West Corner and Stepping-Stone methods, to determine empty vehicle travel flows. Additionally, the Tabu Search (TS) algorithm is applied to branch the 10 stations into two main layout branches. The results obtained from our proposed method demonstrate More >

Kamlesh Kumar Soothar^1,2, Yuanxiang Chen^1,2,*, Arif Hussain Magsi³, Cong Hu¹, Hussain Shah¹

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2697-2721, 2024, DOI:10.32604/cmc.2024.049607

Abstract Efficient optical network management poses significant importance in backhaul and access network communication for preventing service disruptions and ensuring Quality of Service (QoS) satisfaction. The emerging faults in optical networks introduce challenges that can jeopardize the network with a variety of faults. The existing literature witnessed various partial or inadequate solutions. On the other hand, Machine Learning (ML) has revolutionized as a promising technique for fault detection and prevention. Unlike traditional fault management systems, this research has three-fold contributions. First, this research leverages the ML and Deep Learning (DL) multi-classification system and evaluates their accuracy… More >

Deni Ainur Rokhim^1,2, Isma Yanti Vitarisma¹, Sumari Sumari^1,*, 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… More >

Saba Awan^1,*, Zahid Mehmood^2,*

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2535-2555, 2024, DOI:10.32604/cmc.2024.047337

Abstract Highway safety researchers focus on crash injury severity, utilizing deep learning—specifically, deep neural networks (DNN), deep convolutional neural networks (D-CNN), and deep recurrent neural networks (D-RNN)—as the preferred method for modeling accident severity. Deep learning’s strength lies in handling intricate relationships within extensive datasets, making it popular for accident severity level (ASL) prediction and classification. Despite prior success, there is a need for an efficient system recognizing ASL in diverse road conditions. To address this, we present an innovative Accident Severity Level Prediction Deep Learning (ASLP-DL) framework, incorporating DNN, D-CNN, and D-RNN models fine-tuned through More >

Mostafa Diba^*, Hossein Khosravi

Intelligent Automation & Soft Computing, Vol.38, No.3, pp. 315-332, 2023, DOI:10.32604/iasc.2023.046590

Abstract Convolutional neural networks continually evolve to enhance accuracy in addressing various problems, leading to an increase in computational cost and model size. This paper introduces a novel approach for pruning face recognition models based on convolutional neural networks. The proposed method identifies and removes inefficient filters based on the information volume in feature maps. In each layer, some feature maps lack useful information, and there exists a correlation between certain feature maps. Filters associated with these two types of feature maps impose additional computational costs on the model. By eliminating filters related to these categories… More >

Qianqi Sun, Fenhua Bai^*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1733-1754, 2024, DOI:10.32604/cmes.2023.044558

Abstract Blockchain technology has witnessed a burgeoning integration into diverse realms of economic and societal development. Nevertheless, scalability challenges, characterized by diminished broadcast efficiency, heightened communication overhead, and escalated storage costs, have significantly constrained the broad-scale application of blockchain. This paper introduces a novel Encode-and CRT-based Scalability Scheme (ECSS), meticulously refined to enhance both block broadcasting and storage. Primarily, ECSS categorizes nodes into distinct domains, thereby reducing the network diameter and augmenting transmission efficiency. Secondly, ECSS streamlines block transmission through a compact block protocol and robust RS coding, which not only reduces the size of broadcasted More >