Guowei Li^1,*, Shujuan Bu², Xinle Yang², Kaijie Liang¹, Zhengri Shao¹, Xiaobei Song¹, Yitian Tang³, Dejing Zong⁴

Energy Engineering, Vol.121, No.12, pp. 3843-3874, 2024, DOI:10.32604/ee.2024.056195 - 22 November 2024

Abstract A novel dual-pressure organic Rankine cycle system (DPORC) with a dual-stage ejector (DE-DPORC) is proposed. The system incorporates a dual-stage ejector that utilizes a small amount of extraction steam from the high-pressure expander to pressurize a large quantity of exhaust gas to perform work for the low-pressure expander. This innovative approach addresses condensing pressure limitations, reduces power consumption during pressurization, minimizes heat loss, and enhances the utilization efficiency of waste heat steam. A thermodynamic model is developed with net output work, thermal efficiency, and exergy efficiency (W_net, η_t, η_ex) as evaluation criteria, an economic model is established… More >

Abdul Moiz¹, Malik Shahzaib¹, Abdul Ghafoor Memon¹, Laveet Kumar², Mamdouh El Haj Assad^3,*

Energy Engineering, Vol.121, No.11, pp. 3095-3120, 2024, DOI:10.32604/ee.2024.051214 - 21 October 2024

Abstract To meet the escalating electricity demand and rising fuel costs, along with notable losses in power transmission, exploring alternative solutions is imperative. Gas turbines demonstrate high efficiency under ideal International Organization for Standardization (ISO) conditions but face challenges during summer when ambient temperatures reach 40°C. To enhance performance, the proposal suggests cooling inlet air by 15°C using a vapor absorption chiller (VAC), utilizing residual exhaust gases from a combined cycle power plant (CCPP) to maximize power output. Additionally, diverting a portion of exhaust gases to drive an organic Rankine cycle (ORC) for supplementary power generation… More >

Tajim Md. Niamat Ullah Akhund^1,2,*, Waleed M. Al-Nuwaiser³

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3485-3506, 2024, DOI:10.32604/cmc.2024.054222 - 12 September 2024

Abstract This study explores the impact of hyperparameter optimization on machine learning models for predicting cardiovascular disease using data from an IoST (Internet of Sensing Things) device. Ten distinct machine learning approaches were implemented and systematically evaluated before and after hyperparameter tuning. Significant improvements were observed across various models, with SVM and Neural Networks consistently showing enhanced performance metrics such as F1-Score, recall, and precision. The study underscores the critical role of tailored hyperparameter tuning in optimizing these models, revealing diverse outcomes among algorithms. Decision Trees and Random Forests exhibited stable performance throughout the evaluation. While More >

Bingrui Ju^1,2, Wenxiang Sun², Wenzhen Qu^1,2,*, Yan Gu²

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 267-280, 2024, DOI:10.32604/cmes.2024.052159 - 20 August 2024

Abstract In this study, we propose an efficient numerical framework to attain the solution of the extended Fisher-Kolmogorov (EFK) problem. The temporal derivative in the EFK equation is approximated by utilizing the Crank-Nicolson scheme. Following temporal discretization, the generalized finite difference method (GFDM) with supplementary nodes is utilized to address the nonlinear boundary value problems at each time node. These supplementary nodes are distributed along the boundary to match the number of boundary nodes. By incorporating supplementary nodes, the resulting nonlinear algebraic equations can effectively satisfy the governing equation and boundary conditions of the EFK equation. More >

Jiawen Li^1,2, Yuesheng Huang¹, Yayi Lu¹, Leijun Wang^1,*, Yongqi Ren¹, Rongjun Chen¹

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1581-1599, 2024, DOI:10.32604/cmc.2024.052666 - 18 July 2024

Abstract In the context of the accelerated pace of daily life and the development of e-commerce, online shopping is a mainstream way for consumers to access products and services. To understand their emotional expressions in facing different shopping experience scenarios, this paper presents a sentiment analysis method that combines the e-commerce review keyword-generated image with a hybrid machine learning-based model, in which the Word2Vec-TextRank is used to extract keywords that act as the inputs for generating the related images by generative Artificial Intelligence (AI). Subsequently, a hybrid Convolutional Neural Network and Support Vector Machine (CNN-SVM) model… More >

Abdul Sattar Laghari¹, Mohammad Waqas Chandio¹, Laveet Kumar^2,*, Mamdouh El Haj Assad³

Energy Engineering, Vol.121, No.8, pp. 2023-2038, 2024, DOI:10.32604/ee.2024.051082 - 19 July 2024

Abstract The selection of working fluid significantly impacts the geothermal ORC’s Efficiency. Using a mixture as a working fluid is a strategy to improve the output of geothermal ORC. In the current study, modelling and thermodynamic analysis of ORC, using geothermal as a heat source, is carried out at fixed operating conditions. The model is simulated in the Engineering Equation Solver (EES). An environment-friendly mixture of fluids, i.e., R245fa/R600a, with a suitable mole fraction, is used as the operating fluid. The mixture provided the most convenient results compared to the pure working fluid under fixed operating More >

Yongguo Li^1,2, Caiyin Xu^1,2,*, Can Qin^1,2, Dingjian Zheng^1,2

FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.6, pp. 1251-1263, 2024, DOI:10.32604/fdmp.2023.044804 - 27 June 2024

Abstract The so-called ORC (Organic Rankine Cycle) heat recovery technology has attracted much attention with regard to medium and low temperature waste heat recovery. In the present study, it is applied to a Tesla turbine. At the same time, the effects of the disc speed, diameter and inter-disc gap on the internal flow field and output power of the turbine are also investigated by means of CFD (Computational Fluid Dynamics) numerical simulation, by which the pressure, velocity, and output efficiency of the internal flow field are obtained under different internal and external conditions. The highest efficiency More >

Simona-Vasilica Oprea^*, Adela Bâra

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3827-3853, 2024, DOI:10.32604/cmc.2024.051598 - 20 June 2024

Abstract The potential of text analytics is revealed by Machine Learning (ML) and Natural Language Processing (NLP) techniques. In this paper, we propose an NLP framework that is applied to multiple datasets to detect malicious Uniform Resource Locators (URLs). Three categories of features, both ML and Deep Learning (DL) algorithms and a ranking schema are included in the proposed framework. We apply frequency and prediction-based embeddings, such as hash vectorizer, Term Frequency-Inverse Dense Frequency (TF-IDF) and predictors, word to vector-word2vec (continuous bag of words, skip-gram) from Google, to extract features from text. Further, we apply more… More >

Razieh Keshavarzi, Mehdi Jahangiri^*

Energy Engineering, Vol.121, No.6, pp. 1381-1401, 2024, DOI:10.32604/ee.2024.050029 - 21 May 2024

Abstract Nowadays, the use of renewable energies, especially wind, solar, and biomass, is essential as an effective solution to address global environmental and economic challenges. Therefore, the current study examines the energy-economic-environmental analysis of off-grid electricity generation systems using solar panels, wind turbines, and biomass generators in various weather conditions in Iran. Simulations over 25 years were conducted using HOMER v2.81 software, aiming to determine the potential of each region and find the lowest cost of electricity production per kWh. In the end, to identify the most suitable location, the Technique for Order Preference by Similarity… More > Graphic Abstract

Zhonghao Guo^*, Xinyue Xu, Xiangxian Chen

Computer Systems Science and Engineering, Vol.48, No.2, pp. 479-509, 2024, DOI:10.32604/csse.2023.043932 - 19 March 2024

Abstract In software testing, the quality of test cases is crucial, but manual generation is time-consuming. Various automatic test case generation methods exist, requiring careful selection based on program features. Current evaluation methods compare a limited set of metrics, which does not support a larger number of metrics or consider the relative importance of each metric to the final assessment. To address this, we propose an evaluation tool, the Test Case Generation Evaluator (TCGE), based on the learning to rank (L2R) algorithm. Unlike previous approaches, our method comprehensively evaluates algorithms by considering multiple metrics, resulting in… More >