Mo Zhou¹, Jingming Zhao¹, Zili Chen^2,*, Qiushi Fang¹, Hua Li¹, Zhaoyuan Wu²

Energy Engineering, Vol.121, No.10, pp. 3037-3053, 2024, DOI:10.32604/ee.2024.050842

Abstract With the continuous expansion of the scale of renewable energy installation, the demand for energy storage has increased significantly. However, there are significant differences in the value of energy storage in different scenarios, and the phenomenon of diminishing marginal benefits of energy storage is becoming more apparent. Therefore, the multi-dimensional value evolution trend of energy storage has become a key issue. This study selects indicators from three dimensions of energy storage: low-carbon emission reduction, smoothing wind and solar power fluctuations, and saving generation costs, quantifying the economic, environmental, and technical values of energy storage. This… More >

Suogui Shang¹, Kechao Gao¹, Qingbin Wang¹, Xinghua Zhang¹, Pengli Zhou^2,3,*, Jianhua Li^2,3, Peng Chu^2,3

Energy Engineering, Vol.121, No.9, pp. 2505-2524, 2024, DOI:10.32604/ee.2024.050094

Abstract Hydraulic fracturing is a mature and effective method for deep oil and gas production, which provides a foundation for deep oil and gas production. One of the key aspects of implementing hydraulic fracturing technology lies in understanding mechanics response characteristics of rocks in deep reservoirs under complex stress conditions. In this work, based on outcrop core samples, high-stress triaxial compression tests were designed to simulate the rock mechanics behavior of deep reservoirs in Bozhong Sag. Additionally, this study analyzes the deformation and damage law for rock under different stress conditions. Wherein, with a particular focus… More >

Zheyi Chen^1,, Liuchang Xu^1,, Hongting Zheng¹, Luyao Chen¹, Amr Tolba^2,3, Liang Zhao⁴, Keping Yu^5,*, Hailin Feng^1,*

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 1753-1808, 2024, DOI:10.32604/cmc.2024.052618

Abstract Since the 1950s, when the Turing Test was introduced, there has been notable progress in machine language intelligence. Language modeling, crucial for AI development, has evolved from statistical to neural models over the last two decades. Recently, transformer-based Pre-trained Language Models (PLM) have excelled in Natural Language Processing (NLP) tasks by leveraging large-scale training corpora. Increasing the scale of these models enhances performance significantly, introducing abilities like context learning that smaller models lack. The advancement in Large Language Models, exemplified by the development of ChatGPT, has made significant impacts both academically and industrially, capturing widespread… More >

Shoukai Lin^1,3,4,*, Yifan Xiong², Shichang Xu^1,2, Manegdebwaoaga Arthur Fabrice Kabore², Fan Lin⁵, Fuxiang Qiu^1,2,*

Phyton-International Journal of Experimental Botany, Vol.93, No.7, pp. 1485-1502, 2024, DOI:10.32604/phyton.2024.050990

Abstract The SWEET (sugar will eventually be exported transporter) family proteins are a recently identified class of sugar transporters that are essential for various physiological processes. Although the functions of the SWEET proteins have been identified in a number of species, to date, there have been no reports of the functions of the SWEET genes in woodland strawberries (Fragaria vesca). In this study, we identified 15 genes that were highly homologous to the A. thaliana AtSWEET genes and designated them as FvSWEET1–FvSWEET15. We then conducted a structural and evolutionary analysis of these 15 FvSWEET genes. The phylogenetic analysis enabled us… More >

Hongjiang Wang¹, Qingze Shen^2,*, Qin Dai¹, Yingcai Gao², Jing Gao², Tian Zhang^3,*

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 625-642, 2024, DOI:10.32604/cmc.2024.051757

Abstract Deep learning has emerged in many practical applications, such as image classification, fault diagnosis, and object detection. More recently, convolutional neural networks (CNNs), representative models of deep learning, have been used to solve fault detection. However, the current design of CNNs for fault detection of wind turbine blades is highly dependent on domain knowledge and requires a large amount of trial and error. For this reason, an evolutionary YOLOv8 network has been developed to automatically find the network architecture for wind turbine blade-based fault detection. YOLOv8 is a CNN-backed object detection model. Specifically, to reduce… More >

Lin Chen¹, Jiaming Zhu¹, Yuting Xu¹, Huanqin Zheng¹, Shen Su^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2373-2395, 2024, DOI:10.32604/cmes.2024.049996

Abstract In the information age, electronic documents (e-documents) have become a popular alternative to paper documents due to their lower costs, higher dissemination rates, and ease of knowledge sharing. However, digital copyright infringements occur frequently due to the ease of copying, which not only infringes on the rights of creators but also weakens their creative enthusiasm. Therefore, it is crucial to establish an e-document sharing system that enforces copyright protection. However, the existing centralized system has outstanding vulnerabilities, and the plagiarism detection algorithm used cannot fully detect the context, semantics, style, and other factors of the… More >

Javaid Ali¹, Armando Ciancio², Kashif Ali Khan³, Nauman Raza^4,5, Haci Mehmet Baskonus^6,*, Muhammad Luqman¹, Zafar-Ullah Khan⁷

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2275-2296, 2024, DOI:10.32604/cmes.2024.046923

Abstract This study proposes a structure-preserving evolutionary framework to find a semi-analytical approximate solution for a nonlinear cervical cancer epidemic (CCE) model. The underlying CCE model lacks a closed-form exact solution. Numerical solutions obtained through traditional finite difference schemes do not ensure the preservation of the model’s necessary properties, such as positivity, boundedness, and feasibility. Therefore, the development of structure-preserving semi-analytical approaches is always necessary. This research introduces an intelligently supervised computational paradigm to solve the underlying CCE model’s physical properties by formulating an equivalent unconstrained optimization problem. Singularity-free safe Padé rational functions approximate the mathematical More >

Song Deng^1,*, Yiming Yuan²

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3679-3697, 2024, DOI:10.32604/cmc.2024.052008

Abstract Malicious attacks against data are unavoidable in the interconnected, open and shared Energy Internet (EI), Intrusion tolerant techniques are critical to the data security of EI. Existing intrusion tolerant techniques suffered from problems such as low adaptability, policy lag, and difficulty in determining the degree of tolerance. To address these issues, we propose a novel adaptive intrusion tolerance model based on game theory that enjoys two-fold ideas: 1) it constructs an improved replica of the intrusion tolerance model of the dynamic equation evolution game to induce incentive weights; and 2) it combines a tournament competition More >

Yehao Long, Jing Zhong^*, Tongdi Zhang, Li Chen, Lijun Zhang^*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3435-3453, 2024, DOI:10.32604/cmc.2024.051629

Abstract Physical Vapor Deposited (PVD) TiAlN coatings are extensively utilized as protective layers for cutting tools, renowned for their excellent comprehensive performance. To optimize quality control of TiAlN coatings for cutting tools, a multi-scale simulation approach is proposed that encompasses the microstructure evolution of coatings considering the entire preparation and service lifecycle of PVD TiAlN coatings. This scheme employs phase-field simulation to capture the essential microstructure of the PVD-prepared TiAlN coatings. Moreover, cutting simulation is used to determine the service temperature experienced during cutting processes at varying rates. Cahn-Hilliard modeling is finally utilized to consume the More >

Chen Zhang¹, Liming Liu¹, Yufei Yang¹, Yu Sun¹, Jiaxu Ning², Yu Zhang³, Changsheng Zhang^1,4,*, Ying Guo⁴

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5201-5223, 2024, DOI:10.32604/cmc.2024.050863

Abstract The flying foxes optimization (FFO) algorithm, as a newly introduced metaheuristic algorithm, is inspired by the survival tactics of flying foxes in heat wave environments. FFO preferentially selects the best-performing individuals. This tendency will cause the newly generated solution to remain closely tied to the candidate optimal in the search area. To address this issue, the paper introduces an opposition-based learning-based search mechanism for FFO algorithm (IFFO). Firstly, this paper introduces niching techniques to improve the survival list method, which not only focuses on the adaptability of individuals but also considers the population’s crowding degree More >