Abir Abboud¹, Zakaria Triki¹, Rachid Djeffal², Sidi Mohammed El Amine Bekkouche², Hichem Tahraoui^1,3,4, Abdeltif Amrane⁴, Aymen Amin Assadi⁵, Lotfi Khozami⁵, Jie Zhang^6,*

Frontiers in Heat and Mass Transfer, Vol.22, No.3, pp. 769-789, 2024, DOI:10.32604/fhmt.2024.050330

Abstract As buildings evolve to meet the challenges of energy efficiency and indoor comfort, phase change materials (PCM) emerge as a promising solution due to their ability to store and release latent heat. This paper explores the transformative impact of incorporating PCM on the hygrothermal dynamics of multi-zone constructions. The study focuses on analyzing heat transfer, particularly through thermal conduction, in a wall containing PCM. A novel approach was proposed, wherein the studied system (sensitive balance) interacts directly with a latent balance to realistically define the behavior of specific humidity and mass flow rates. In addition, More >

Jihoon Sung¹, Yeunwoong Kyung^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2891-2911, 2024, DOI:10.32604/cmes.2024.051772

Abstract Industrial wireless mesh networks (WMNs) have been widely deployed in various industrial sectors, providing services such as manufacturing process monitoring, equipment control, and sensor data collection. A notable characteristic of industrial WMNs is their distinct traffic pattern, where the majority of traffic flows originate from mesh nodes and are directed towards mesh gateways. In this context, this paper adopts and revisits a routing algorithm known as ALFA (autonomous load-balancing field-based anycast routing), tailored specifically for anycast (one-to-one-of-many) networking in WMNs, where traffic flows can be served through any one of multiple gateways. In essence, the… More >

Iqbal M. Batiha^1,2,*, Rania Saadeh³, Iqbal H. Jebril¹, Ahmad Qazza³, Abeer A. Al-Nana⁴, Shaher Momani^2,5

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2729-2745, 2024, DOI:10.32604/cmes.2024.051588

Abstract The aim of this research is to demonstrate a novel scheme for approximating the Riemann-Liouville fractional integral operator. This would be achieved by first establishing a fractional-order version of the -point Trapezoidal rule and then by proposing another fractional-order version of the -composite Trapezoidal rule. In particular, the so-called divided-difference formula is typically employed to derive the -point Trapezoidal rule, which has accordingly been used to derive a more accurate fractional-order formula called the -composite Trapezoidal rule. Additionally, in order to increase the accuracy of the proposed approximations by reducing the true errors, we incorporate More >

Jifu Song, Zhibin Guan, Maojiang Li, Sha Sha, Chao Song, Zhiwei Gao, Yongli Zhao

Oncology Research, Vol.26, No.6, pp. 847-856, 2018, DOI:10.3727/096504017X15016337254632

Abstract MicroRNAs (miRNAs) have emerged as pivotal regulators of the development and progression of gastric cancer. Studies have shown that miR-154 is a novel cancer-associated miRNA involved in various cancers. However, the role of miR-154 in gastric cancer remains unknown. Here we aimed to investigate the biological function and the potential molecular mechanism of miR-154 in gastric cancer. We found that miR-154 was significantly downregulated in gastric cancer tissues and cell lines. The overexpression of miR-154 significantly repressed the growth and invasion of gastric cancer cells. Bioinformatics analysis and Dual-Luciferase Reporter Assay data showed that miR-154… More >

Ali S. Alghamdi^1,*, Mohamed A. Zohdy², Saad Aldoihi^3,4

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1339-1370, 2024, DOI:10.32604/cmes.2024.048839

Abstract In the contemporary era, the global expansion of electrical grids is propelled by various renewable energy sources (RESs). Efficient integration of stochastic RESs and optimal power flow (OPF) management are critical for network optimization. This study introduces an innovative solution, the Gaussian Bare-Bones Levy Cheetah Optimizer (GBBLCO), addressing OPF challenges in power generation systems with stochastic RESs. The primary objective is to minimize the total operating costs of RESs, considering four functions: overall operating costs, voltage deviation management, emissions reduction, voltage stability index (VSI) and power loss mitigation. Additionally, a carbon tax is included in… More >

Sohaib Latif^1,*, M. Saad Bin Ilyas¹, Azhar Imran², Hamad Ali Abosaq³, Abdulaziz Alzubaidi⁴, Vincent Karovič Jr.⁵

Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 353-379, 2024, DOI:10.32604/iasc.2024.047080

Abstract The Internet of Things (IoT) is growing rapidly and impacting almost every aspect of our lives, from wearables and healthcare to security, traffic management, and fleet management systems. This has generated massive volumes of data and security, and data privacy risks are increasing with the advancement of technology and network connections. Traditional access control solutions are inadequate for establishing access control in IoT systems to provide data protection owing to their vulnerability to single-point OF failure. Additionally, conventional privacy preservation methods have high latency costs and overhead for resource-constrained devices. Previous machine learning approaches were… More >

Sujithra Sankar^1,*, S. Sathyalakshmi²

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 3111-3138, 2024, DOI:10.32604/cmc.2024.048408

Abstract In the era of advanced machine learning techniques, the development of accurate predictive models for complex medical conditions, such as thyroid cancer, has shown remarkable progress. Accurate predictive models for thyroid cancer enhance early detection, improve resource allocation, and reduce overtreatment. However, the widespread adoption of these models in clinical practice demands predictive performance along with interpretability and transparency. This paper proposes a novel association-rule based feature-integrated machine learning model which shows better classification and prediction accuracy than present state-of-the-art models. Our study also focuses on the application of SHapley Additive exPlanations (SHAP) values as… More >

Sharaf J. Malebary^*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1301-1317, 2024, DOI:10.32604/cmc.2024.047917

Abstract Brain tumors are a pressing public health concern, characterized by their high mortality and morbidity rates. Nevertheless, the manual segmentation of brain tumors remains a laborious and error-prone task, necessitating the development of more precise and efficient methodologies. To address this formidable challenge, we propose an advanced approach for segmenting brain tumor Magnetic Resonance Imaging (MRI) images that harnesses the formidable capabilities of deep learning and convolutional neural networks (CNNs). While CNN-based methods have displayed promise in the realm of brain tumor segmentation, the intricate nature of these tumors, marked by irregular shapes, varying sizes,… More >

Ruchun Jia, Jianwei Zhang^*, Yi Lin

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2391-2418, 2024, DOI:10.32604/cmc.2024.044149

Abstract With the popularization of the Internet and the development of technology, cyber threats are increasing day by day. Threats such as malware, hacking, and data breaches have had a serious impact on cybersecurity. The network security environment in the era of big data presents the characteristics of large amounts of data, high diversity, and high real-time requirements. Traditional security defense methods and tools have been unable to cope with the complex and changing network security threats. This paper proposes a machine-learning security defense algorithm based on metadata association features. Emphasize control over unauthorized users through… More >

Chein-Shan Liu¹, Jian-Hung Shen², Chung-Lun Kuo¹, Yung-Wei Chen^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1317-1335, 2024, DOI:10.32604/cmes.2023.030618

Abstract This study sets up two new merit functions, which are minimized for the detection of real eigenvalue and complex eigenvalue to address nonlinear eigenvalue problems. For each eigen-parameter the vector variable is solved from a nonhomogeneous linear system obtained by reducing the number of eigen-equation one less, where one of the nonzero components of the eigenvector is normalized to the unit and moves the column containing that component to the right-hand side as a nonzero input vector. 1D and 2D golden section search algorithms are employed to minimize the merit functions to locate real and… More >