Xiaochu Luo¹, Xiaobing Qi², Zhao Luo³, Zhonghao Li⁴, Ruiquan Liao¹, Xingkai Zhang^1,*

FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.6, pp. 1233-1249, 2024, DOI:10.32604/fdmp.2023.045737

Abstract Conventional conductivity methods for measuring the void fraction in gas-liquid multiphase systems are typically affected by accuracy problems due to the presence of fluid flow and salinity. This study presents a novel approach for determining the void fraction based on a reciprocating dynamic conductivity probe used to measure the liquid film thickness under forced annular-flow conditions. The measurement system comprises a cyclone, a conductivity probe, a probe reciprocating device, and a data acquisition and processing system. This method ensures that the flow pattern is adjusted to a forced annular flow, thereby minimizing the influence of More >

Marya Iqbal¹, Yaser Hafeez¹, Nabil Almashfi², Amjad Alsirhani³, Faeiz Alserhani⁴, Sadia Ali¹, Mamoona Humayun^5,*, Muhammad Jamal⁶

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5031-5049, 2024, DOI:10.32604/cmc.2024.051371

Abstract Embracing software product lines (SPLs) is pivotal in the dynamic landscape of contemporary software development. However, the flexibility and global distribution inherent in modern systems pose significant challenges to managing SPL variability, underscoring the critical importance of robust cybersecurity measures. This paper advocates for leveraging machine learning (ML) to address variability management issues and fortify the security of SPL. In the context of the broader special issue theme on innovative cybersecurity approaches, our proposed ML-based framework offers an interdisciplinary perspective, blending insights from computing, social sciences, and business. Specifically, it employs ML for demand analysis, More >

Djeldjli Halima^1,*, Benatiallah Djelloul¹, Ghasri Mehdi², Tanougast Camel³, Benatiallah Ali⁴, Benabdelkrim Bouchra¹

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4725-4740, 2024, DOI:10.32604/cmc.2024.051002

Abstract When designing solar systems and assessing the effectiveness of their many uses, estimating sun irradiance is a crucial first step. This study examined three approaches (ANN, GA-ANN, and ANFIS) for estimating daily global solar radiation (GSR) in the south of Algeria: Adrar, Ouargla, and Bechar. The proposed hybrid GA-ANN model, based on genetic algorithm-based optimization, was developed to improve the ANN model. The GA-ANN and ANFIS models performed better than the standalone ANN-based model, with GA-ANN being better suited for forecasting in all sites, and it performed the best with the best values in the… More > Graphic Abstract

Siwan Noh¹, Kyung-Hyune Rhee^2,*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3805-3826, 2024, DOI:10.32604/cmc.2024.050949

Abstract In Decentralized Machine Learning (DML) systems, system participants contribute their resources to assist others in developing machine learning solutions. Identifying malicious contributions in DML systems is challenging, which has led to the exploration of blockchain technology. Blockchain leverages its transparency and immutability to record the provenance and reliability of training data. However, storing massive datasets or implementing model evaluation processes on smart contracts incurs high computational costs. Additionally, current research on preventing malicious contributions in DML systems primarily focuses on protecting models from being exploited by workers who contribute incorrect or misleading data. However, less… More >

Mohammad Aldossary^1,*, Hatem A. Alharbi², Nasir Ayub³

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4603-4620, 2024, DOI:10.32604/cmc.2024.050862

Abstract Cloud computing has become increasingly popular due to its capacity to perform computations without relying on physical infrastructure, thereby revolutionizing computer processes. However, the rising energy consumption in cloud centers poses a significant challenge, especially with the escalating energy costs. This paper tackles this issue by introducing efficient solutions for data placement and node management, with a clear emphasis on the crucial role of the Internet of Things (IoT) throughout the research process. The IoT assumes a pivotal role in this study by actively collecting real-time data from various sensors strategically positioned in and around… More >

Zhenjiang Dong¹, Xin Ge¹, Yuehua Huang¹, Jiankuo Dong¹, Jiang Xu^2,*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4021-4044, 2024, DOI:10.32604/cmc.2024.049233

Abstract This paper presents a comprehensive exploration into the integration of Internet of Things (IoT), big data analysis, cloud computing, and Artificial Intelligence (AI), which has led to an unprecedented era of connectivity. We delve into the emerging trend of machine learning on embedded devices, enabling tasks in resource-limited environments. However, the widespread adoption of machine learning raises significant privacy concerns, necessitating the development of privacy-preserving techniques. One such technique, secure multi-party computation (MPC), allows collaborative computations without exposing private inputs. Despite its potential, complex protocols and communication interactions hinder performance, especially on resource-constrained devices. Efforts… More >

Chunhui Li^1,*, Hua Jiang²

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4491-4512, 2024, DOI:10.32604/cmc.2024.048431

Abstract In the context of enterprise systems, intrusion detection (ID) emerges as a critical element driving the digital transformation of enterprises. With systems spanning various sectors of enterprises geographically dispersed, the necessity for seamless information exchange has surged significantly. The existing cross-domain solutions are challenged by such issues as insufficient security, high communication overhead, and a lack of effective update mechanisms, rendering them less feasible for prolonged application on resource-limited devices. This study proposes a new cross-domain collaboration scheme based on federated chains to streamline the server-side workload. Within this framework, individual nodes solely engage in… More >

Quan Zhou^*, Haiyang Li

Energy Engineering, Vol.121, No.7, pp. 1781-1803, 2024, DOI:10.32604/ee.2024.049387

Abstract Renewable energy is becoming more attractive as traditional fossil fuels are rapidly depleted and expensive, and their use would release pollutants. Power systems that use both wind and solar energy are more reliable and efficient than those that utilize only one energy. Hybrid renewable energy systems (HRES) are viable for remote areas operating in standalone mode. This paper aims to present the state-of-the-art research on off-grid solar-wind hybrid energy systems over the last two decades. More than 1500 published articles extracted from the Web of Science are analyzed by bibliometric methods and processed by CiteSpace… More >

Xiaodan Wu¹, Jie Li^1,*, Yongbin Cai², Sihui Huang¹

Energy Engineering, Vol.121, No.7, pp. 1963-1990, 2024, DOI:10.32604/ee.2024.048477

Abstract Aiming at the problems of large energy consumption and serious pollution of winter heating existing in the rural buildings in Southern Xinjiang, a combined active-passive heating system was proposed, and the simulation software was used to optimize the parameters of the system, according to the parameters obtained from the optimization, a test platform was built and winter heating test was carried out. The simulation results showed that the thickness of the air layer of 75 mm, the total area of the vent holes of 0.24 m, and the thickness of the insulation layer of 120… More >

Yosr Laatiri, Habib Sammouda, Fadhel Aloulou^*

Journal of Renewable Materials, Vol.12, No.4, pp. 771-798, 2024, DOI:10.32604/jrm.2024.047022

Abstract This article aims to present the feasibility of storing thermal energy in buildings for solar water heating while maintaining the comfort environment for residential buildings. Our contribution is the creation of insulating composite panels made of bio-based phase change materials (bio-PCM is all from coconut oil), cement and renewable materials (treated wood fiber and organic clay). The inclusion of wood fibers improved the thermal properties; a simple 2% increase of wood fiber decreased the heat conductivity by approximately 23.42%. The issues of bio-PCM leakage in the cement mortar and a roughly 56.5% reduction in thermal… More > Graphic Abstract