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

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 >

Karam H. Mohammed¹, Ashraf E. Al-Mirani¹, Bashar Mahmood Ali², Omar Rafae Alomar^1,*

Frontiers in Heat and Mass Transfer, Vol.22, No.3, pp. 839-854, 2024, DOI:10.32604/fhmt.2024.052671

Abstract This work involves an experimental study on the performance of automobile air-conditioning systems by adding AlO nanoparticles to oil compressors to investigate their impacts on the enhancement of the speed cooling of refrigeration systems and to compare it with the system operated using only oil. The AlO nanoparticles have been added to the oil compressor for different ranges of mass concentration (Ø = 0.1%, Ø = 0.15% and Ø = 0.2%). The stability of AlO nanoparticles has been tested by direct observation for different time periods. The results indicated that the air conditioning system that More >

Shuai Li¹, Xiaodong Zhao^1,2,*, Jinghu Zhou¹, Xiyue Wang¹

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2587-2611, 2024, DOI:10.32604/cmes.2024.052203

Abstract Accurate quantification of the uncertainty in the mechanical characteristics of dielectric solids is crucial for advancing their application in high-precision technological domains, necessitating the development of robust computational methods. This paper introduces a Conditional Generation Adversarial Network Isogeometric Analysis (CGAN-IGA) to assess the uncertainty of dielectric solids’ mechanical characteristics. IGA is utilized for the precise computation of electric potentials in dielectric, piezoelectric, and flexoelectric materials, leveraging its advantage of integrating seamlessly with Computer-Aided Design (CAD) models to maintain exact geometrical fidelity. The CGAN method is highly efficient in generating models for piezoelectric and flexoelectric materials, More >

DAN GUO¹, XI XIA^2,*, JIAN YANG^1,*

BIOCELL, Vol.48, No.7, pp. 1023-1036, 2024, DOI:10.32604/biocell.2024.051095

Abstract Liver regeneration and the development of effective therapies for liver failure remain formidable challenges in modern medicine. In recent years, the utilization of 3D cell-based strategies has emerged as a promising approach for addressing these urgent clinical requirements. This review provides a thorough analysis of the application of 3D cell-based approaches to liver regeneration and their potential impact on patients with end-stage liver failure. Here, we discuss various 3D culture models that incorporate hepatocytes and stem cells to restore liver function and ameliorate the consequences of liver failure. Furthermore, we explored the challenges in transitioning More >

Fatma S. Alrayes¹, Mohammed Zakariah², Syed Umar Amin^3,*, Zafar Iqbal Khan³, Jehad Saad Alqurni⁴

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4319-4347, 2024, DOI:10.32604/cmc.2024.050586

Abstract Intrusion detection systems (IDS) are essential in the field of cybersecurity because they protect networks from a wide range of online threats. The goal of this research is to meet the urgent need for small-footprint, highly-adaptable Network Intrusion Detection Systems (NIDS) that can identify anomalies. The NSL-KDD dataset is used in the study; it is a sizable collection comprising 43 variables with the label’s “attack” and “level.” It proposes a novel approach to intrusion detection based on the combination of channel attention and convolutional neural networks (CNN). Furthermore, this dataset makes it easier to conduct… More >

Amerah Alabrah^*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3897-3912, 2024, DOI:10.32604/cmc.2024.048528

Abstract The emergence of digital networks and the wide adoption of information on internet platforms have given rise to threats against users’ private information. Many intruders actively seek such private data either for sale or other inappropriate purposes. Similarly, national and international organizations have country-level and company-level private information that could be accessed by different network attacks. Therefore, the need for a Network Intruder Detection System (NIDS) becomes essential for protecting these networks and organizations. In the evolution of NIDS, Artificial Intelligence (AI) assisted tools and methods have been widely adopted to provide effective solutions. However,… More >

Widad Elbakri¹, Maheyzah Md. Siraj^1,*, Bander Ali Saleh Al-rimy¹, Sultan Noman Qasem², Tawfik Al-Hadhrami³

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3725-3756, 2024, DOI:10.32604/cmc.2024.048105

Abstract Cloud computing environments, characterized by dynamic scaling, distributed architectures, and complex workloads, are increasingly targeted by malicious actors. These threats encompass unauthorized access, data breaches, denial-of-service attacks, and evolving malware variants. Traditional security solutions often struggle with the dynamic nature of cloud environments, highlighting the need for robust Adaptive Cloud Intrusion Detection Systems (CIDS). Existing adaptive CIDS solutions, while offering improved detection capabilities, often face limitations such as reliance on approximations for change point detection, hindering their precision in identifying anomalies. This can lead to missed attacks or an abundance of false alarms, impacting overall… More >

Yang Cao^*, Xuhui Meng

Frontiers in Heat and Mass Transfer, Vol.22, No.2, pp. 583-595, 2024, DOI:10.32604/fhmt.2024.049111

Abstract In this paper, a new approach called the Eulerian species method was proposed for simulating the convective and/or boiling heat transfer of nanofluids. The movement of nanoparticles in nanofluids is tracked by the species transport equation, and the boiling process of nanofluids is computed by the Eulerian multiphase method coupled with the RPI boiling model. The validity of the species transport equation for simulating nanoparticles movement was verified by conducting a simulation of nanofluids convective heat transfer. Simulation results of boiling heat transfer of nanofluids were obtained by using the commercial CFD software ANSYS Fluent More >

Kadhum Audaa Jehhef¹, Ali J. Ali², Salah H. Abid Aun¹, Akram H. Abed^3,*

Frontiers in Heat and Mass Transfer, Vol.22, No.2, pp. 557-581, 2024, DOI:10.32604/fhmt.2024.047814

Abstract The cavity with lid-driven is greatly used in mixing, coating, and drying applications and is a substantial issue in the study of thermal performance rate and fluid field. A numerical approach is presented to study the thermal distribution and passage of fluid in a lid-driven cavity with an upper oscillating surface and an attached baffle. The walls of a cavity at the left and right were maintained at 350 and 293 K, respectively. The upper oscillating surface was equipped with a variable height to baffle to increase the convection of the three kinds of TiO,… More >

Farah Mohammad^1,*, Saad Al-Ahmadi², Jalal Al-Muhtadi^1,2

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 3175-3192, 2024, DOI:10.32604/cmc.2023.042873

Abstract Electricity theft is a widespread non-technical issue that has a negative impact on both power grids and electricity users. It hinders the economic growth of utility companies, poses electrical risks, and impacts the high energy costs borne by consumers. The development of smart grids is crucial for the identification of power theft since these systems create enormous amounts of data, including information on client consumption, which may be used to identify electricity theft using machine learning and deep learning techniques. Moreover, there also exist different solutions such as hardware-based solutions to detect electricity theft that… More >