Vladimir Konovalov^*

FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.11, pp. 2541-2563, 2024, DOI:10.32604/fdmp.2024.051219 - 28 October 2024

Abstract The regime of horizontal subcooled film boiling is characterized by the formation of a thin layer of vapor covering the surface of a flat horizontal heater. Based on the equations of motion of a viscous incompressible fluid and the equation of heat transfer, the stability of such a vapor film is investigated. The influence of the modulation of the gravity field caused by vertical vibrations of the heater of finite frequency, as well as a constant electric field applied normal to the surface of the heater, is taken into account. It is shown that in… More >

Jinyu Gu¹, Yingjun Wang^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.29, No.2, pp. 1-1, 2024, DOI:10.32604/icces.2024.010958

Abstract In engineering practice, most components are subjected to variable-amplitude cyclic loading, resulting in fatigue damage, which is one of the main forms of damage in engineering structures. Nonlinear damage rule is developed based on linear damage rule, which can predict the fatigue life of structures more accurately. Therefore, we present a topology optimization method considering nonlinear fatigue damage accumulation in the time domain. For the time domain, we adopted the rainflow counting method to evaluate the stress level generated by cyclic loading and the Basquin equation to describe the S-N curve. We applied Morrow's plastic… More >

Christina Khokhryakova^1,*, Konstantin Kostarev², Irina Mizeva³

FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.10, pp. 2205-2218, 2024, DOI:10.32604/fdmp.2024.051053 - 23 September 2024

Abstract Magnetic fluids, also known as ferrofluids, are versatile functional materials with a wide range of applications. These applications span from industrial uses such as vacuum seals, actuators, and acoustic devices to medical uses, including serving as contrast agents for magnetic resonance imaging (MRI), delivering medications to specific locations within the body, and magnetic hyperthermia for cancer treatment. The use of a non-wettable immiscible liquid substrate to support a layer of magnetic fluid opens up new possibilities for studying various fluid flows and related instabilities in multi-phase systems with both a free surface and an interface.… More > Graphic Abstract

Daekook Kang¹, Ramya Lakshmanaraj², Samayan Narayanamoorthy², Navaneethakrishnan Suganthi Keerthana Devi², Samayan Kalaiselvan³, Ranganathan Saraswathy⁴, Dragan Pamucar^5,6,7,*, Vladimir Simic^8,9

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4947-4972, 2024, DOI:10.32604/cmc.2024.055260 - 12 September 2024

Abstract This socialized environment among educated and developed people causes them to focus more on their appearance and health, which turns them towards medical-related treatments, leading us to discuss anti-aging treatment methods for each age group, particularly for urban people who are interested in this. Some anti-aging therapies are used to address the alterations brought on by aging in human life without the need for surgery or negative effects. Five anti-aging therapies such as microdermabrasion or dermabrasion, laser resurfacing anti-aging skin treatments, chemical peels, dermal fillers for aged skin, and botox injections are considered in this… More >

Changyu Liu¹, Hao Huang¹, Guogang Huang^2,*, Chunyin Wu¹, Yingqi Liang³

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4219-4242, 2024, DOI:10.32604/cmc.2024.053625 - 12 September 2024

Abstract Laboratory safety is a critical area of broad societal concern, particularly in the detection of abnormal actions. To enhance the efficiency and accuracy of detecting such actions, this paper introduces a novel method called TubeRAPT (Tubelet Transformer based on Adapter and Prefix Training Module). This method primarily comprises three key components: the TubeR network, an adaptive clustering attention mechanism, and a prefix training module. These components work in synergy to address the challenge of knowledge preservation in models pre-trained on large datasets while maintaining training efficiency. The TubeR network serves as the backbone for spatio-temporal… More >

Yangrong Chen^1,2, June Li^3,*, Yu Xia³, Ruiwen Zhang³, Lingling Li^1,2, Xiaoyu Li^1,2, Lin Ge^1,2

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2579-2609, 2024, DOI:10.32604/cmc.2024.053230 - 15 August 2024

Abstract Intelligent electronic devices (IEDs) are interconnected via communication networks and play pivotal roles in transmitting grid-related operational data and executing control instructions. In the context of the heightened security challenges within smart grids, IEDs pose significant risks due to inherent hardware and software vulnerabilities, as well as the openness and vulnerability of communication protocols. Smart grid security, distinct from traditional internet security, mainly relies on monitoring network security events at the platform layer, lacking an effective assessment mechanism for IEDs. Hence, we incorporate considerations for both cyber-attacks and physical faults, presenting security assessment indicators and… More > Graphic Abstract

Atif Mehmood^1,2, Zhonglong Zheng^1,*, Rizwan Khan¹, Ahmad Al Smadi³, Farah Shahid^1,2, Shahid Iqbal⁴, Mutasem K. Alsmadi⁵, Yazeed Yasin Ghadi⁶, Syed Aziz Shah⁸, Mostafa M. Ibrahim⁷

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2081-2098, 2024, DOI:10.32604/cmc.2024.046869 - 15 August 2024

Abstract Neuroimaging has emerged over the last few decades as a crucial tool in diagnosing Alzheimer’s disease (AD). Mild cognitive impairment (MCI) is a condition that falls between the spectrum of normal cognitive function and AD. However, previous studies have mainly used handcrafted features to classify MCI, AD, and normal control (NC) individuals. This paper focuses on using gray matter (GM) scans obtained through magnetic resonance imaging (MRI) for the diagnosis of individuals with MCI, AD, and NC. To improve classification performance, we developed two transfer learning strategies with data augmentation (i.e., shear range, rotation, zoom… More >

Tingting Su¹, Jia Wang^1,*, Wei Hu^2,*, Gaoqiang Dong¹, Jeon Gwanggil³

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4433-4448, 2024, DOI:10.32604/cmc.2024.051535 - 20 June 2024

Abstract Along with the progression of Internet of Things (IoT) technology, network terminals are becoming continuously more intelligent. IoT has been widely applied in various scenarios, including urban infrastructure, transportation, industry, personal life, and other socio-economic fields. The introduction of deep learning has brought new security challenges, like an increment in abnormal traffic, which threatens network security. Insufficient feature extraction leads to less accurate classification results. In abnormal traffic detection, the data of network traffic is high-dimensional and complex. This data not only increases the computational burden of model training but also makes information extraction more… More >

Yuanyao Lu^1,*, Wei Chen², Zhanhe Yu¹, Jingxuan Wang¹, Chaochao Yang²

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5051-5066, 2024, DOI:10.32604/cmc.2024.050865 - 20 June 2024

Abstract With the rapid advancement of social economies, intelligent transportation systems are gaining increasing attention. Central to these systems is the detection of abnormal vehicle behavior, which remains a critical challenge due to the complexity of urban roadways and the variability of external conditions. Current research on detecting abnormal traffic behaviors is still nascent, with significant room for improvement in recognition accuracy. To address this, this research has developed a new model for recognizing abnormal traffic behaviors. This model employs the R3D network as its core architecture, incorporating a dense block to facilitate feature reuse. This… More >

Yunfeng Cai¹, Ran Qin¹, Jin Tang¹, Long Zhang¹, Xiaotian Bi¹, Qing Yang^2,*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4979-4994, 2024, DOI:10.32604/cmc.2024.050435 - 20 June 2024

Abstract Electric power training is essential for ensuring the safety and reliability of the system. In this study, we introduce a novel Abnormal Action Recognition (AAR) system that utilizes a Lightweight Pose Estimation Network (LPEN) to efficiently and effectively detect abnormal fall-down and trespass incidents in electric power training scenarios. The LPEN network, comprising three stages—MobileNet, Initial Stage, and Refinement Stage—is employed to swiftly extract image features, detect human key points, and refine them for accurate analysis. Subsequently, a Pose-aware Action Analysis Module (PAAM) captures the positional coordinates of human skeletal points in each frame. Finally, More >