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  • Open Access

    ARTICLE

    Comparative Investigations on Fracture Toughness and Damping Response of Fabric Reinforced Epoxy Composites

    GAURAV AGARWAL

    Journal of Polymer Materials, Vol.39, No.3-4, pp. 255-267, 2022, DOI:10.32381/JPM.2022.39.3-4.6

    Abstract Studies were conducted to observe the effect of fracture toughness and damping response on fabric reinforced epoxy polymer composites. The samples of glass fabric, kevlar fabric and carbon fabric having 15wt%, 25wt%, 35wt%, 45wt% and 55wt % fabric content were prepared and tested following ASTM standards. Fracture toughness, peak load and increase in energy absorption are determined for the fabric-epoxy composites. Effect of temperature on storage modulus, loss modulus and tan delta values for various percentages of fabric epoxy composites are noticed and corresponding damping response behaviour is determined. The results revealed that reduction in strength at higher percentage of… More >

  • Open Access

    ARTICLE

    An Enhanced Ensemble-Based Long Short-Term Memory Approach for Traffic Volume Prediction

    Duy Quang Tran1, Huy Q. Tran2,*, Minh Van Nguyen3

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3585-3602, 2024, DOI:10.32604/cmc.2024.047760

    Abstract With the advancement of artificial intelligence, traffic forecasting is gaining more and more interest in optimizing route planning and enhancing service quality. Traffic volume is an influential parameter for planning and operating traffic structures. This study proposed an improved ensemble-based deep learning method to solve traffic volume prediction problems. A set of optimal hyperparameters is also applied for the suggested approach to improve the performance of the learning process. The fusion of these methodologies aims to harness ensemble empirical mode decomposition’s capacity to discern complex traffic patterns and long short-term memory’s proficiency in learning temporal relationships. Firstly, a dataset for… More >

  • Open Access

    ARTICLE

    CONJUGATE HEAT TRANSFER FROM A FLAT PLATE WITH SHOWER HEAD IMPINGING JETS

    Rajesh Kumar Panda, B.V.S.S.S. Prasad*

    Frontiers in Heat and Mass Transfer, Vol.2, No.1, pp. 1-10, 2011, DOI:10.5098/hmt.v2.1.3008

    Abstract Conjugate heat transfer from a flat circular disk is investigated both computationally and experimentally with a constant heat flux imposed on its bottom surface and a shower head of air jets impinging on the top surface. The shower head consists of a central jet surrounded by four neighboring perimeter jets. Measured temperature data at twelve locations within the plate are compared with the conjugate heat transfer result obtained at the same locations computationally by Shear Stress Transport (SST) κ-ω turbulence model. The spacing to orifice diameter ratio (H/d = 1 to 4), the jet Reynolds number (7115 to 10674) and… More >

  • Open Access

    ARTICLE

    Computational Verification of Low-Frequency Broadband Noise from Wind Turbine Blades Using Semi-Empirical Methods

    Vasishta Bhargava Nukala*, Chinmaya Prasad Padhy

    Sound & Vibration, Vol.58, pp. 133-150, 2024, DOI:10.32604/sv.2024.047762

    Abstract A significant aerodynamic noise from wind turbines arises when the rotating blades interact with turbulent flows. Though the trailing edge of the blade is an important source of noise at high frequencies, the present work deals with the influence of turbulence distortion on leading edge noise from wind turbine blades which becomes significant in low-frequency regions. Four quasi-empirical methods are studied to verify the accuracy of turbulent inflow noise predicted at low frequencies for a 2 MW horizontal axis wind turbine. Results have shown that all methods exhibited a downward linear trend in noise spectra for a given mean wind… More >

  • Open Access

    ARTICLE

    An Empirical Study on the Effectiveness of Adversarial Examples in Malware Detection

    Younghoon Ban, Myeonghyun Kim, Haehyun Cho*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3535-3563, 2024, DOI:10.32604/cmes.2023.046658

    Abstract Antivirus vendors and the research community employ Machine Learning (ML) or Deep Learning (DL)-based static analysis techniques for efficient identification of new threats, given the continual emergence of novel malware variants. On the other hand, numerous researchers have reported that Adversarial Examples (AEs), generated by manipulating previously detected malware, can successfully evade ML/DL-based classifiers. Commercial antivirus systems, in particular, have been identified as vulnerable to such AEs. This paper firstly focuses on conducting black-box attacks to circumvent ML/DL-based malware classifiers. Our attack method utilizes seven different perturbations, including Overlay Append, Section Append, and Break Checksum, capitalizing on the ambiguities present… More >

  • Open Access

    ARTICLE

    Parental Educational Expectations, Academic Pressure, and Adolescent Mental Health: An Empirical Study Based on CEPS Survey Data

    Tao Xu1,*, Fangqiang Zuo1, Kai Zheng2,*

    International Journal of Mental Health Promotion, Vol.26, No.2, pp. 93-103, 2024, DOI:10.32604/ijmhp.2023.043226

    Abstract Background: This study aimed to investigate the relationship between parental educational expectations and adolescent mental health problems, with academic pressure as a moderating variable. Methods: This study was based on the baseline data of the China Education Panel Survey, which was collected within one school year during 2013–2014. It included 19,958 samples from seventh and ninth graders, who ranged from 11 to 18 years old. After removing missing values and conducting relevant data processing, the effective sample size for analysis was 16344. The OLS (Ordinary Least Squares) multiple linear regression analysis was used to examine the relationship between parental educational… More >

  • Open Access

    ARTICLE

    Evaluating the Efficacy of Latent Variables in Mitigating Data Poisoning Attacks in the Context of Bayesian Networks: An Empirical Study

    Shahad Alzahrani1, Hatim Alsuwat2, Emad Alsuwat3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1635-1654, 2024, DOI:10.32604/cmes.2023.044718

    Abstract Bayesian networks are a powerful class of graphical decision models used to represent causal relationships among variables. However, the reliability and integrity of learned Bayesian network models are highly dependent on the quality of incoming data streams. One of the primary challenges with Bayesian networks is their vulnerability to adversarial data poisoning attacks, wherein malicious data is injected into the training dataset to negatively influence the Bayesian network models and impair their performance. In this research paper, we propose an efficient framework for detecting data poisoning attacks against Bayesian network structure learning algorithms. Our framework utilizes latent variables to quantify… More >

  • Open Access

    ARTICLE

    C-CORE: Clustering by Code Representation to Prioritize Test Cases in Compiler Testing

    Wei Zhou1, Xincong Jiang2,*, Chuan Qin2

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 2069-2093, 2024, DOI:10.32604/cmes.2023.043248

    Abstract Edge devices, due to their limited computational and storage resources, often require the use of compilers for program optimization. Therefore, ensuring the security and reliability of these compilers is of paramount importance in the emerging field of edge AI. One widely used testing method for this purpose is fuzz testing, which detects bugs by inputting random test cases into the target program. However, this process consumes significant time and resources. To improve the efficiency of compiler fuzz testing, it is common practice to utilize test case prioritization techniques. Some researchers use machine learning to predict the code coverage of test… More >

  • Open Access

    PROCEEDINGS

    Damping Properties in Gradient Nano-Grained Metals

    Sheng Qian1, Qi Tong1,*

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.27, No.3, pp. 1-1, 2023, DOI:10.32604/icces.2023.010116

    Abstract Applications such as aircrafts and electronic devices require the noise and vibration reduction without much extra burden, such as extra damping systems. High damping metallic materials that exhibit the ability to dissipate mechanical energy are potential candidates in these application via directly being part of the functional components, such as the frame materials. The energy damping in polycrystalline metals depends on the activities of defects such as dislocation and grain boundary. However, operating defects has the opposite effect on strength and damping capacity. In the quest for high damping metals, maintaining the level of strength is desirable in practice. In… More >

  • Open Access

    ARTICLE

    Developing Transparent IDS for VANETs Using LIME and SHAP: An Empirical Study

    Fayaz Hassan1,*, Jianguo Yu1, Zafi Sherhan Syed2, Arif Hussain Magsi3, Nadeem Ahmed4

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3185-3208, 2023, DOI:10.32604/cmc.2023.044650

    Abstract Vehicular Ad-hoc Networks (VANETs) are mobile ad-hoc networks that use vehicles as nodes to create a wireless network. Whereas VANETs offer many advantages over traditional transportation networks, ensuring security in VANETs remains a significant challenge due to the potential for malicious attacks. This study addresses the critical issue of security in VANETs by introducing an intelligent Intrusion Detection System (IDS) that merges Machine Learning (ML)–based attack detection with Explainable AI (XAI) explanations. This study ML pipeline involves utilizing correlation-based feature selection followed by a Random Forest (RF) classifier that achieves a classification accuracy of 100% for the binary classification task… More >

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