Tianyi Li^1,2, Xin Gu², Qing Zhang^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 339-361, 2024, DOI:10.32604/cmes.2024.047566

Abstract This study proposes a comprehensive, coupled thermomechanical model that replaces local spatial derivatives in classical differential thermomechanical equations with nonlocal integral forms derived from the peridynamic differential operator (PDDO), eliminating the need for calibration procedures. The model employs a multi-rate explicit time integration scheme to handle varying time scales in multi-physics systems. Through simulations conducted on granite and ceramic materials, this model demonstrates its effectiveness. It successfully simulates thermal damage behavior in granite arising from incompatible mineral expansion and accurately calculates thermal crack propagation in ceramic slabs during quenching. To account for material heterogeneity, the model utilizes the Shuffle algorithm… More >

Liliane Nga¹, Benoit Ndiwe^1,2, Achille Bernard Biwolé¹, Antonio Pizzi^3,*, Jean Jalin Eyinga Biwole¹, Joseph Zobo Mfomo¹

Journal of Renewable Materials, Vol.12, No.3, pp. 553-568, 2024, DOI:10.32604/jrm.2023.046568

Abstract Extracts of plant origin, particularly tannins, are attracting growing interest for the sustainable development of materials in the industrial sector. The discovery of new tannins is therefore necessary. The aim of this work was to contribute to the understanding of the properties of Paraberlinia bifoliolata tannin by Matrix Assisted Laser Desorption Ionization Time of Flight Mass Spectroscopy MALDI-TOF/MS and Carbon 13 Nuclear Magnetic Resonance (¹³C NMR). The chemical composition of tannin extracted from Paraberlinia bifoliolata bark was determined, as was the mechanical strength of the resin hardened with Acacia nilotica extracts. Yield by successive water extraction was 35%. MALDI-TOF/MS analysis… More >

ZHAO JUN^a, LIU AIQIN^a, ZHOU HONG^a, LUO JUN^b,*, AND LIU YUAN^b,*

Journal of Polymer Materials, Vol.37, No.1-2, pp. 29-42, 2020, DOI:10.32381/JPM.2020.37.1-2.3

Abstract Heteroatoms (N, O, and F) and hydrogen groups are important elements for forming the H-bonds. It is well known that a large number of hydrogen groups are formed after curing reaction of epoxy. However, literatures about epoxy resins containing heteroaromatic ring and the H-bonds after cure reaction of the epoxy resins are seldom published. To bridge the gap, a kind of new epoxy monomer containing pyridine ring (EMP) has been synthesized in this work, andit is further cured by 4,4-diaminodiphenyl methane (DDM). The properties of cured EMP/ DDM are evaluated by DSC, DMA, and static contact angle measurement. Moreover, the… More >

Moran Wang^a, Bin-Yang Cao^b, Zeng-Yuan Guo^b,*

Frontiers in Heat and Mass Transfer, Vol.1, No.1, pp. 1-8, 2010, DOI:10.5098/hmt.v1.1.3004

Abstract The thermomass theory regards heat owning mass-energy duality, exhibiting energy-like features in conversion and mass-like features in transfer processes. The equivalent mass of thermal energy is determined by the mass-energy equivalence of Einstein, which therefore leads to the inertia of heat in transfer. In this work, we build up a thermomass gas model based on this theory to describe the fluid-flow-like heat conduction process in a medium. The equation of state and the governing equations for transport for the thermomass gas have been derived based on methodologies of the classical mechanics since the drift speed of thermomass gas is generally… More >

Jinbo Yang¹, Hai Huang¹, Lailai Yin², Jiaxing Qu³, Wanjuan Xie^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3085-3099, 2024, DOI:10.32604/cmes.2023.045417

Abstract Diagnosing multi-stage diseases typically requires doctors to consider multiple data sources, including clinical symptoms, physical signs, biochemical test results, imaging findings, pathological examination data, and even genetic data. When applying machine learning modeling to predict and diagnose multi-stage diseases, several challenges need to be addressed. Firstly, the model needs to handle multimodal data, as the data used by doctors for diagnosis includes image data, natural language data, and structured data. Secondly, privacy of patients’ data needs to be protected, as these data contain the most sensitive and private information. Lastly, considering the practicality of the model, the computational requirements should… More >

Yujing Ma^1,*, Leilei Chen^2,3, Haojie Lian^3,4, Zhongwang Wang^2,3

The International Conference on Computational & Experimental Engineering and Sciences, Vol.28, No.1, pp. 1-1, 2023, DOI:10.32604/icces.2023.010585

Abstract An efficient method of moments (MoM) based on polynomial chaos expansion(PCE) is applied to quickly calculate the electromagnetic scattering problems. The triangle basic functions are used to discretize the surface integral equations. The PCE is utilized to accelerate the MoM by constructing a surrogate model for univariate and bivariate analysis[1]. The mathematical expressions of the surrogate model for the radar cross-section (RCS) are established by considering uncertain parameters such as bistatic angle, incident frequency, and dielectric constant[2,3]. By using the example of a scattering cylinder with analytical solution, it is verified that the MoM accelerated by PCE presents a considerable… More >

Mohanad A. Mohammed^*, Hala B. Abdul Wahab

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1731-1748, 2024, DOI:10.32604/cmes.2023.030528

Abstract Blockchain technology has garnered significant attention from global organizations and researchers due to its potential as a solution for centralized system challenges. Concurrently, the Internet of Things (IoT) has revolutionized the Fourth Industrial Revolution by enabling interconnected devices to offer innovative services, ultimately enhancing human lives. This paper presents a new approach utilizing lightweight blockchain technology, effectively reducing the computational burden typically associated with conventional blockchain systems. By integrating this lightweight blockchain with IoT systems, substantial reductions in implementation time and computational complexity can be achieved. Moreover, the paper proposes the utilization of the Okamoto Uchiyama encryption algorithm, renowned for… More >

Jayaraj Thankappan¹, Delphin Raj Kesari Mary², Dong Jin Yoon³, Soo-Hyun Park^4,*

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 1053-1079, 2023, DOI:10.32604/cmc.2023.038437

Abstract Flooding is a hazardous natural calamity that causes significant damage to lives and infrastructure in the real world. Therefore, timely and accurate decision-making is essential for mitigating flood-related damages. The traditional flood prediction techniques often encounter challenges in accuracy, timeliness, complexity in handling dynamic flood patterns and leading to substandard flood management strategies. To address these challenges, there is a need for advanced machine learning models that can effectively analyze Internet of Things (IoT)-generated flood data and provide timely and accurate flood predictions. This paper proposes a novel approach-the Adaptive Momentum and Backpropagation (AM-BP) algorithm-for flood prediction and management in… More >

Tao Yin¹, Changgen Peng^2,*, Weijie Tan³, Dequan Xu⁴, Hanlin Tang⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 827-843, 2024, DOI:10.32604/cmes.2023.029039

Abstract In the assessment of car insurance claims, the claim rate for car insurance presents a highly skewed probability distribution, which is typically modeled using Tweedie distribution. The traditional approach to obtaining the Tweedie regression model involves training on a centralized dataset, when the data is provided by multiple parties, training a privacy-preserving Tweedie regression model without exchanging raw data becomes a challenge. To address this issue, this study introduces a novel vertical federated learning-based Tweedie regression algorithm for multi-party auto insurance rate setting in data silos. The algorithm can keep sensitive data locally and uses privacy-preserving techniques to achieve intersection… More >

Zhengmao Yang^1,*, Junjie Yang², Yang Chen³, Fulei Jing⁴

The International Conference on Computational & Experimental Engineering and Sciences, Vol.26, No.4, pp. 1-1, 2023, DOI:10.32604/icces.2023.09229

Abstract Woven ceramic matrix composites (CMCs), exhibiting excellent thermomechanical properties at high temperatures, are promising as alternative materials to the conventional nickel-based superalloys in the hot section components of aero-engines. Therefore, understanding and predicting the lifetime of CMCs is critical. Fatigue prediction of woven CMCs currently involves long-term and costly testing. A feasible alternative is to use predictive modelling based on a deep understanding of the damage mechanisms. Therefore, this study develops a multiscale analysis modelling method for predicting the fatigue life of CMC materials at high temperature by investigating the thermomechanical fatigue damage evolution. To represent the global thermomechanical properties… More >