Mingxu Zhang¹, Mingliang Wang², Wei Zhang^3,*

Structural Durability & Health Monitoring, Vol.18, No.5, pp. 599-622, 2024, DOI:10.32604/sdhm.2024.051949

Abstract This paper examines the mechanical behavior of fiber reinforced polymer (FRP)-confined concrete under cyclic compression using the 3D cohesive zone model (CZM). A numerical modeling method was developed, employing zero-thickness cohesive elements to represent the stress-displacement relationship of concrete potential fracture surfaces and FRP-concrete interfaces. Additionally, mixed-mode damage plastic constitutive models were proposed for the concrete potential fracture surfaces and FRP-concrete interface, considering interfacial friction. Furthermore, an anisotropic plastic constitutive model was developed for the FRP composite jacket. The CZM model proposed in this study was validated using experimental data from plain concrete and large More >

Dmitry Bratsun^*, Natalia Elenskaya, Ramil Siraev, Mikhail Tashkinov

FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.7, pp. 1463-1479, 2024, DOI:10.32604/fdmp.2024.047928

Abstract In this work, we numerically study the hydrodynamic permeability of new-generation artificial porous materials used as scaffolds for cell growth in a perfusion bioreactor. We consider two popular solid matrix designs based on triply periodic minimal surfaces, the Schwarz P (primitive) and D (diamond) surfaces, which enable the creation of materials with controlled porosity gradients. The latter property is crucial for regulating the shear stress field in the pores of the scaffold, which makes it possible to control the intensity of cell growth. The permeability of functionally graded materials is studied within the framework of… More > Graphic Abstract

Roseline Oluwaseun Ogundokun^1,2, Joseph Bamidele Awotunde³, Hakeem Babalola Akande⁴, Cheng-Chi Lee^5,6,*, Agbotiname Lucky Imoize^7,8

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 139-161, 2024, DOI:10.32604/cmc.2024.052153

Abstract Mobile technology is developing significantly. Mobile phone technologies have been integrated into the healthcare industry to help medical practitioners. Typically, computer vision models focus on image detection and classification issues. MobileNetV2 is a computer vision model that performs well on mobile devices, but it requires cloud services to process biometric image information and provide predictions to users. This leads to increased latency. Processing biometrics image datasets on mobile devices will make the prediction faster, but mobiles are resource-restricted devices in terms of storage, power, and computational speed. Hence, a model that is small in size,… More >

Dinh Phu Cuong Le^1,2, Dong Wang¹, Viet-Tuan Le^3,*

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 37-60, 2024, DOI:10.32604/cmc.2024.050790

Abstract Transformer models have emerged as dominant networks for various tasks in computer vision compared to Convolutional Neural Networks (CNNs). The transformers demonstrate the ability to model long-range dependencies by utilizing a self-attention mechanism. This study aims to provide a comprehensive survey of recent transformer-based approaches in image and video applications, as well as diffusion models. We begin by discussing existing surveys of vision transformers and comparing them to this work. Then, we review the main components of a vanilla transformer network, including the self-attention mechanism, feed-forward network, position encoding, etc. In the main part of More >

Nidhi Parashar¹, Prashant Johri¹, Arfat Ahmad Khan⁵, Nitin Gaur¹, Seifedine Kadry^2,3,4,*

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 389-425, 2024, DOI:10.32604/cmc.2024.050240

Abstract The growing global requirement for food and the need for sustainable farming in an era of a changing climate and scarce resources have inspired substantial crop yield prediction research. Deep learning (DL) and machine learning (ML) models effectively deal with such challenges. This research paper comprehensively analyses recent advancements in crop yield prediction from January 2016 to March 2024. In addition, it analyses the effectiveness of various input parameters considered in crop yield prediction models. We conducted an in-depth search and gathered studies that employed crop modeling and AI-based methods to predict crop yield. The… More >

Ronghao Pan, José Antonio García-Díaz^*, Rafael Valencia-García

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2849-2868, 2024, DOI:10.32604/cmes.2024.049631

Abstract Large Language Models (LLMs) are increasingly demonstrating their ability to understand natural language and solve complex tasks, especially through text generation. One of the relevant capabilities is contextual learning, which involves the ability to receive instructions in natural language or task demonstrations to generate expected outputs for test instances without the need for additional training or gradient updates. In recent years, the popularity of social networking has provided a medium through which some users can engage in offensive and harmful online behavior. In this study, we investigate the ability of different LLMs, ranging from zero-shot… More >

LISANNE K.A. NEIJENHUIS^1,2,3,#, LEUTA L. NAUMANN^4,#, SONIA A.M. FERKEL¹, SAMUEL J.S. RUBIN¹, STEPHAN ROGALLA^1,*

Oncology Research, Vol.32, No.7, pp. 1163-1172, 2024, DOI:10.32604/or.2024.050907

Abstract Background: Osteosarcoma is the most common malignant primary bone tumor. The prognosis for patients with disseminated disease remains very poor despite recent advancements in chemotherapy. Moreover, current treatment regimens bear a significant risk of serious side effects. Thus, there is an unmet clinical need for effective therapies with improved safety profiles. Taurolidine is an antibacterial agent that has been shown to induce cell death in different types of cancer cell lines. Methods: In this study, we examined both the antineoplastic and antiangiogenic effects of taurolidine in animal models of osteosarcoma. K7M2 murine osteosarcoma cells were… More >

Samia Allaoua Chelloug^*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4845-4861, 2024, DOI:10.32604/cmc.2024.051539

Abstract Intrusion detection is a predominant task that monitors and protects the network infrastructure. Therefore, many datasets have been published and investigated by researchers to analyze and understand the problem of intrusion prediction and detection. In particular, the Network Security Laboratory-Knowledge Discovery in Databases (NSL-KDD) is an extensively used benchmark dataset for evaluating intrusion detection systems (IDSs) as it incorporates various network traffic attacks. It is worth mentioning that a large number of studies have tackled the problem of intrusion detection using machine learning models, but the performance of these models often decreases when evaluated on… 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 >

Yi-Chun Lai¹, Shu-Yin Chiang², Yao-Chiang Kan³, Hsueh-Chun Lin^4,*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3783-3803, 2024, DOI:10.32604/cmc.2024.050376

Abstract Artificial intelligence (AI) technology has become integral in the realm of medicine and healthcare, particularly in human activity recognition (HAR) applications such as fitness and rehabilitation tracking. This study introduces a robust coupling analysis framework that integrates four AI-enabled models, combining both machine learning (ML) and deep learning (DL) approaches to evaluate their effectiveness in HAR. The analytical dataset comprises 561 features sourced from the UCI-HAR database, forming the foundation for training the models. Additionally, the MHEALTH database is employed to replicate the modeling process for comparative purposes, while inclusion of the WISDM database, renowned… More > Graphic Abstract