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  • Open Access

    ARTICLE

    An Investigation into the Compressive Strength, Permeability and Microstructure of Quartzite-Rock-Sand Mortar

    Wei Chen*, Wuwen Liu, Yue Liang

    FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.4, pp. 859-872, 2024, DOI:10.32604/fdmp.2023.029310

    Abstract River sand is an essential component used as a fine aggregate in mortar and concrete. Due to unrestrained exploitation, river sand resources are gradually being exhausted. This requires alternative solutions. This study deals with the properties of cement mortar containing different levels of manufactured sand (MS) based on quartzite, used to replace river sand. The river sand was replaced at 20%, 40%, 60% and 80% with MS (by weight or volume). The mechanical properties, transfer properties, and microstructure were examined and compared to a control group to study the impact of the replacement level. The results indicate that the compressive… More >

  • Open Access

    ARTICLE

    On the Preparation of Low-Temperature-Rise and Low-Shrinkage Concrete Based on Phosphorus Slag

    Jianlong Jin, Jingjing Ding, Long Xiong, Ming Bao, Peng Zeng*

    FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.4, pp. 803-814, 2024, DOI:10.32604/fdmp.2023.027311

    Abstract The effects of different contents of a MgO expansive agent and phosphorus slag on the mechanical properties, shrinkage behavior, and the heat of hydration of concrete were studied. The slump flow, setting time, dry shrinkage, and hydration heat were used as sensitive parameters to assess the response of the considered specimens. As shown by the results, in general, with an increase in the phosphorus slag content, the hydration heat of concrete decreases for all ages, but the early strength displays a downward trend and the dry shrinkage rate increases. The 90-d strength and dry shrinkage of concrete could be improved… More > Graphic Abstract

    On the Preparation of Low-Temperature-Rise and Low-Shrinkage Concrete Based on Phosphorus Slag

  • Open Access

    ARTICLE

    The Turbulent Schmidt Number for Transient Contaminant Dispersion in a Large Ventilated Room Using a Realizable k-ε Model

    Fei Wang, Qinpeng Meng, Jinchi Zhao, Xin Wang, Yuhong Liu, Qianru Zhang*

    FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.4, pp. 829-846, 2024, DOI:10.32604/fdmp.2023.026917

    Abstract Buildings with large open spaces in which chemicals are handled are often exposed to the risk of explosions. Computational fluid dynamics is a useful and convenient way to investigate contaminant dispersion in such large spaces. The turbulent Schmidt number (Sct) concept has typically been used in this regard, and most studies have adopted a default value. We studied the concentration distribution for sulfur hexafluoride (SF6) assuming different emission rates and considering the effect of Sct. Then we examined the same problem for a light gas by assuming hydrogen gas (H2) as the contaminant. When SF6 was considered as the contaminant… More >

  • Open Access

    ARTICLE

    Analysis and Optimization of the Electrohydraulic Forming Process of Sinusoidal Corrugation Tubes

    Da Cai, Yinlong Song, Hao Jiang, Guangyao Li, Junjia Cui*

    FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.4, pp. 873-887, 2024, DOI:10.32604/fdmp.2023.025833

    Abstract Aluminum alloy thin-walled structures are widely used in the automotive industry due to their advantages related to light weight and crashworthiness. They can be produced at room temperature by the electrohydraulic forming process. In the present study, the influence of the related parameters on the forming quality of a 6063 aluminum alloy sinusoidal corrugation tube has been assessed. In particular, the orthogonal experimental design (OED) and central composite design (CCD) methods have been used. Through the range analysis and variance analysis of the experimental data, the influence degree of wire diameter (WD) and discharge energy (DE) on the forming quality… More >

  • Open Access

    ARTICLE

    Assessment of Particle Matter Pollution during Post-Earthquake Debris Removal in Adiyaman City

    Ercan Vural*

    Revue Internationale de Géomatique, Vol.33, pp. 37-50, 2024, DOI:10.32604/rig.2024.047908

    Abstract Severe earthquakes in the world and in Turkey can cause great loss of life and property, environmental problems and health problems. In addition to the loss of life and property, earthquakes are closely related to ecosystems, air, water, and soil pollution. Particularly in post-earthquake debris removal, very large amounts of particulate matter are released and may have negative effects on the health of the local population. This study aimed to detect two types of particle matter pollution during debris removal in 25 different locations in Adiyaman City using a CEM DT 9880 particle matter measuring device in May and August… More >

  • Open Access

    ARTICLE

    VKFQ: A Verifiable Keyword Frequency Query Framework with Local Differential Privacy in Blockchain

    Youlin Ji, Bo Yin*, Ke Gu

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4205-4223, 2024, DOI:10.32604/cmc.2024.049086

    Abstract With its untameable and traceable properties, blockchain technology has been widely used in the field of data sharing. How to preserve individual privacy while enabling efficient data queries is one of the primary issues with secure data sharing. In this paper, we study verifiable keyword frequency (KF) queries with local differential privacy in blockchain. Both the numerical and the keyword attributes are present in data objects; the latter are sensitive and require privacy protection. However, prior studies in blockchain have the problem of trilemma in privacy protection and are unable to handle KF queries. We propose an efficient framework that… More >

  • Open Access

    ARTICLE

    BCCLR: A Skeleton-Based Action Recognition with Graph Convolutional Network Combining Behavior Dependence and Context Clues

    Yunhe Wang1, Yuxin Xia2, Shuai Liu2,*

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4489-4507, 2024, DOI:10.32604/cmc.2024.048813

    Abstract In recent years, skeleton-based action recognition has made great achievements in Computer Vision. A graph convolutional network (GCN) is effective for action recognition, modelling the human skeleton as a spatio-temporal graph. Most GCNs define the graph topology by physical relations of the human joints. However, this predefined graph ignores the spatial relationship between non-adjacent joint pairs in special actions and the behavior dependence between joint pairs, resulting in a low recognition rate for specific actions with implicit correlation between joint pairs. In addition, existing methods ignore the trend correlation between adjacent frames within an action and context clues, leading to… More >

  • Open Access

    ARTICLE

    Aspect-Level Sentiment Analysis Based on Deep Learning

    Mengqi Zhang1, Jiazhao Chai2, Jianxiang Cao3, Jialing Ji3, Tong Yi4,*

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3743-3762, 2024, DOI:10.32604/cmc.2024.048486

    Abstract In recent years, deep learning methods have developed rapidly and found application in many fields, including natural language processing. In the field of aspect-level sentiment analysis, deep learning methods can also greatly improve the performance of models. However, previous studies did not take into account the relationship between user feature extraction and contextual terms. To address this issue, we use data feature extraction and deep learning combined to develop an aspect-level sentiment analysis method. To be specific, we design user comment feature extraction (UCFE) to distill salient features from users’ historical comments and transform them into representative user feature vectors.… More >

  • Open Access

    ARTICLE

    WebFLex: A Framework for Web Browsers-Based Peer-to-Peer Federated Learning Systems Using WebRTC

    Mai Alzamel1,*, Hamza Ali Rizvi2, Najwa Altwaijry1, Isra Al-Turaiki1

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4177-4204, 2024, DOI:10.32604/cmc.2024.048370

    Abstract Scalability and information personal privacy are vital for training and deploying large-scale deep learning models. Federated learning trains models on exclusive information by aggregating weights from various devices and taking advantage of the device-agnostic environment of web browsers. Nevertheless, relying on a main central server for internet browser-based federated systems can prohibit scalability and interfere with the training process as a result of growing client numbers. Additionally, information relating to the training dataset can possibly be extracted from the distributed weights, potentially reducing the privacy of the local data used for training. In this research paper, we aim to investigate… More >

  • Open Access

    ARTICLE

    Deep Learning and Tensor-Based Multiple Clustering Approaches for Cyber-Physical-Social Applications

    Hongjun Zhang1,2, Hao Zhang2, Yu Lei3, Hao Ye1, Peng Li1,*, Desheng Shi1

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4109-4128, 2024, DOI:10.32604/cmc.2024.048355

    Abstract The study delves into the expanding role of network platforms in our daily lives, encompassing various mediums like blogs, forums, online chats, and prominent social media platforms such as Facebook, Twitter, and Instagram. While these platforms offer avenues for self-expression and community support, they concurrently harbor negative impacts, fostering antisocial behaviors like phishing, impersonation, hate speech, cyberbullying, cyberstalking, cyberterrorism, fake news propagation, spamming, and fraud. Notably, individuals also leverage these platforms to connect with authorities and seek aid during disasters. The overarching objective of this research is to address the dual nature of network platforms by proposing innovative methodologies aimed… More >

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