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  • Open Access

    ARTICLE

    Analyzing COVID-19 Discourse on Twitter: Text Clustering and Classification Models for Public Health Surveillance

    Pakorn Santakij1, Samai Srisuay2,*, Pongporn Punpeng1

    Computer Systems Science and Engineering, Vol.48, No.3, pp. 665-689, 2024, DOI:10.32604/csse.2024.045066

    Abstract Social media has revolutionized the dissemination of real-life information, serving as a robust platform for sharing life events. Twitter, characterized by its brevity and continuous flow of posts, has emerged as a crucial source for public health surveillance, offering valuable insights into public reactions during the COVID-19 pandemic. This study aims to leverage a range of machine learning techniques to extract pivotal themes and facilitate text classification on a dataset of COVID-19 outbreak-related tweets. Diverse topic modeling approaches have been employed to extract pertinent themes and subsequently form a dataset for training text classification models.… More >

  • Open Access

    ARTICLE

    Optimizing Two-Phase Flow Heat Transfer: DCS Hybrid Modeling and Automation in Coal-Fired Power Plant Boilers

    Ming Yan1, Caijiang Lu2,*, Pan Shi1,*, Meiling Zhang3, Jiawei Zhang1, Liang Wang1

    Frontiers in Heat and Mass Transfer, Vol.22, No.2, pp. 615-631, 2024, DOI:10.32604/fhmt.2024.048333

    Abstract In response to escalating challenges in energy conservation and emission reduction, this study delves into the complexities of heat transfer in two-phase flows and adjustments to combustion processes within coal-fired boilers. Utilizing a fusion of hybrid modeling and automation technologies, we develop soft measurement models for key combustion parameters, such as the net calorific value of coal, flue gas oxygen content, and fly ash carbon content, within the Distributed Control System (DCS). Validated with performance test data, these models exhibit controlled root mean square error (RMSE) and maximum absolute error (MAXE) values, both within the… More > Graphic Abstract

    Optimizing Two-Phase Flow Heat Transfer: DCS Hybrid Modeling and Automation in Coal-Fired Power Plant Boilers

  • Open Access

    ARTICLE

    Artificial Neural Network Modeling for Predicting Thermal Conductivity of EG/Water-Based CNC Nanofluid for Engine Cooling Using Different Activation Functions

    Md. Munirul Hasan1, Md Mustafizur Rahman2,*, Mohammad Saiful Islam3, Wong Hung Chan4, Yasser M. Alginahi5, Muhammad Nomani Kabir6, Suraya Abu Bakar1, Devarajan Ramasamy2

    Frontiers in Heat and Mass Transfer, Vol.22, No.2, pp. 537-556, 2024, DOI:10.32604/fhmt.2024.047428

    Abstract A vehicle engine cooling system is of utmost importance to ensure that the engine operates in a safe temperature range. In most radiators that are used to cool an engine, water serves as a cooling fluid. The performance of a radiator in terms of heat transmission is significantly influenced by the incorporation of nanoparticles into the cooling water. Concentration and uniformity of nanoparticle distribution are the two major factors for the practical use of nanofluids. The shape and size of nanoparticles also have a great impact on the performance of heat transfer. Many researchers are… More > Graphic Abstract

    Artificial Neural Network Modeling for Predicting Thermal Conductivity of EG/Water-Based CNC Nanofluid for Engine Cooling Using Different Activation Functions

  • Open Access

    ARTICLE

    Fusion of Spiral Convolution-LSTM for Intrusion Detection Modeling

    Fei Wang, Zhen Dong*

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2315-2329, 2024, DOI:10.32604/cmc.2024.048443

    Abstract Aiming at the problems of low accuracy and slow convergence speed of current intrusion detection models, SpiralConvolution is combined with Long Short-Term Memory Network to construct a new intrusion detection model. The dataset is first preprocessed using solo thermal encoding and normalization functions. Then the spiral convolution-Long Short-Term Memory Network model is constructed, which consists of spiral convolution, a two-layer long short-term memory network, and a classifier. It is shown through experiments that the model is characterized by high accuracy, small model computation, and fast convergence speed relative to previous deep learning models. The model More >

  • Open Access

    ARTICLE

    MAIPFE: An Efficient Multimodal Approach Integrating Pre-Emptive Analysis, Personalized Feature Selection, and Explainable AI

    Moshe Dayan Sirapangi1, S. Gopikrishnan1,*

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2229-2251, 2024, DOI:10.32604/cmc.2024.047438

    Abstract Medical Internet of Things (IoT) devices are becoming more and more common in healthcare. This has created a huge need for advanced predictive health modeling strategies that can make good use of the growing amount of multimodal data to find potential health risks early and help individuals in a personalized way. Existing methods, while useful, have limitations in predictive accuracy, delay, personalization, and user interpretability, requiring a more comprehensive and efficient approach to harness modern medical IoT devices. MAIPFE is a multimodal approach integrating pre-emptive analysis, personalized feature selection, and explainable AI for real-time health… More >

  • Open Access

    ARTICLE

    Aggravation of Cancer, Heart Diseases and Diabetes Subsequent to COVID-19 Lockdown via Mathematical Modeling

    Fatma Nese Efil1, Sania Qureshi1,2,3, Nezihal Gokbulut1,4, Kamyar Hosseini1,3, Evren Hincal1,4,*, Amanullah Soomro2

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 485-512, 2024, DOI:10.32604/cmes.2024.047907

    Abstract The global population has been and will continue to be severely impacted by the COVID-19 epidemic. The primary objective of this research is to demonstrate the future impact of COVID-19 on those who suffer from other fatal conditions such as cancer, heart disease, and diabetes. Here, using ordinary differential equations (ODEs), two mathematical models are developed to explain the association between COVID-19 and cancer and between COVID-19 and diabetes and heart disease. After that, we highlight the stability assessments that can be applied to these models. Sensitivity analysis is used to examine how changes in… More >

  • Open Access

    ARTICLE

    Modularized and Parametric Modeling Technology for Finite Element Simulations of Underground Engineering under Complicated Geological Conditions

    Jiaqi Wu1, Li Zhuo1,*, Jianliang Pei1, Yao Li2, Hongqiang Xie1, Jiaming Wu1, Huaizhong Liu1

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 621-645, 2024, DOI:10.32604/cmes.2024.046398

    Abstract The surrounding geological conditions and supporting structures of underground engineering are often updated during construction, and these updates require repeated numerical modeling. To improve the numerical modeling efficiency of underground engineering, a modularized and parametric modeling cloud server is developed by using Python codes. The basic framework of the cloud server is as follows: input the modeling parameters into the web platform, implement Rhino software and FLAC3D software to model and run simulations in the cloud server, and return the simulation results to the web platform. The modeling program can automatically generate instructions that can run… More >

  • Open Access

    ARTICLE

    Influence of Bottom Inclination on the Flow Structure in a Rotating Convective Layer

    Andrei Vasiliev, Andrei Sukhanovskii*, Elena Popova

    FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.4, pp. 739-748, 2024, DOI:10.32604/fdmp.2024.048092

    Abstract The formation of convective flows in a rotating cylindrical layer with an inclined bottom and free surface is studied. Convection is driven by localized cooling at the center of the upper free surface and by rim heating at the bottom near the sidewall. The horizontal temperature difference in a rotating layer leads to the formation of a convective flow with a complex structure. The mean meridional circulation, consisting of three cells, provides a strongly non-uniform differential rotation. As a result of the instability of the main cyclonic zonal flow, the train of baroclinic waves appears… More >

  • Open Access

    ARTICLE

    Analysis and Modeling of Time Output Characteristics for Distributed Photovoltaic and Energy Storage

    Kaicheng Liu1,3,*, Chen Liang2, Xiaoyang Dong2, Liping Liu1

    Energy Engineering, Vol.121, No.4, pp. 933-949, 2024, DOI:10.32604/ee.2023.043658

    Abstract Due to the unpredictable output characteristics of distributed photovoltaics, their integration into the grid can lead to voltage fluctuations within the regional power grid. Therefore, the development of spatial-temporal coordination and optimization control methods for distributed photovoltaics and energy storage systems is of utmost importance in various scenarios. This paper approaches the issue from the perspective of spatiotemporal forecasting of distributed photovoltaic (PV) generation and proposes a Temporal Convolutional-Long Short-Term Memory prediction model that combines Temporal Convolutional Networks (TCN) and Long Short-Term Memory (LSTM). To begin with, an analysis of the spatiotemporal distribution patterns of More >

  • Open Access

    ARTICLE

    Electromechanical Transient Modeling Analysis of Large-Scale New Energy Grid Connection

    Shichao Cao*, Yonggang Dong, Xiaoying Liu

    Energy Engineering, Vol.121, No.4, pp. 1109-1125, 2024, DOI:10.32604/ee.2023.043004

    Abstract The synchronous virtual machine uses inverter power to imitate the performance of the conventional synchronous machine. It also has the same inertia, damping, frequency, voltage regulation, and other external performance as the generator. It is the key technology to realize new energy grid connections’ stable and reliable operation. This project studies a dynamic simulation model of an extensive new energy power system based on the virtual synchronous motor. A new energy storage method is proposed. The mathematical energy storage model is established by combining the fixed rotor model of a synchronous virtual machine with the… More >

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