REZA YADOLLAHVANDMIANDOAB^1,#, MEHRSA JALALIZADEH^1,#, FRANCIELE APARECIDA VECHIA DIONATO¹, KEINI BUOSI¹, PATRÍCIA A. F. LEME¹, LUCIANA S. B. DAL COL¹, CRISTIANE F. GIACOMELLI¹, ALEX DIAS ASSIS¹, NASIM BASHIRICHELKASARI¹, LEONARDO OLIVEIRA REIS^1,2,*

Oncology Research, Vol., , DOI:10.32604/or.2024.045442

Abstract Bladder cancer (BC) is the 10th most common cancer worldwide, with about 0.5 million reported new cases and about 0.2 million deaths per year. In this scoping review, we summarize the current evidence regarding the clinical implications of single-cell sequencing for bladder cancer based on PRISMA guidelines. We searched PubMed, CENTRAL, Embase, and supplemented with manual searches through the Scopus, and Web of Science for published studies until February 2023. We included original studies that used at least one single-cell technology to study bladder cancer. Forty-one publications were included in the review. Twenty-nine studies showed that this technology can identify… More > Graphic Abstract

Xinqiao Liu^*, Linxin Zhang, Xinran Zhang

International Journal of Mental Health Promotion, Vol., , DOI:10.32604/ijmhp.2023.042359

Abstract Under the effects of COVID-19 and a number of ongoing lockdown tactics, anxiety symptoms and poor sleep quality have become common mental health issues among college freshmen and are intimately related to their emotional adaptation. To explore this connection, this study gathered data from a sample of 256 freshmen enrolled in an elite university in China in September 2022. The association between sleep quality, anxiety symptoms, and emotional adaptation was clarified using correlation analysis. Additionally, the mediating function of anxiety symptoms between sleep quality and emotional adaptation was investigated using a structural equation model. The results reveal that: (1) Chinese… More >

Mostafa Diba^*, Hossein Khosravi

Intelligent Automation & Soft Computing, Vol., , DOI:10.32604/iasc.2023.046590

Abstract Convolutional neural networks continually evolve to enhance accuracy in addressing various problems, leading to an increase in computational cost and model size. This paper introduces a novel approach for pruning face recognition models based on convolutional neural networks. The proposed method identifies and removes inefficient filters based on the information volume in feature maps. In each layer, some feature maps lack useful information, and there exists a correlation between certain feature maps. Filters associated with these two types of feature maps impose additional computational costs on the model. By eliminating filters related to these categories of feature maps, the reduction… More >

Ruiqiong Wang¹, Tiantian Du², Jinchen Cao^2,* , Guoqiang Wang³

Frontiers in Heat and Mass Transfer, Vol., , DOI:10.32604/fhmt.2024.047825

Abstract With the advancement of oilfield extraction technology, since oil-water emulsions in waxy crude oil are prone to be deposited on the pipe wall, increasing the difficulty of crude oil extraction. In this paper, the mesoscopic dissipative particle dynamics method is used to study the mechanism of the crystallization and deposition adsorbed on the wall. The results show that in the absence of water molecules, the paraffin molecules near the substrate are deposited on the metallic surface with a horizontal morphology, while the paraffin molecules close to the fluid side are arranged in a vertical column morphology. In the emulsified system,… More >

Mustafa Abdullah^*

Frontiers in Heat and Mass Transfer, Vol., , DOI:10.32604/fhmt.2024.046788

Abstract This study investigates the influence of periodic heat flux and viscous dissipation on magnetohydrodynamic (MHD) flow through a vertical channel with heat generation. A theoretical approach is employed. The channel is exposed to a perpendicular magnetic field, while one side experiences a periodic heat flow, and the other side undergoes a periodic temperature variation. Numerical solutions for the governing partial differential equations are obtained using a finite difference approach, complemented by an eigenfunction expansion method for analytical solutions. Visualizations and discussions illustrate how different variables affect the flow velocity and temperature fields. This offers comprehensive insights into MHD flow behavior… More >

Irfan Haider¹, Muhammad Attique Khan^1,*, Muhammad Nazir¹, Taerang Kim², Jae-Hyuk Cha²

Computer Systems Science and Engineering, Vol., , DOI:10.32604/csse.2023.042080

Abstract Fruit infections have an impact on both the yield and the quality of the crop. As a result, an automated recognition system for fruit leaf diseases is important. In artificial intelligence (AI) applications, especially in agriculture, deep learning shows promising disease detection and classification results. The recent AI-based techniques have a few challenges for fruit disease recognition, such as low-resolution images, small datasets for learning models, and irrelevant feature extraction. This work proposed a new fruit leaf leaf leaf disease recognition framework using deep learning features and improved pathfinder optimization. Three fruit types have been employed in this work for… More >

Haitao Liu^1,2,*, Jiaming Wang¹, Xiuliang Zhang¹, Yanji Jiang², Qian Xiao¹

CMES-Computer Modeling in Engineering & Sciences, Vol., , DOI:10.32604/cmes.2024.047129

Abstract The expansion chamber serves as the primary silencing structure within the exhaust pipeline. However, it can also act as a sound-emitting structure when subjected to airflow. This article presents a hybrid method for numerically simulating and analyzing the unsteady flow and aerodynamic noise in an expansion chamber under the influence of airflow. A fluid simulation model is established, utilizing the Large Eddy Simulation (LES) method to calculate the unsteady flow within the expansion chamber. The simulation results effectively capture the development and changes of the unsteady flow and vorticity inside the cavity, exhibiting a high level of consistency with experimental… More >

Yuanle Chen¹, Haobo Wang¹, Chunyu Liu¹, Linyi Wang², Jiaxin Liu¹, Wei Wu^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol., , DOI:10.32604/cmes.2023.046837

Abstract Recently, there have been significant advancements in the study of semantic communication in single-modal scenarios. However, the ability to process information in multi-modal environments remains limited. Inspired by the research and applications of natural language processing across different modalities, our goal is to accurately extract frame-level semantic information from videos and ultimately transmit high-quality videos. Specifically, we propose a deep learning-based Multi-Modal Mutual Enhancement Video Semantic Communication system, called M3E-VSC. Built upon a Vector Quantized Generative Adversarial Network (VQGAN), our system aims to leverage mutual enhancement among different modalities by using text as the main carrier of transmission. With it,… More >

Kuosheng Jiang¹, Chengrui Han¹, Yasheng Chang^2,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol., , DOI:10.32604/cmes.2024.046729

Abstract Angular contact ball bearings have been widely used in machine tool spindles, and the bearing preload plays an important role in the performance of the spindle. In order to solve the problems of the traditional optimal preload prediction method limited by actual conditions and uncertainties, a roller bearing preload test method based on the improved D-S evidence theory multi-sensor fusion method was proposed. First, a novel controllable preload system is proposed and evaluated. Subsequently, multiple sensors are employed to collect data on the bearing parameters during preload application. Finally, a multisensor fusion algorithm is used to make predictions, and a… More >

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

CMES-Computer Modeling in Engineering & Sciences, Vol., , 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 >