HAIQING HU^1,#,*, HAO YANG^2,#, SHUAISHUAI FAN³, XUE JIA³, YING ZHAO³, HONGRUI LI³

Oncology Research, Vol.32, No.8, pp. 1335-1346, 2024, DOI:10.32604/or.2024.044174

Abstract Long non-coding RNAs (lncRNAs) have been implicated in cancer progression and drug resistance development. Moreover, there is evidence that lncRNA HOX transcript antisense intergenic RNA (HOTAIR) is involved in colorectal cancer (CRC) progression. The present study aimed to examine the functional role of lncRNA HOTAIR in conferring radiotherapy resistance in CRC cells, as well as the underlying mechanism. The relative expression levels of HOTAIR were examined in 70 pairs of CRC tumor and para-cancerous tissues, as well as in radiosensitive and radioresistant samples. The correlations between HOTAIR expression levels and clinical features of patients with… More >

Ali Altalbe^1,2,*, Abdul Rehman Javed³

Computer Systems Science and Engineering, Vol.48, No.4, pp. 1075-1081, 2024, DOI:10.32604/csse.2024.054179

Abstract This article has no abstract. More >

Sana Sahar Guia¹, Abdelkader Laouid¹, Mohammad Hammoudeh^2,*, Mostafa Kara^1,3

Computer Systems Science and Engineering, Vol.48, No.4, pp. 915-935, 2024, DOI:10.32604/csse.2024.046414

Abstract Artificial intelligence has been involved in several domains. Despite the advantages of using artificial intelligence techniques, some crucial limitations prevent them from being implemented in specific domains and locations. The accuracy, poor quality of gathered data, and processing time are considered major concerns in implementing machine learning techniques, certainly in low-end smart devices. This paper aims to introduce a novel pre-treatment technique dedicated to image text detection that uses the images’ pixel divergence and similarity to reduce the image size. Mitigating the image size while keeping its features improves the model training time with an… More >

M. Sridevi¹, B. Shankar Goud², Ali Hassan^3,4,*, D. Mahendar⁵

Frontiers in Heat and Mass Transfer, Vol.22, No.3, pp. 939-953, 2024, DOI:10.32604/fhmt.2024.050929

Abstract This study intends to evaluate the influence of temperature stratification on an unsteady fluid flow past an accelerated vertical plate in the existence of viscous dissipation. It is assumed that the medium under study is a grey, non-scattered fluid that both fascinates and transmits radiation. The leading equations are discretized using the finite difference method (FDM). Using MATLAB software, the impacts of flow factors on flow fields are revealed with particular examples in graphs and a table. In this regard, FDM results show that the velocity and temperature gradients increase with an increase of Eckert More >

Md. Fayz-Al-Asad^1,4, F. Mebarek-Oudina^2,*, H. Vaidya³, Md. Shamim Hasan⁴, Md. Manirul Alam Sarker⁴, A. I. Ismail⁵

Frontiers in Heat and Mass Transfer, Vol.22, No.3, pp. 817-837, 2024, DOI:10.32604/fhmt.2024.050814

Abstract The goal of this paper is to represent a numerical study of magnetohydrodynamic mixed convection heat transfer in a lid-driven vertical wavy enclosure with a fin attached to the bottom wall. We use a finite element method based on Galerkin weighted residual (GWR) techniques to set up the appropriate governing equations for the present flow model. We have conducted a parametric investigation to examine the impact of Hartmann and Richardson numbers on the flow pattern and heat transmission features inside a wavy cavity. We graphically represent the numerical results, such as isotherms, streamlines, velocity profiles,… More >

Mian Muhammad Danyal^1,2, Sarwar Shah Khan^3,4,*, Rahim Shah Khan⁵, Saifullah Jan², Naeem ur Rahman⁶

Journal of Intelligent Medicine and Healthcare, Vol.2, pp. 35-53, 2024, DOI:10.32604/jimh.2024.051340

Abstract Multi-modality medical images are essential in healthcare as they provide valuable insights for disease diagnosis and treatment. To harness the complementary data provided by various modalities, these images are amalgamated to create a single, more informative image. This fusion process enhances the overall quality and comprehensiveness of the medical imagery, aiding healthcare professionals in making accurate diagnoses and informed treatment decisions. In this study, we propose a new hybrid pre-processing approach, Laplacian Filter + Discrete Fourier Transform (LF+DFT), to enhance medical images before fusion. The LF+DFT approach highlights key details, captures small information, and sharpens… More >

Md. Atiqur Rahman¹, Mustavi Ibne Masum¹, Khan Md Hasib², M. F. Mridha^3,*, Sultan Alfarhood⁴, Mejdl Safran^4,*, Dunren Che⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2425-2448, 2024, DOI:10.32604/cmes.2024.050760

Abstract Brain tumors pose a significant threat to human lives and have gained increasing attention as the tenth leading cause of global mortality. This study addresses the pressing issue of brain tumor classification using Magnetic resonance imaging (MRI). It focuses on distinguishing between Low-Grade Gliomas (LGG) and High-Grade Gliomas (HGG). LGGs are benign and typically manageable with surgical resection, while HGGs are malignant and more aggressive. The research introduces an innovative custom convolutional neural network (CNN) model, Glioma-CNN. GliomaCNN stands out as a lightweight CNN model compared to its predecessors. The research utilized the BraTS 2020 More >

Madiha Hameed^1,3, Aneela Zameer^1,*, Muhammad Asif Zahoor Raja²

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2131-2164, 2024, DOI:10.32604/cmes.2024.050124

Abstract The International Skin Imaging Collaboration (ISIC) datasets are pivotal resources for researchers in machine learning for medical image analysis, especially in skin cancer detection. These datasets contain tens of thousands of dermoscopic photographs, each accompanied by gold-standard lesion diagnosis metadata. Annual challenges associated with ISIC datasets have spurred significant advancements, with research papers reporting metrics surpassing those of human experts. Skin cancers are categorized into melanoma and non-melanoma types, with melanoma posing a greater threat due to its rapid potential for metastasis if left untreated. This paper aims to address challenges in skin cancer detection… More >

Hongchi Liu¹, Xing Deng^1,*, Haijian Shao^1,2

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2397-2424, 2024, DOI:10.32604/cmes.2024.049737

Abstract The degradation of optical remote sensing images due to atmospheric haze poses a significant obstacle, profoundly impeding their effective utilization across various domains. Dehazing methodologies have emerged as pivotal components of image preprocessing, fostering an improvement in the quality of remote sensing imagery. This enhancement renders remote sensing data more indispensable, thereby enhancing the accuracy of target identification. Conventional defogging techniques based on simplistic atmospheric degradation models have proven inadequate for mitigating non-uniform haze within remotely sensed images. In response to this challenge, a novel UNet Residual Attention Network (URA-Net) is proposed. This paradigmatic approach… More > Graphic Abstract

Hui Tian¹, Dandan Zhao¹, Yannan Wu^2,3,*, Xingyu Yi¹, Jun Ma¹, Xiang Zhou⁴

FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.6, pp. 1265-1277, 2024, DOI:10.32604/fdmp.2023.044833

Abstract The low porosity and low permeability of tight oil reservoirs call for improvements in the current technologies for oil recovery. Traditional chemical solutions with large molecular size cannot effectively flow through the nano-pores of the reservoir. In this study, the feasibility of Nanofluids has been investigated using a high pressure high temperature core-holder and nuclear magnetic resonance (NMR). The results of the experiments indicate that the specified Nanofluids can enhance the tight oil recovery significantly. The water and oil relative permeability curve shifts to the high water saturation side after Nanofluid flooding, thereby demonstrating an More > Graphic Abstract