JIANYUN XIE^1,*, LINJIE LU¹, JIALI ZHANG¹, QIRUI LI², WEIDONG CHEN^1,*

Oncology Research, Vol., , DOI:10.32604/or.2023.031511

Abstract Objective: Circular ribose nucleic acids (circRNAs) are implicated in tumor progression and drug resistance of prostate cancer (PCa). The current work explored the function of circ_0005203 (circTHSD4) in the malignancy and docetaxel (DTX) resistance of PCa. Methods: circTHSD4 expression within PCa as well as matched non-carcinoma samples was measured through real-time reverse transcription-quantitative polymerase chain reaction (RT-qPCR). In addition, a subcellular fraction assay was conducted to determine circTHSD4 subcellular localization within PCa cells. In addition, we performed a Western blot (WB) assay to detect high-mobility-group A2 protein (HMGA2) levels. Besides, functional associations of two molecules were investigated through dual luciferase… More >

HASSAN BAYDOUN¹, YUJI KATO¹, HIROKI KAMO¹, ANNA HÜSCH^1,2, HAYATO MIZUTA¹, RYOTA KAWAHARA¹, SIRO SIMIZU^1,*

Oncology Research, Vol., , DOI:10.32604/or.2023.030304

Abstract C-mannosylation is a post-translational modification that occurs intracellularly in the endoplasmic reticulum. In humans, biosynthesis of C-mannosylation in proteins containing thrombospondin type 1 repeat is catalyzed by the DPY19 family; nonetheless, biological functions of protein C-mannosylation are not yet fully understood, especially in tumor progression. Vasculogenic mimicry (VM) is the formation of fluid-conducting channels by highly invasive and genetically deregulated tumor cells, enabling the tumors to form matrix-embedded vasculogenic structures, containing plasma and blood cells to meet the metabolic demands of rapidly growing tumors. In this study, we focused on DPY19L3, a C-mannosyltransferase, and aimed to unravel its role in… More >

Emad Ul Haq Qazi^1,*, Tanveer Zia¹, Muhammad Imran², Muhammad Hamza Faheem¹

Intelligent Automation & Soft Computing, Vol., , DOI:10.32604/iasc.2023.041181

Abstract Deep learning is considered one of the most efficient and reliable methods through which the legitimacy of a digital image can be verified. In the current cyber world where deepfakes have shaken the global community, confirming the legitimacy of a digital image is of great importance. With the advancements made in deep learning techniques, now we can efficiently train and develop state-of-the-art digital image forensic models. The most traditional and widely used method by researchers is convolution neural networks (CNN) for verification of image authenticity but it consumes a considerable number of resources and requires a large dataset for training.… More >

Ibrahim M. Alwayle¹, Hala J. Alshahrani², Saud S. Alotaibi³, Khaled M. Alalayah¹, Amira Sayed A. Aziz⁴, Khadija M. Alaidarous¹, Ibrahim Abdulrab Ahmed⁵, Manar Ahmed Hamza^6,*

Intelligent Automation & Soft Computing, Vol., , DOI:10.32604/iasc.2023.034718

Abstract Abstractive text summarization is crucial to produce summaries of natural language with basic concepts from large text documents. Despite the achievement of English language-related abstractive text summarization models, the models that support Arabic language text summarization are fewer in number. Recent abstractive Arabic summarization models encounter different issues that need to be resolved. Syntax inconsistency is a crucial issue resulting in the low-accuracy summary. A new technique has achieved remarkable outcomes by adding topic awareness in the text summarization process that guides the module by imitating human awareness. The current research article presents Abstractive Arabic Text Summarization using Hyperparameter Tuned… More >

Al Hussien Seddik Saad¹, Mohammed S. Reda², Gamal M. Behery², Ahmed A. El-harby², Mohammed Baz³, Ahmed Ismail Ebada², Mohamed Abouhawwash^4,5,*

Intelligent Automation & Soft Computing, Vol., , DOI:10.32604/iasc.2023.032084

Abstract The trend of digital information transformation has become a topic of interest. Many data are threatening; thus, protecting such data from attackers is considered an essential process. Recently, a new methodology for data concealing has been suggested by researchers called coverless steganography. Coverless steganography can be accomplished either by building an image database to match its image subblocks with the secret message to obtain the stego image or by generating an image. This paper proposes a coverless image steganography system based on pure image generation using secret message bits with a capacity higher than the other traditional systems. The system… More >

Elias Hossain¹, Abdullah Alshahrani², Wahidur Rahman^3,*

Intelligent Automation & Soft Computing, Vol., , DOI:10.32604/iasc.2022.029511

Abstract This paper aims to develop Machine Learning algorithms to classify electronic articles related to this phenomenon by retrieving information and topic modelling. The Methodology of this study is categorized into three phases: the Text Classification Approach (TCA), the Proposed Algorithms Interpretation (PAI), and finally, Information Retrieval Approach (IRA). The TCA reflects the text preprocessing pipeline called a clean corpus. The Global Vectors for Word Representation (Glove) pre-trained model, FastText, Term Frequency-Inverse Document Frequency (TF-IDF), and Bag-of-Words (BOW) for extracting the features have been interpreted in this research. The PAI manifests the Bidirectional Long Short-Term Memory (Bi-LSTM) and Convolutional Neural Network… More >

Fawziea M. Hussien¹, Atheer S. Hassoon^2,*, Ghaidaa M. Ahmed¹

Frontiers in Heat and Mass Transfer, Vol., , DOI:10.32604/fhmt.2023.047177

Abstract A heat exchanger’s performance depends heavily on the operating fluid’s transfer of heat capacity and thermal conductivity. Adding nanoparticles of high thermal conductivity materials is a significant way to enhance the heat transfer fluid's thermal conductivity. This research used engine oil containing alumina (Al₂O₃) nanoparticles and copper oxide (CuO) to test whether or not the heat exchanger’s efficiency could be improved. To establish the most effective elements for heat transfer enhancement, the heat exchangers thermal performance was tested at 0.05% and 0.1% concentrations for Al₂O₃ and CuO nanoparticles. The simulation results showed that the percentage increase in Nusselt number (Nu)… More >

Abdelrahman Maharek^1,2,*, Amr Abozeid^2,3, Rasha Orban¹, Kamal ElDahshan²

Computer Systems Science and Engineering, Vol., , DOI:10.32604/csse.2024.039436

Abstract What causes object detection in video to be less accurate than it is in still images? Because some video frames have degraded in appearance from fast movement, out-of-focus camera shots, and changes in posture. These reasons have made video object detection (VID) a growing area of research in recent years. Video object detection can be used for various healthcare applications, such as detecting and tracking tumors in medical imaging, monitoring the movement of patients in hospitals and long-term care facilities, and analyzing videos of surgeries to improve technique and training. Additionally, it can be used in telemedicine to help diagnose… More >

Jingyu Li^1,2, Mushui Wang^1,2,*, Zhaoyuan Wu^1,3, Guizhen Tian^1,2, Na Zhang^1,2, Guangchen Liu^1,2

Energy Engineering, Vol., , DOI:10.32604/ee.2023.044862

Abstract Given the “double carbon” objective and the drive toward low-carbon power, investigating the integration and interaction within the carbon-electricity market can enhance renewable energy utilization and facilitate energy conservation and emission reduction endeavors. However, further research is necessary to explore operational optimization methods for establishing a regional energy system using Power-to-Hydrogen (P2H) technology, focusing on participating in combined carbon-electricity market transactions. This study introduces an innovative Electro-Hydrogen Regional Energy System (EHRES) in this context. This system integrates renewable energy sources, a P2H system, cogeneration units, and energy storage devices. The core purpose of this integration is to optimize renewable energy… More > Graphic Abstract

Kaicheng Liu^1,3,*, Chen Liang², Xiaoyang Dong², Liping Liu¹

Energy Engineering, Vol., , 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 PV generation is conducted, and… More >