Farhad Mortezapour Shiri^*, Thinagaran Perumal, Norwati Mustapha, Raihani Mohamed

Journal on Artificial Intelligence, Vol.6, pp. 301-360, 2024, DOI:10.32604/jai.2024.054314 - 20 November 2024

Abstract Deep learning (DL) has emerged as a powerful subset of machine learning (ML) and artificial intelligence (AI), outperforming traditional ML methods, especially in handling unstructured and large datasets. Its impact spans across various domains, including speech recognition, healthcare, autonomous vehicles, cybersecurity, predictive analytics, and more. However, the complexity and dynamic nature of real-world problems present challenges in designing effective deep learning models. Consequently, several deep learning models have been developed to address different problems and applications. In this article, we conduct a comprehensive survey of various deep learning models, including Convolutional Neural Network (CNN), Recurrent… More >

Shaoxiong Wu¹, Ruoxin Li¹, Xiaofeng Tao¹, Hailong Wu^1,*, Ping Miao¹, Yang Lu¹, Yanyan Lu¹, Qi Liu², Li Pan²

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3063-3077, 2024, DOI:10.32604/cmc.2024.055381 - 18 November 2024

Abstract Time series prediction has always been an important problem in the field of machine learning. Among them, power load forecasting plays a crucial role in identifying the behavior of photovoltaic power plants and regulating their control strategies. Traditional power load forecasting often has poor feature extraction performance for long time series. In this paper, a new deep learning framework Residual Stacked Temporal Long Short-Term Memory (RST-LSTM) is proposed, which combines wavelet decomposition and time convolutional memory network to solve the problem of feature extraction for long sequences. The network framework of RST-LSTM consists of two More >

WEITAO ZHENG¹, DONG JIANG², SONGEN CHEN¹, MEILING WU¹, BAOQI YAN², JIAHUI ZHAI², YUNQIANG SHI², BIN XIE¹, XINGWANG XIE², KANGHONG HU^1,*, WENXUE MA^3,*

Oncology Research, Vol.32, No.12, pp. 1837-1850, 2024, DOI:10.32604/or.2024.056565 - 13 November 2024

Abstract Objectives: The Kirsten rat sarcoma virus (KRAS) G12D oncogenic mutation poses a significant challenge in treating solid tumors due to the lack of specific and effective therapeutic interventions. This study aims to explore innovative approaches in T cell receptor (TCR) engineering and characterization to target the KRAS G12D_7-16 mutation, providing potential strategies for overcoming this therapeutic challenge. Methods: In this innovative study, we engineered and characterized two T cell receptors (TCRs), KDA11-01 and KDA11-02 with high affinity for the KRAS G12D_7-16 mutation. These TCRs were isolated from tumor-infiltrating lymphocytes (TILs) derived from tumor tissues of patients More >

CHUAN JIANG^1,2, CHUNLEI LIU^1,3, XI YAO^1,3, JINGYA SU^1,2, WEI LU^1,3, ZHENGBO WEI^3,*, YING XIE^1,2,*

Oncology Research, Vol.32, No.12, pp. 1935-1948, 2024, DOI:10.32604/or.2024.052244 - 13 November 2024

Abstract Background: Head and neck squamous cell carcinoma (HNSCC) is a prevalent form of cancer globally, with chemoresistance posing a major challenge in treatment outcomes. The efficacy of the commonly used chemotherapeutic agent, cisplatin, is diminished in patients with poor prognoses. Methods: Various bioinformatics databases were utilized to examine Carboxylesterase 1 (CES1) gene expression, clinicopathologic features, patient survival analysis, and gene function. An organoid model of HNSCC was established, along with the induction of drug-resistant HNSCC in the organoid model. CES1 expression was assessed using qRT-PCR and Western Blot, and differential markers were identified through transcriptome… More >

ZEYIN LAI¹, HANGYUAN ZHAO¹, HONG DENG^1,2,*

BIOCELL, Vol.48, No.11, pp. 1569-1578, 2024, DOI:10.32604/biocell.2024.053983 - 07 November 2024

Abstract In the development of colorectal cancer (CRC), cancer-associated fibroblasts (CAFs) play a pivotal role in establishing tumor-permissive extracellular matrix structures, angiogenesis, and modulating the immune status of the tumor microenvironment (TME), thereby influencing tumor metastasis and resistance to radiotherapy and chemotherapy. The pleiotropic effects of CAFs in the TME may be attributed to the heterogeneous origin and high plasticity of their population. Given the specificity of CAFs, they provide a variety of potential target molecules for future CRC treatment, which may play an indispensable role in CRC therapeutic strategies. This review summarizes the origin of More >

Yuan Li^1,3,*, Zhiqiang Fan^1,2, Tao Suo^1,3

The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.3, pp. 1-2, 2024, DOI:10.32604/icces.2024.013336

Abstract Light armored vehicles, as the primary means of force transport on contemporary battlefields, require not only high mobility but also better protection to meet the complex battlefield environment and mission requirements. Composite armor is widely used in the design of light armored vehicles due to its lightweight and excellent defensible performance. In this paper, the damage law of the composite armor of an infantry fighting vehicle, when penetrated by fragment-simulated projectiles (FSP), is studied by numerical simulation, and the homogeneous equivalent targets surrogating a combination of local protective armor and vulnerable parts are constructed based More >

Lei Peng^1,*, Jin Zhou², Xianfeng Zhang³, Zhongwei Guan^4,5

The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.3, pp. 1-1, 2024, DOI:10.32604/icces.2024.011196

Abstract This paper presents the numerical modelling of the ballistic response of hybrid composite structures subjected to 7.62 mm projectile impact. This study focuses on the modelling of composites made of various materials, including ceramics, Ultra-High-Molecular-Weight Polyethylene (UHMWPE), Kevlar, and compressed wood, with fabrication of hybrid laminated structures that offer promising ballistic resistance capabilities. By employing a range of constitutive models and failure criteria, the finite element model simulates the ballistic behaviors of the constituent materials, facilitating a comprehensive understanding of their performance under high-velocity impacts. The core of the study lies in the comparison between… More >

Mohammed A. Mahdi¹, Shahanawaj Ahamad², Sawsan A. Saad³, Alaa Dafhalla³, Alawi Alqushaibi⁴, Rizwan Qureshi^5,*

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2351-2373, 2024, DOI:10.32604/cmes.2024.055723 - 31 October 2024

Abstract The segmentation of head and neck (H&N) tumors in dual Positron Emission Tomography/Computed Tomography (PET/CT) imaging is a critical task in medical imaging, providing essential information for diagnosis, treatment planning, and outcome prediction. Motivated by the need for more accurate and robust segmentation methods, this study addresses key research gaps in the application of deep learning techniques to multimodal medical images. Specifically, it investigates the limitations of existing 2D and 3D models in capturing complex tumor structures and proposes an innovative 2.5D UNet Transformer model as a solution. The primary research questions guiding this study… More >

Alaa U. Khawaja¹, Ahmad Shaf^2,*, Faisal Al Thobiani³, Tariq Ali⁴, Muhammad Irfan⁵, Aqib Rehman Pirzada², Unza Shahkeel²

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2399-2420, 2024, DOI:10.32604/cmes.2024.054257 - 31 October 2024

Abstract Electric motor-driven systems are core components across industries, yet they’re susceptible to bearing faults. Manual fault diagnosis poses safety risks and economic instability, necessitating an automated approach. This study proposes FTCNNLSTM (Fine-Tuned TabNet Convolutional Neural Network Long Short-Term Memory), an algorithm combining Convolutional Neural Networks, Long Short-Term Memory Networks, and Attentive Interpretable Tabular Learning. The model preprocesses the CWRU (Case Western Reserve University) bearing dataset using segmentation, normalization, feature scaling, and label encoding. Its architecture comprises multiple 1D Convolutional layers, batch normalization, max-pooling, and LSTM blocks with dropout, followed by batch normalization, dense layers, and More >

Catalina Vargas-Acevedo¹, Gareth J. Morgan¹, Rhynn Soderstrom², Richard Ing³, Nicholas Houska³, Jenny E. Zablah^1,*

Congenital Heart Disease, Vol.19, No.4, pp. 389-397, 2024, DOI:10.32604/chd.2024.055121 - 31 October 2024

Abstract Introduction: Pulmonary hemorrhage (PHm) is a life-threatening complication that can occur after catheter-based interventions in patients with pulmonary vein stenosis (PVS). Inhaled racemic epinephrine (iRE) and tranexamic acid (iTXA) have been used in other conditions, but a standardized approach in PVS has not been described. We aimed to describe the current management of PHm after PVS catheter-based interventions. Methods: We present a retrospective review of episodes of PHm from July 2022 to February 2024. PHm was defined as frank blood suctioned from the endotracheal tube including blood-tinged secretions and >3% decrease in saturations and/or ventilatory… More >