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  • Open Access

    ARTICLE

    Reinforcement Learning Based Quantization Strategy Optimal Assignment Algorithm for Mixed Precision

    Yuejiao Wang, Zhong Ma*, Chaojie Yang, Yu Yang, Lu Wei

    CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2024.047108

    Abstract The quantization algorithm compresses the original network by reducing the numerical bit width of the model, which improves the computation speed. Because different layers have different redundancy and sensitivity to data bit width. Reducing the data bit width will result in a loss of accuracy. Therefore, it is difficult to determine the optimal bit width for different parts of the network with guaranteed accuracy. Mixed precision quantization can effectively reduce the amount of computation while keeping the model accuracy basically unchanged. In this paper, a hardware-aware mixed precision quantization strategy optimal assignment algorithm adapted to low bit width is proposed,… More >

  • Open Access

    ARTICLE

    Anomaly Detection Algorithm of Power System Based on Graph Structure and Anomaly Attention

    Yifan Gao*, Jieming Zhang, Zhanchen Chen, Xianchao Chen

    CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2024.048615

    Abstract In this paper, we propose a novel anomaly detection method for data centers based on a combination of graph structure and abnormal attention mechanism. The method leverages the sensor monitoring data from target power substations to construct multidimensional time series. These time series are subsequently transformed into graph structures, and corresponding adjacency matrices are obtained. By incorporating the adjacency matrices and additional weights associated with the graph structure, an aggregation matrix is derived. The aggregation matrix is then fed into a pre-trained graph convolutional neural network (GCN) to extract graph structure features. Moreover, both the multidimensional time series segments and… More >

  • Open Access

    ARTICLE

    Weakly Supervised Network with Scribble-Supervised and Edge-Mask for Road Extraction from High-Resolution Remote Sensing Images

    Supeng Yu1, Fen Huang1,*, Chengcheng Fan2,3,4,*

    CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2024.048608

    Abstract Significant advancements have been achieved in road surface extraction based on high-resolution remote sensing image processing. Most current methods rely on fully supervised learning, which necessitates enormous human effort to label the image. Within this field, other research endeavors utilize weakly supervised methods. These approaches aim to reduce the expenses associated with annotation by leveraging sparsely annotated data, such as scribbles. This paper presents a novel technique called a weakly supervised network using scribble-supervised and edge-mask (WSSE-net). This network is a three-branch network architecture, whereby each branch is equipped with a distinct decoder module dedicated to road extraction tasks. One… More >

  • Open Access

    ARTICLE

    A Deep Learning Framework for Mass-Forming Chronic Pancreatitis and Pancreatic Ductal Adenocarcinoma Classification Based on Magnetic Resonance Imaging

    Luda Chen1, Kuangzhu Bao2, Ying Chen2, Jingang Hao2,*, Jianfeng He1,3,*

    CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2024.048507

    Abstract Pancreatic diseases, including mass-forming chronic pancreatitis (MFCP) and pancreatic ductal adenocarcinoma (PDAC), present with similar imaging features, leading to diagnostic complexities. Deep Learning (DL) methods have been shown to perform well on diagnostic tasks. Existing DL pancreatic lesion diagnosis studies based on Magnetic Resonance Imaging (MRI) utilize the prior information to guide models to focus on the lesion region. However, over-reliance on prior information may ignore the background information that is helpful for diagnosis. This study verifies the diagnostic significance of the background information using a clinical dataset. Consequently, the Prior Difference Guidance Network (PDGNet) is proposed, merging decoupled lesion… More >

  • Open Access

    ARTICLE

    KurdSet: A Kurdish Handwritten Characters Recognition Dataset Using Convolutional Neural Network

    Sardar Hasen Ali*, Maiwan Bahjat Abdulrazzaq

    CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2024.048356

    Abstract Handwritten character recognition (HCR) involves identifying characters in images, documents, and various sources such as forms surveys, questionnaires, and signatures, and transforming them into a machine-readable format for subsequent processing. Successfully recognizing complex and intricately shaped handwritten characters remains a significant obstacle. The use of convolutional neural network (CNN) in recent developments has notably advanced HCR, leveraging the ability to extract discriminative features from extensive sets of raw data. Because of the absence of pre-existing datasets in the Kurdish language, we created a Kurdish handwritten dataset called (KurdSet). The dataset consists of Kurdish characters, digits, texts, and symbols. The dataset… More >

  • Open Access

    ARTICLE

    Correlation and Pathway Analysis of the Carbon, Nitrogen, and Phosphorus in Soil-Microorganism-Plant with Main Quality Components of Tea (Camellia sinensis)

    Chun Mao1, Ji He1,*, Xuefeng Wen1, Yangzhou Xiang2, Jihong Feng1, Yingge Shu1

    Phyton-International Journal of Experimental Botany, Vol., , DOI:10.32604/phyton.2024.048246

    Abstract The contents of carbon (C), nitrogen (N), and phosphorus (P) in soil-microorganisms-plant significantly affect tea quality by altering the main quality components of tea, such as tea polyphenols, amino acids, and caffeine. However, few studies have quantified the effects of these factors on the main quality components of tea. The study aimed to explore the interactions of C, N, and P in soil-microorganisms-plants and the effects of these factors on the main quality components of tea by using the path analysis method. The results indicated that (1) The contents of C, N, and P in soil, microorganisms, and tea plants… More >

  • Open Access

    ARTICLE

    MIDNet: Deblurring Network for Material Microstructure Images

    Jiaxiang Wang1, Zhengyi Li1, Peng Shi1, Hongying Yu2, Dongbai Sun1,3,*

    CMC-Computers, Materials & Continua, Vol., , pp. 1-18, DOI:10.32604/cmc.2024.046929

    Abstract Scanning electron microscopy (SEM) is a crucial tool in the field of materials science, providing valuable insights into the microstructural characteristics of materials. Unfortunately, SEM images often suffer from blurriness caused by improper hardware calibration or imaging automation errors, which present challenges in analyzing and interpreting material characteristics. Consequently, rectifying the blurring of these images assumes paramount significance to enable subsequent analysis. To address this issue, we introduce a Material Images Deblurring Network (MIDNet) built upon the foundation of the Nonlinear Activation Free Network (NAFNet). MIDNet is meticulously tailored to address the blurring in images capturing the microstructure of materials.… More >

  • Open Access

    ARTICLE

    Safety-Constrained Multi-Agent Reinforcement Learning for Power Quality Control in Distributed Renewable Energy Networks

    Yongjiang Zhao, Haoyi Zhong, Chang Cyoon Lim*

    CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2024.048771

    Abstract This paper examines the difficulties of managing distributed power systems, notably due to the increasing use of renewable energy sources, and focuses on voltage control challenges exacerbated by their variable nature in modern power grids. To tackle the unique challenges of voltage control in distributed renewable energy networks, researchers are increasingly turning towards multi-agent reinforcement learning (MARL). However, MARL raises safety concerns due to the unpredictability in agent actions during their exploration phase. This unpredictability can lead to unsafe control measures. To mitigate these safety concerns in MARL-based voltage control, our study introduces a novel approach: Safety-Constrained Multi-Agent Reinforcement Learning… More >

  • Open Access

    ARTICLE

    A comparative in vitro study on the effect of SGLT2 inhibitors on chemosensitivity to doxorubicin in MCF-7 breast cancer cells

    SHAHID KARIM1,*, ALANOUD NAHER ALGHANMI1, MAHA JAMAL1, HUDA ALKREATHY1, ALAM JAMAL2, HIND A. ALKHATABI3, MOHAMMED BAZUHAIR1, AFTAB AHMAD4,5

    Oncology Research, Vol., , DOI:10.32604/or.2024.048988

    Abstract Cancer frequently develops resistance to the majority of chemotherapy treatments. This study aimed to examine the synergistic cytotoxic and antitumor effects of SGLT2 inhibitors, specifically Canagliflozin (CAN), Dapagliflozin (DAP), Empagliflozin (EMP), and Doxorubicin (DOX), using in vitro experimentation. The precise combination of CAN+DOX has been found to greatly enhance the cytotoxic effects of doxorubicin (DOX) in MCF-7 cells. Interestingly, it was shown that cancer cells exhibit an increased demand for glucose and ATP in order to support their growth. Notably, when these medications were combined with DOX, there was a considerable inhibition of glucose consumption, as well as reductions in… More > Graphic Abstract

    A comparative <i>in vitro</i> study on the effect of SGLT2 inhibitors on chemosensitivity to doxorubicin in MCF-7 breast cancer cells

  • Open Access

    ARTICLE

    GNAS mutations suppress cell invasion by activating MEG3 in growth hormone–secreting pituitary adenoma

    CHAO TANG1,#, CHUNYU ZHONG2,#, JUNHAO ZHU1, FENG YUAN1, JIN YANG1, YONG XU3,*, CHIYUAN MA1,3,4,5,*

    Oncology Research, Vol., , DOI:10.32604/or.2024.046007

    Abstract Approximately 30%–40% of growth hormone–secreting pituitary adenomas (GHPAs) harbor somatic activating mutations in GNAS (α subunit of stimulatory G protein). Mutations in GNAS are associated with clinical features of smaller and less invasive tumors. However, the role of GNAS mutations in the invasiveness of GHPAs is unclear. GNAS mutations were detected in GHPAs using a standard polymerase chain reaction (PCR) sequencing procedure. The expression of mutation-associated maternally expressed gene 3 (MEG3) was evaluated with RT-qPCR. MEG3 was manipulated in GH3 cells using a lentiviral expression system. Cell invasion ability was measured using a Transwell assay, and epithelial–mesenchymal transition (EMT)-associated proteins… More >

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