Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (1,197)
  • Open Access

    ARTICLE

    Impact of Window Layers on Selenium Distribution and Photovoltaic Performance in CdSexTe1−x/CdTe Solar Cells

    Junyan Tian1, Qingyuan Zhang1, Lili Wu1,2,*, Xia Hao1,2, Guanggen Zeng1, Wenwu Wang1, Jingquan Zhang1,2

    Chalcogenide Letters, Vol.23, No.1, 2026, DOI:10.32604/cl.2026.076362 - 26 January 2026

    Abstract The incorporation of the Se element in CdTe solar cells is critical, while the low bandgap CdSexTe1−x, formed by the interdiffusion of CdTe and CdSe during device preparation, can promote the carrier lifetime. Different window layers formed by CdSe w/o MZO or CdS have different Se distributions. This paper systematically evaluates the influence of four types of window layers (CdSe, CdS/CdSe, MZO/CdSe and MZO/CdS/CdSe) on the performance of CdTe solar cells, and focuses on the correlation between the window layers and the Se distribution characteristic, carrier recombination mechanism, and device efficiency. The results show that CdSe… More >

  • Open Access

    ARTICLE

    Enhanced COVID-19 and Viral Pneumonia Classification Using Customized EfficientNet-B0: A Comparative Analysis with VGG16 and ResNet50

    Williams Kyei*, Chunyong Yin, Kelvin Amos Nicodemas, Khagendra Darlami

    Journal on Artificial Intelligence, Vol.8, pp. 19-38, 2026, DOI:10.32604/jai.2026.074988 - 20 January 2026

    Abstract The COVID-19 pandemic has underscored the need for rapid and accurate diagnostic tools to differentiate respiratory infections from normal cases using chest X-rays (CXRs). Manual interpretation of CXRs is time-consuming and prone to errors, particularly in distinguishing COVID-19 from viral pneumonia. This research addresses these challenges by proposing a customized EfficientNet-B0 model for ternary classification (COVID-19, Viral Pneumonia, Normal) on the COVID-19 Radiography Database. Employing transfer learning with architectural modifications, including a tailored classification head and regularization techniques, the model achieves superior performance. Evaluated via accuracy, F1-score (macro-averaged), AUROC (macro-averaged), precision (macro-averaged), recall (macro-averaged), inference… More >

  • Open Access

    ARTICLE

    The Impact of SWMF Features on the Performance of Random Forest, LSTM and Neural Network Classifiers for Detecting Trojans

    Fatemeh Ahmadi Abkenari*, Melika Zandi, Shanmugapriya Gopalakrishnan

    Journal of Cyber Security, Vol.8, pp. 93-109, 2026, DOI:10.32604/jcs.2026.074197 - 20 January 2026

    Abstract Nowadays, cyberattacks are considered a significant threat not only to the reputation of organizations through the theft of customers’ data or reducing operational throughput, but also to their data ownership and the safety and security of their operations. In recent decades, machine learning techniques have been widely employed in cybersecurity research to detect various types of cyberattacks. In the domain of cybersecurity data, and especially in Trojan detection datasets, it is common for datasets to record multiple statistical measures for a single concept. We referred to them as SWMF features in this paper, which include… More >

  • Open Access

    ARTICLE

    Advancing Breast Cancer Molecular Subtyping: A Comparative Study of Convolutional Neural Networks and Vision Transformers on Mammograms

    Chee Chin Lim1,2,*, Hui Wen Tiu1, Qi Wei Oung1,3, Chiew Chea Lau4, Xiao Jian Tan2,5

    CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.070468 - 12 January 2026

    Abstract Breast cancer remains one of the leading causes of cancer mortality world-wide, with accurate molecular subtyping is critical for guiding treatment and improving patient outcomes. Traditional molecular subtyping via immuno-histochemistry (IHC) test is invasive, time-consuming, and may not fully represent tumor heterogeneity. This study proposes a non-invasive approach using digital mammography images and deep learning algorithm for classifying breast cancer molecular subtypes. Four pretrained models, including two Convolutional Neural Networks (MobileNet_V3_Large and VGG-16) and two Vision Transformers (ViT_B_16 and ViT_Base_Patch16_Clip_224) were fine-tuned to classify images into HER2-enriched, Luminal, Normal-like, and Triple Negative subtypes. Hyperparameter tuning,… More >

  • Open Access

    ARTICLE

    A Novel Quantitative Detection of Sleeve Grouting Compactness Based on Ultrasonic Time-Frequency Dual-Domain Analysis

    Longqi Liao1, Jing Li2, Yuhua Li3, Yuemin Wang3, Jinhua Li1,*, Liyuan Cao4,*, Chunxiang Li4,*

    Structural Durability & Health Monitoring, Vol.20, No.1, 2026, DOI:10.32604/sdhm.2025.072237 - 08 January 2026

    Abstract Quantitative detection of sleeve grouting compactness is a technical challenge in civil engineering testing. This study explores a novel quantitative detection method based on ultrasonic time-frequency dual-domain analysis. It establishes a mapping relationship between sleeve grouting compactness and characteristic parameters. First, this study made samples with gradient defects for two types of grouting sleeves, G18 and G20. These included four cases: 2D, 4D, 6D defects (where D is the diameter of the grouting sleeve), and no-defect. Then, an ultrasonic input/output data acquisition system was established. Three-dimensional sound field distribution data were obtained through an orthogonal… More >

  • Open Access

    ARTICLE

    Suppression of Dry-Coupled Rubber Layer Interference in Ultrasonic Thickness Measurement: A Comparative Study of Empirical Mode Decomposition Variants

    Weichen Wang1, Shaofeng Wang1, Wenjing Liu1,*, Luncai Zhou2, Erqing Zhang1, Ting Gao3, Grigory Petrishin4

    Structural Durability & Health Monitoring, Vol.20, No.1, 2026, DOI:10.32604/sdhm.2025.071278 - 08 January 2026

    Abstract In dry-coupled ultrasonic thickness measurement, thick rubber layers introduce high-amplitude parasitic echoes that obscure defect signals and degrade thickness accuracy. Existing methods struggle to resolve overlap-ping echoes under variable coupling conditions and non-stationary noise. This study proposes a novel dual-criterion framework integrating energy contribution and statistical impulsivity metrics to isolate specimen re-flections from coupling-layer interference. By decomposing A-scan signals into Intrinsic Mode Functions (IMFs), the framework employs energy contribution thresholds (>85%) and kurtosis indices (>3) to autonomously select IMFs containing valid specimen echoes. Hybrid time-frequency thresholding further suppresses interference through amplitude filtering and spectral focusing. More >

  • Open Access

    ARTICLE

    Evaluation of the Failure Impact of Jet Fire from Natural Gas Leakage on Parallel Pipelines

    Zezhi Wen1, Kai Zhang1, Shanlin Liang2, Liqiong Chen1,*, Zijian Xiong1

    Structural Durability & Health Monitoring, Vol.20, No.1, 2026, DOI:10.32604/sdhm.2025.066408 - 08 January 2026

    Abstract Maintaining the structural integrity of parallel natural gas pipelines during leakage-induced jet fires remains a critical engineering challenge. Existing methods often fail to account for the complex interactions among heat transfer, material behavior, and pipeline geometry, which can lead to overly simplified and potentially unsafe assessments. To address these limitations, this study develops a multiphysics approach that integrates small-orifice leakage theory with detailed thermo-fluid-structural simulations. The proposed framework contributes to a more accurate failure analysis through three main components: (1) coupled modeling that tracks transient heat flow and stress development as fire conditions evolve; (2)… More >

  • Open Access

    REVIEW

    Ferroptosis: Mechanisms, Comparison with Cuproptosis and Emerging Horizons in Therapeutics

    Shujie Yin1, Zong Li1, Wen-Bin Ou1,2,*

    Oncology Research, Vol.34, No.1, 2026, DOI:10.32604/or.2025.069049 - 30 December 2025

    Abstract Ferroptosis is an iron-dependent, excessive lipid peroxidation-driven form of regulated cell death. The core mechanisms of ferroptosis include lipid peroxidation cascade, System Xc-glutathioneglutathione peroxidase 4 axis, iron and lipid metabolism chaos, the NAD(P)Hferroptosis suppressor protein 1—ubiquinone axis, and GTP cyclohydrolase 1 tetrahydrobiopterin-dihydrofolate reductase axis. Cuproptosis is triggered by copper ions and involves ferredoxin 1-mediated aggregation of lipoylated proteins, differing fundamentally from ferroptosis. Both ferroptosis and cuproptosis exhibit dual roles (promote or inhibit) in cancers. And the sensitivity of different cancer types to ferroptosis varies, which may depend on special metabolic signatures (e.g., E-cadherin loss causes epithelial–mesenchymal More > Graphic Abstract

    Ferroptosis: Mechanisms, Comparison with Cuproptosis and Emerging Horizons in Therapeutics

  • Open Access

    ARTICLE

    Optimizing Resource Allocation in Blockchain Networks Using Neural Genetic Algorithm

    Malvinder Singh Bali1, Weiwei Jiang2,*, Saurav Verma3, Kanwalpreet Kour4, Ashwini Rao3

    CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-19, 2026, DOI:10.32604/cmc.2025.070866 - 09 December 2025

    Abstract In recent years, Blockchain Technology has become a paradigm shift, providing Transparent, Secure, and Decentralized platforms for diverse applications, ranging from Cryptocurrency to supply chain management. Nevertheless, the optimization of blockchain networks remains a critical challenge due to persistent issues such as latency, scalability, and energy consumption. This study proposes an innovative approach to Blockchain network optimization, drawing inspiration from principles of biological evolution and natural selection through evolutionary algorithms. Specifically, we explore the application of genetic algorithms, particle swarm optimization, and related evolutionary techniques to enhance the performance of blockchain networks. The proposed methodologies More >

  • Open Access

    ARTICLE

    Porosity-Impact Strength Relationship in Material Extrusion: Insights from MicroCT, and Computational Image Analysis

    Jia Yan Lim1,2, Siti Madiha Muhammad Amir3, Roslan Yahya3, Marta Peña Fernández2, Tze Chuen Yap1,*

    CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-19, 2026, DOI:10.32604/cmc.2025.070707 - 09 December 2025

    Abstract Additive Manufacturing, also known as 3D printing, has transformed conventional manufacturing by building objects layer by layer, with material extrusion or fused deposition modeling standing out as particularly popular. However, due to its manufacturing process and thermal nature, internal voids and pores are formed within the thermoplastic materials being fabricated, potentially leading to a decrease in mechanical properties. This paper discussed the effect of printing parameters on the porosity and the mechanical properties of the 3D printed polylactic acid (PLA) through micro-computed tomography (microCT), computational image analysis, and Charpy impact testing. The results for both… More >

Displaying 11-20 on page 2 of 1197. Per Page