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  • Open Access

    REVIEW

    Public Health Implications of Road Construction and Traffic Congestion in a Hydrocarbon-Polluted Environment: An Assessment of Air and Noise Pollution

    Idongesit Sunday Ambrose1, Sunday Edet Etuk2, Okechukwu Ebuka Agbasi3,*, Ijah Ioryue Silas4, Unyime Udoette Saturday5, Eyo Edet Orok6

    Revue Internationale de Géomatique, Vol.34, pp. 335-350, 2025, DOI:10.32604/rig.2025.064552 - 13 June 2025

    Abstract Road construction and traffic congestion are increasingly recognized as major contributors to environmental and public health challenges in urban Nigeria, particularly in Rivers State. Despite growing urbanization, a gap remains in localized data on the combined effects of air and noise pollution in hydrocarbon-polluted environments. This study addresses that gap by conducting a preliminary environmental health assessment focused on the Port Harcourt Ring Road project. Air quality and noise levels were monitored in situ at 20 strategically selected locations, with five control points included for baseline comparison. Digital portable meters were used to measure concentrations of… More >

  • Open Access

    ARTICLE

    DEMGAN: A Machine Learning-Based Intrusion Detection System Evasion Scheme

    Dawei Xu1,2,3, Yue Lv1, Min Wang1, Baokun Zheng4,*, Jian Zhao1,3, Jiaxuan Yu5

    CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1731-1746, 2025, DOI:10.32604/cmc.2025.064833 - 09 June 2025

    Abstract Network intrusion detection systems (IDS) are a prevalent method for safeguarding network traffic against attacks. However, existing IDS primarily depend on machine learning (ML) models, which are vulnerable to evasion through adversarial examples. In recent years, the Wasserstein Generative Adversarial Network (WGAN), based on Wasserstein distance, has been extensively utilized to generate adversarial examples. Nevertheless, several challenges persist: (1) WGAN experiences the mode collapse problem when generating multi-category network traffic data, leading to subpar quality and insufficient diversity in the generated data; (2) Due to unstable training processes, the authenticity of the data produced by… More >

  • Open Access

    ARTICLE

    Determination of Favorable Factors for Cloud IP Recognition Technology

    Yuanyuan Ma1, Cunzhi Hou1, Ang Chen1, Jinghui Zhang1, Ruixia Jin2, Ruixiang Li3,*

    CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1437-1456, 2025, DOI:10.32604/cmc.2025.064523 - 09 June 2025

    Abstract Identifying cloud IP usage scenarios is critical for cybersecurity applications, yet existing machine learning methods rely heavily on numerous features, resulting in high complexity and low interpretability. To address these issues, this paper proposes an approach to identify cloud IPs from the perspective of network attributes. We employ data mining and crowdsourced collection strategies to gather IP addresses from various usage scenarios, which including cloud IPs and non-cloud IPs. On this basis, we establish a cloud IP identification feature set that includes attributes such as Autonomous System Number (ASN) and organization information. By analyzing the… More >

  • Open Access

    ARTICLE

    Edge-Based Data Hiding and Extraction Algorithm to Increase Payload Capacity and Data Security

    Hanan Hardan1,*, Osama A. Khashan2,*, Mohammad Alshinwan1

    CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1681-1710, 2025, DOI:10.32604/cmc.2025.061659 - 09 June 2025

    Abstract This study introduces an Edge-Based Data Hiding and Extraction Algorithm (EBDHEA) to address the problem of data embedding in images while preserving robust security and high image quality. The algorithm produces three classes of pixels from the pixels in the cover image: edges found by the Canny edge detection method, pixels arising from the expansion of neighboring edge pixels, and pixels that are neither edges nor components of the neighboring edge pixels. The number of Least Significant Bits (LSBs) that are used to hide data depends on these classifications. Furthermore, the lossless compression method, Huffman… More >

  • Open Access

    ARTICLE

    FuzzyStego: An Adaptive Steganographic Scheme Using Fuzzy Logic for Optimizing Embeddable Areas in Spatial Domain Images

    Mardhatillah Shevy Ananti1, Adifa Widyadhani Chanda D’Layla1, Ntivuguruzwa Jean De La Croix1,2, Tohari Ahmad1,*

    CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1031-1054, 2025, DOI:10.32604/cmc.2025.061246 - 09 June 2025

    Abstract In the evolving landscape of secure communication, steganography has become increasingly vital to secure the transmission of secret data through an insecure public network. Several steganographic algorithms have been proposed using digital images with a common objective of balancing a trade-off between the payload size and the quality of the stego image. In the existing steganographic works, a remarkable distortion of the stego image persists when the payload size is increased, making several existing works impractical to the current world of vast data. This paper introduces FuzzyStego, a novel approach designed to enhance the stego… More >

  • Open Access

    ARTICLE

    Numerical Treatments for a Crossover Cholera Mathematical Model Combining Different Fractional Derivatives Based on Nonsingular and Singular Kernels

    Seham M. AL-Mekhlafi1,*, Kamal R. Raslan2, Khalid K. Ali2, Sadam. H. Alssad2,3, Nehaya R. Alsenaideh4

    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1927-1953, 2025, DOI:10.32604/cmes.2025.063971 - 30 May 2025

    Abstract This study introduces a novel mathematical model to describe the progression of cholera by integrating fractional derivatives with both singular and non-singular kernels alongside stochastic differential equations over four distinct time intervals. The model incorporates three key fractional derivatives: the Caputo-Fabrizio fractional derivative with a non-singular kernel, the Caputo proportional constant fractional derivative with a singular kernel, and the Atangana-Baleanu fractional derivative with a non-singular kernel. We analyze the stability of the core model and apply various numerical methods to approximate the proposed crossover model. To achieve this, the approximation of Caputo proportional constant fractional… More >

  • Open Access

    ARTICLE

    Exploring the correlation and mechanism of natural killer cell cytotoxic sensitivity against gastric cancer

    WENZHUO YANG1,#, HAODONG CHEN2,#, ZHILAN ZHANG3, ZHIYONG XIA3, YUANYUAN JIN1,*, ZHAOYONG YANG1,*

    Oncology Research, Vol.33, No.6, pp. 1485-1494, 2025, DOI:10.32604/or.2025.059426 - 29 May 2025

    Abstract Background: Human natural killer (NK) cells have attracted widespread attention as a potential adoptive cell therapy (ACT). However, the therapeutic effects of NK cell infusion in patients with solid tumors are limited. There is an urgent need to explore a suitable new treatment plan to overcome weaknesses and support the superior therapeutic activity of NK cells. Methods: In this study, the mechanisms underlying the susceptibility of gastric cancer (GC) cell lines AGS, HGC-27, and NCI-N87 to NK cell-mediated cytotoxicity were explored. Results: Lactic dehydrogenase (LDH) release assays showed that all three GC cell lines were susceptible… More >

  • Open Access

    REVIEW

    From Model Organism to Pharmaceutical Powerhouse: Innovative Applications of Yeast in Modern Drug Research

    Xiaobing Li1,2, Yongsheng Liu1, Limin Wei1, Li Rao1, Jingxin Mao1,*, Xuemei Li3,*

    BIOCELL, Vol.49, No.5, pp. 813-832, 2025, DOI:10.32604/biocell.2025.062124 - 27 May 2025

    Abstract Yeast-based models have become a powerful platform in pharmaceutical research, offering significant potential for producing complex drugs, vaccines, and therapeutic agents. While many current drugs were discovered before fully understanding their molecular mechanisms, yeast systems now provide valuable insights for drug discovery and personalized medicine. Recent advancements in genetic engineering, metabolic engineering, and synthetic biology have improved the efficiency and scalability of yeast-based production systems, enabling more sustainable and cost-effective manufacturing processes. This paper reviews the latest developments in yeast-based technologies, focusing on their use as model organisms to study disease mechanisms, identify drug targets,… More >

  • Open Access

    ARTICLE

    Expo-GAN: A Style Transfer Generative Adversarial Network for Exhibition Hall Design Based on Optimized Cyclic and Neural Architecture Search

    Qing Xie*, Ruiyun Yu

    CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4757-4774, 2025, DOI:10.32604/cmc.2025.063345 - 19 May 2025

    Abstract This study presents a groundbreaking method named Expo-GAN (Exposition-Generative Adversarial Network) for style transfer in exhibition hall design, using a refined version of the Cycle Generative Adversarial Network (CycleGAN). The primary goal is to enhance the transformation of image styles while maintaining visual consistency, an area where current CycleGAN models often fall short. These traditional models typically face difficulties in accurately capturing expansive features as well as the intricate stylistic details necessary for high-quality image transformation. To address these limitations, the research introduces several key modifications to the CycleGAN architecture. Enhancements to the generator involve… More >

  • Open Access

    ARTICLE

    CFGANLDA: A Collaborative Filtering and Graph Attention Network-Based Method for Predicting Associations between lncRNAs and Diseases

    Dang Hung Tran, Van Tinh Nguyen*

    CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4679-4698, 2025, DOI:10.32604/cmc.2025.063228 - 19 May 2025

    Abstract It is known that long non-coding RNAs (lncRNAs) play vital roles in biological processes and contribute to the progression, development, and treatment of various diseases. Obviously, understanding associations between diseases and lncRNAs significantly enhances our ability to interpret disease mechanisms. Nevertheless, the process of determining lncRNA-disease associations is costly, labor-intensive, and time-consuming. Hence, it is expected to foster computational strategies to uncover lncRNA-disease relationships for further verification to save time and resources. In this study, a collaborative filtering and graph attention network-based LncRNA-Disease Association (CFGANLDA) method was nominated to expose potential lncRNA-disease associations. First, it… More >

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