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  • Open Access

    PROCEEDINGS

    AI-Assisted Generative Inverse Design of Heterogeneous Meta-Biomaterials Based on TPMS for Biomimetic Tissue Engineering

    Xiaolong Zhu, Feng Chen, Yuntian Chen, Wei Zhu, Xiaoxiao Han*

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.33, No.3, pp. 1-1, 2025, DOI:10.32604/icces.2025.012584

    Abstract Human tissues and organs exhibit not only intricate anatomical architectures but also spatially heterogeneous distributions of elastic modulus—for example, between cancellous and cortical bone, across the epidermis, dermis, and subcutaneous layers, and between healthy and fibrotic liver tissues. Conventional biomaterials often fail to replicate such mechanical heterogeneity, thereby limiting their capacity to recreate biomimetic physiological microenvironments essential for applications like tissue regeneration and disease modeling. Meta-biomaterials, artificially engineered through the rational structural design of continuous materials, have emerged as a promising class of materials owing to their highly tunable mechanical and biological properties. These attributes… More >

  • Open Access

    ARTICLE

    Integrated Sharing Platform for Genetic Data of Rare and Precious Metal Materials

    Lin Huang1,2, Ying Zhou2, Jingjing Yang1,*

    CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4587-4606, 2025, DOI:10.32604/cmc.2025.068370 - 23 October 2025

    Abstract The construction of centralized and standardized material databases is essential to support both scientific innovation and industrial application. However, for rare and precious metal materials, existing data resources are often decentralized. This results in persistent issues such as data silos and fragmentation, which significantly hinder efficient data utilization and collaboration. In response to these challenges, this study investigates the development of an integrated platform for sharing genetic data of rare and precious metal materials. The research begins by analyzing current trends in material data platforms, both domestically and internationally. These insights help inform the architectural… More >

  • Open Access

    REVIEW

    A Review of PEO (Polyethylene Oxide) Assisted Electrospinning of Chitosan: Innovation, Production, and Application

    Md. Tanvir Raihan1, Md. Himel Mahmud2, Badhon Chandra Mazumder2, Md. Nazif Hasan Chowdhury3, Mohammad Tajul Islam1,*

    Journal of Polymer Materials, Vol.42, No.3, pp. 677-711, 2025, DOI:10.32604/jpm.2025.068356 - 30 September 2025

    Abstract Electrospinning has gained significant importance across various fields, including biomedicine, filtration, and packaging due to the control it provides over the properties of the resulting materials, such as fiber diameter and membrane thickness. Chitosan is a biopolymer that can be utilized with both natural and synthetic copolymers, owing to its therapeutic potential, biocompatibility, and biodegradability. However, producing electrospun chitosan is challenging due to its high solution viscosity, which often results in the formation of beads instead of uniform fibers. To address this issue, the spinnability of chitosan is significantly enhanced, and the production of continuous More >

  • Open Access

    ARTICLE

    Type-I Heavy-Tailed Burr XII Distribution with Applications to Quality Control, Skewed Reliability Engineering Systems and Lifetime Data

    Okechukwu J. Obulezi1,*, Hatem E. Semary2, Sadia Nadir3, Chinyere P. Igbokwe4, Gabriel O. Orji1, A. S. Al-Moisheer2, Mohammed Elgarhy5

    CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 2991-3027, 2025, DOI:10.32604/cmes.2025.069553 - 30 September 2025

    Abstract This study introduces the type-I heavy-tailed Burr XII (TIHTBXII) distribution, a highly flexible and robust statistical model designed to address the limitations of conventional distributions in analyzing data characterized by skewness, heavy tails, and diverse hazard behaviors. We meticulously develop the TIHTBXII’s mathematical foundations, including its probability density function (PDF), cumulative distribution function (CDF), and essential statistical properties, crucial for theoretical understanding and practical application. A comprehensive Monte Carlo simulation evaluates four parameter estimation methods: maximum likelihood (MLE), maximum product spacing (MPS), least squares (LS), and weighted least squares (WLS). The simulation results consistently show… More >

  • Open Access

    ARTICLE

    ScalaDetect-5G: Ultra High-Precision Highly Elastic Deep Intrusion Detection System for 5G Network

    Shengjia Chang, Baojiang Cui*, Shaocong Feng

    CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3805-3827, 2025, DOI:10.32604/cmes.2025.067756 - 30 September 2025

    Abstract With the rapid advancement of mobile communication networks, key technologies such as Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV) have enhanced the quality of service for 5G users but have also significantly increased the complexity of network threats. Traditional static defense mechanisms are inadequate for addressing the dynamic and heterogeneous nature of modern attack vectors. To overcome these challenges, this paper presents a novel algorithmic framework, SD-5G, designed for high-precision intrusion detection in 5G environments. SD-5G adopts a three-stage architecture comprising traffic feature extraction, elastic representation, and adaptive classification. Specifically, an enhanced Concrete… More >

  • Open Access

    PROCEEDINGS

    Cryogenic 3D Printing: A New Approach to Produce Hard Polyester-Based Tissue Engineering Scaffolds with In Situ Dual Delivery of Growth Factors and Cells

    Chong Wang*

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.33, No.1, pp. 1-1, 2025, DOI:10.32604/icces.2025.012164

    Abstract Please Creating mechanically robust tissue engineering scaffolds capable of delivering growth factors and stem cells in situ for hard tissue repair remains a significant challenge. Inspired by the spiral structure of ice cream, our group developed an advanced 3D printing technique known as cryogenic 3D printing to fabricate polyester-based scaffolds with embedded growth factors. This method utilizes water-in-oil (w/o) polyester emulsions containing growth factors as the printing ink, which is patterned onto a cryogenic substrate. The resulting scaffolds feature a hierarchically porous structure, allowing mesenchymal stem cells (MSCs) to easily attach and proliferate. Additionally, the… More >

  • Open Access

    PROCEEDINGS

    From Cell to Cell-Free Strategies: New Developments in Cartilage and Cardiac Tissue Repair

    Xiaolin Cui1, Khoon Lim2,*

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.33, No.1, pp. 1-2, 2025, DOI:10.32604/icces.2025.011173

    Abstract 1 Introduction
    Joint and cardiovascular diseases, such as osteoarthritis (OA) and myocardial infarction (MI), pose significant clinical challenges due to their limited regenerative capacity. The key to mitigating tissue damage and preventing the progression of OA and MI is to repair or even regenerate the infarcted tissue. At present, cell-based therapy is the primary strategy for tissue repair. Delivered cells could either differentiate into functional cells or secrete paracrine signals to promote onsite cell function. Both mechanisms have demonstrated potential in cartilage and cardiac tissue repair, both preclinically and clinically. In addition to cell therapy,… More >

  • Open Access

    ARTICLE

    Redefining the Programmer: Human-AI Collaboration, LLMs, and Security in Modern Software Engineering

    Elyson De La Cruz*, Hanh Le, Karthik Meduri, Geeta Sandeep Nadella*, Hari Gonaygunta

    CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3569-3582, 2025, DOI:10.32604/cmc.2025.068137 - 23 September 2025

    Abstract The rapid integration of artificial intelligence (AI) into software development, driven by large language models (LLMs), is reshaping the role of programmers from traditional coders into strategic collaborators within Industry 4.0 ecosystems. This qualitative study employs a hermeneutic phenomenological approach to explore the lived experiences of Information Technology (IT) professionals as they navigate a dynamic technological landscape marked by intelligent automation, shifting professional identities, and emerging ethical concerns. Findings indicate that developers are actively adapting to AI-augmented environments by engaging in continuous upskilling, prompt engineering, interdisciplinary collaboration, and heightened ethical awareness. However, participants also voiced… More >

  • Open Access

    ARTICLE

    Intelligent Estimation of ESR and C in AECs for Buck Converters Using Signal Processing and ML Regression

    Acácio M. R. Amaral1,2,*

    CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3825-3859, 2025, DOI:10.32604/cmc.2025.067179 - 23 September 2025

    Abstract Power converters are essential components in modern life, being widely used in industry, automation, transportation, and household appliances. In many critical applications, their failure can lead not only to financial losses due to operational downtime but also to serious risks to human safety. The capacitors forming the output filter, typically aluminum electrolytic capacitors (AECs), are among the most critical and susceptible components in power converters. The electrolyte in AECs often evaporates over time, causing the internal resistance to rise and the capacitance to drop, ultimately leading to component failure. Detecting this fault requires measuring the… More >

  • Open Access

    ARTICLE

    A Flexible Exponential Log-Logistic Distribution for Modeling Complex Failure Behaviors in Reliability and Engineering Data

    Hadeel AlQadi1, Fatimah M. Alghamdi2, Hamada H. Hassan3, Mohamed E. Mead4, Ahmed Z. Afify5,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 2029-2061, 2025, DOI:10.32604/cmes.2025.069801 - 31 August 2025

    Abstract Parametric survival models are essential for analyzing time-to-event data in fields such as engineering and biomedicine. While the log-logistic distribution is popular for its simplicity and closed-form expressions, it often lacks the flexibility needed to capture complex hazard patterns. In this article, we propose a novel extension of the classical log-logistic distribution, termed the new exponential log-logistic (NExLL) distribution, designed to provide enhanced flexibility in modeling time-to-event data with complex failure behaviors. The NExLL model incorporates a new exponential generator to expand the shape adaptability of the baseline log-logistic distribution, allowing it to capture a… More >

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