Ulla Milbreta^1,2, Laura Andze¹, Juris Zoldners¹, Ilze Irbe¹, Marite Skute¹, Inese Filipova^1,*

Journal of Renewable Materials, Vol.13, No.3, pp. 583-597, 2025, DOI:10.32604/jrm.2024.057663 - 20 March 2025

Abstract This study investigated the potential of locally sourced mushrooms as a sustainable alternative to marine-derived chitosan in papermaking. Chitosan was extracted from four local (Boletus edulis, Suillus luteus, Leccinum aurantiacum, Suillus variegatus), one commercially available (Agaricus bisporus) and one laboratory-grown (Phanerochaete chrysosporium) fungal species. Paper handsheets were prepared using either 100% regenerated paper or a 50/50 blend of regenerated paper and hemp fibres. 2.5% chitosan (based on dry mass) was incorporated into the paper mass, using chitosan sourced from B. edulis, A. bisporus, P. chrysosporium, and crustacean chitosan. Fungal chitosan sources were selected based on multiple factors. B. edulis exhibited the highest chitosan yield… More >

Denis S. Ryabov¹, Igor V. Kosarev^2,3, Daxing Xiong⁴, Aleksey A. Kudreyko⁵, Sergey V. Dmitriev^2,6,*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 3797-3820, 2025, DOI:10.32604/cmc.2025.062079 - 06 March 2025

Abstract Molecular dynamics (MD) is a powerful method widely used in materials science and solid-state physics. The accuracy of MD simulations depends on the quality of the interatomic potentials. In this work, a special class of exact solutions to the equations of motion of atoms in a body-centered cubic (bcc) lattice is analyzed. These solutions take the form of delocalized nonlinear vibrational modes (DNVMs) and can serve as an excellent test of the accuracy of the interatomic potentials used in MD modeling for bcc crystals. The accuracy of the potentials can be checked by comparing the… More >

Kassymbek Ozhikenov¹, Zhadyra Alimbayeva^1,*, Chingiz Alimbayev^1,2,*, Aiman Ozhikenova¹, Yeldos Altay¹

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5257-5284, 2025, DOI:10.32604/cmc.2025.061508 - 06 March 2025

Abstract Myocardial infarction (MI) is one of the leading causes of death globally among cardiovascular diseases, necessitating modern and accurate diagnostics for cardiac patient conditions. Among the available functional diagnostic methods, electrocardiography (ECG) is particularly well-known for its ability to detect MI. However, confirming its accuracy—particularly in identifying the localization of myocardial damage—often presents challenges in practice. This study, therefore, proposes a new approach based on machine learning models for the analysis of 12-lead ECG data to accurately identify the localization of MI. In particular, the learning vector quantization (LVQ) algorithm was applied, considering the contribution… More >

Sachin Sharma^1,2,*, Brajesh Kumar Singh³, Hitendra Garg²

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4691-4708, 2025, DOI:10.32604/cmc.2025.061252 - 06 March 2025

Abstract Image tampering detection and localization have emerged as a critical domain in combating the pervasive issue of image manipulation due to the advancement of the large-scale availability of sophisticated image editing tools. The manual forgery localization is often reliant on forensic expertise. In recent times, machine learning (ML) and deep learning (DL) have shown promising results in automating image forgery localization. However, the ML-based method relies on hand-crafted features. Conversely, the DL method automatically extracts shallow spatial features to enhance the accuracy. However, DL-based methods lack the global co-relation of the features due to this… More >

Xinrong Zhang¹, Bo Chang^2,*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5079-5095, 2025, DOI:10.32604/cmc.2025.059469 - 06 March 2025

Abstract In the RSSI-based positioning algorithm, regarding the problem of a great conflict between precision and cost, a low-power and low-cost synergic localization algorithm is proposed, where effective methods are adopted in each phase of the localization process and fully use the detective information in the network to improve the positioning precision and robustness. In the ranging period, the power attenuation factor is obtained through the wireless channel modeling, and the RSSI value is transformed into distance. In the positioning period, the preferred reference nodes are used to calculate coordinates. In the position optimization period, Taylor… More >

Chengzhang Zhu^1,2, Ahmed Alasri¹, Tao Xu³, Yalong Xiao^1,2,*, Abdulrahman Noman¹, Raeed Alsabri¹, Xuanchu Duan⁴, Monir Abdullah⁵

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5153-5167, 2025, DOI:10.32604/cmc.2024.058647 - 06 March 2025

Abstract Globally, diabetic retinopathy (DR) is the primary cause of blindness, affecting millions of people worldwide. This widespread impact underscores the critical need for reliable and precise diagnostic techniques to ensure prompt diagnosis and effective treatment. Deep learning-based automated diagnosis for diabetic retinopathy can facilitate early detection and treatment. However, traditional deep learning models that focus on local views often learn feature representations that are less discriminative at the semantic level. On the other hand, models that focus on global semantic-level information might overlook critical, subtle local pathological features. To address this issue, we propose an… More >

Qi Mu^*, Yuanjie Guo, Xiangfu Ge, Xinyue Wang, Zhanli Li

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4669-4690, 2025, DOI:10.32604/cmc.2025.058495 - 06 March 2025

Abstract In low-light image enhancement, prevailing Retinex-based methods often struggle with precise illumination estimation and brightness modulation. This can result in issues such as halo artifacts, blurred edges, and diminished details in bright regions, particularly under non-uniform illumination conditions. We propose an innovative approach that refines low-light images by leveraging an in-depth awareness of local content within the image. By introducing multi-scale effective guided filtering, our method surpasses the limitations of traditional isotropic filters, such as Gaussian filters, in handling non-uniform illumination. It dynamically adjusts regularization parameters in response to local image characteristics and significantly integrates… More >

Gang Hao, Peng Liang^*, Ziyuan Li, Huimin Zhao, Hong Zhang

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5221-5238, 2025, DOI:10.32604/cmc.2025.055739 - 06 March 2025

Abstract In recent years, the detection of image copy-move forgery (CMFD) has become a critical challenge in verifying the authenticity of digital images, particularly as image manipulation techniques evolve rapidly. While deep convolutional neural networks (DCNNs) have been widely employed for CMFD tasks, they are often hindered by a notable limitation: the progressive reduction in spatial resolution during the encoding process, which leads to the loss of critical image details. These details are essential for the accurate detection and localization of image copy-move forgery. To overcome the limitations of existing methods, this paper proposes a Transformer-based… More >

Oswaldo González-Gaxiola¹, Anjan Biswas^2,3,4,*, Ahmed H. Arnous⁵, Yakup Yildirim^6,7

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2513-2525, 2025, DOI:10.32604/cmes.2025.062177 - 03 March 2025

Abstract Computational modeling plays a vital role in advancing our understanding and application of soliton theory. It allows researchers to both simulate and analyze complex soliton phenomena and discover new types of soliton solutions. In the present study, we computationally derive the bright and dark optical solitons for a Schrödinger equation that contains a specific type of nonlinearity. This nonlinearity in the model is the result of the combination of the parabolic law and the non-local law of self-phase modulation structures. The numerical simulation is accomplished through the application of an algorithm that integrates the classical… More >

Youngjin Hwang, Seungyoon Kang, Junseok Kim^*

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 1811-1838, 2025, DOI:10.32604/cmc.2025.061071 - 17 February 2025

Abstract This review paper provides a comprehensive introduction to various numerical methods for the phase-field model used to simulate the phase separation dynamics of diblock copolymer melts. Diblock copolymer systems form complex structures at the nanometer scale and play a significant role in various applications. The phase-field model, in particular, is essential for describing the formation and evolution of these structures and is widely used as a tool to effectively predict the movement of phase boundaries and the distribution of phases over time. In this paper, we discuss the principles and implementations of various numerical methodologies More >