Ye Zeng^1,*, Bingmei M. FU^2,*

BIOCELL, Vol.49, No.6, pp. 925-930, 2025, DOI:10.32604/biocell.2025.065879 - 24 June 2025

Abstract This article has no abstract. More >

Ya-Jie Sun¹, Li-Wei Qiao¹, Sai Ji^1,2,*

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1769-1785, 2025, DOI:10.32604/cmc.2025.062950 - 09 June 2025

Abstract Vehicle re-identification involves matching images of vehicles across varying camera views. The diversity of camera locations along different roadways leads to significant intra-class variation and only minimal inter-class similarity in the collected vehicle images, which increases the complexity of re-identification tasks. To tackle these challenges, this study proposes AG-GCN (Attention-Guided Graph Convolutional Network), a novel framework integrating several pivotal components. Initially, AG-GCN embeds a lightweight attention module within the ResNet-50 structure to learn feature weights automatically, thereby improving the representation of vehicle features globally by highlighting salient features and suppressing extraneous ones. Moreover, AG-GCN adopts More >

Hussam Qushtom, Ahmad Hasasneh^*, Sari Masri

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1379-1395, 2025, DOI:10.32604/cmc.2025.061995 - 09 June 2025

Abstract This study presents an enhanced convolutional neural network (CNN) model integrated with Explainable Artificial Intelligence (XAI) techniques for accurate prediction and interpretation of wheat crop diseases. The aim is to streamline the detection process while offering transparent insights into the model’s decision-making to support effective disease management. To evaluate the model, a dataset was collected from wheat fields in Kotli, Azad Kashmir, Pakistan, and tested across multiple data splits. The proposed model demonstrates improved stability, faster convergence, and higher classification accuracy. The results show significant improvements in prediction accuracy and stability compared to prior works,… More >

Trong Hieu Luu¹, Phan Nguyen Ky Phuc², Quang Hieu Ngo^1,*, Thanh Tam Nguyen³, Huu Cuong Nguyen¹

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5643-5656, 2025, DOI:10.32604/cmc.2025.064007 - 19 May 2025

Abstract This study presents a drone-based aerial imaging method for automated rice seedling detection and counting in paddy fields. Utilizing a drone equipped with a high-resolution camera, images are captured 14 days post-sowing at a consistent altitude of six meters, employing autonomous flight for uniform data acquisition. The approach effectively addresses the distinct growth patterns of both single and clustered rice seedlings at this early stage. The methodology follows a two-step process: first, the GoogleNet deep learning network identifies the location and center points of rice plants. Then, the U-Net deep learning network performs classification and… More >

Yuhang Li¹, Li Sun¹, Tao Wang¹, Bingjun Yu², Zhihong Gao³, Xiaochun Shu¹, Tengyue Yan¹, Weibing Zhuang^1,2,*, Zhong Wang^1,*

Phyton-International Journal of Experimental Botany, Vol.94, No.4, pp. 1211-1223, 2025, DOI:10.32604/phyton.2025.063647 - 30 April 2025

Abstract bHLH transcription factors, widely exist in various plants, and are vital for the growth and development of these plants. Among them, many have been implicated in anthocyanin biosynthesis across various plants. In the present study, a PdbHLH57 gene, belonging to the bHLH IIIf group, was characterized, which was isolated and cloned from the colored-leaf poplar ‘Zhongshancaiyun’ (ZSCY). The cDNA sequence of PdbHLH57 was 1887 base pairs, and the protein encoded by PdbHLH57 had 628 amino acids, the isoelectric point and molecular weight of which were 6.26 and 69.75 kDa, respectively. Through bioinformatics analysis, PdbHLH57 has been classified… More >

Poonam Sharma¹, Bhisham Sharma^2,*, Dhirendra Prasad Yadav³, Surbhi Bhatia Khan^4,5,6,*, Ahlam Almusharraf⁷

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3227-3245, 2025, DOI:10.32604/cmc.2025.062376 - 16 April 2025

Abstract Rapid and precise diagnostic tools for Monkeypox (Mpox) lesions are crucial for effective treatment because their symptoms are similar to those of other pox-related illnesses, like smallpox and chickenpox. The morphological similarities between smallpox, chickenpox, and monkeypox, particularly in how they appear as rashes and skin lesions, which can sometimes make diagnosis challenging. Chickenpox lesions appear in many simultaneous phases and are more diffuse, often beginning on the trunk. In contrast, monkeypox lesions emerge progressively and are typically centralized on the face, palms, and soles. To provide accessible diagnostics, this study introduces a novel method… More >

Zhiyong Li, Xinlian Zhou^*

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 739-760, 2025, DOI:10.32604/cmc.2025.059807 - 26 March 2025

Abstract Brain tumor classification is crucial for personalized treatment planning. Although deep learning-based Artificial Intelligence (AI) models can automatically analyze tumor images, fine details of small tumor regions may be overlooked during global feature extraction. Therefore, we propose a brain tumor Magnetic Resonance Imaging (MRI) classification model based on a global-local parallel dual-branch structure. The global branch employs ResNet50 with a Multi-Head Self-Attention (MHSA) to capture global contextual information from whole brain images, while the local branch utilizes VGG16 to extract fine-grained features from segmented brain tumor regions. The features from both branches are processed through More >

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 >