Chia-Hui Liu^*

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.072887

Abstract The Industrial Internet of Things (IIoT) has emerged as a cornerstone of Industry 4.0, enabling large-scale automation and data-driven decision-making across factories, supply chains, and critical infrastructures. However, the massive interconnection of resource-constrained devices also amplifies the risks of eavesdropping, data tampering, and device impersonation. While digital signatures are indispensable for ensuring authenticity and non-repudiation, conventional schemes such as RSA and ECC are vulnerable to quantum algorithms, jeopardizing long-term trust in IIoT deployments. This study proposes a lightweight, stateless, hash-based signature scheme that achieves post-quantum security while addressing the stringent efficiency demands of IIoT. The… More >

Sushruta Mishra¹, Sunil Kumar Mohapatra², Kshira Sagar Sahoo³, Anand Nayyar⁴, Tae-Kyung Kim^5,*

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.072493

Abstract With an increase in internet-connected devices and a dependency on online services, the threat of Distributed Denial of Service (DDoS) attacks has become a significant concern in cybersecurity. The proposed system follows a multi-step process, beginning with the collection of datasets from different edge devices and network nodes. To verify its effectiveness, experiments were conducted using the CICDoS2017, NSL-KDD, and CICIDS benchmark datasets alongside other existing models. Recursive feature elimination (RFE) with random forest is used to select features from the CICDDoS2019 dataset, on which a BiLSTM model is trained on local nodes. Local models… More >

Mingming Huang¹, Yunfan Ye^1,*, Zhiping Cai²

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.072399

Abstract As a fundamental component in computer vision, edges can be categorized into four types based on discontinuities in reflectance, illumination, surface normal, or depth. While deep CNNs have significantly advanced generic edge detection, real-time multi-class semantic edge detection under resource constraints remains challenging. To address this, we propose a lightweight framework based on PiDiNet that enables fine-grained semantic edge detection. Our model simultaneously predicts background and four edge categories from full-resolution inputs, balancing accuracy and efficiency. Key contributions include: a multi-channel output structure expanding binary edge prediction to five classes, supported by a deep supervision More >

Yuyao Huang, Hui Shu, Fei Kang^*

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.071552

Abstract Message structure reconstruction is a critical task in protocol reverse engineering, aiming to recover protocol field structures without access to source code. It enables important applications in network security, including malware analysis and protocol fuzzing. However, existing methods suffer from inaccurate field boundary delineation and lack hierarchical relationship recovery, resulting in imprecise and incomplete reconstructions. In this paper, we propose , a novel method for reconstructing protocol field structures based on program execution slice embedding. extracts code slices from protocol parsing at runtime, converts them into embedding vectors using a data flow-sensitive assembly language model, More >

Ran Liu, Yawen Chen, Dong Yang^*, Jingjing Yang^*

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.070734

Abstract In the field of smart agriculture, accurate and efficient object detection technology is crucial for automated crop management. A particularly challenging task in this domain is small object detection, such as the identification of immature fruits or early stage disease spots. These objects pose significant difficulties due to their small pixel coverage, limited feature information, substantial scale variations, and high susceptibility to complex background interference. These challenges frequently result in inadequate accuracy and robustness in current detection models. This study addresses two critical needs in the cashew cultivation industry—fruit maturity and anthracnose detection—by proposing an… More >

Jiajun Sun, Shunshun Ji, Chao Zhang^*

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.068668

Abstract Asparagus stem blight is a devastating crop disease, and the early detection of its pathogenic spores is essential for effective disease control and prevention. However, spore detection is still hindered by complex backgrounds, small target sizes, and high annotation costs, which limit its practical application and widespread adoption. To address these issues, a semi-supervised spore detection framework is proposed for use under complex background conditions. Firstly, a difficulty perception scoring function is designed to quantify the detection difficulty of each image region. For regions with higher difficulty scores, a masking strategy is applied, while the… More >

Hongji Chen, Jianxun Zhang^*, Tianze Yu, Yingzhu Zeng, Huan Zeng

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.067041

Abstract Low-light image enhancement aims to improve the visibility of severely degraded images captured under insufficient illumination, alleviating the adverse effects of illumination degradation on image quality. Traditional Retinex-based approaches, inspired by human visual perception of brightness and color, decompose an image into illumination and reflectance components to restore fine details. However, their limited capacity for handling noise and complex lighting conditions often leads to distortions and artifacts in the enhanced results, particularly under extreme low-light scenarios. Although deep learning methods built upon Retinex theory have recently advanced the field, most still suffer from insufficient interpretability… More >

Iván De-la-Cruz-Chacón, Christian Anabí Riley-Saldaña, Marisol Castro-Moreno, Claudia Azucena Durán-Ruiz, Alma Rosa González-Esquinca^*

Phyton-International Journal of Experimental Botany, DOI:10.32604/phyton.2025.072586

Abstract The seedling phase represents an initial and critical stage for the establishment of individuals in the ecosystem. During this stage, specialized metabolites contribute to survival; however, studies analyzing the presence of these molecules and the reasons for their production and accumulation are still scarce. Annonaceae is a botanical family recognized for the chemodiversity of its secondary metabolites; nearly 1000 alkaloids have been reported in approximately 150 adult specimens. The aim of this study was to determine whether alkaloid biosynthesis in Annonaceae is expressed from early stages. For this purpose, Annona macroprophyllata, Annona muricata, Annona purpurea, and Annona reticulata seedlings,… More >

Yue Ma^1,2, Douwen Qin^1,2, Weiqiang Liu^1,2, Xiuting Ju^1,2,*

Phyton-International Journal of Experimental Botany, DOI:10.32604/phyton.2025.069643

Abstract Comprehensive evaluation based on mineral element content is one of the effective methods for the exploration and utilization of wild tulip germplasm resources. In this study, Tulipa iliensis, Tulipa tianschanica and Tulipa heterophylla distributed in China were used as the research objects. The contents of 10 mineral elements (N, K, P, S, Ca, Mg, Cu, Zn, Fe, Mn) in roots, bulbs and leaves were determined, and the three wild tulips were comprehensively evaluated by correlation analysis, principal component analysis and cluster analysis. The results showed distinct variations in mineral element content among different organs of T. iliensis, T. tianschanica and T.… More >

Xu Guo¹, Yanan Yu², Xiaolin Ma³, Yuanjie Cai^1,*

Oncology Research, DOI:10.32604/or.2025.069274

Abstract Background: Long non-coding RNAs (lncRNAs) act as epigenetic regulators for tumor hallmarks. This investigation sought to probe the carcinogenic trait of PAN3-AS1 across pan-cancer comprehensively. Methods: We studied the diagnostic and prognostic features and the immune landscape of PAN3-AS1 across pan-cancer by bioinformatics approaches. The hierarchical regulatory networks governing PAN3-AS1 expression in colon cancer were explored via chromatin immunoprecipitation, luciferase activity assays, and RNA immunoprecipitation, etc. We screened drugs sensitive to WAP four-disulfide core domain 13 (WFDC13) by virtual screening and molecular docking. Results: Single-cell transcriptomics demonstrated that a variety of immune populations abnormally expressed PAN3-AS1… More >