Meng Wang¹, Jinghan Cai¹, Wenzheng Liu¹, Xue Yang¹, Jingjing Zhang¹, Qiangmin Zhou¹, Fanzhen Wang¹, Hang Zhang^1,*, Tonghai Liu^2,*

Phyton-International Journal of Experimental Botany, Vol.95, No.1, 2026, DOI:10.32604/phyton.2025.075541 - 30 January 2026

Abstract Tomato is a major economic crop worldwide, and diseases on tomato leaves can significantly reduce both yield and quality. Traditional manual inspection is inefficient and highly subjective, making it difficult to meet the requirements of early disease identification in complex natural environments. To address this issue, this study proposes an improved YOLO11-based model, YOLO-SPDNet (Scale Sequence Fusion, Position-Channel Attention, and Dual Enhancement Network). The model integrates the SEAM (Self-Ensembling Attention Mechanism) semantic enhancement module, the MLCA (Mixed Local Channel Attention) lightweight attention mechanism, and the SPA (Scale-Position-Detail Awareness) module composed of SSFF (Scale Sequence Feature… More >

Hasan Kaan Kucukerdem^*

Phyton-International Journal of Experimental Botany, Vol.95, No.1, 2026, DOI:10.32604/phyton.2026.074188 - 30 January 2026

Abstract Root-zone temperature (RZT) strongly affects plant growth, nutrient uptake and tolerance to environmental stress, making its regulation a key challenge in greenhouse cultivation in cold climates. This study aimed to assess the potential of passive techniques, namely black polyethylene mulch and row covers, for modifying RZT dynamics in lettuce (Lactuca sativa L.) production and to evaluate the predictive performance of the eXtreme Gradient Boosting (XGBoost) algorithm. Experiments were conducted in Iğdır, Türkiye, over a 61-day period, with soil temperature continuously monitored at depths of 1–30 cm under mulched and non-mulched conditions, alongside measurements of greenhouse air… More >

Raviduth Ramful^*

Journal of Renewable Materials, Vol.14, No.1, 2026, DOI:10.32604/jrm.2025.02025-0116 - 23 January 2026

Abstract Typically used thermal insulation materials such as foam insulation and fibreglass may pose notable health risks and environmental impacts thereby resulting in respiratory irritation and waste disposal issues, respectively. While these materials are affordable and display good thermal insulation, their unsustainable traits pertaining to an intensive manufacturing process and poor disposability are major concerns. Alternative insulation materials with enhanced sustainable characteristics are therefore being explored, and one type of material which has gained notable attention owing to its low carbon footprint and low thermal conductivity is natural fibre. Among the few review studies conducted on… More > Graphic Abstract

Fatemeh Ahmadi Abkenari^*, Melika Zandi, Shanmugapriya Gopalakrishnan

Journal of Cyber Security, Vol.8, pp. 93-109, 2026, DOI:10.32604/jcs.2026.074197 - 20 January 2026

Abstract Nowadays, cyberattacks are considered a significant threat not only to the reputation of organizations through the theft of customers’ data or reducing operational throughput, but also to their data ownership and the safety and security of their operations. In recent decades, machine learning techniques have been widely employed in cybersecurity research to detect various types of cyberattacks. In the domain of cybersecurity data, and especially in Trojan detection datasets, it is common for datasets to record multiple statistical measures for a single concept. We referred to them as SWMF features in this paper, which include… More >

Xiang Luo¹, Yuxuan Peng², Renghong Xie¹, Peng Li³, Yuwen Qian^3,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073700 - 12 January 2026

Abstract Deep learning has made significant progress in the field of oriented object detection for remote sensing images. However, existing methods still face challenges when dealing with difficult tasks such as multi-scale targets, complex backgrounds, and small objects in remote sensing. Maintaining model lightweight to address resource constraints in remote sensing scenarios while improving task completion for remote sensing tasks remains a research hotspot. Therefore, we propose an enhanced multi-scale feature extraction lightweight network EM-YOLO based on the YOLOv8s architecture, specifically optimized for the characteristics of large target scale variations, diverse orientations, and numerous small objects… More >

Yongmei Zhang^*, Tianxin Zhang, Linghua Tian

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073159 - 12 January 2026

Abstract Marine forecasting is critical for navigation safety and disaster prevention. However, traditional ocean numerical forecasting models are often limited by substantial errors and inadequate capture of temporal-spatial features. To address the limitations, the paper proposes a TimeXer-based numerical forecast correction model optimized by an exogenous-variable attention mechanism. The model treats target forecast values as internal variables, and incorporates historical temporal-spatial data and seven-day numerical forecast results from traditional models as external variables based on the embedding strategy of TimeXer. Using a self-attention structure, the model captures correlations between exogenous variables and target sequences, explores intrinsic More >

Wang Zhang^1,#, Haozhuo Cao^2,#, Qiangqiang Yao^1,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072948 - 12 January 2026

Abstract Recent advances in deep learning have significantly improved image deblurring; however, existing approaches still suffer from limited global context modeling, inadequate detail restoration, and poor texture or edge perception, especially under complex dynamic blur. To address these challenges, we propose the Multi-Resolution Fusion Network (MRFNet), a blind multi-scale deblurring framework that integrates progressive residual connectivity for hierarchical feature fusion. The network employs a three-stage design: (1) TransformerBlocks capture long-range dependencies and reconstruct coarse global structures; (2) Nonlinear Activation Free Blocks (NAFBlocks) enhance local detail representation and mid-level feature fusion; and (3) an optimized residual subnetwork… More >

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

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072493 - 12 January 2026

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 >

Minghui Li¹, Hongbo Li^1,*, Jiaqi Zhu², Xupeng Zhang¹

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072406 - 12 January 2026

Abstract To address the challenge of real-time detection of unauthorized drone intrusions in complex low-altitude urban environments such as parks and airports, this paper proposes an enhanced MBS-YOLO (Multi-Branch Small Target Detection YOLO) model for anti-drone object detection, based on the YOLOv8 architecture. To overcome the limitations of existing methods in detecting small objects within complex backgrounds, we designed a C2f-Pu module with excellent feature extraction capability and a more compact parameter set, aiming to reduce the model’s computational complexity. To improve multi-scale feature fusion, we construct a Multi-Branch Feature Pyramid Network (MB-FPN) that employs a… More >

Haohui Su¹, Xuan Zhang^1,*, Lvjun Zheng¹, Xiaojie Shen², Hua Liao¹

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072093 - 12 January 2026

Abstract Distributed Denial-of-Service (DDoS) attacks pose severe threats to Industrial Control Networks (ICNs), where service disruption can cause significant economic losses and operational risks. Existing signature-based methods are ineffective against novel attacks, and traditional machine learning models struggle to capture the complex temporal dependencies and dynamic traffic patterns inherent in ICN environments. To address these challenges, this study proposes a deep feature-driven hybrid framework that integrates Transformer, BiLSTM, and KNN to achieve accurate and robust DDoS detection. The Transformer component extracts global temporal dependencies from network traffic flows, while BiLSTM captures fine-grained sequential dynamics. The learned… More >