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  • Open Access

    ARTICLE

    YOLO-SPDNet: Multi-Scale Sequence and Attention-Based Tomato Leaf Disease Detection Model

    Meng Wang1, Jinghan Cai1, Wenzheng Liu1, Xue Yang1, Jingjing Zhang1, Qiangmin Zhou1, Fanzhen Wang1, Hang Zhang1,*, Tonghai Liu2,*

    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 >

  • Open Access

    ARTICLE

    Integrative Analysis of Genetic-Ecological Factors Shaping Epimedium Chemical Diversity

    Ziying Huang1, Ruikang Ma1, Anning Li2, Yufei Cheng1, Xiaolin Lin2, Mengzhi Li3, Yu Zhang2, Liping Shi1, Linlin Dong1,*

    Phyton-International Journal of Experimental Botany, Vol.95, No.1, 2026, DOI:10.32604/phyton.2025.074419 - 30 January 2026

    Abstract Epimedium is commonly used to treat bone injury and kidney disease, with prenylated flavonol glycosides (PFGs) as its active ingredients. It has attracted much attention due to prominent healthcare and therapeutic effects, but faces problems of adulteration with closely related species and confusion about geographical origins. In this study, multiple technical approaches were employed to identify its genetic characteristics and metabolic differences. Based on DNA barcoding, 20 batches of samples were analyzed. The genetic distances of matK, ITS and psbA-trnH within species were all smaller than those between species, and psbA-trnH along with ITS + psbA-trnH proved most effective… More >

  • Open Access

    ARTICLE

    The Connection Paradox: How Social Support Facilitates Short Video Addiction and Solitary Well-Being among Older Adults in China

    Yue Cui1, Ziqing Yang2, Hao Gao1,*

    International Journal of Mental Health Promotion, Vol.28, No.1, 2026, DOI:10.32604/ijmhp.2025.072986 - 28 January 2026

    Abstract Background: In the Chinese context, the impact of short video applications on the psychological well-being of older adults is contested. While often examined through a pathological lens of addiction, this perspective may overlook paradoxical, context-dependent positive outcomes. Therefore, the main objective of this study is to challenge the traditional Compensatory Internet Use Theory by proposing and testing a chained mediation model that explores a paradoxical pathway from social support to life satisfaction via problematic social media use. Methods: Data were collected between July and August 2025 via the Credamo online survey platform, yielding 384 valid responses… More >

  • Open Access

    ARTICLE

    An Integrated DNN-FEA Approach for Inverse Identification of Passive, Heterogeneous Material Parameters of Left Ventricular Myocardium

    Zhuofan Li1, Daniel H. Pak2, James S. Duncan2, Liang Liang3, Minliang Liu1,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.073757 - 29 January 2026

    Abstract Patient-specific finite element analysis (FEA) is a promising tool for noninvasive quantification of cardiac and vascular structural mechanics in vivo. However, inverse material property identification using FEA, which requires iteratively solving nonlinear hyperelasticity problems, is computationally expensive which limits the ability to provide timely patient-specific insights to clinicians. In this study, we present an inverse material parameter identification strategy that integrates deep neural networks (DNNs) with FEA, namely inverse DNN-FEA. In this framework, a DNN encodes the spatial distribution of material parameters and effectively regularizes the inverse solution, which aims to reduce susceptibility to local optima… More >

  • Open Access

    REVIEW

    Circulating Tumor DNA in Cervical Cancer: Clinical Utility and Medico-Legal Perspectives

    Abdulrahman K. Sinno1, Aisha Mustapha1, Navya Nair1, Simona Zaami2, Lina De Paola2, Valentina Billone3, Eleonora Conti3, Giuseppe Gullo3,*, Pasquale Patrizio4

    Oncology Research, Vol.34, No.1, 2026, DOI:10.32604/or.2025.072176 - 30 December 2025

    Abstract Cervical cancer related to human papillomavirus (HPV) is a leading cause of cancer-related mortality among women worldwide. Cancer cells release fragments of their DNA, known as circulating tumor DNA (ctDNA), which can be detected in bodily fluids. A PubMed search using the terms “ctHPV” or “circulating tumor DNA” and “cervical cancer”, limited to the past ten years, identified 104 articles, complemented by hand-searching for literature addressing medico-legal implications. Studies were evaluated for relevance and methodological quality. Detection and characterization of circulating tumor HPV DNA (ctHPV DNA) have emerged as promising tools for assessing prognosis and More >

  • Open Access

    ARTICLE

    FeatherGuard: A Data-Driven Lightweight Error Protection Scheme for DNN Inference on Edge Devices

    Dong Hyun Lee1, Na Kyung Lee2, Young Seo Lee1,2,*

    CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-17, 2026, DOI:10.32604/cmc.2025.069976 - 09 December 2025

    Abstract There has been an increasing emphasis on performing deep neural network (DNN) inference locally on edge devices due to challenges such as network congestion and security concerns. However, as DRAM process technology continues to scale down, the bit-flip errors in the memory of edge devices become more frequent, thereby leading to substantial DNN inference accuracy loss. Though several techniques have been proposed to alleviate the accuracy loss in edge environments, they require complex computations and additional parity bits for error correction, thus resulting in significant performance and storage overheads. In this paper, we propose FeatherGuard,… More >

  • Open Access

    ARTICLE

    MewCDNet: A Wavelet-Based Multi-Scale Interaction Network for Efficient Remote Sensing Building Change Detection

    Jia Liu1, Hao Chen1, Hang Gu1, Yushan Pan2,3, Haoran Chen1, Erlin Tian4, Min Huang4, Zuhe Li1,*

    CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-24, 2026, DOI:10.32604/cmc.2025.068162 - 10 November 2025

    Abstract Accurate and efficient detection of building changes in remote sensing imagery is crucial for urban planning, disaster emergency response, and resource management. However, existing methods face challenges such as spectral similarity between buildings and backgrounds, sensor variations, and insufficient computational efficiency. To address these challenges, this paper proposes a novel Multi-scale Efficient Wavelet-based Change Detection Network (MewCDNet), which integrates the advantages of Convolutional Neural Networks and Transformers, balances computational costs, and achieves high-performance building change detection. The network employs EfficientNet-B4 as the backbone for hierarchical feature extraction, integrates multi-level feature maps through a multi-scale fusion… More >

  • Open Access

    ARTICLE

    Donor-gifted nephrolithiasis: case-based analysis and comparative study

    Maxwell Sandberg1,*, Mark Xu1, Randall Bissette1, Jacob Malakismail2, Niara East3, Robenson Nguyen4, Jackson Nowatzke1, Robert Stratta5, Dean Assimos1,6, Colin Kleinguetl1

    Canadian Journal of Urology, Vol.32, No.6, pp. 633-641, 2025, DOI:10.32604/cju.2025.069091 - 30 December 2025

    Abstract Objectives: Donor-gifted nephrolithiasis—the presence of a stone in a donor kidney at the time of transplantation—is rare. Research is limited, and no consensus high-quality evidence guidelines exist, leaving selection criteria and management to individual provider discretion. We aimed to estimate the frequency and analyze patient and graft outcomes of deceased donor (DD) transplant recipients with stones in their kidneys at Wake Forest Baptist Medical Center. Methods: All DD renal transplants or patients receiving most of their care postoperatively after DD renal transplantation at our institution from 1979 to 2025 were reviewed. Stones were considered donor-gifted… More >

  • Open Access

    ARTICLE

    A New Dataset for Network Flooding Attacks in SDN-Based IoT Environments

    Nader Karmous1, Wadii Jlassi1, Mohamed Ould-Elhassen Aoueileyine1, Imen Filali2,*, Ridha Bouallegue1

    CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4363-4393, 2025, DOI:10.32604/cmes.2025.074178 - 23 December 2025

    Abstract This paper introduces a robust Distributed Denial-of-Service attack detection framework tailored for Software-Defined Networking based Internet of Things environments, built upon a novel, synthetic multi-vector dataset generated in a Mininet-Ryu testbed using real-time flow-based labeling. The proposed model is based on the XGBoost algorithm, optimized with Principal Component Analysis for dimensionality reduction, utilizing lightweight flow-level features extracted from OpenFlow statistics to classify attacks across critical IoT protocols including TCP, UDP, HTTP, MQTT, and CoAP. The model employs lightweight flow-level features extracted from OpenFlow statistics to ensure low computational overhead and fast processing. Performance was rigorously… More >

  • Open Access

    ARTICLE

    An Explainable Deep Learning Framework for Kidney Cancer Classification Using VGG16 and Layer-Wise Relevance Propagation on CT Images

    Asma Batool1, Fahad Ahmed1, Naila Sammar Naz1, Ayman Altameem2, Ateeq Ur Rehman3,4, Khan Muhammad Adnan5,*, Ahmad Almogren6,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4129-4152, 2025, DOI:10.32604/cmes.2025.073149 - 23 December 2025

    Abstract Early and accurate cancer diagnosis through medical imaging is crucial for guiding treatment and enhancing patient survival. However, many state-of-the-art deep learning (DL) methods remain opaque and lack clinical interpretability. This paper presents an explainable artificial intelligence (XAI) framework that combines a fine-tuned Visual Geometry Group 16-layer network (VGG16) convolutional neural network with layer-wise relevance propagation (LRP) to deliver high-performance classification and transparent decision support. This approach is evaluated on the publicly available Kaggle kidney cancer imaging dataset, which comprises labeled cancerous and non-cancerous kidney scans. The proposed model achieved 98.75% overall accuracy, with precision, More >

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