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  • Open Access

    REVIEW

    A review on pathobiology of circulating tumour plasma cells: The sine qua non of poor prognosis in plasma cell neoplasms

    PRATIBHA SUKU1, AISHWARYA DASH1, ARAVIND RADHAKRISHNAN1, PANKAJ MALHOTRA2, MAN UPDESH SINGH SACHDEVA1,*

    Oncology Research, Vol.33, No.5, pp. 1055-1068, 2025, DOI:10.32604/or.2024.055154 - 18 April 2025

    Abstract Circulating plasma cells (CPCs) in patients of plasma cell neoplasm have been an area of intense research in recent decades. Circulating tumor plasma cells (CTPCs) might represent a sub-clone of tumor cells that have exited into peripheral blood as a result of the dynamic interactions between the bone marrow (BM) microenvironment and neoplastic plasma cells. Chemokine receptors like chemokine receptor 4 (CXCR4) and integrins are known to play a role in homing and migration of plasma cells (PCs). The hypoxic microenvironment in the BM niche also contributes to their circulation through various mechanisms. In addition,… More >

  • Open Access

    ARTICLE

    TransSSA: Invariant Cue Perceptual Feature Focused Learning for Dynamic Fruit Target Detection

    Jianyin Tang, Zhenglin Yu*, Changshun Shao

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2829-2850, 2025, DOI:10.32604/cmc.2025.063287 - 16 April 2025

    Abstract In the field of automated fruit harvesting, precise and efficient fruit target recognition and localization play a pivotal role in enhancing the efficiency of harvesting robots. However, this domain faces two core challenges: firstly, the dynamic nature of the automatic picking process requires fruit target detection algorithms to adapt to multi-view characteristics, ensuring effective recognition of the same fruit from different perspectives. Secondly, fruits in natural environments often suffer from interference factors such as overlapping, occlusion, and illumination fluctuations, which increase the difficulty of image capture and recognition. To address these challenges, this study conducted… More >

  • Open Access

    ARTICLE

    Machine Learning for Smart Soil Monitoring

    Khaoula Ben Abdellafou1, Kamel Zidi2, Ahamed Aljuhani1, Okba Taouali1,*, Mohamed Faouzi Harkat3

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3007-3023, 2025, DOI:10.32604/cmc.2025.063146 - 16 April 2025

    Abstract Environmental protection requires identifying, investigating, and raising awareness about safeguarding nature from the harmful effects of both anthropogenic and natural events. This process of environmental protection is essential for maintaining human well-being. In this context, it is critical to monitor and safeguard the personal environment, which includes maintaining a healthy diet and ensuring plant safety. Living in a balanced environment and ensuring the safety of plants for green spaces and a healthy diet require controlling the nature and quality of the soil in our environment. To ensure soil quality, it is imperative to monitor and… More >

  • Open Access

    ARTICLE

    Integrating Edge Intelligence with Blockchain-Driven Secured IoT Healthcare Optimization Model

    Khulud Salem Alshudukhi1, Mamoona Humayun2,*, Ghadah Naif Alwakid1

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 1973-1986, 2025, DOI:10.32604/cmc.2025.063077 - 16 April 2025

    Abstract The Internet of Things (IoT) and edge computing have substantially contributed to the development and growth of smart cities. It handled time-constrained services and mobile devices to capture the observing environment for surveillance applications. These systems are composed of wireless cameras, digital devices, and tiny sensors to facilitate the operations of crucial healthcare services. Recently, many interactive applications have been proposed, including integrating intelligent systems to handle data processing and enable dynamic communication functionalities for crucial IoT services. Nonetheless, most solutions lack optimizing relaying methods and impose excessive overheads for maintaining devices’ connectivity. Alternatively, data More >

  • Open Access

    ARTICLE

    Ensemble of Deep Learning with Crested Porcupine Optimizer Based Autism Spectrum Disorder Detection Using Facial Images

    Jagadesh Balasubramani1, Surendran Rajendran1,*, Mohammad Zakariah2, Abeer Alnuaim2

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2793-2807, 2025, DOI:10.32604/cmc.2025.062266 - 16 April 2025

    Abstract Autism spectrum disorder (ASD) is a multifaceted neurological developmental condition that manifests in several ways. Nearly all autistic children remain undiagnosed before the age of three. Developmental problems affecting face features are often associated with fundamental brain disorders. The facial evolution of newborns with ASD is quite different from that of typically developing children. Early recognition is very significant to aid families and parents in superstition and denial. Distinguishing facial features from typically developing children is an evident manner to detect children analyzed with ASD. Presently, artificial intelligence (AI) significantly contributes to the emerging computer-aided… More >

  • Open Access

    ARTICLE

    Two-Hop Delay-Aware Energy Efficiency Resource Allocation in Space-Air-Ground Integrated Smart Grid Network

    Qinghai Ou1, Min Yang1, Jingcai Kong1, Yang Yang2,*

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2429-2447, 2025, DOI:10.32604/cmc.2025.062067 - 16 April 2025

    Abstract The lack of communication infrastructure in remote regions presents significant obstacles to gathering data from smart power sensors (SPSs) in smart grid networks. In such cases, a space-air-ground integrated network serves as an effective emergency solution. This study addresses the challenge of optimizing the energy efficiency of data transmission from SPSs to low Earth orbit (LEO) satellites through unmanned aerial vehicles (UAVs), considering both effective capacity and fronthaul link capacity constraints. Due to the non-convex nature of the problem, the objective function is reformulated, and a delay-aware energy-efficient power allocation and UAV trajectory design (DEPATD)… More >

  • Open Access

    ARTICLE

    Deterministic Convergence Analysis for GRU Networks via Smoothing Regularization

    Qian Zhu1, Qian Kang1, Tao Xu2, Dengxiu Yu3,*, Zhen Wang1

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 1855-1879, 2025, DOI:10.32604/cmc.2025.061913 - 16 April 2025

    Abstract In this study, we present a deterministic convergence analysis of Gated Recurrent Unit (GRU) networks enhanced by a smoothing regularization technique. While GRU architectures effectively mitigate gradient vanishing/exploding issues in sequential modeling, they remain prone to overfitting, particularly under noisy or limited training data. Traditional regularization, despite enforcing sparsity and accelerating optimization, introduces non-differentiable points in the error function, leading to oscillations during training. To address this, we propose a novel smoothing regularization framework that replaces the non-differentiable absolute function with a quadratic approximation, ensuring gradient continuity and stabilizing the optimization landscape. Theoretically, we rigorously… More >

  • Open Access

    ARTICLE

    Entropy-Bottleneck-Based Privacy Protection Mechanism for Semantic Communication

    Kaiyang Han1, Xiaoqiang Jia1, Yangfei Lin2, Tsutomu Yoshinaga2, Yalong Li2, Jiale Wu2,*

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2971-2988, 2025, DOI:10.32604/cmc.2025.061563 - 16 April 2025

    Abstract With the rapid development of artificial intelligence and the Internet of Things, along with the growing demand for privacy-preserving transmission, the need for efficient and secure communication systems has become increasingly urgent. Traditional communication methods transmit data at the bit level without considering its semantic significance, leading to redundant transmission overhead and reduced efficiency. Semantic communication addresses this issue by extracting and transmitting only the most meaningful semantic information, thereby improving bandwidth efficiency. However, despite reducing the volume of data, it remains vulnerable to privacy risks, as semantic features may still expose sensitive information. To… More >

  • Open Access

    ARTICLE

    GMS: A Novel Method for Detecting Reentrancy Vulnerabilities in Smart Contracts

    Dawei Xu1,2, Fan Huang1, Jiaxin Zhang1, Yunfang Liang1, Baokun Zheng3,*, Jian Zhao1

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2207-2220, 2025, DOI:10.32604/cmc.2025.061455 - 16 April 2025

    Abstract With the rapid proliferation of Internet of Things (IoT) devices, ensuring their communication security has become increasingly important. Blockchain and smart contract technologies, with their decentralized nature, provide strong security guarantees for IoT. However, at the same time, smart contracts themselves face numerous security challenges, among which reentrancy vulnerabilities are particularly prominent. Existing detection tools for reentrancy vulnerabilities often suffer from high false positive and false negative rates due to their reliance on identifying patterns related to specific transfer functions. To address these limitations, this paper proposes a novel detection method that combines pattern matching… More >

  • Open Access

    ARTICLE

    DAFPN-YOLO: An Improved UAV-Based Object Detection Algorithm Based on YOLOv8s

    Honglin Wang1, Yaolong Zhang2,*, Cheng Zhu3

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 1929-1949, 2025, DOI:10.32604/cmc.2025.061363 - 16 April 2025

    Abstract UAV-based object detection is rapidly expanding in both civilian and military applications, including security surveillance, disaster assessment, and border patrol. However, challenges such as small objects, occlusions, complex backgrounds, and variable lighting persist due to the unique perspective of UAV imagery. To address these issues, this paper introduces DAFPN-YOLO, an innovative model based on YOLOv8s (You Only Look Once version 8s). The model strikes a balance between detection accuracy and speed while reducing parameters, making it well-suited for multi-object detection tasks from drone perspectives. A key feature of DAFPN-YOLO is the enhanced Drone-AFPN (Adaptive Feature… More >

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