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  • Open Access

    ARTICLE

    PD-1+ and TIM-3+ T cells widely express common γ-chain cytokine receptors in multiple myeloma patients, and IL-2, IL-7, IL-15 stimulation up-regulates PD-1 and TIM-3 on T cells

    EGOR V. BATOROV1,2,*, ALISA D. INESHINA2, TATIANA A. ARISTOVA3, VERA V. DENISOVA3, SVETLANA A. SIZIKOVA3, DARIA S. BATOROVA3, GALINA Y. USHAKOVA3, EKATERINA Y. SHEVELA1, ELENA R. CHERNYKH1

    Oncology Research, Vol.32, No.10, pp. 1575-1587, 2024, DOI:10.32604/or.2024.047893

    Abstract Background: Immune checkpoint ligand-receptor interactions appear to be associated with multiple myeloma (MM) progression. Simultaneously, previous studies showed the possibility of PD-1 and TIM-3 expression on T cells upon stimulation with common γ-chain family cytokines in vitro and during homeostatic proliferation. The aim of the present work was to study the impact of homeostatic proliferation on the expansion of certain T cell subsets up-regulating PD-1 and TIM-3 checkpoint molecules. Methods: The expression of CD25, CD122, CD127 common γ-chain cytokine receptors, phosphorylated signal transducer and activator of transcription-5 (pSTAT5) and eomesodermin (EOMES) was comparatively assessed with flow… More >

  • Open Access

    ARTICLE

    Performance of Deep Learning Techniques in Leaf Disease Detection

    Robertas Damasevicius1,*, Faheem Mahmood2, Yaseen Zaman3, Sobia Dastgeer2, Sajid Khan2

    Computer Systems Science and Engineering, Vol.48, No.5, pp. 1349-1366, 2024, DOI:10.32604/csse.2024.050359

    Abstract Plant diseases must be identified as soon as possible since they have an impact on the growth of the corresponding species. Consequently, the identification of leaf diseases is essential in this field of agriculture. Diseases brought on by bacteria, viruses, and fungi are a significant factor in reduced crop yields. Numerous machine learning models have been applied in the identification of plant diseases, however, with the recent developments in deep learning, this field of study seems to hold huge potential for improved accuracy. This study presents an effective method that uses image processing and deep… More >

  • Open Access

    ARTICLE

    Associated Factors of Anxiety Symptoms in Patients with Keratinocyte Carcinoma: A Cross-Sectional Study

    Qian Liu1,#, Hui Zhang1,#, Juan Gao2, Meiping Sha1, Lijun Shen1, Xianfeng Cheng3,*, Hao Chen4,*

    Psycho-Oncologie, Vol.18, No.3, pp. 213-221, 2024, DOI:10.32604/po.2024.052607

    Abstract Background: Keratinocyte carcinoma (KC) is a common malignancy characterized by a high recurrence rate and considerable psychological distress. The incidence of KC is increasing in China, raising concerns about its psychological consequences and adverse effects on quality of life. Demographic and clinical factors are thought to influence mental health outcomes in these patients. Nonetheless, data on the prevalence of anxiety in Chinese patients with KC and the factors associated with this anxiety are notably lacking. Therefore, a comprehensive investigation into the anxiety of patients with KC is imperative. Objective: This study aimed to investigate the… More >

  • Open Access

    ARTICLE

    Enhancing Safety in Autonomous Vehicle Navigation: An Optimized Path Planning Approach Leveraging Model Predictive Control

    Shih-Lin Lin*, Bo-Chen Lin

    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3555-3572, 2024, DOI:10.32604/cmc.2024.055456

    Abstract This paper explores the application of Model Predictive Control (MPC) to enhance safety and efficiency in autonomous vehicle (AV) navigation through optimized path planning. The evolution of AV technology has progressed rapidly, moving from basic driver-assistance systems (Level 1) to fully autonomous capabilities (Level 5). Central to this advancement are two key functionalities: Lane-Change Maneuvers (LCM) and Adaptive Cruise Control (ACC). In this study, a detailed simulation environment is created to replicate the road network between Nantun and Wuri on National Freeway No. 1 in Taiwan. The MPC controller is deployed to optimize vehicle trajectories,… More >

  • Open Access

    ARTICLE

    Infrared Fault Detection Method for Dense Electrolytic Bath Polar Plate Based on YOLOv5s

    Huiling Yu1, Yanqiu Hang2, Shen Shi1, Kangning Wu1, Yizhuo Zhang1,*

    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4859-4874, 2024, DOI:10.32604/cmc.2024.055403

    Abstract Electrolysis tanks are used to smelt metals based on electrochemical principles, and the short-circuiting of the pole plates in the tanks in the production process will lead to high temperatures, thus affecting normal production. Aiming at the problems of time-consuming and poor accuracy of existing infrared methods for high-temperature detection of dense pole plates in electrolysis tanks, an infrared dense pole plate anomalous target detection network YOLOv5-RMF based on You Only Look Once version 5 (YOLOv5) is proposed. Firstly, we modified the Real-Time Enhanced Super-Resolution Generative Adversarial Network (Real-ESRGAN) by changing the U-shaped network (U-Net)… More >

  • Open Access

    ARTICLE

    Enhanced UAV Pursuit-Evasion Using Boids Modelling: A Synergistic Integration of Bird Swarm Intelligence and DRL

    Weiqiang Jin1,#, Xingwu Tian1,#, Bohang Shi1, Biao Zhao1,*, Haibin Duan2, Hao Wu3

    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3523-3553, 2024, DOI:10.32604/cmc.2024.055125

    Abstract The UAV pursuit-evasion problem focuses on the efficient tracking and capture of evading targets using unmanned aerial vehicles (UAVs), which is pivotal in public safety applications, particularly in scenarios involving intrusion monitoring and interception. To address the challenges of data acquisition, real-world deployment, and the limited intelligence of existing algorithms in UAV pursuit-evasion tasks, we propose an innovative swarm intelligence-based UAV pursuit-evasion control framework, namely “Boids Model-based DRL Approach for Pursuit and Escape” (Boids-PE), which synergizes the strengths of swarm intelligence from bio-inspired algorithms and deep reinforcement learning (DRL). The Boids model, which simulates collective… More >

  • Open Access

    ARTICLE

    Machine Fault Diagnosis Using Audio Sensors Data and Explainable AI Techniques-LIME and SHAP

    Aniqua Nusrat Zereen1, Abir Das2, Jia Uddin3,*

    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3463-3484, 2024, DOI:10.32604/cmc.2024.054886

    Abstract Machine fault diagnostics are essential for industrial operations, and advancements in machine learning have significantly advanced these systems by providing accurate predictions and expedited solutions. Machine learning models, especially those utilizing complex algorithms like deep learning, have demonstrated major potential in extracting important information from large operational datasets. Despite their efficiency, machine learning models face challenges, making Explainable AI (XAI) crucial for improving their understandability and fine-tuning. The importance of feature contribution and selection using XAI in the diagnosis of machine faults is examined in this study. The technique is applied to evaluate different machine-learning More >

  • Open Access

    ARTICLE

    A Path Planning Algorithm Based on Improved RRT Sampling Region

    Xiangkui Jiang*, Zihao Wang, Chao Dong

    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4303-4323, 2024, DOI:10.32604/cmc.2024.054640

    Abstract

    For the problem of slow search and tortuous paths in the Rapidly Exploring Random Tree (RRT) algorithm, a feedback-biased sampling RRT, called FS-RRT, is proposed based on RRT. Firstly, to improve the sampling efficiency of RRT to shorten the search time, the search area of the random tree is restricted to improve the sampling efficiency. Secondly, to obtain better information about obstacles to shorten the path length, a feedback-biased sampling strategy is used instead of the traditional random sampling, the collision of the expanding node with an obstacle generates feedback information so that the next

    More >

  • Open Access

    ARTICLE

    Value Function Mechanism in WSNs-Based Mango Plantation Monitoring System

    Wen-Tsai Sung1, Indra Griha Tofik Isa1,2, Sung-Jung Hsiao3,*

    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3733-3759, 2024, DOI:10.32604/cmc.2024.053634

    Abstract Mango fruit is one of the main fruit commodities that contributes to Taiwan’s income. The implementation of technology is an alternative to increasing the quality and quantity of mango plantation product productivity. In this study, a Wireless Sensor Networks (“WSNs”)-based intelligent mango plantation monitoring system will be developed that implements deep reinforcement learning (DRL) technology in carrying out prediction tasks based on three classifications: “optimal,” “sub-optimal,” or “not-optimal” conditions based on three parameters including humidity, temperature, and soil moisture. The key idea is how to provide a precise decision-making mechanism in the real-time monitoring system.… More >

  • Open Access

    ARTICLE

    Explainable AI-Based DDoS Attacks Classification Using Deep Transfer Learning

    Ahmad Alzu’bi1,*, Amjad Albashayreh2, Abdelrahman Abuarqoub3, Mai A. M. Alfawair4

    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3785-3802, 2024, DOI:10.32604/cmc.2024.052599

    Abstract In the era of the Internet of Things (IoT), the proliferation of connected devices has raised security concerns, increasing the risk of intrusions into diverse systems. Despite the convenience and efficiency offered by IoT technology, the growing number of IoT devices escalates the likelihood of attacks, emphasizing the need for robust security tools to automatically detect and explain threats. This paper introduces a deep learning methodology for detecting and classifying distributed denial of service (DDoS) attacks, addressing a significant security concern within IoT environments. An effective procedure of deep transfer learning is applied to utilize More >

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