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  • Open Access

    ARTICLE

    Drought Stress Alleviation in Chenopodium quinoa through Synergistic Effect of Silicon and Molybdenum via Triggering of SNF1-Associated Protein Kinase 2 Signaling Mechanism

    Asmat Askar1,#, Humaira Gul1,#, Mamoona Rauf1, Muhammad Arif2, Bokyung Lee3, Sajid Ali4,*, Abdulwahed Fahad Alrefaei5, Mikhlid H. Almutairi5, Zahid Ali Butt6, Ho-Youn Kim7, Muhammad Hamayun1,*

    Phyton-International Journal of Experimental Botany, Vol.93, No.9, pp. 2455-2478, 2024, DOI:10.32604/phyton.2024.054508 - 30 September 2024

    Abstract Drought stress negatively impacts agricultural crop yields. By using mineral fertilizers and chemical regulators to encourage plant development and growth, its impact can be mitigated. The current study revealed that exogenous silicon (Si) (potassium silicate; K2Si2O5 at 1000 ppm) and molybdenum (Mo) (ammonium molybdate; (NH4)6Mo7O24•4H2O at 100 ppm) improved drought tolerance in quinoa (Chenopodium quinoa Willd). The research was conducted in a randomized complete block design with three biological replicates. The treatments comprised T0 (control, water spray), T4 (drought stress), and T1, T2, T3, T5, T6, and T7, i.e., foliar applications of silicon and molybdenum solutions individually… More >

  • Open Access

    ARTICLE

    Numerical Simulation of Wet Particles Motion in a Vertical Powder Dryer

    Long Yu, Dongdong Pang*, Minmin She, Hongwei Qiu, Ping Cao, Xiongwei You

    FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.8, pp. 1823-1846, 2024, DOI:10.32604/fdmp.2024.048093 - 06 August 2024

    Abstract In this study, the motion of wet particles in the drying unit of a vertical powder dryer is investigated by using a Discrete element method (DEM) coupled with a liquid bridge force. In particular, by varying parameters such as the particle mass flow rates, the superficial gas velocities, and superficial gas temperatures, the influence of the moisture content on the flow behavior is examined. The results show that when the moisture content increases, the mean particle velocity decreases while the bed mean solid “holdup” and the mean residence time (MRT) of particles grow. It is More > Graphic Abstract

    Numerical Simulation of Wet Particles Motion in a Vertical Powder Dryer

  • Open Access

    ARTICLE

    Film Flow of Nano-Micropolar Fluid with Dissipation Effect

    Abuzar Abid Siddiqui1, Mustafa Turkyilmazoglu2,3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2487-2512, 2024, DOI:10.32604/cmes.2024.050525 - 08 July 2024

    Abstract The physical problem of the thin film flow of a micropolar fluid over a dynamic and inclined substrate under the influence of gravitational and thermal forces in the presence of nanoparticles is formulated. Five different types of nanoparticle samples are accounted for in this current study, namely gold Au, silver Ag, molybdenum disulfide MoS2, aluminum oxide Al2O3, and silicon dioxide SiO2. Blood, a micropolar fluid, serves as the common base fluid. An exact closed-form solution for this problem is derived for the first time in the literature. The results are particularly validated against those for the Newtonian fluid… More >

  • Open Access

    ARTICLE

    BDPartNet: Feature Decoupling and Reconstruction Fusion Network for Infrared and Visible Image

    Xuejie Wang1, Jianxun Zhang1,*, Ye Tao2, Xiaoli Yuan1, Yifan Guo1

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4621-4639, 2024, DOI:10.32604/cmc.2024.051556 - 20 June 2024

    Abstract While single-modal visible light images or infrared images provide limited information, infrared light captures significant thermal radiation data, whereas visible light excels in presenting detailed texture information. Combining images obtained from both modalities allows for leveraging their respective strengths and mitigating individual limitations, resulting in high-quality images with enhanced contrast and rich texture details. Such capabilities hold promising applications in advanced visual tasks including target detection, instance segmentation, military surveillance, pedestrian detection, among others. This paper introduces a novel approach, a dual-branch decomposition fusion network based on AutoEncoder (AE), which decomposes multi-modal features into intensity… More >

  • Open Access

    REVIEW

    The pathogenesis of chronic subdural hematoma in the perspective of neomembrane formation and related mechanisms

    MINGYUE HUANG1,#, JUNFEI DAI1,#, XIANLIANG ZHONG2, JIN WANG2, JIANZHONG XU2, BO DU2,*

    BIOCELL, Vol.48, No.6, pp. 889-896, 2024, DOI:10.32604/biocell.2024.050097 - 10 June 2024

    Abstract Chronic subdural hematoma (CSDH) is a disease characterized by capsuled blood products that progressively occupy the intracranial space, causing intracranial hypertension and compression in the brain. CSDH frequently occurs in all demographics, especially in the elderly, but the pathogenesis of CSDH remains unclear. In this review, we discuss the origin, development, and current treatment strategies of CSDH. For the first time, we analyzed the cellular and molecular compositions of hematoma membranes with a focus on neomembrane formation, a complex early-stage interactive event in hematoma pathogenesis. We hypothesize that in patients with CSDH, dural border cells… More >

  • Open Access

    ARTICLE

    A Planning Method for Operational Test of UAV Swarm Based on Mission Reliability

    Jingyu Wang1, Ping Jiang1,*, Jianjun Qi2

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1889-1918, 2024, DOI:10.32604/cmes.2024.049813 - 20 May 2024

    Abstract The unmanned aerial vehicle (UAV) swarm plays an increasingly important role in the modern battlefield, and the UAV swarm operational test is a vital means to validate the combat effectiveness of the UAV swarm. Due to the high cost and long duration of operational tests, it is essential to plan the test in advance. To solve the problem of planning UAV swarm operational test, this study considers the multi-stage feature of a UAV swarm mission, composed of launch, flight and combat stages, and proposes a method to find test plans that can maximize mission reliability.… More >

  • Open Access

    ARTICLE

    Generalized nth-Order Perturbation Method Based on Loop Subdivision Surface Boundary Element Method for Three-Dimensional Broadband Structural Acoustic Uncertainty Analysis

    Ruijin Huo1,2,3, Qingxiang Pei1,2,3, Xiaohui Yuan1,*, Yanming Xu3

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 2053-2077, 2024, DOI:10.32604/cmes.2024.049185 - 20 May 2024

    Abstract In this paper, a generalized th-order perturbation method based on the isogeometric boundary element method is proposed for the uncertainty analysis of broadband structural acoustic scattering problems. The Burton-Miller method is employed to solve the problem of non-unique solutions that may be encountered in the external acoustic field, and the th-order discretization formulation of the boundary integral equation is derived. In addition, the computation of loop subdivision surfaces and the subdivision rules are introduced. In order to confirm the effectiveness of the algorithm, the computed results are contrasted and analyzed with the results under Monte More >

  • Open Access

    ARTICLE

    Sepsis Prediction Using CNNBDLSTM and Temporal Derivatives Feature Extraction in the IoT Medical Environment

    Sapiah Sakri1, Shakila Basheer1, Zuhaira Muhammad Zain1, Nurul Halimatul Asmak Ismail2,*, Dua’ Abdellatef Nassar1, Manal Abdullah Alohali1, Mais Ayman Alharaki1

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1157-1185, 2024, DOI:10.32604/cmc.2024.048051 - 25 April 2024

    Abstract Background: Sepsis, a potentially fatal inflammatory disease triggered by infection, carries significant health implications worldwide. Timely detection is crucial as sepsis can rapidly escalate if left undetected. Recent advancements in deep learning (DL) offer powerful tools to address this challenge. Aim: Thus, this study proposed a hybrid CNNBDLSTM, a combination of a convolutional neural network (CNN) with a bi-directional long short-term memory (BDLSTM) model to predict sepsis onset. Implementing the proposed model provides a robust framework that capitalizes on the complementary strengths of both architectures, resulting in more accurate and timelier predictions. Method: The sepsis prediction… More >

  • Open Access

    ARTICLE

    Test Case Generation Evaluator for the Implementation of Test Case Generation Algorithms Based on Learning to Rank

    Zhonghao Guo*, Xinyue Xu, Xiangxian Chen

    Computer Systems Science and Engineering, Vol.48, No.2, pp. 479-509, 2024, DOI:10.32604/csse.2023.043932 - 19 March 2024

    Abstract In software testing, the quality of test cases is crucial, but manual generation is time-consuming. Various automatic test case generation methods exist, requiring careful selection based on program features. Current evaluation methods compare a limited set of metrics, which does not support a larger number of metrics or consider the relative importance of each metric to the final assessment. To address this, we propose an evaluation tool, the Test Case Generation Evaluator (TCGE), based on the learning to rank (L2R) algorithm. Unlike previous approaches, our method comprehensively evaluates algorithms by considering multiple metrics, resulting in… More >

  • Open Access

    ARTICLE

    MDCN: Modified Dense Convolution Network Based Disease Classification in Mango Leaves

    Chirag Chandrashekar1, K. P. Vijayakumar1,*, K. Pradeep1, A. Balasundaram1,2

    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2511-2533, 2024, DOI:10.32604/cmc.2024.047697 - 27 February 2024

    Abstract The most widely farmed fruit in the world is mango. Both the production and quality of the mangoes are hampered by many diseases. These diseases need to be effectively controlled and mitigated. Therefore, a quick and accurate diagnosis of the disorders is essential. Deep convolutional neural networks, renowned for their independence in feature extraction, have established their value in numerous detection and classification tasks. However, it requires large training datasets and several parameters that need careful adjustment. The proposed Modified Dense Convolutional Network (MDCN) provides a successful classification scheme for plant diseases affecting mango leaves. More >

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