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  • Open Access


    Endophytic Occupation in Nodules of Rhynchosia Plants from Semiarid Regions of Argentina

    Cinthia T. Lucero1, María de los Á. Ruíz2, Fabiola Pagliero1, Carolina Castaño1, Mariela L. Ambrosino1, Graciela S. Lorda1,*

    Phyton-International Journal of Experimental Botany, Vol.93, No.6, pp. 1081-1099, 2024, DOI:10.32604/phyton.2024.050762

    Abstract Beneficial microbes can improve soil health by promoting soil structure, nutrient cycling, and disease suppression. In addition, a wide array of rhizospheric microbes are responsible for producing metabolically active compounds including various types of plant growth regulators. So, microbial biodiversity studies could contribute to the improvement of agricultural practices in deprived areas, such as the Pampean semiarid region. The vast majority of studies conducted on endophytic microorganisms have focused on intensive crop legume species. In contrast, little attention has been paid to microorganisms of native legumes, whose ecology is not directly affected by human action.… More >

  • Open Access


    Identification of Damage in Steel‒Concrete Composite Beams Based on Wavelet Analysis and Deep Learning

    Chengpeng Zhang, Junfeng Shi*, Caiping Huang

    Structural Durability & Health Monitoring, Vol.18, No.4, pp. 465-483, 2024, DOI:10.32604/sdhm.2024.048705

    Abstract In this paper, an intelligent damage detection approach is proposed for steel-concrete composite beams based on deep learning and wavelet analysis. To demonstrate the feasibility of this approach, first, following the guidelines provided by relevant standards, steel-concrete composite beams are designed, and six different damage incidents are established. Second, a steel ball is used for free-fall excitation on the surface of the steel-concrete composite beams and a low-temperature-sensitive quasi-distributed long-gauge fiber Bragg grating (FBG) strain sensor is used to obtain the strain signals of the steel-concrete composite beams with different damage types. To reduce the… More >

  • Open Access


    Silencing of lncRNA CCDC26 Restrains the Growth and Migration of Glioma Cells In Vitro and In Vivo via Targeting miR-203

    Shilei Wang*, Yuzuo Hui*, Xiaoming Li, Qingbin Jia*

    Oncology Research, Vol.26, No.8, pp. 1143-1154, 2018, DOI:10.3727/096504017X14965095236521

    Abstract Gliomas are the most common primary brain tumors with high mortality. The treatment for gliomas is largely limited due to its uncomprehending pathological mechanism. Here we aimed to investigate the effect of long noncoding RNA (lncRNA) coiled-coil domain-containing 26 (CCDC26) in glioma progression. In our study, the expression of CCDC26 was found upregulated in glioma tissues and cell lines compared with normal tissues and cell lines. Further exploration detected decreased cell proliferation and increased cell apoptosis in U-251 and M059J cells transfected with CCDC26-siRNA. In addition, the silencing of CCDC26 strongly reduced the wound closing… More >

  • Open Access


    Intelligent Power Grid Load Transferring Based on Safe Action-Correction Reinforcement Learning

    Fuju Zhou*, Li Li, Tengfei Jia, Yongchang Yin, Aixiang Shi, Shengrong Xu

    Energy Engineering, Vol.121, No.6, pp. 1697-1711, 2024, DOI:10.32604/ee.2024.047680

    Abstract When a line failure occurs in a power grid, a load transfer is implemented to reconfigure the network by changing the states of tie-switches and load demands. Computation speed is one of the major performance indicators in power grid load transfer, as a fast load transfer model can greatly reduce the economic loss of post-fault power grids. In this study, a reinforcement learning method is developed based on a deep deterministic policy gradient. The tedious training process of the reinforcement learning model can be conducted offline, so the model shows satisfactory performance in real-time operation, More >

  • Open Access


    Transient Stability Preventive Control of Wind Farm Connected Power System Considering the Uncertainty

    Yuping Bian*, Xiu Wan, Xiaoyu Zhou

    Energy Engineering, Vol.121, No.6, pp. 1637-1656, 2024, DOI:10.32604/ee.2024.047678

    Abstract To address uncertainty as well as transient stability constraints simultaneously in the preventive control of wind farm systems, a novel three-stage optimization strategy is established in this paper. In the first stage, the probabilistic multi-objective particle swarm optimization based on the point estimate method is employed to cope with the stochastic factors. The transient security region of the system is accurately ensured by the interior point method in the second stage. Finally, the verification of the final optimal objectives and satisfied constraints are enforced in the last stage. Furthermore, the proposed strategy is a general More >

  • Open Access


    Energy-Saving Distributed Flexible Job Shop Scheduling Optimization with Dual Resource Constraints Based on Integrated Q-Learning Multi-Objective Grey Wolf Optimizer

    Hongliang Zhang1,2, Yi Chen1, Yuteng Zhang1, Gongjie Xu3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1459-1483, 2024, DOI:10.32604/cmes.2024.049756

    Abstract The distributed flexible job shop scheduling problem (DFJSP) has attracted great attention with the growth of the global manufacturing industry. General DFJSP research only considers machine constraints and ignores worker constraints. As one critical factor of production, effective utilization of worker resources can increase productivity. Meanwhile, energy consumption is a growing concern due to the increasingly serious environmental issues. Therefore, the distributed flexible job shop scheduling problem with dual resource constraints (DFJSP-DRC) for minimizing makespan and total energy consumption is studied in this paper. To solve the problem, we present a multi-objective mathematical model for… More >

  • Open Access


    Numerical Exploration of Asymmetrical Impact Dynamics: Unveiling Nonlinearities in Collision Problems and Resilience of Reinforced Concrete Structures

    AL-Bukhaiti Khalil1, Yanhui Liu1,*, Shichun Zhao1, Daguang Han2

    Structural Durability & Health Monitoring, Vol.18, No.3, pp. 223-254, 2024, DOI:10.32604/sdhm.2024.044751

    Abstract This study provides a comprehensive analysis of collision and impact problems’ numerical solutions, focusing on geometric, contact, and material nonlinearities, all essential in solving large deformation problems during a collision. The initial discussion revolves around the stress and strain of large deformation during a collision, followed by explanations of the fundamental finite element solution method for addressing such issues. The hourglass mode’s control methods, such as single-point reduced integration and contact-collision algorithms are detailed and implemented within the finite element framework. The paper further investigates the dynamic response and failure modes of Reinforced Concrete (RC)… More >

  • Open Access


    Nonlinear Registration of Brain Magnetic Resonance Images with Cross Constraints of Intensity and Structure

    Han Zhou1,2, Hongtao Xu1,2, Xinyue Chang1,2, Wei Zhang1,2, Heng Dong1,2,*

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2295-2313, 2024, DOI:10.32604/cmc.2024.047754

    Abstract Many deep learning-based registration methods rely on a single-stream encoder-decoder network for computing deformation fields between 3D volumes. However, these methods often lack constraint information and overlook semantic consistency, limiting their performance. To address these issues, we present a novel approach for medical image registration called the Dual-VoxelMorph, featuring a dual-channel cross-constraint network. This innovative network utilizes both intensity and segmentation images, which share identical semantic information and feature representations. Two encoder-decoder structures calculate deformation fields for intensity and segmentation images, as generated by the dual-channel cross-constraint network. This design facilitates bidirectional communication between grayscale More >

  • Open Access


    A Multi-Constraint Path Optimization Scheme Based on Information Fusion in Software Defined Network

    Jinlin Xu1,2, Wansu Pan1,*, Longle Cheng1,2, Haibo Tan1,2, Munan Yuan1,*, Xiaofeng Li1,2

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1399-1418, 2024, DOI:10.32604/cmc.2024.049622

    Abstract The existing multipath routing in Software Defined Network (SDN) is relatively blind and inefficient, and there is a lack of cooperation between the terminal and network sides, making it difficult to achieve dynamic adaptation of service requirements and network resources. To address these issues, we propose a multi-constraint path optimization scheme based on information fusion in SDN. The proposed scheme collects network topology and network state information on the network side and computes disjoint paths between end hosts. It uses the Fuzzy Analytic Hierarchy Process (FAHP) to calculate the weight coefficients of multiple constrained parameters… More >

  • Open Access


    Safety-Constrained Multi-Agent Reinforcement Learning for Power Quality Control in Distributed Renewable Energy Networks

    Yongjiang Zhao, Haoyi Zhong, Chang Cyoon Lim*

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 449-471, 2024, DOI:10.32604/cmc.2024.048771

    Abstract This paper examines the difficulties of managing distributed power systems, notably due to the increasing use of renewable energy sources, and focuses on voltage control challenges exacerbated by their variable nature in modern power grids. To tackle the unique challenges of voltage control in distributed renewable energy networks, researchers are increasingly turning towards multi-agent reinforcement learning (MARL). However, MARL raises safety concerns due to the unpredictability in agent actions during their exploration phase. This unpredictability can lead to unsafe control measures. To mitigate these safety concerns in MARL-based voltage control, our study introduces a novel… More >

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