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  • Open Access

    ARTICLE

    Correlation and Pathway Analysis of the Carbon, Nitrogen, and Phosphorus in Soil-Microorganism-Plant with Main Quality Components of Tea (Camellia sinensis)

    Chun Mao1, Ji He1,*, Xuefeng Wen1, Yangzhou Xiang2, Jihong Feng1, Yingge Shu1

    Phyton-International Journal of Experimental Botany, Vol.93, No.3, pp. 487-502, 2024, DOI:10.32604/phyton.2024.048246

    Abstract The contents of carbon (C), nitrogen (N), and phosphorus (P) in soil-microorganisms-plant significantly affect tea quality by altering the main quality components of tea, such as tea polyphenols, amino acids, and caffeine. However, few studies have quantified the effects of these factors on the main quality components of tea. The study aimed to explore the interactions of C, N, and P in soil-microorganisms-plants and the effects of these factors on the main quality components of tea by using the path analysis method. The results indicated that (1) The contents of C, N, and P in soil, microorganisms, and tea plants… More >

  • Open Access

    ARTICLE

    A CORRELATION FOR NUSSELT NUMBER UNDER TURBULENT MIXED CONVECTION USING TRANSIENT HEAT TRANSFER EXPERIMENTS

    N. Gnanasekaran, C. Balaji*

    Frontiers in Heat and Mass Transfer, Vol.2, No.2, pp. 1-6, 2011, DOI:10.5098/hmt.v2.2.3008

    Abstract This paper reports the results of an experimental investigation of transient, turbulent mixed convection in a vertical channel in which one of the walls is heated and the other is adiabatic. The goal is to simultaneously estimate the constants in a Nusselt number correlation whose form is assumed a priori by synergistically marrying the experimental results with repeated numerical calculations that assume guess values of the constants. The convective heat transfer coefficient “h”is replaced by the Nusselt number (Nu) and the constants in the Nusselt number are to be evaluated. From the experimentally obtained temperature time history and the simulated… More >

  • Open Access

    ARTICLE

    Cross-Project Software Defect Prediction Based on SMOTE and Deep Canonical Correlation Analysis

    Xin Fan1,2, Shuqing Zhang1,2,*, Kaisheng Wu1,2, Wei Zheng1,2, Yu Ge1,2

    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1687-1711, 2024, DOI:10.32604/cmc.2023.046187

    Abstract Cross-Project Defect Prediction (CPDP) is a method that utilizes historical data from other source projects to train predictive models for defect prediction in the target project. However, existing CPDP methods only consider linear correlations between features (indicators) of the source and target projects. These models are not capable of evaluating non-linear correlations between features when they exist, for example, when there are differences in data distributions between the source and target projects. As a result, the performance of such CPDP models is compromised. In this paper, this paper proposes a novel CPDP method based on Synthetic Minority Oversampling Technique (SMOTE)… More >

  • Open Access

    ARTICLE

    Machine Learning Security Defense Algorithms Based on Metadata Correlation Features

    Ruchun Jia, Jianwei Zhang*, Yi Lin

    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2391-2418, 2024, DOI:10.32604/cmc.2024.044149

    Abstract With the popularization of the Internet and the development of technology, cyber threats are increasing day by day. Threats such as malware, hacking, and data breaches have had a serious impact on cybersecurity. The network security environment in the era of big data presents the characteristics of large amounts of data, high diversity, and high real-time requirements. Traditional security defense methods and tools have been unable to cope with the complex and changing network security threats. This paper proposes a machine-learning security defense algorithm based on metadata association features. Emphasize control over unauthorized users through privacy, integrity, and availability. The… More >

  • Open Access

    ARTICLE

    Spatiotemporal Prediction of Urban Traffics Based on Deep GNN

    Ming Luo1, Huili Dou2, Ning Zheng3,*

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 265-282, 2024, DOI:10.32604/cmc.2023.040067

    Abstract Traffic prediction already plays a significant role in applications like traffic planning and urban management, but it is still difficult to capture the highly non-linear and complicated spatiotemporal correlations of traffic data. As well as to fulfil both long-term and short-term prediction objectives, a better representation of the temporal dependency and global spatial correlation of traffic data is needed. In order to do this, the Spatiotemporal Graph Neural Network (S-GNN) is proposed in this research as a method for traffic prediction. The S-GNN simultaneously accepts various traffic data as inputs and investigates the non-linear correlations between the variables. In terms… More >

  • Open Access

    ARTICLE

    Variation Characteristics of Root Traits of Different Alfalfa Cultivars under Saline-Alkaline Stress and their Relationship with Soil Environmental Factors

    Tian-Jiao Wei1, Guang Li1, Yan-Ru Cui1, Jiao Xie1, Xing-Ai Gao1, Xing Teng1, Xin-Ying Zhao1, Fa-Chun Guan1,*, Zheng-Wei Liang2,*

    Phyton-International Journal of Experimental Botany, Vol.93, No.1, pp. 29-43, 2024, DOI:10.32604/phyton.2023.046078

    Abstract Soil salinization is the main factor that threatens the growth and development of plants and limits the increase of yield. It is of great significance to study the key soil environmental factors affecting plant root traits to reveal the adaptation strategies of plants to saline-alkaline-stressed soil environments. In this study, the root biomass, root morphological parameters and root mineral nutrient content of two alfalfa cultivars with different sensitivities to alkaline stress were analyzed with black soil as the control group and the mixed saline-alkaline soil with a ratio of 7:3 between black soil and saline-alkaline soil as the saline-alkaline treatment… More >

  • Open Access

    ARTICLE

    Learning Dual-Domain Calibration and Distance-Driven Correlation Filter: A Probabilistic Perspective for UAV Tracking

    Taiyu Yan1, Yuxin Cao1, Guoxia Xu1, Xiaoran Zhao2, Hu Zhu1, Lizhen Deng3,*

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3741-3764, 2023, DOI:10.32604/cmc.2023.039828

    Abstract Unmanned Aerial Vehicle (UAV) tracking has been possible because of the growth of intelligent information technology in smart cities, making it simple to gather data at any time by dynamically monitoring events, people, the environment, and other aspects in the city. The traditional filter creates a model to address the boundary effect and time filter degradation issues in UAV tracking operations. But these methods ignore the loss of data integrity terms since they are overly dependent on numerous explicit previous regularization terms. In light of the aforementioned issues, this work suggests a dual-domain Jensen-Shannon divergence correlation filter (DJSCF) model address… More >

  • Open Access

    ARTICLE

    Influence of Hydro-Morphometric Parameters on Water Flow in the Tshopo Sub-Catchments, Democratic Republic of Congo

    Influence des paramètres hydro-morphométriques sur l’écoulement des eaux des sous-bassins versants de la Tshopo, République démocratique du Congo

    Faidance Mashauri1,2,*, Mokili Mbuluyo1,3, Nsalambi Nkongolo2,4

    Revue Internationale de Géomatique, Vol.32, pp. 79-98, 2023, DOI:10.32604/RIG.2023.044124

    Abstract The most characteristic hydro-morphometric parameters controlling water flow in the Tshopo catchment have not yet been determined. Correlation analysis, multiple linear regression and hierarchical ascending classification were applied to all the data in order to identify the most characteristic variables that significantly influence water flow velocity, and to group together physically similar sub-catchments. The results highlight the importance of topography on water flow. Three topographical variables, namely median altitude (H50), overall gradient (Dg) and specific gradient (Ds), have a significant influence (p-value ≤ 0.05) on surface water flow velocity (Ve) in the Tshopo sub-catchments. Two opposing groups (G1 and G2)… More >

  • Open Access

    ARTICLE

    Correlation Analysis of Turbidity and Total Phosphorus in Water Quality Monitoring Data

    Wenwu Tan1, Jianjun Zhang1,*, Xing Liu1, Jiang Wu1, Yifu Sheng1, Ke Xiao2, Li Wang2, Haijun Lin1, Guang Sun3, Peng Guo4

    Journal on Big Data, Vol.5, pp. 85-97, 2023, DOI:10.32604/jbd.2022.030908

    Abstract At present, water pollution has become an important factor affecting and restricting national and regional economic development. Total phosphorus is one of the main sources of water pollution and eutrophication, so the prediction of total phosphorus in water quality has good research significance. This paper selects the total phosphorus and turbidity data for analysis by crawling the data of the water quality monitoring platform. By constructing the attribute object mapping relationship, the correlation between the two indicators was analyzed and used to predict the future data. Firstly, the monthly mean and daily mean concentrations of total phosphorus and turbidity outliers… More >

  • Open Access

    ARTICLE

    Bearing Fault Diagnosis Based on Deep Discriminative Adversarial Domain Adaptation Neural Networks

    Jinxi Guo1, Kai Chen1,2, Jiehui Liu1, Yuhao Ma2, Jie Wu2,*, Yaochun Wu2, Xiaofeng Xue3, Jianshen Li1

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2619-2640, 2024, DOI:10.32604/cmes.2023.031360

    Abstract Intelligent diagnosis driven by big data for mechanical fault is an important means to ensure the safe operation of equipment. In these methods, deep learning-based machinery fault diagnosis approaches have received increasing attention and achieved some results. It might lead to insufficient performance for using transfer learning alone and cause misclassification of target samples for domain bias when building deep models to learn domain-invariant features. To address the above problems, a deep discriminative adversarial domain adaptation neural network for the bearing fault diagnosis model is proposed (DDADAN). In this method, the raw vibration data are firstly converted into frequency domain… More >

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