Ahmad A. A. Alkhatib^*, Qusai Abed-Al

Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1071-1087, 2022, DOI:10.32604/iasc.2022.020461

Abstract Wireless sensor networks have been a very important means in forest monitoring applications. A clustered sensor network comprises a set of cluster members and one cluster head. The cluster members are normally located close to each other, with overlaps among their sensing coverage within the cluster. The cluster members concurrently detect the same event to send to the Cluster Head node. This is where data aggregation is deployed to remove redundant data at the cost of data accuracy, where some data generated by the sensing process might be an outlier. Thus, it is important to conserve the aggregated data’s accuracy… More >

Xin Hu^1,2,#, Fangyu Jing^3,#, Qingjun Wang^1,4, Linyang Shi¹, Yunfeng Cao^4,5, Zhitu Zhu^1,4,*

Oncologie, Vol.23, No.2, pp. 203-217, 2021, DOI:10.32604/Oncologie.2021.016155

Abstract It is noteworthy that colon cancer is the fourth place in new cases and the fifth in mortalities according to global cancer statistics 2018. Tumorigenesis displays specific correlation with metabolic alterations. A variety of metabolites, including ornithine (Orn), are related to colon cancer according to sources of disease metabolic information retrieval in human metabolome database. The metabolic regulation of Orn pathway is a key link in the survival of cancer cells. In this study, the plasma Orn levels in colon cancer patients and healthy participants were measured by liquid chromatography tandem mass spectrometry, and the metabolic disturbances of Orn in… More >

Masoud Muhammed Hassan¹, Adel Sabry Eesa^1,*, Ahmed Jameel Mohammed², Wahab Kh. Arabo¹

CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 451-469, 2021, DOI:10.32604/cmc.2021.018280

Abstract Learning from imbalanced data is one of the greatest challenging problems in binary classification, and this problem has gained more importance in recent years. When the class distribution is imbalanced, classical machine learning algorithms tend to move strongly towards the majority class and disregard the minority. Therefore, the accuracy may be high, but the model cannot recognize data instances in the minority class to classify them, leading to many misclassifications. Different methods have been proposed in the literature to handle the imbalance problem, but most are complicated and tend to simulate unnecessary noise. In this paper, we propose a simple… More >

Xueping Fan^*, Guanghong Yang, Zhipeng Shang, Xiaoxiong Zhao, Yuefei Liu^*

Structural Durability & Health Monitoring, Vol.15, No.1, pp. 69-83, 2021, DOI:10.32604/sdhm.2021.011922

Abstract This article presented a new data fusion approach for reasonably predicting dynamic serviceability reliability of the long-span bridge girder. Firstly, multivariate Bayesian dynamic linear model (MBDLM) considering dynamic correlation among the multiple variables is provided to predict dynamic extreme deflections; secondly, with the proposed MBDLM, the dynamic correlation coefficients between any two performance functions can be predicted; finally, based on MBDLM and Gaussian copula technique, a new data fusion method is given to predict the serviceability reliability of the long-span bridge girder, and the monitoring extreme deflection data from an actual bridge is provided to illustrated the feasibility and application… More >

R. Uma Maheswari^1,*, R. Umamaheswari²

Intelligent Automation & Soft Computing, Vol.26, No.3, pp. 479-488, 2020, DOI:10.32604/iasc.2020.013924

Abstract To enhance the predictive condition-based maintenance (CBMS), a reliable automatic Drivetrain fault detection technique based on vibration monitoring is proposed. Accelerometer sensors are mounted on a wind turbine drivetrain at different spatial locations to measure the vibration from multiple vibration sources. In this work, multi-channel signals are fused and monocomponent modes of oscillation are reconstructed by the Multivariate Empirical Mode Decomposition (MEMD) Technique. Noise assisted methodology is adapted to palliate the mixing of modes with common frequency scales. The instantaneous amplitude envelope and instantaneous frequency are estimated with the Hilbert transform. Low order and high order statistical moments, signal feature… More >

Hongsheng Su^1,2,∗, Shuangshuang Wang¹, Dengfei Wang²

Computer Systems Science and Engineering, Vol.33, No.5, pp. 351-360, 2018, DOI:10.32604/csse.2018.33.351

Abstract To better solve the issue with abnormal failure of electric motor unit (EMU) brake pad resulted from various random factors in the ever-changing operating environment, in this paper, a new evaluation method of performance prediction and abnormity decision is proposed based on the Multivariate integrated random walk (MIRW) model. In this method, the state space model of the EMU brake pad performance degradation is firstly established. And then based on the observed data, the brake pad performance degradation trend is extracted by the fixed interval forward - backward smoothing algorithm. Based on it, the future degradation state can be predicted… More >

Jianhua Dong¹, Lifeng Wu², Xiaogang Liu^{1, *}, Cheng Fan¹, Menghui Leng³, Qiliang Yang¹

CMES-Computer Modeling in Engineering & Sciences, Vol.123, No.1, pp. 49-73, 2020, DOI: 10.32604/cmes.2020.09014

Abstract Solar radiation is an important parameter in the fields of computer modeling, engineering technology and energy development. This paper evaluated the ability of three machine learning models, i.e., Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM) and Multivariate Adaptive Regression Splines (MARS), to estimate the daily diffuse solar radiation (R_d). The regular meteorological data of 1966-2015 at five stations in China were taken as the input parameters (including mean average temperature (T_a), theoretical sunshine duration (N), actual sunshine duration (n), daily average air relative humidity (RH), and extra-terrestrial solar radiation (R_a)). And their estimation accuracies were subjected to comparative analysis.… More >

M. A. BEMVENUTI

BIOCELL, Vol.30, Suppl.S, pp. 111-118, 2006

Abstract This article has no abstract. More >

Kandus¹ M, D Almorza³, R Boggio Ronceros², JC Salerno¹

Phyton-International Journal of Experimental Botany, Vol.79, pp. 39-46, 2010, DOI:10.32604/phyton.2010.79.039

Abstract Our objective was to determine the genotype-environment interaction (GxE) in a hybrid integrated by maize lines either carrying or not balanced lethal systems. Experiments were conducted in three locations over a period of two years considering each yearlocation combination as a different environment. Yield data were analysed using the Additive Main Effects and Multiplicative Interaction (AMMI) model and the Sites Regression Analysis (SREG). Results were represented by biplots. The AMMI analysis was the best model for determining the interaction. More >

Castañón-Nájera² G, L Latournerie-Moreno³, M Mendoza-Elos⁴, A Vargas-López⁵, H Cárdenas-Morales⁶

Phyton-International Journal of Experimental Botany, Vol.77, pp. 189-202, 2008, DOI:10.32604/phyton.2008.77.189

Abstract A morphological characterization in situ of Capsicum spp was made in 13 localities in the state of Tabasco, México, during 2004 and 2005. The objective was to sample and identify different morphotypes of chilli pepper which grow under wild and cultivated conditions. Eleven chilli pepper morphotypes were found. Most of them were wild (Amashito, Corazón de pollo, Muela, Garbanzo, Garbanzo raro and Desconocido) and corresponded to C. annuum. Another morphotype was half wild (Picopaloma) and corresponded to C. frutescens. The remaining morphotypes corresponded to C. annuum and C. chinenese. Data were analyzed with multivariate statistics. Principal component analysis (PCA) indicated… More >