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  • Open Access


    Dynamic Changes in Left and Right Cerebral Oxygen Saturation during Selective Cerebral Perfusion in Young Infants

    Hwa-Young Jang1, Sang-Jun Beon2, Sung-Hoon Kim1, In-Kyung Song1, Won-Jung Shin1,*

    Congenital Heart Disease, Vol.18, No.6, pp. 639-647, 2023, DOI:10.32604/chd.2023.030065

    Abstract Objectives: We investigated whether the selective cerebral perfusion (SCP) technique causes differences in changes in cerebral perfusion between both hemispheres in young infants, using cerebral oxygen saturation (ScO2) as an index. Further, we determined the association between the discrepancy in ScO2 and cerebral perfusion pressure during SCP. Methods: The difference in ScO2 between the left and right cerebral hemispheres (ΔScO2 Rt-Lt) was calculated during clamping of the innominate artery (IA) and during SCP. Results: In 25 infants (aged 2 to 78 days), the left and right ScO2 were well maintained (median 63.2% and 60.9% during IA clamping, respectively; 64.0% and… More > Graphic Abstract

    Dynamic Changes in Left and Right Cerebral Oxygen Saturation during Selective Cerebral Perfusion in Young Infants

  • Open Access


    Functional Nonparametric Predictions in Food Industry Using Near-Infrared Spectroscopy Measurement

    Ibrahim M. Almanjahie1,2,*, Omar Fetitah3, Mohammed Kadi Attouch3, Tawfik Benchikh3,4

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6307-6319, 2023, DOI:10.32604/cmc.2023.033441

    Abstract Functional statistics is a new technique for dealing with data that can be viewed as curves or images. Parallel to this approach, the Near-Infrared Reflectance (NIR) spectroscopy methodology has been used in modern chemistry as a rapid, low-cost, and exact means of assessing an object’s chemical properties. In this research, we investigate the quality of corn and cookie dough by analyzing the spectroscopic technique using certain cutting-edge statistical models. By analyzing spectral data and applying functional models to it, we could predict the chemical components of corn and cookie dough. Kernel Functional Classical Estimation (KFCE), Kernel Functional Quantile Estimation (KFQE),… More >

  • Open Access


    Physical and Mechanical Properties of Catalpa bungei Clones and Estimation of the Properties by Near-Infrared Spectroscopy

    Rui Wang, Lanlan Shi, Yurong Wang*

    Journal of Renewable Materials, Vol.10, No.12, pp. 3285-3302, 2022, DOI:10.32604/jrm.2022.020546

    Abstract Air-dry density, modulus of rupture (MOR), modulus of elasticity (MOE), compressive strength parallel to grain, and hardness of Catalpa bungei clones were investigated in this study with feasibility of predicting these properties by near-infrared (NIR) spectroscopy. The best candidate ‘Luoqiu 3’ has been selected from three clones based on wood physical and mechanical property indices. Lower values of wood physical and mechanical properties have been found in the corewood compared to the outerwood. There were significant positive correlations between the air-dry density and mechanical properties. Information from cross section for air-dry density, compressive strength parallel to grain, and hardness yielded… More >

  • Open Access


    Industrial Food Quality Analysis Using New k-Nearest-Neighbour methods

    Omar Fetitah1, Ibrahim M. Almanjahie2,3, Mohammed Kadi Attouch1,*, Salah Khardani4

    CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2681-2694, 2021, DOI:10.32604/cmc.2021.015469

    Abstract The problem of predicting continuous scalar outcomes from functional predictors has received high levels of interest in recent years in many fields, especially in the food industry. The k-nearest neighbor (k-NN) method of Near-Infrared Reflectance (NIR) analysis is practical, relatively easy to implement, and becoming one of the most popular methods for conducting food quality based on NIR data. The k-NN is often named k nearest neighbor classifier when it is used for classifying categorical variables, while it is called k-nearest neighbor regression when it is applied for predicting noncategorical variables. The objective of this paper is to use the… More >

  • Open Access


    Extreme Learning Machines Based on Least Absolute Deviation and Their Applications in Analysis Hard Rate of Licorice Seeds

    Liming Yang1,2, Junjian Bai1, Qun Sun3

    CMES-Computer Modeling in Engineering & Sciences, Vol.108, No.1, pp. 49-65, 2015, DOI:10.3970/cmes.2015.108.049

    Abstract Extreme learning machine (ELM) has demonstrated great potential in machine learning and data mining fields owing to its simplicity, rapidity and good generalization performance. In this work, a general framework for ELM regression is first investigated based on least absolute deviation (LAD) estimation (called LADELM), and then we develop two regularized LADELM formulations with the l2-norm and l1-norm regularization, respectively. Moreover, the proposed models are posed as simple linear programming or quadratic programming problems. Furthermore, the proposed models are used directly to analyze the hard rate of licorice seeds using near-infrared spectroscopy data. Experimental results on eight different spectral regions… More >

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