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linear regression analysis of properties related to moisture management using cotton-polyester knitted fabrics

TEXTILE RESEARCH JOURNAL 2024年第15-16期94卷 1737-1751页

作者： Asfand, Norina Petraitiene, Stase Daukantiene, Virginija Kaunas Univ Technol Studentu Str 56 LT-51424 Kaunas Lithuania

The complex evaluation of thermo-physiological comfort for a particular garment is still challenging, as it depends on the different structural parameters and individual properties of textiles. Measurement of relevant fabric characteristics requires very specific laboratory equipment, such as an M 290 moisture management tester (SDL ATLAS) or similar. For this reason, it is obvious that there is a great demand to predict the overall moisture management capability (OMMC) based on the individual properties that are responsible for clothing comfort and testing according to different standards rather than OMMC-specific calculation using the M 290 tester. Therefore, in this research, linear regression analysis was performed using MATLAB software to predict the OMMC for cotton-polyester fabrics knitted in two patterns, namely 1 x 1 rib and half-Milano rib, using four percentages of fibers. Water vapor permeability, water vapor resistance, water absorption capacity, water absorption time, and air permeability were used as input variables for linear regression analysis to predict the OMMC of fabrics. The performed analysis has shown that the OMMC is directly dependent on the relative water vapor permeability and air permeability, and the linear regression equation suggested in this research can predict the suitability of a textile for a particular garment concerning its moisture management behavior.

关键词： Moisture management linear regression analysis knitted fabric antistatic polyester 1 x 1 rib half-Milano

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THE DESIGN OF JUJUBE IRRIGATION SYSTEM USING linear regression analysis, BP NEURAL NETWORK AND RANDOM FOREST

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INMATEH-AGRICULTURAL ENGINEERING 2023年第2期70卷 165-172页

作者： Dou, Wenhao Sun, Sanmin Xu, Pengxiang Tarim Univ Coll Water Conservancy & Architecture Engn Alar Xinjiang Peoples R China

This paper evaluates linear regression analysis, BP neural network, and a random forest prediction model for the prediction of jujube water demand. The results highlight that the R2 of the random forest is 0.941 and the residual distribution is the most stable. Hence, the random forest is more suitable for prediction, and therefore, an intelligent irrigation system is established employing random forest, where the cloud server is the upper computer and a Raspberry Pi is the lower computer, and at the same time, a PC and a mobile interface was built to present various information about the developed irrigation system.

关键词： Random Forest BP neural network linear regression analysis Intelligent irrigation system

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Development of pricing assessment methodology for used vehicles in Bangladesh's automotive industry utilizing Delphi technique, analytic hierarchy process, and linear regression analysis

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JOURNAL OF REVENUE AND PRICING MANAGEMENT 2025年 1-24页

作者： Hossain, Md. Rasfeq Rayhan, Dewan Sabbir Ahammed Bhuiyan, Iftekhar Uddin Bangladesh Univ Engn & Technol BUET Inst Appropriate Technol IAT Dhaka Bangladesh Kazi Farms Ltd Transport Div Dhaka Bangladesh Natl Inst Text Engn & Res NITER Dept Ind & Prod Engn IPE Dhaka Bangladesh

The used vehicle business segment of Bangladesh's automotive industry is expanding tremendously though this business faces significant challenges due to the absence of a justified pricing methodology. Traditionally, financial entities like banks and insurance companies often rely on a generic approach, applying a flat depreciation rate of 15% for assessing the price of used vehicles, disregarding the vehicle's technical conditions. This study aimed to resolve this problem by introducing a comprehensive pricing assessment methodology that evaluates the technical conditions of the used vehicle and provides a fair price for the vehicle so that both the buyers and sellers are in a win-win situation. This study integrated the market surveys, the Delphi technique, the Analytical Hierarchy Process (AHP), and linear regression analysis to develop a more equitable and comprehensive pricing mechanism for used vehicles named as 'Pricing Chart'. The market survey was conducted through a meticulously designed questionnaire targeting current buyers and sellers of the used vehicle market. The survey, conducted in two phases, had a sample size of a hundred participants, determined through a sample size formula. The survey identified forty-three (43) pivotal factors that customers want while negotiating the price of a used vehicle. The most influential pivotal factors were the performance of the transmission (98% of respondents), vehicle brand (96%), and spare availability (95%). Moreover, the SWOT analysis based on the survey identified that the lack of technical assessment facilities, the absence of a recognized body, and the lack of structured pricing methods were the major weaknesses of the used vehicle market. Further, the Delphi technique elucidated 28 influential decision factors from the 43 pivotal factors, among them vehicle age, technical soundness, overall appearance, brand, and after-sales performance stand as the most critical factors. These factors were then prioritize

关键词： Bangladesh automotive industry Used vehicle assessment and pricing SWOT analysis Delphi technique Analytical hierarchy process (AHP) linear regression analysis

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An Accurate and Fast Computational Python Based Module for linear regression analysis in Data Science Applications

An Accurate and Fast Computational Python Based Module for L...

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IEEE International Conference on Intelligent Systems, Smart and Green Technologies (ICISSGT)

作者： Vangara, Vikram Koti Mourya Vuddanti, Sandeep Kakani, Bhaskar Cent Univ Kamakaka ECE Dept Gulbarga India BITSILICA Pvt Ltd Res Intern Hyderabad India NIT Andhra Pradesh Dept Elect Engn Tadepalligudem India

ISBN: (纸本)9781665404167

Due to the increase in computational power of digital electronics in past decades, Data Science has become an emerging field and attracted much research interest for development in various sectors. The usage of Python programming language is tremendously growing due to the large community and its ease of use and its support with powerful scripting algorithms for many engineering applications. One of the simple and basic technique in Data Science is linear regression. Traditional Libraries like 'scikit-learn' and 'SciPy' are built on top of other libraries like 'NumPy' and 'pandas'. In this study, a python module is created for performing simple linear regression named "LinReg" using only python's built-in functions and data types. This allows faster computation of the regression coefficients and the evaluation metrics like slope, intercept, and mean absolute error. A test is also conducted to compare the computational time of the module with the above mentioned two libraries. However, the analogies are given to compare and pros and cons of this work and its future scope is discussed on such modules for various other algorithms in Data Science.

关键词： Python linear regression analysis Data Science

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The Dielectric Constant of Ba6-3x(Sm1-yNdy)8+2xTi18O54 (x=2/3) Ceramics for Microwave Communication by linear regression analysis

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MATERIALS 2020年第24期13卷 5733页

作者： He, Tiancheng Lv, Caihuan Li, Wenhao Huang, Guohua Hu, Zhihui Xu, Jianmei China Univ Geosci Fac Mat Sci & Chem Wuhan 430074 Peoples R China Nanning Normal Univ Key Lab New Elect Funct Mat Guangxi Coll & Univ Nanning 530023 Peoples R China

The electronics related to the fifth generation mobile communication technology (5G) are projected to possess significant market potential. High dielectric constant microwave ceramics used as filters and resonators in 5G have thus attracted great attention. The Ba6-3x(Sm1-yNdy)(8+2x)Ti18O54 (x = 2/3) ceramic system has aroused people's interest due to its underlying excellent microwave dielectric properties. In this paper, the relationships between the dielectric constant, Nd-doped content, sintering temperature and the density of Ba6-3x(Sm1-yNdy)(8+2x)Ti18O54 (x = 2/3) ceramics were studied. The linear regression equation was established by statistical product and service solution (SPSS) data analysis software, and the factors affecting the dielectric constant have been analyzed by using the enter and stepwise methods, respectively. It is found that the model established by the stepwise method is practically significant with Y = -71.168 + 6.946x(1) + 25.799x(3), where Y, x(1) and x(3) represent the dielectric constant, Nd content and the density, respectively. According to this model, the influence of density on the dielectric constant is greater than that of Nd doping concentration. We bring the linear regression analysis method into the research field of microwave dielectric ceramics, hoping to provide an instructive for the optimization of ceramic technology.

关键词： microwave dielectric ceramics Ba6-3x(Sm1-yNdy)(8+2x)Ti18O54 (x=2/3) system dielectric constant linear regression analysis

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Using linear regression analysis and Defense in Depth to Protect Networks during the Global Corona Pandemic

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Journal of Information Security 2020年第4期11卷 261-291页

作者： Rodney Alexander Hutchinson Community College Hutchinson Kansas USA

The purpose of this research was to determine whether the linear regression analysis can be effectively applied to the prioritization of defense-in-depth security tools and procedures to reduce cyber threats during the Global Corona Virus Pandemic. The way this was determined or methods used in this study consisted of scanning 20 peer reviewed Cybersecurity Articles from prominent Cybersecurity Journals for a list of defense in depth measures (tools and procedures) and the threats that those measures were designed to reduce. The methods further involved using the Likert Scale Model to create an ordinal ranking of the measures and threats. The defense in depth tools and procedures were then compared to see whether the Likert scale and linear regression analysis could be effectively applied to prioritize and combine the measures to reduce pandemic related cyber threats. The results of this research reject the H0 null hypothesis that linear regression analysis does not affect the relationship between the prioritization and combining of defense in depth tools and procedures (independent variables) and pandemic related cyber threats (dependent variables).

关键词： Information Assurance Defense in Depth Information Technology Network Security Cybersecurity linear regression analysis Pandemic

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Simultaneous determination of synthetic cannabinoids in illegal herbal products and blood by LC/TOF-MS, and linear regression analysis of retention time using log Pow

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FORENSIC CHEMISTRY 2020年 17卷

作者： Saito, Koichi Kokaji, Yoshie Muranaka, Yuki Ito, Rie Hoshi Univ Fac Pharmaceut Sci Dept Analyt Chem Shinagawa Ku 2-4-41 Ebara Tokyo 1428501 Japan

A method for the simultaneous determination of 47 synthetic cannabinoids in herbal products and blood (serum and whole blood) by liquid chromatography/time-of-flight mass spectrometry (LC/TOF-MS) was developed. In addition, linear regression analysis of logarithm of retention time and log P-ow was conducted to identify unknown synthetic cannabinoids. The target compounds were separated on a reversed-phase Poroshell 120 EC-C18 (100 mm x 4.6 mm I.D, 2.7 mu m) column using a mixture of acetonitrile/10 mM ammonium formate buffer solution (70:30) as the mobile phase in the isocratic elution mode. The herbal products were extracted with methanol and the extracts were filtered. The strong linear correlation between log P-ow and logarithm of retention time suggests that linear regression analysis would be useful for deducing unknown synthetic cannabinoids in combination with accurate mass measurement by TOF-MS. Blood samples were deproteinized with acetonitrile and subjected to solid-phase dispersive extraction for sample extraction and cleanup. The average recoveries for blood analysis spiked at 10 ng/mL were 61.8% for serum and 61.0% for whole blood. One-way analysis of variance revealed that the mean repeatability value and the mean intermediate precision value for serum were 7.4% and 10.5%, respectively, and those for whole blood were 6.1% and 11.4%, respectively. The developed method was applicable as a screening method for synthetic cannabinoids in illegal herbal products as well as serum and whole blood samples.

关键词： Synthetic cannabinoid linear regression analysis LC/TOF-MS Blood Solid-phase dispersive extraction

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Prediction of UVAT Response parameters by Using linear regression analysis 9

Prediction of UVAT Response parameters by Using Linear Regre...

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9th International Conference of Materials Processing and Characterization (ICMPC)

作者： Vivekananda, K. Kumar, Anshuman Sahoo, S. K. ICFAI Fdn Higher Educ Fac Sci & Technol Dept Mech Engn Hyderabad Telangana India Koneru Lakshmaiah Educ Fdn Dept Mech Engn Vaddeswaram Andhra Pradesh India Natl Inst Technol Dept Mech Engn Rourkela Odisha India

Recent decades improving the quality of the product with respect to the cost is quintessential. For obtaining such type of quality product, it is necessary to develop new manufacturing techniques which can improve the machinability characteristics. In recent years many researchers are investigated sensibly towards improving the machinability characteristics. Ultrasonic vibration assisted manufacturing process is one of them, which has gained a lot of attention due to its sporadic cutting mechanism for achieving a better quality product. In this present paper, concentrates on ultrasonic vibration assisted turning (UVAT) process because turning is one of the important manufacturing processes which can shape the metals by removal of the undesired material to get a required shape and size of the product. For understanding and easy industrial use of UVAT technique, a full factorial design of experiments (125) has been performed. A linear regression mathematical model has been developed to validate the UVAT experimental results. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Inconel 825 linear regression analysis UVAT UVT

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linear regression analysis of Energy Consumption Data for Smart Homes 41

Linear Regression Analysis of Energy Consumption Data for Sm...

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41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO)

作者： Moletsane, Phenyo Phemelo Motlhamme, Tebogo Judith Malekian, Reza Bogatmoska, Dijana Capeska Univ Pretoria MIT C Stream Data Sci Dept Comp Sci ZA-0002 Pretoria South Africa Univ Pretoria Dept Elect Elect & Comp Engn ZA-0002 Pretoria South Africa Univ Informat Sci & Technol St Paul Apostle Dept Comp Sci & Engn Ohrid North Macedonia

ISBN: (纸本)9789532330953

Due to the population and economic growth, global demand for energy is expected to increase considerably in the coming years. A large part of the energy demand comes from energy use in buildings. Consequently, energy efficiency requirement in buildings is considered to be one of the most important goals to promote global energy sustainability. This challenge has motivated recent research in the design of smart home energy management systems based on a set of sensors that analyse how energy is consumed in buildings. In this paper, linear regression model is used to analyse energy consumption data from two real smart homes. The datasets include operational data in two homes and were collected as part of the Smart* project.

关键词： Smart homes linear regression analysis energy efficiency

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Effect of removing the common mode errors on linear regression analysis of noise amplitudes in position time series of a regional GPS network & a case study of GPS stations in Southern California

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Advances in Space Research 2018年第10期61卷 2521-2530页

作者： Jiang, Weiping Ma, Jun Li, Zhao Zhou, Xiaohui Zhou, Boye GNSS Research Centre Wuhan University 129 Luoyu Road Wuhan 430079 China School of Geodesy and Geomatics Wuhan University 129 Luoyu Road Wuhan 430079 China Faculté des Sciences de la Technologieet de la Communication University of Luxembourg 6 rue Richard Coudenhove-Kalergi Luxembourg City 1359 Luxembourg

The analysis of the correlations between the noise in different components of GPS stations has positive significance to those trying to obtain more accurate uncertainty of velocity with respect to station motion. Previous research into noise in GPS position time series focused mainly on single component evaluation, which affects the acquisition of precise station positions, the velocity field, and its uncertainty. In this study, before and after removing the common-mode error (CME), we performed one-dimensional linear regression analysis of the noise amplitude vectors in different components of 126 GPS stations with a combination of white noise, flicker noise, and random walking noise in Southern California. The results show that, on the one hand, there are above-moderate degrees of correlation between the white noise amplitude vectors in all components of the stations before and after removal of the CME, while the correlations between flicker noise amplitude vectors in horizontal and vertical components are enhanced from un-correlated to moderately correlated by removing the CME. On the other hand, the significance tests show that, all of the obtained linear regression equations, which represent a unique function of the noise amplitude in any two components, are of practical value after removing the CME. According to the noise amplitude estimates in two components and the linear regression equations, more accurate noise amplitudes can be acquired in the two components. © 2018 COSPAR

关键词： Correlation GPS time series linear regression analysis Noise Regional GPS network

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