Results 1 to 10 of about 12,005 (181)
Sewing Thread Consumption for Chain Stitches of Class 400 using Geometrical and Multilinear Regression Models [PDF]
AUTEX Research Journal, 2021 This work deals with determination of rapid and precise methods to predict the amount of sewing thread needed to sew a garment using different chain stitches of the class 400 (from 401 to 407 chain stitches).
Malek Sarah +3 more
doaj +4 more sources
Prediction of Compression Index of Marine clay Using Artificial Neural Network and Multilinear Regression Models [PDF]
Journal of Soft Computing in Civil Engineering, 2021 Compression Index (CI) is one of the frequently used soil parameters for the determination of possible settlement. In this study, the Compression Index of Marine clay is predicted using Artificial Neural network (ANN).
Ramachandiran Saisubramanian +1 more
doaj +3 more sources
Ultraviolet Erythemal Irradiance (UVER) under Different Sky Conditions in Burgos, Spain: Multilinear Regression and Artificial Neural Network Models [PDF]
Applied Sciences, 2023 Different strategies for modeling Global Horizontal UltraViolet Erythemal irradiance (GHUVE) based on meteorological parameters measured in Burgos (Spain) have been developed. The experimental campaign ran from September 2020 to June 2022.
S. García-Rodríguez +4 more
doaj +5 more sources
Modeling spatiotemporal patterns of microplastic pollution in the lupit river using multilinear regression. [PDF]
Sci RepAbstract Despite increasing awareness of microplastics as contaminants, their sources and abundance factors remain poorly understood. This study found widespread microplastic pollution in the Lupit River. Microplastic concentrations were determined from surface water samples once per season.
Raab K +4 more
europepmc +4 more sources
Water Science and Technology, 2023 The aim of this study is to model a relationship between the amount of the suspended sediment load by considering the physiographic characteristics of the Lake Urmia watershed. For this purpose, the information from different stations was used to develop
Niloofar Nejatian +4 more
doaj +5 more sources
Multilinear Regression Model to Estimate Mud Weight [PDF]
Nigerian Journal of Environmental Sciences and Technology, 2021 Estimation of mud weight poses a serious challenge to mud industries. In this study, a model was developed to tackle the problem of estimation of mud weight using multilinear regression techniques. The model was developed using data obtained from production records.
J. Oloro, T. E. Akhihiero
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Multilinear Regression Model for Biogas Production Prediction from Dry Anaerobic Digestion of OFMSW [PDF]
Sustainability, 2022 The aim of this study was to develop a multiple linear regression (MLR) model to predict the specific methane production (SMP) from dry anaerobic digestion (AD) of the organic fraction of municipal solid waste (OFMSW). A data set from an experimental test on a pilot-scale plug-flow reactor (PFR) including 332 observations was used to build the model ...
Elena Rossi +2 more
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Analysis of enterprise management technology and innovation based on multilinear regression model [PDF]
Applied Mathematics and Nonlinear Sciences, 2021 Abstract With the continuous innovation of scientific research, technological level and social and economic development, the technological innovation of enterprises in the continuous management is also facing a new breakthrough and upgrade.
Fangfang Du +2 more
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Horizontal axis wind turbine modelling and data analysis by multilinear regression [PDF]
Mechanical Sciences, 2020 Abstract. The modelling of each horizontal axis wind turbine (HAWT) differs due to variation in operating conditions, dynamic parameters, and components. Thus, the choice of profiles also varies for specific applications. So for the better choice of profiles, the wind turbine performance is analysed for different parameters and working conditions.
Paulaiyan Tittus, P. M. Diaz
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Jurnal Kejuruteraan, 2021 Increased volumes of solid waste generation continue unabated due mainly to rapid population increase, urban migration, economic enhancements, and modern lifestyles. Two significant factors contributing directly to the higher volumes of solid waste generation are population and gross domestic product (GDP).
Faridah Zulkipli +3 more
openalex +2 more sources