Prediction of some physico-chemical parameters in red wines from ultraviolet–visible spectra using a partial least-squares model in latent variables

The Analyst ◽  
1995 ◽  
Vol 120 (7) ◽  
pp. 1891-1896 ◽  
Author(s):  
Carmen García-Jares ◽  
Bernard Médina
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Latent Variables ◽  
Red Wines ◽  
Chemical Parameters ◽  
Physico Chemical ◽  
Partial Least Squares Model ◽  
Visible Spectra

Smart shopping: the adoption of grocery shopping apps

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Lee-Andra Bruwer ◽  
Nkosivile Welcome Madinga ◽  
Nqobile Bundwini
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Latent Variables ◽  
Moderating Effect ◽  
Ease Of Use ◽  
Perceived Usefulness ◽  
Equation Modeling ◽  
Grocery Shopping ◽  
Intention To Use ◽  
Content Type

PurposeThe purpose of this paper is to determine the key factors influencing the adoption of grocery shopping and to examine the moderating effect of education between antecedents of the adoption of grocery shopping apps and user attitude and intention to purchase.Design/methodology/approachThis study adopted partial least squares structural equation modeling (PLS-SEM) to evaluate the relationship between the latent variables: perceived usefulness, perceived ease of use, attitude and intention to use grocery shopping apps. Partial least squares multigroup analysis (PLS-MGA) was used to examine the moderating effect of education. A total of 305 grocery shopping apps users were surveyed using a structural questionnaire.FindingsThe results indicated that all the factors considered in the framework were significant in predicting the intention to use the grocery shopping apps. The findings show that education has no significant impact on any relationship.Practical implicationsA better understanding of the factors that affect the acceptance of mobile grocery shopping apps is important for developing better strategic management plans.Originality/valueThis is one of the first studies to research the adoption of grocery shopping apps in a developing country, as well as the first to focus on consumers in South Africa.

scholarly journals Effective fault detection in structural health monitoring systems

2019 ◽  
Vol 11 (9) ◽  
pp. 168781401987323 ◽  
Author(s):  
Marwa Chaabane ◽  
Majdi Mansouri ◽  
Kamaleldin Abodayeh ◽  
Ahmed Ben Hamida ◽  
Hazem Nounou ◽  
...  
Keyword(s):  
Fault Detection ◽  
Least Squares ◽  
Partial Least Squares ◽  
Least Squares Method ◽  
Random Noise ◽  
Ratio Test ◽  
Multiscale Representation ◽  
Kernel Partial Least Squares ◽  
Partial Least Squares Model ◽  
Partial Least Squares Method

A new fault detection technique is considered in this article. It is based on kernel partial least squares, exponentially weighted moving average, and generalized likelihood ratio test. The developed approach aims to improve monitoring the structural systems. It consists of computing an optimal statistic that merges the current information and the previous one and gives more weight to the most recent information. To improve the performances of the developed kernel partial least squares model even further, multiscale representation of data will be used to develop a multiscale extension of this method. Multiscale representation is a powerful data analysis way that presents efficient separation of deterministic characteristics from random noise. Thus, multiscale kernel partial least squares method that combines the advantages of the kernel partial least squares method with those of multiscale representation will be developed to enhance the structural modeling performance. The effectiveness of the proposed approach is assessed using two examples: synthetic data and benchmark structure. The simulation study proves the efficiency of the developed technique over the classical detection approaches in terms of false alarm rate, missed detection rate, and detection speed.

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