Set Of Independent Variables Research Articles

Indirect caffeine modeling in an urban river

Caffeine is used worldwide as a chemical tracer to identify anthropic pressures on urban water resources. Nevertheless, its quantification demands great financial investments. This research created a model that would indirectly determine a range of possible caffeine concentrations along an urban river, without the need for extensive laboratory work. The model is based on Canonical Correlation Analysis (CCA), which can correlate two sets of different-sized independent and dependent variables in order to generate a single empirical equation. This equation takes as input the concentrations of ammonia nitrogen and orthophosphate, as well as the total population and the population inhabiting irregular housing areas. From the model’s results, it was possible to elaborate a spectrum of possible concentrations of caffeine along the Atuba River (Curitiba-Brazil). The tendency of water quality degradation of this river was also predicted. This model could become a useful preliminary analysis for water resource managers and researchers alike.
 Keywords: caffeine, canonical correlation analysis, water quality modeling.

Methodology for Predicting MAP-21 Interstate Travel Time Reliability Measure Target in Virginia

This study develops a target setting methodology for the (Moving Ahead for Progress) MAP-21 Interstate Travel Time Reliability Measure of “Percent of the Person-Miles Traveled on the Interstate that are Reliable” (PMTR-IS). The study uses data specific to Virginia for a set of independent variables (Hourly Volume, and Volume/Capacity Ratio, Truck Percentage, Equivalent Property Damage Only Rate, Lane Impacting Incident Rate, Number of Lanes, Presence of Safety Service Patrol, Terrain, Urban Designation) to predict whether a MAP-21 reporting segment is reliable. This is used to estimate predicted PMTR-IS with the MAP-21 specified formula. CART (classification and regression trees) models were used with 1,536 different configurations. Of the four best performing models, one was selected using engineering judgement. Datasets from 2017 and 2018 were used for training, and from 2019 and 2020 for testing. The predicted and calculated PMTR-IS were compared, and the error percentage was lower than 1% for 2019, which can be considered negligible. The 2020 error rate was higher and can perhaps be attributed to unusual reliability because of the impacts of the pandemic on travel. Sensitivity analyses revealed that the predicted PMTR-IS is reactive to capacity increase in unreliable sections at a local level, slower to respond to local AADT (annual average daily traffic) increase, and stable to small statewide AADT oscillations. The authors recommend using this methodology for target setting, taking into consideration the state authorities’ other strategic priorities.

Determinants of financial inclusion across gender in Saudi Arabia: evidence from the World Bank's Global Financial Inclusion survey

PurposeThis paper investigates the magnitude of financial inclusion in terms of ownership and usage of financial products across gender in Saudi Arabia based on the World Bank's Global Financial Inclusion (GFI) survey data.Design/methodology/approachThis study derives the data from the GFI survey, covering 1,009 respondents from Saudi Arabia. The data on ownership and usage of financial products along with demographic characteristics of the surveyed respondents have been extracted for the analysis. The data have been analyzed with the help of statistical techniques, such as the chi-square test, and marginal effects for identifying the factors affecting financial inclusion across gender.FindingsThere is a significant association between financial inclusion and gender in terms of ownership and usage. About 82% of males have reported having a bank account with financial institutions, whereas only 60% females have reported ownership of a bank account in Saudi Arabia. The ownership and usage of financial products are comparatively more among males than females. The analysis of the marginal effect of gender shows a significant and positive impact on financial inclusion, implying that males are 10 and 13% more likely to own and use financial products, respectively, as compared to females. Further, marginal effect estimates for ownership and usage for males and females indicate that a set of independent variables related to age, level of education, occupation and income level of the respondents have a significant impact on financial inclusion.Practical implicationsFinancial inclusion across gender is the first step of creating an inclusive society and empowering both males and females equally. Findings indicated an inclination of financial inclusion towards males. The research findings provide key policy insights for achieving the Vision 2030 of Saudi Arabia by strengthening gender inclusion in its growth story and ensuring the participation of females at workplaces.Originality/valueMost of the studies have included bank account ownership in a financial institution as an indicator of financial inclusion. The authors have included ownership and usage of a variety of financial products for assessing the determinants of financial inclusion across gender, which provides empirical evidence on the magnitude of financial inclusion.

Regression Models to Study the Total LOS Related to Valvuloplasty.

Background: Valvular heart diseases are diseases that affect the valves by altering the normal circulation of blood within the heart. In recent years, the use of valvuloplasty has become recurrent due to the increase in calcific valve disease, which usually occurs in the elderly, and mitral valve regurgitation. For this reason, it is critical to be able to best manage the patient undergoing this surgery. To accomplish this, the length of stay (LOS) is used as a quality indicator. Methods: A multiple linear regression model and four other regression algorithms were used to study the total LOS function of a set of independent variables related to the clinical and demographic characteristics of patients. The study was conducted at the University Hospital “San Giovanni di Dio e Ruggi d’Aragona” of Salerno (Italy) in the years 2010–2020. Results: Overall, the MLR model proved to be the best, with an R2 value of 0.720. Among the independent variables, age, pre-operative LOS, congestive heart failure, and peripheral vascular disease were those that mainly influenced the output value. Conclusions: LOS proves, once again, to be a strategic indicator for hospital resource management, and simple linear regression models have shown excellent results to analyze it.

Minimum feature size control in level set topology optimization via density fields

A level set topology optimization approach that uses an auxiliary density field to nucleate holes during the optimization process and achieves minimum feature size control in optimized designs is explored. The level set field determines the solid-void interface and the density field describes the distribution of a fictitious porous material using the solid isotropic material with penalization. These fields are governed by two sets of independent optimization variables which are initially coupled using a penalty for hole nucleation. The strength of the density field penalization and projection is gradually increased during the optimization process to promote a 0–1 density distribution. In addition, a second penalty regulates the evolution of the density field in the void phase. The treatment of the density field combined with the second penalty mitigate the appearance of small design features. The minimum feature size of optimized designs is controlled by the radius of the linear filter applied to the density optimization variables. The structural response is predicted by the extended finite element method, the sensitivities by the adjoint method, and the optimization variables are updated by a gradient-based optimization algorithm. Numerical examples investigate the robustness of this approach with respect to algorithmic parameters and mesh refinement. The results show the applicability of the combined density level set topology optimization approach for both optimal hole nucleation and for minimum feature size control in 2D and 3D. This comes, however, at the cost of a more complex problem formulation and additional computational cost due to an increased number of optimization variables.

Effect of Human Resources Quality, Performance Evaluation, and Incentives on Employee Productivity at Raharja High School

This study used a sample of 30 employees of Raharja College, Cikokol, Tangerang which were taken randomly to test the effect of the quality of human resources, job evaluation, incentives on employee productivity. The survey method with path analysis technique was used to analyze the pattern of relationships between variables with the aim of knowing the direct or indirect effect of a set of independent variables (exogenous) on the dependent variable (endogenous) with data analysis using the smart PLS 3.0 version software. The results show that the quality of human resources has a positive effect on employee productivity with a path coefficient value of 0.145, the quality of human resources has a positive effect on incentives with a path coefficient value of 0.121, incentives have a positive effect on productivity with a path coefficient value of 0.784, while performance evaluation has no positive effect on employee productivity with a path coefficient value of -0.103. It can be said that all the hypotheses in this study only one that does not have a positive effect while the rest have a positive effect. From the results of the data analysis, it can be seen that the quality of human resources directly affects employee productivity at Raharja College. Thus, the hypothesis which states that the quality of HR has a direct effect on employee productivity is accepted.

Prostate tumor eccentricity predicts Gleason score better than prostate tumor volume.

Prostate tumor volume predicts biochemical recurrence, metastases, and tumor proliferation. A recent study showed that prostate tumor eccentricity (elongation or roundness) correlated with Gleason score. No studies examined the relationship among the prostate tumor's shape, volume, and potential aggressiveness. Of the 26 patients that were analyzed, 18 had volumes >1 cc for the histology-based study, and 25 took up contrast material for the MRI portion of this study. This retrospective study quantitatively compared tumor eccentricity and volume measurements from pathology assessment sectioned wholemount prostates and multi-parametric MRI to Gleason scores. Multi-parametric MRI (T1, T2, diffusion, dynamic contrast-enhanced images) were resized, translated, and stitched to form spatially registered multi-parametric cubes. Multi-parametric signatures that characterize prostate tumors were inserted into a target detection algorithm (Adaptive Cosine Estimator, ACE). Various detection thresholds were applied to discriminate tumor from normal tissue. Pixel-based blobbing, and labeling were applied to digitized pathology slides and threshold ACE images. Tumor volumes were measured by counting voxels within the blob. Eccentricity calculation used moments of inertia from the blobs. From wholemount prostatectomy slides, fitting two sets of independent variables, prostate tumor eccentricity (largest blob eccentricity, weighted eccentricity, filtered weighted eccentricity) and tumor volume (largest blob volume, average blob volume, filtered average blob volume) to Gleason score in a multivariate analysis, yields correlation coefficient R=0.798 to 0.879 with P<0.01. The eccentricity t-statistic exceeded the volume t-statistic. Fitting histology-based total prostate tumor volume against Gleason score yields R=0.498, P=0.0098. From multi-parametric MRI, the correlation coefficient R between the Gleason score and the largest blob eccentricity for varying thresholds (0.30 to 0.55) ranged from -0.51 to -0.672 (P<0.01). For varying thresholds (0.60 to 0.80) for MRI detection, the R between the largest blob volume eccentricity against the Gleason score ranged from 0.46 to 0.50 (P<0.03). Combining tumor eccentricity and tumor volume in multivariate analysis failed to increase Gleason score prediction. Prostate tumor eccentricity, determined by histology or MRI, more accurately predicted Gleason score than prostate tumor volume. Combining tumor eccentricity with volume from histology-based analysis enhanced Gleason score prediction, unlike MRI.

The Impact of Ownership Structure and Corporate Governance on Capital Structure of Jordanian Industrial Companies

This study investigates the impact of ownership structure and corporate governance (CG) on the capital structure using 798 firm-year observations of listed companies in the Amman Stock Exchange (2005-2018). The Ordinary Least Squares is utilized to examine the relationships between the dependent variable (i.e., leverage) and a set of independent variables, including ownership concentration factors (proxied by the institutional and largest shareholder) and CG factors (proxied by board size, CEO/chairman duality, board composition, a committee of nominations and remuneration, meetings number). Empirical The data reveal a strong negative (positive) relationship between the largest shareholder (institutional shareholder) and capital structure. Regarding the CG factors, the regression results show that board size, composition, and several meetings are the only factors correlated significantly positively with capital structure. Our examination is primarily motivated by the inconclusive and limited empirical evidence on the association between ownership and governance factors and capital structure. It enriches the literature by providing an updated model on capital structure factors from a non-Western setting. This study adds new evidence by capturing the effect of the unique characteristics of developing countries and the institutional environment. Using data from one of the Middle Eastern nations (Jordan), this study is the first to examine ownership and governance elements in capital structure research over an extended period. The results of this investigation benefit regulatory authorities in monitoring and regulating the corporates. This led to considering the factors affecting Jordanian firms' capital structure. The evidence generated in our study supports the development of strict CG schema rules by protecting the safety of stakeholders and policymakers.

Analysis of human exposure to landslides with a GIS multiscale approach

Decision-making plays a key role in reducing landslide risk and preventing natural disasters. Land management, recovery of degraded lands, urban planning, and environmental protection in general are fundamental for mitigating landslide hazard and risk. Here, we present a GIS-based multi-scale approach to highlight where and when a country is affected by a high probability of landslide occurrence. In the first step, a landslide human exposure equation is developed considering the landslide susceptibility triggered by rain as hazard, and the population density as exposed factor. The output, from this overview analysis, is a global GIS layer expressing the number of potentially affected people by month, where the monthly rain is used to weight the landslide hazard. As following step, Logistic Regression (LR) analysis was implemented at a national and local level. The Receiver Operating Characteristic indicator is used to understand the goodness of a LR model. The LR models are defined by a dependent variable, presence–absence of landslide points, versus a set of independent environmental variables. The results demonstrate the relevance of a multi-scale approach, at national level the biophysical variables are able to detect landslide hotspot areas, while at sub-regional level geomorphological aspects, like land cover, topographic wetness, and local climatic condition have greater explanatory power.

Procjena profitabilnosti banke u evroizovanim ekonomijama

Financial euroization is a significant phenomenon in developing countries. The key financial institutions for macroeconomic stability in these countries are the banks. Conversion of most of the financial assets and liabilities into foreign currency creates a currency mismatch between the assets and liabilities in domestic and foreign currency. Banks in the role of intermediaries between depositors and borrowers face the risks of accepting foreign currency deposits and placing foreign currency loans. With this, financial euroization can impact the performance of banks. The paper's main objective is to analyze the impacts of euroization on the performance of banks in North Macedonia, Serbia, and Bosnia and Herzegovina for the period from 2010 to 2020. For performance measure, we use bank profitability. The paper uses a pre-set empirical model, in which the dependent variable is ROA (annual data for the return on assets - as a variable for measuring the profitability of banks) and a set of independent variables, including the degree of euroization. The results suggest that the model is robust, and the variables are statistically significant. In conclusion, this outcome can be ascribed to the segmentation of the bank credit portfolio in credit denominated in domestic versus foreign currency credit denomination. Moreover, the degree of euroization proved to be statistically significant for predicting the performance of banks, hence is high bank management decision-making information.

A Macro-microscopic Coupled Constitutive Model for Fluid-Saturated Porous Media with Compressible Constituents

The paper provides a macro-microscopic coupled constitutive model for fluid-saturated porous media with respect to the compressibility of the solid skeleton, the real solid material and the fluid phase. The derivation of the model is carried out based on the porous media theory and is consistent with the second law of thermodynamics. In the present paper, two different sets of independent variables are introduced to implement the coupled behavior between the compressibility of the solid skeleton and the real solid material. Altogether the proposed model exploits five independent variables, i.e. the deviatoric part of the right Cauchy-Green deformation tensor, the partial density of solid phase, the density of the real solid material, the density of the real fluid material and the relative velocity of the fluid phase. Subsequently, the linearized version of the proposed constitutive model is also presented and compared with some models by other authors. It is found that Biot's model can also be derived based on the linearized version of the proposed model, which indicates that the present work bridges the gap between the porous media theory and Biot's model. Compared with Biot's model, the present model can provide the evolution of the porosity by considering the volumetric strain of the solid skeleton and the volumetric strain of the real solid material.

Self-efficacy and social support as protective variables that predict resistance to gravity in suicidal attempts

Background: Suicide attempts (SA) is a global mental health problem, especially in Spain. Classical research has focused on determining the risk variables; however, few studies focused on protective variables. The aim was to assess which protective variables are more predictive of the greater or lesser lethality of a made SA. Methods: The sample consisted of 156 people who had been admitted to Emergency Department (ED) for SA, aged between 18 and 49 years Mean (M) = 26, Standard Deviation (SD) = ± 10.8. The sample was divided into three groups defined according to the level of lethality of the made SA. Protective variables related to resilience were assessed. Results: The results suggest that self-efficacy and social support are protective predictors for future SA, along with being male. The protective variables explain the degree of lower suicide lethality, with the model proposed (set of independent variables) being significant and explaining between 0.12 and 0.68 of the dependent variable, correctly classifying 84.8% of the cases (R2 de Nagelkerke = 0.76). Conclusions: This study is further evidence of the need for effective suicide prevention and intervention plans adapted to gender differences in behaviour and further research in this line of work. Future studies on large patient samples are now needed, especially with a longitudinal design.

TOPOLOGY OPTIMIZATION OF ADDITIVE MANUFACTURING COMPONENTS WITH LATTICE STRUCTURES FOR MULTIPHYSICS APPLICATIONS

This paper aims to optimize additive manufacturing parts containing lattice structures by means of topology optimization. The proposed method relies on numerical homogenization to model the behavior of the mesoscale lattices inside the macroscale component with an equivalent material model. While the topology of the unit cell is fixed a priori, the material constitutive model is considered to be a function of multiple size parameters of the lattice struts and the orientation of the lattice cell. Both the size parameters and the orientation are considered as design fields in the design domain of the optimization in order to optimize the distribution of orthotropic lattice structures while adapting their orientation to the macroscale loads. In addition, the shape of the macroscale part is described by a separate level set function. Besides avoiding the appearance of degenerate lattice structures, the strict control over the macroscale shape permits the inclusion of different manufacturing constraints. The design fields describing the lattice geometry and macroscale shape are parametrized explicitly by filtering sets of independent optimization variables. This explicit design parametrization allows one to simultaneously optimize the shape of the macroscale part and the lattice structures inside the part. Moreover, the optimization problem is expressed and solved as a standard nonlinear programming problem, which allows one to easily adopt the formulation for different design problems. This interesting feature is illustrated in the numerical examples which deal with different physical problems, and several objective and constraint functions.

Use of GIS and machine learning to predict disease in shrimp farmed on the east coast of the Mekong Delta, Vietnam

Diseases in shrimp farms in the Mekong Delta of Vietnam cause significant crop losses and are therefore of great concern to producers. Once a pond becomes infected, it is difficult to prevent spread of the disease to nearby shrimp farming areas. Thus, predicting the occurrence of disease is an essential part of reducing the risk for shrimp farmers. In this study, we applied an integrated geographic information system and machine learning system to predict three serious diseases of shrimp, namely, acute hepatopancreatic necrosis, white spot syndrome disease, and Enterocytozoon hepatopenaei infection, based on data collected from shrimp farms in the Tra Vinh, Bac Lieu, Soc Trang, and Ca Mau provinces of Vietnam. We first constructed a map showing the distribution of these diseases using the locations of affected farms, and then we conducted spatial analysis to acquire the geographical features of the affected locations. This latter information was combined with environmental factors and clinical signs to form the set of independent variables affecting the outbreak of diseases. The neural network model outperformed the logistic regression, random forest, and gradient boosting methods in terms of predicting infection to estimate the probability of disease occurrence in farmed areas. Acute hepatopancreatic necrosis disease infected farms downstream of the Co Chien and Hau Rivers of Tra Vinh and west of Ca Mau. Enterocytozoon hepatopenaei infection is distributed in Soc Trang Province, while white spot syndrome virus has spread to the coastal districts of Soc Trang and Bac Lieu Provinces, where it is highly associated to water from a complex canal system.

Development of an analytical model for copper heap leaching from secondary sulfides in chloride media in an industrial environment

In multivariate analysis, a predictive model is a mathematical/statistical model that relates a set of independent variables to dependent or response variable(s). This work presents a descriptive model that explains copper recovery from secondary sulfide minerals (chalcocite) taking into account the effects of time, heap height, superficial velocity of leaching flow, chloride concentration, particle size, porosity, and effective diffusivity of the solute within particle pores. Copper recovery is then modelled by a system of first-order differential equations. The results indicated that the heap height and superficial velocity of leaching flow are the most critical independent variables while the others are less influential under operational conditions applied. In the present study representative adjustment parameters are obtained, so that the model could be used to explore copper recovery in chloride media as a part of the extended value chain of the copper sulfides processing.

Stpope generisanog saobraćaja različitih vrsta objekata u urbanoj sredini

The paper presents the methodology and results of research on the basic characteristics of movement and generated traffic of different land purposes in the city of Loznica. By determining the basic travel characteristics for each selected zone and forming an adequate set of independent parameters for the calculation of rates. The goal is to establish links between land use and generated traffic. Based on foreign literature, scientific papers, manuals and models, a methodology was created that is adapted to local conditions for each research zone. Data on each zone were processed and analyzed in order to determine the basic travel characteristics. The basic travel characteristics for related zones are presented in groups in order to better compare similarities and differences. Based on the obtained results on generated traffic and the selected set of independent variables for each zone, the trip rates of generated traffic were calculated and compared with the results of the American manual of generated traffic. Also, conclusions were drawn on the usability of the generated traffic rates from the American manual and the relationship between the basic travel characteristics and their impact on the calculated generated trip rates.

THE STATUS OF GRADE EIGHT STUDENTS’ ACADEMIC ACHIEVEMENT IN MATHEMATICS AND FACTORS AFFECTING: THE CASE OF SOME SELECTED PRIMARY SCHOOLS IN NEKEMTE TOWN

The study's main objective was to investigate the status of grade eight students' academic achievements in mathematics in governmental primary schools of Nekemte Town and the factors affecting them. For this research, 144 students were selected using simple random sampling, and 20 teachers were selected via purposive sampling techniques. On the other hand, 15 respondents were selected via purposive sampling for an interview out of 5 sample schools; the study design was employed a sequential explanatory research approach thereby, questionnaires, post-semi-structural interviews, and observation tools that analyzed and interpreted using descriptive statistics, independent sample t-test, multiple regression, and one-way ANOVA computed by SPSS- version 24:00. The results of students' achievement test corresponding with predicted factors was indicating a low average mean score of algebra and Geometry tests rated by accepted value (75%) of Cronbach alpha, ANOVA test scores between teachers and students respondents indicated that school facilities, teacher's quality, parental support and, parental level of education revealed (F&gt;5.161.05) values. The results of multiple regression confirmed that the set of independent variables explained (P≤ .05) dependent variable was regressed on predicted variables such as school facility, teachers' quality, and parental educational level; however, attitude and parental support (P-v&gt;.05). The R2=.357 depicted that the model explains 35.70% of the variance in achievement tests in mathematics. Similarly, qualitative data probe that independent study variables mainly affect students’ academic achievement in mathematics. From the finding, the researcher forwards recommendations for the unexplained variables that require diagnosis in further investigation to fit the model summary

Computing Independent Variable Sets for Polynomial Ideals

Computing independent variable sets for polynomial ideals plays an important role in solving high‐dimensional polynomial equations. The computation of a Gröbner basis for an ideal, with respect to a block lexicographical order in classic methods, is huge, and then an improved algorithm is given. Based on the quasi‐Gröbner basis of the extended ideal, a criterion of assigning independent variables is gained. According to the criteria, a maximal independent variable set for a polynomial ideal can be computed by assigning indeterminates gradually. The key point of the algorithm is to reduce dimensions so that the unit of computation is one variable instead of a set, which turns a multivariate problem into a single‐variable problem and turns the computation of rational function field into that of the fundamental number field. Hence, the computation complexity is reduced. The algorithm has been analysed by an example, and the results reveal that the algorithm is correct and effective.

Spatial modeling of radon potential mapping using deep learning algorithms

Radon potential mapping is challenging due to the limited availability of information. In this study, a new modeling process using deep learning models based on convolution neural network (CNN), long short-term memory (LSTM), and recurrent neural network (RNN) is presented to predict radon potential in the northwestern part of Gangwon Province, South Korea. The used data in this study are in two sets of dependent variables (measured soil gas radon concentrations) and independent variables (radon conditioning factors: lithology; distance from lineament; mean soil calcium oxide [Cao], potassium oxide [K2O], and ferric oxide [Fe2O3] concentrations; effective soil depth; topsoil texture; and soil drainage). The models were validated based on the area under the receiver operating curve (), mean squared error (), root mean square error (), and standard deviation (). The CNN model with values of 0.906 and 0.905 in the learning and testing stages, respectively, is introduced as the optimal model. The lowest and values were from the CNN, LSTM, and RNN models, respectively. Our results show that the use of deep learning models to generate radon potential maps is promising and reliable.

Improved modelling for low-correlated multiple responses by common-subset-of-independent-variables partial-least-squares

In this study, a new approach for PLS modelling for low-correlated multiple responses, called Common-Subset-of-Independent-Variables Partial-Least-Squares, denoted as CSIV-PLS1, is proposed and evaluated. In CSIV-PLS1, for each response vector, individual PLS1 models with individual model complexities are developed, based on one common set of independent variables, obtained after variable selection by the Final Complexity Adapted Models method, using the absolute values of the PLS regression coefficients, denoted as FCAM-REG.CSIV-PLS1 combines a common variable set for all response vectors, which is a characteristic of PLS2, with the individual model complexity for each response, which is a characteristic of PLS1. These characteristics make CSIV-PLS1 more flexible than PLS2.The selective and predictive abilities of the proposed CSIV-PLS1 method are investigated using one simulated and four real data sets with low-correlated multiple responses from different sources. The simulated data set is used to test the general applicability of the CSIV-PLS1 method. The predictive abilities, measured by the RMSEP values, resulting from CSIV-PLS1 models, are statistically compared with those of the corresponding PLS1 and PLS2 models, using one-tailed paired t-tests.The selective ability of the CSIV-PLS1 method is good, because mostly variables with an informative meaning to the responses are selected. The RMSEP values resulting from the CSIV-PLS1 method are (i) significantly lower at the 95% confidence level than those of the corresponding PLS2 method, and (ii) borderline significantly lower at the 90–95% confidence level than those of the corresponding PLS1 methods. In case of low-correlated multiple responses, the predictive ability of the CSIV-PLS1 method is significantly better than that of the PLS2 method, and borderline significantly better than those of the corresponding PLS1 methods. Therefore, CSIV-PLS1 modelling may be an alternative for PLS1 or PLS2.