Partial Least Squares Algorithm Research Articles

Striking the Balance: How Workplace Flexibility Enhances Engagement and Performance of Employees working in Public and Private settings

This study investigates the relationships between workplace flexibility, employee engagement, and performance of employees working in public and private settings. The purpose is to understand how flexible work arrangements influence employee engagement and overall performance across various industries. This research was conducted using a descriptive quantitative method. A structured questionnaire was used to collect data from 400 employees across finance, technology, healthcare, and manufacturing industries. The measurement model’s reliability was evaluated using the partial least square (PLS) structural equation modeling (SEM) approach (see table 3). Cronbach alpha (α) and composite reliability (CR) values were used. A prognosis model evaluation shows that all of the variables have SRMR values of 0.082 and NFI values of 0.914, suggesting that the model accurately matches with the experimental data. The Q2 predict value for employee performance (EP) of 0.352 shows excellent predictive relevance with a significant impact size. In comparison, the Q2 predict predictive value for Employee Engagement (EENG) is 0.185, indicating good predictive relevance with a modest impact size. These Q2Predict values are higher than the required minimum threshold of 0.00, indicating that the model is highly predictive for each variable. Furthermore, the Q2 effect sizes for EENG and EPC show that they have a significant impact on the endogenous variable (WPFX). SmartPLS 3.3.9 used the PLS algorithm as well as the bootstrapping approach to evaluate the presented hypothesis. Table 6 shows the beta values that demonstrate the intensity and significance of the positive link between the dependent and independent variables, where employee engagement acts as both a moderator and mediator. By including both mediation and moderation in the same model, you can gain a more comprehensive understanding of how workplace flexibility impacts employee performance, considering the mediating role of employee engagement and the moderating effect of its level. In addition to the above analysis, In addition, demographic factors are tested by Manova.

Rapid UV-Vis spectrophotometric method aided by firefly-PLS models for the simultaneous quantification of ciprofloxacin, lomefloxacin, and enrofloxacin in their laboratory mixture, dosage forms and water samples: greenness and blueness assessment

Herein, a novel UV spectrophotometric method coupled with chemometric tools was developed for the simultaneous determination of three fluoroquinolone antibiotics: ciprofloxacin, lomefloxacin, and enrofloxacin. Such integration of UV spectroscopy and chemometric analysis proved to be a simple, rapid, and cost-effective approach for the quantification of these clinically important pharmaceutical compounds and aid in their quality control analysis. The method employed firefly algorithm for variable selection and partial least squares (PLS) regression for model calibration. The developed method was validated by independent test set in addition the accuracy, intra and inter-day precision as per ICH guidelines which showed a satisfactory performance with mean recovery ranged between 98.18 and 101.83 with %RSD < 2. Besides, the developed method displayed ultrasensitive levels with LODs (0.0803, 0.1125, 0.1309 µg/mL) and LOQs (0.2434, 0.3409, 0.3968 µg/mL) for ciprofloxacin, lomefloxacin, and enrofloxacin, respectively. The greenness and blueness of the developed method were also evaluated using the recently proposed Analytical GREEnness metric approach (AGREE) and Blue applicability grade index (BAGI) tools, which showed a high AGREE score of 0.79 and a BAGI score of 77.5. These results indicate that the developed method provides an environmentally friendly alternative to the traditionally used chromatographic techniques, while maintaining high analytical practicability. Finally, the application of the developed methodology was demonstrated on real pharmaceutical and tap water samples, and the results were in good agreement with those obtained by the reference HPLC method indicating the reliability and suitability of the proposed spectrophotometric method for routine analysis of fluoroquinolone antibiotics.

Efficient global sensitivity analysis method for dynamic models in high dimensions

AbstractDynamic models generating time‐dependent model predictions are typically associated with high‐dimensional input spaces and high‐dimensional output spaces, in particular if time is discretized. It is computationally prohibitive to apply traditional global sensitivity analysis (SA) separately on each time output, as is common in the literature on multivariate SA. As an alternative, we propose a novel method for efficient global SA of dynamic models with high‐dimensional inputs by combining a new polynomial chaos expansion (PCE)‐driven partial least squares (PLS) algorithm with the analysis of variance. PLS is used to simultaneously reduce the dimensionality of the input and output variables spaces, by identifying the input and output latent variables that account for most of their joint variability. PCE is incorporated into the PLS algorithm to capture the non‐linear behavior of the physical system. We derive the sensitivity indices associated with each output latent variable, based on which we propose generalized sensitivity indices that synthesize the influence of each input on the variance of entire output time series. All sensitivities can be computed analytically by post‐processing the coefficients of the PLS‐PCE representation. Hence, the computational cost of global SA for dynamic models essentially reduces to the cost for estimating these coefficients. We numerically compare the proposed method with existing methods by several dynamic models with high‐dimensional inputs. The results show that the PLS‐PCE method can obtain accurate sensitivity indices at low computational cost, even for models with strong interaction among the inputs.

Efficient Quantification of Soluble and Insoluble Oxalates in Clay Mineral Mixtures

ABSTRACT Quantification of oxalate salts in soil clay minerals is necessary to study oxalate biogeochemistry, but existing analytical techniques are expensive and time-consuming. We aim to develop an efficient attenuated total reflectance mid-infrared (MIR) spectroscopic technique to quantify oxalate salts in a clay mineral matrix. We calibrated MIR models for analysis of oxalate anion concentrations in standard solutions (0–0.01 M) by using a partial least-squares regression algorithm. MIR models were also developed for analysis of sodium oxalate (NaOx) and calcium oxalate (CaOx) content in clay mineral mixtures with composition like soils of a semi-arid region and with contrasting concentrations (0–1.0 g g−1 for both oxalate salts) to test sensitivity of analyses. Validation plots (true vs predicted values) showed excellent model fit (R2 ≥ 0.96) and accuracy (normalized root mean squared error of prediction ≤ 0.06) for CaOx, NaOx and oxalic acid components. Once predictive models are stored in analytical software, MIR spectroscopic analyses of samples are much more efficient than chemical techniques. Our MIR spectral-based models are suitable for direct quantification of oxalate salts in clay mineral mixtures for samples like those used for model calibration.

Simultaneous Rapid Detection of Multiple Physicochemical Properties of Jet Fuel Using Near-Infrared Spectroscopy.

Jet fuel is the primary fuel used in the aviation industry, and its quality has a direct impact on the safety and operational efficiency of aircraft. The accurate quantitative detection and analysis of various physicochemical property indicators are important for improving and ensuring the quality of jet fuel in the domestic market. This study used near-infrared (NIR) spectroscopy to establish a suitable model for the simultaneous and rapid detection of multiple physicochemical properties in jet fuel. Using more than 40 different sources of jet fuel, a rapid detection model was established by optimizing the spectral processing methods. The measurement models were separately built using the partial least-squares (PLS) and orthogonal PLS algorithms, and the model parameters were optimized. The results show that after the Savitzky-Golay second derivative preprocessing, the PLS model built using the feature spectra selected by the uninformative variable elimination wavelength algorithm achieved the best measurement performance. Compared with the PLS model without preprocessing, the range of the resulting accuracy improvement was at least 15.01%. Under the optimal model parameters, the calibration set regression coefficient (Rc2) of the 11 jet fuel property index models ranged from 0.9102 to 0.9763, with the root-mean-square error of calibration values up to 0.8468 °C (for flash points). The regression coefficient (Rp2) of the validation set ranged from 0.8239 to 0.9557, with the root-mean-square error of prediction values up to 1.1354 °C (for flash points). The ratios of prediction to deviation (RPD) values were all in the range of 1.9-3.0, indicating high accuracy and reliability of the model. The rapid NIR analysis method established in this study enables the simultaneous and rapid detection of multiple physicochemical properties of jet fuel, thereby providing effective technical support for ensuring the quality of jet fuel in the market.

A new conceptual model to investigate the role of hospital's capabilities on sustainable learning

The health-care industry is in a state of constant flux, with new challenges and opportunities emerging regularly. Hospitals, as the cornerstone of health-care delivery, must adapt and embrace change to provide optimal patient care. One crucial aspect that plays a significant role in the success of hospitals is sustainable learning. Sustainable learning refers to acquiring knowledge, skills, and competencies that enable health-care professionals to adapt to changes, implement best practices, and deliver high-quality care. Sustainable learning, a concept gaining prominence, emphasizes the ability of hospitals to learn from experiences and adapt to changing circumstances while maintaining quality health-care delivery. This article aims to investigate the role of hospital capabilities in sustainable learning and explore how hospitals can foster an environment that promotes continuous learning and development. Another goal of the paper is to test the relationships between cultural capabilities, structural capabilities, knowledge management capabilities, Information Technology (IT) infrastructure, top management support, application capabilities, and sustainable learning. The Partial Least-Squares (PLS) algorithm was performed using SmartPLS 3.0 to attain this goal. The results successfully support the study goals. This study verified that cultural capability, structural capabilities, knowledge management capabilities, IT infrastructure, top management support, and application capabilities positively affected sustainable learning. This investigation contributes to hospital, management, and education research by developing an integrated paradigm for sustainable learning. In conclusion, the new conceptual model presented here provides a robust framework for investigating the role of hospital capabilities in sustainable learning. By understanding and improving their capabilities, hospitals can not only adapt to change but also thrive in an ever-changing health-care landscape.

Comparison between Variable-Selection Algorithms in PLS Regression with Near-Infrared Spectroscopy to Predict Selected Metals in Soil.

Soil is one of the Earth's most important natural resources. The presence of metals can decrease environmental quality if present in excessive amounts. Analyzing soil metal contents can be costly and time consuming, but near-infrared (NIR) spectroscopy coupled with chemometric tools can offer an alternative. The most important multivariate calibration method to predict concentrations or physical, chemical or physicochemical properties as a chemometric tool is partial least-squares (PLS) regression. However, a large number of irrelevant variables may cause problems of accuracy in the predictive chemometric models. Thus, stochastic variable-selection techniques, such as the Firefly algorithm by intervals in PLS (FFiPLS), can provide better solutions for specific problems. This study aimed to evaluate the performance of FFiPLS against deterministic PLS algorithms for the prediction of metals in river basin soils. The samples had their spectra collected from the region of 1000-2500 nm. Predictive models were then built from the spectral data, including PLS, interval-PLS (iPLS), successive projections algorithm for interval selection in PLS (iSPA-PLS), and FFiPLS. The chemometric models were built with raw data and preprocessed data by using different methods such as multiplicative scatter correction (MSC), standard normal variate (SNV), mean centering, adjustment of baseline and smoothing by the Savitzky-Golay method. The elliptical joint confidence region (EJCR) used in each chemometric model presented adequate fit. FFiPLS models of iron and titanium obtained a relative prediction deviation (RPD) of more than 2. The chemometric models for determination of aluminum obtained an RPD of more than 2 in the preprocessed data with SNV, MSC and baseline (offset + linear) and with raw data. The metals Be, Gd and Y failed to obtain adequate models in terms of residual prediction deviation (RPD). These results are associated with the low values of metals in the samples. Considering the complexity of the samples, the relative error of prediction (REP) obtained between 10 and 25% of the values adequate for this type of sample. Root mean square error of calibration and prediction (RMSEC and RMSEP, respectively) presented the same profile as the other quality parameters. The FFiPLS algorithm outperformed deterministic algorithms in the construction of models estimating the content of Al, Be, Gd and Y. This study produced chemometric models with variable selection able to determine metals in the Ipojuca River watershed soils using reflectance-mode NIR spectrometry.

Identifying the active ingredients of carbonized Typhae Pollen by spectrum-effect relationship combined with MBPLS, PLS, and SVM algorithms

Typhae Pollen (TP) and its carbonized product (carbonized Typhae Pollen, CTP), as cut-and-dried herbal drugs, have been widely used in the form of slices in clinical settings. However, the two drugs exhibit a great difference in terms of their clinical efficacy, for TP boasts an effect of removing blood stasis and promoting blood circulation, while CTP typically presents a hemostatic function. Since the active ingredients of CTP, so far, still remain unclear, this study aimed at identifying the active ingredients of CTP by spectrum-effect relationship approach coupled with multi-block partial least squares (MBPLS), partial least squares (PLS), and support vector machine (SVM) algorithms. In this study, the chemical profiles of a series of CTP samples which were stir-fried for different duration (denoted as CTP0∼CTP9) were firstly characterized by UHPLC-QE-Orbitrap MS. Then the hemostatic effect of the CTP samples was evaluated from the perspective of multiple parameters—APTT, PT, TT, FIB, TXB2, 6-keto-PGF1α, PAI-1 and t-PA—using established rat models with functional uterine bleeding. Subsequently, MBPLS, PLS and SVM were combined to perform spectrum-effect relationship analysis to identify the active ingredients of CTP, followed by an in vitro hemostatic bioactivity test for verification. As a result, a total of 77 chemical ingredients were preliminarily identified from the CTP samples, and the variations occurred in these ingredients were also analyzed during the carbonizing process. The study revealed that all the CTP samples, to a varying degree, showed a hemostatic effect, among which CTP6 and CTP7 were superior to the others in terms of the hemostatic effect. The block importance in the projection (BIP) indexes of MBPLS model indicated that flavonoids and organic acids made more contributions to the hemostatic effect of CTP in comparison to other ingredients. Consequently, 9 bioactive ingredients, including quercetin-3-O-glucoside, kaempferol-3-O-rutinoside, quercetin, kaempferol, isorhamnetin, 2-methylenebutanedioic acid, pentanedioic acid, benzoic acid and 3-hydroxybenzoic acid, were further identified as the potential active ingredients based on PLS and SVM models as well as the in vitro verification. This study successfully revealed the bioactive ingredients of CTP associated with its hemostatic effect, and also provided a scientific basis for further understanding the mechanism of TP processing. In addition, it proposed a novel path to identify the active ingredients for Chinese herbal medicines.

An Assessment of m-Health Effect on Covid-19 Management Using PLS Modeling Approach

Introduction: The aim of the present study was to investigate the different roles of m-Health in pandemic management using the Partial Least Square (PLS) modeling technique. Owing to the limited existing literature regarding theorizing and the lack of the default model in predicting the role of m-Health in pandemic management, this method was used for exploratory modeling.Material and Methods: The PLS model was performed with smart-PLS software for the following steps: estimating weight ratios, considering weight ratios as input, estimating parameters, model-fitting and testing hypotheses. In addition, Factor scores in regression equations were used to estimate structural parameters. PLS algorithm, Cronbach's alpha, and Composite Reliability were used for the measurement and reliability evaluation model Goodness-of-fit. In addition, the R2 index was used to evaluate the model adequacy. Bootstrapping was used for significant coefficients. The Goodness-of-fit of the model was examined via the Standardized Root Mean Square Residual (SRMR) criterion.Results: It is determined the measurement models goodness-of-fit which the alpha values were as follows: diagnosis construct=0.786, follow-up=0.772, treatment=0.796, health care providers=0.704 and education=0.839 with more than 0.7 for all measures for Composite Reliability, the structural model measures such as R2 were higher than 0.6 for all areas and the overall model goodness-of-fit was -0.007 for SRMR, the five hypotheses developed in the model were confirmed according standardized coefficients more than 1.96 for all paths. Furthermore, the proposed model concerning the positive and significant role of m-Health in diagnosis, treatment and follow-up, education and health providers during the pandemic era was approved.Conclusion: The results of the present study can be used as a theoretical basis in developing models related to the role of m-Health in pandemic management. Also, health policymakers and practitioners could use the results to manage current and post-coronary conditions and to promote services based on various m-Health apps.

Combination four different ensemble algorithms with the generalized linear model (GLM) for predicting forest fire susceptibility

In this study, the generalized linear model (GLM) and four ensemble methods (partial least squares (PLS), boosting, bagging, and Bayesian) were applied to predict forest fire hazard in the Chalus Rood watershed in the Mazandaran Province, Iran. Data from 108 historical forest fire events collected through field surveys were applied as the basis of the analysis. About 70% of the data were used for training the models, while the remaining 30% was used for testing. A total of 14 environmental, climatic, and vegetation variables were used as input features to the models to predict forest fire probability. After conducting a multicollinearity test on the independent variables, the GLM and the ensemble models were applied for modeling. The efficiency of the models was evaluated using receiver operating characteristic (ROC) curve parameters. Results from the validation process, based on the area under the ROC curve (AUC), showed that the GLM, PLS-GLM, boosted-GLM, Bagging-GLM, and Bayesian-GLM models had efficiencies of 0.79, 0.75, 0.81, 0.84, and 0.85, respectively. The results indicated that all ensemble methods, except the PLS algorithm, improved the performance of the GLM model in modeling forest fire hazards in the Chalus Rood watershed, with the Bayesian algorithm being the most efficient method among them.

Application of partial least squares algorithm to explore bioactive components of crude and stir-baked hawthorn for invigorating spleen and promoting digestion

This study was aimed at identifying the bioactive components of the crude and stir-baked hawthorn for invigorating spleen and promoting digestion, respectively, to clarify the processing mechanism of hawthorn by applying the partial least squares(PLS) algorithm to build the spectrum-effect relationship model. Firstly, different polar fractions of crude and stir-baked hawthorn aqueous extracts and combinations of different fractions were prepared, respectively. Then, the contents of 24 chemical components were determined by ultra-high performance liquid chromatography-mass spectrometry. The effects of different polar fractions of crude hawthorn and stir-baked hawthorn aqueous extracts and combinations of different fractions were evaluated by measuring the gastric emptying rate and small intestinal propulsion rate. Finally, the PLS algorithm was used to establish the spectrum-effect relationship model. The results showed that there were significant differences in the contents of 24 chemical components for different polar fractions of crude and stir-baked hawthorn aqueous extracts and combinations of different fractions, and the gastric emptying rate and small intestinal propulsion rate of model rats were improved by administration of different polar fractions of crude and stir-baked hawthorn aqueous extracts and combinations of different fractions. The bioactive components of crude hawthorn identified by PLS models were vitexin-4″-O-glucoside, vitexin-2″-O-rhamnoside, neochlorogenic acid, rutin, gallic acid, vanillic acid, citric acid, malic acid, quinic acid and fumaric acid, while neochlorogenic acid, cryptochlorogenic acid, rutin, gallic acid, vanillic acid, citric acid, quinic acid and fumaric acid were the bioactive components of stir-baked hawthorn. This study provided data support and scientific basis for identifying the bioactive components of crude and stir-baked hawthorn, and clarifying the processing mechanism of hawthorn.

Research on a nondestructive model for the detection of the nitrogen content of tomato.

The timely detection of information on crop nutrition is of great significance for improving the production efficiency of facility crops. In this study, the terahertz (THz) spectral information of tomato plant leaves with different nitrogen levels was obtained. The noise reduction of the THz spectral data was then carried out by using the Savitzky-Golay (S-G) smoothing algorithm. The sample sets were then analyzed by using Kennard-Stone (KS) and random sampling (RS) methods, respectively. The KS algorithm was optimized to divide the sample sets. The stability competitive adaptive reweighted sampling (SCARS), uninformative variable elimination (UVE), and interval partial least-squares (iPLS) algorithms were then used to screen the pre-processed THz spectral data. Based on the selected characteristic frequency bands, a model for the detection of the nitrogen content of tomato based on the THz spectrum was established by the radial basis function neural network (RBFNN) and backpropagation neural network (BPNN) algorithms, respectively. The results show that the root-mean-square error of correction (RMSEC) and root-mean-square error of prediction (RMSEP) of the BPNN model were respectively 0.1722% and 0.1843%, and the determination coefficients of the correction set (Rc 2) and prediction set (Rp 2) were respectively 0.8447 and 0.8375. The RMSEC and RMSEP values of the RBFNN model were respectively 0.1322% and 0.1855%, and the Rc 2 and Rp 2 values were respectively 0.8714 and 0.8463. Thus, the accuracy of the model established by the RBFNN algorithm was slightly higher. Therefore, the nitrogen content of tomato leaves can be detected by THz spectroscopy. The results of this study can provide a theoretical basis for the research and development of equipment for the detection of the nitrogen content of tomato leaves.

Partial least squares, steepest descent, and conjugate gradient for regularized predictive modeling

AbstractIn this article, we explore the connection of partial least squares (PLS) to other regularized regression algorithms including the Lasso and ridge regression, and consider a steepest descent alternative to the PLS algorithm. First, the PLS latent variable analysis is emphasized and formulated as a standalone procedure. The PLS connections to the conjugate gradient, Krylov space, and the Cayley–Hamilton theorem for matrix pseudo‐inverse are explored based on known results in the literature. Comparison of PLS with the Lasso and ridge regression are given in terms of the different resolutions along the regularization paths, leading to an explanation of why PLS sometimes does not outperform the Lasso and ridge regression. As an attempt to increase resolutions along the regularization paths, a steepest descent PLS is formulated as a regularized regression alternative to PLS and is compared to other regularized algorithms via simulations and an industrial case study.

Rapid determination of chemical compositions in chicken flesh by mining hyperspectral data

Hyperspectral data in 900–1700 nm range was mined for rapid and non-destructive determination of chicken chemical compositions. Hyperspectral images of chicken flesh samples were acquired and spectral data within the hyperspectral images were extracted, preprocessed to relate to moisture, protein and ash contents by partial least squares (PLS). The results showed that PLS model built with Savitzky-Golay convolution smoothing (SGCS) spectra (SGCS-PLS model), RAW spectra (RAW-PLS model), normalization (NOR) spectra (NOR-PLS model) had the best performance in predicting moisture (RP = 0.925, RMSEP = 0.391%), protein (RP = 0.926, RMSEP = 0.589%) and ash (RP = 0.938, RMSEP = 0.114%) contents, respectively. Four different methods including regression coefficients (RC), stepwise regression (SR), successive projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS) were then applied to select optimal wavelengths for simplifying the original PLS models. As a result, 23 and 15 optimal wavelengths were respectively selected by CARS method from SGCS spectra (for moisture) and NOR spectra (for ash) and the original PLS models were optimized (CARS-SGCS-PLS model, CARS-NOR-PLS model) with RP of 0.932, 0.932 and RMSEP of 0.376%, 0.120%, respectively. Thirteen optimal wavelengths were selected by RC method from RAW spectra (for protein) and RC-RAW-PLS model was constructed with RP of 0.923 and RMSEP of 0.588%. F test and t test results further verified the statistical soundness of the three best optimized models. The chemical maps of moisture, protein and ash were finally generated by transferring the models into each pixel of images of chicken samples to visualize the chemical distribution. The overall study indicated that hyperspectral data combined with PLS algorithm could be potentially used for predicting the chemical compositions of chicken flesh.

Method for demodulating the overlapping absorption spectra of CO and CH4.

When measuring the concentrations of individual gases in gas mixtures via laser absorption spectroscopy, the widening of adjacent absorption spectral lines can cause them to overlap, which hinders the calculation of the gas concentrations. In this study, the causes of this hindrance are analyzed. By using the Partial Least Squares (PLS) algorithm, the relative error in the measured CO concentration for a mixture of CO and CH4 gases was less than 10% even when the volume ratio VCH4/CO (The ratio of CH4 gas concentration to CO gas concentration) reached 100. These results show that the PLS algorithm is able to determine accurate concentrations even with significant broadening and interference of spectral lines in mixed gases.

A Ring Gyroscope With On-Chip Capacitive Stress Compensation

We present long-term stress compensation results for a 3.2mm diameter ring gyroscope integrated with 16 capacitive stress sensors for the first time in this work. A bridge-type capacitive sensor is preferred due to its compact size and temperature insensitivity for on-chip stress measurements. The ring design enables a high level of integration and stress sensor-gyroscope output correlation. We first demonstrate the stress sensor operation on a stress test-bed. The drift test for sixteen days at mismatched mode and the drift test for eight days at matched mode in room temperature reveal that the stress compensation can eliminate the gyroscope drift. The stability of the stress compensated gyroscope output can reach 0.008°/h in mismatched mode and 0.003°/h in matched mode at an averaging time of two days with no signs of long-term drift. High gyroscope stability is achieved with a partial least-squares fitting algorithm; however, we believe that stress and gyroscope output relation might be linear time-variant with possible nonlinear and hysteresis effects. Analysis of the drive and sense mode frequencies shows that only temperature cannot explain the frequency variations, and the inclusion of stress can comprehensively describe the frequency changes. [2022-0108]

Thermal risk modeling and safety optimization of an arylamine diazo reaction based on PLS algorithm

In this study, a multi-factor reaction thermal risk model and a safety optimization method based on partial least squares (PLS) method were proposed. First, orthogonal experiments of key influencing factors were designed to obtain the correlation data between influencing factors and characteristic indicators of thermal risk. Then, the PLS method was employed to establish the reaction thermal risk model between them. Finally, this model was utilized to predict the worst-case scenario and optimize the permissible scope of influencing factors. The proposed method was applied to an arylamine diazo reaction as a representative example. The influencing factors included sulfuric acid concentration and diazonium salt concentration, while the characteristic indicator of thermal risk was the initiation temperature of adiabatic induction period of x hours (TDx). Setting TD24 > 30 °C, TD8 > 35 °C, TD4 > 40 °C, and TD1 > 45 °C as constraints, the optimal result was sulfuric acid concentration ≥ 72.8% and diazonium salt concentration ≤ 41.2%. Moreover, if 5% industrial deviation was tolerated, the optimal result was sulfuric acid concentration ≥ 76.2% and diazonium salt concentration ≤ 38.2%.

A reliability analysis method based on adaptive Kriging and partial least squares

In recent years, reliability analysis based on adaptive Kriging (AK) has been extensively studied. However, constructing the Kriging model of high-dimensional systems during adaptive learning faces huge computational challenges. This paper combines partial least squares (PLS) with AK to develop an efficient method for reliability analysis of high-dimensional systems, which is named as PLS-AK. The proposed method takes PLS algorithm as the dimensionality reduction (DR) technique of high-dimensional input variables. Then, in the reduced space of input principal components (PCs), a low-dimensional Kriging model is constructed with U learning function for reliability analysis. Since PLS usually requires adequate samples to determine the latent relationship between input and output variables, using too many initial samples before adaptive learning will weaken the advantages of AK. To avoid this problem, this paper also proposes a synchronous updating method for both the projection direction and AK model. In this way, only a few initial samples are required to determine the initial input projection, and the input projection will be updated and converge gradually as the number of training samples increases. At last, three examples can prove the effectiveness of the proposed PLS-AK method.

META-PLS modelling: An integrated approach to automatic model optimization for near-infrared spectra

Predictive latent space near-infrared (NIR) spectral modelling with PLS (Partial Least Squares) has two main tasks that require user input to achieve optimal models. The first is the selection of the optimal pre-processing of NIR spectra and the second is the selection of the optimal number of PLS model components assuming the data is outlier free. Often the two tasks are performed in an exhaustive search to find the best pre-processing as well as the optimal number of model components. We propose a novel approach called meta partial least square (META-PLS) which drops the need for both the pre-processing optimisation and exhaustive search for optimal model components. We utilise the stepwise nature of the PLS algorithm to learn complementary information from different pre-processed forms of the same data set as performed in multiblock pre-processing ensemble models to avoid pre-processing selection but receive help from the pre-processing ensembles, and deploy a weighted randomisation test to decide the optimal number of model components automatically. The performance of the approach for performing automatic NIR spectral modelling is demonstrated with several real data sets.

Effect of Green Supply Chain Management Practices on Environmental Performance: Case of Mexican Manufacturing Companies

Managers implement several Green Supply Chain Management (GSCM) practices to improve sustainability and economic performance, such as environmental management systems (EMS), eco-design (ED), source reduction (SR) and attending to external environmental management (EEM) requirements; however, the relationship among them requires a deep study. This paper reports the case of the Mexican maquiladora industry, analyzing the main relationships among GSCM practices with environmental impact (EI) and environmental cost savings (ECS). The analysis reports three structural equation models (SEM) developed as simple, second-order, and mediating models. Those relationships are tested using 160 responses to a survey applied to the Mexican maquiladora industry and with partial least squares algorithms (PLS), where conditional probabilities for different scenarios in latent variables are also reported. Findings indicate that EMS has a direct effect on EI (β = 0.442) and ECS (β = 0.227), indicating that EMS reduces EI and cost associated with the production process; however, ED has no direct effect on EI (β = 0.019) and ECS ((β = 0.006), and it can be due to the maquiladora nature as foreign companies focused on manufacturing and not to product design.