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The purpose of this paper is to initiate investigations to develop near infrared (NIR) spectroscopy coupled with spectral dimensionality reduction and multivariate calibration methods to rapidly measure cotton content in blend fabrics.

In total, 124 and 41 samples were used to calibrate models and assess the performance of the models, respectively. The raw spectra are transformed into wavelet coefficients. Multivariate calibration methods of partial least square (PLS), extreme learning machine (ELM) and least square support vector machine (LS-SVM) were employed to develop the models using 100 wavelet coefficients. Through comparing the performance of PLS, ELM and LS-SVM models with new samples, the optimal model of cotton content was obtained with the LS-SVM model.

The correlation coefficient of prediction (r_p) and root mean square errors of prediction were 0.99 and 4.37 percent, respectively. The results suggest that NIR spectroscopy, combining with the LS-SVM method, has significant potential to quantitatively analyze cotton content in blend fabrics.

It may have commercial and regulatory potential to avoid time-consuming work, costly and laborious chemical analysis for cotton content in blend fabrics.

The purpose of this paper is to develop near infrared (NIR) techniques coupled with multivariate calibration methods to rapid measure cotton content in blend fabrics.

In total, 124 and 41 samples were used to calibrate models and assess the performance of the models, respectively. Multivariate calibration methods of partial least square (PLS), extreme learning machine (ELM) and least square support vector machine (LS-SVM) were employed to develop the models. Through comparing the performance of PLS, ELM and LS-SVM models with new samples, the optimal model of cotton content was obtained with LS-SVM model. The correlation coefficient of prediction (r _p ) and root mean square errors of prediction were 0.98 and 4.50 percent, respectively.

The results suggest that NIR technique combining with LS-SVM method has significant potential to quantitatively analyze cotton content in blend fabrics.

It may have commercial and regulatory potential to avoid time consuming work, costly and laborious chemical analysis for cotton content in blend fabrics.

The purpose of this paper is to develop FT‐NIR technique for determination of moisture content in bael pulp.

Calibration and validation sets were designed for the conception and evaluation of the method adequacy in the range of moisture content 70 to 95 per cent (wb). The prediction models based on partial least squares (PLS) regression, were developed in the near‐infrared region (4,000‐2,500cm^‐1). Conventional criteria such as the R², the root mean square errors of cross validation (RMSECV), root mean square errors of estimation (RMSEE) as well as the number of PLS factors were considered for the selection of three pre‐processing (vector normalization, minimum‐maximum normalization and multiplicative scatter correction) methods.

The best calibration model was developed with min‐max normalization (MMN) spectral pre‐processing (R²=99.3). The MMN pre‐processing method was found most suitable and the maximum coefficient of determination (R²) value of 0.993 was obtained for the calibration model developed. The developed results indicated that FTNIR spectroscopy could be used for rapid detection of moisture content in bael pulp samples without any sample destruction.

The research in this paper is useful for the quick detection of moisture content of bael fruit pulp during processing.

There is recent emphasis on designing new materials and alloys specifically for metal additive manufacturing (AM) processes, in contrast to AM of existing alloys that were developed for other traditional manufacturing methods involving considerably different physics. Process optimization to determine processing recipes for newly developed materials is expensive and time-consuming. The purpose of the current work is to use a systematic printability assessment framework developed by the co-authors to determine windows of processing parameters to print defect-free parts from a binary nickel-niobium alloy (NiNb5) using laser powder bed fusion (LPBF) metal AM.

The printability assessment framework integrates analytical thermal modeling, uncertainty quantification and experimental characterization to determine processing windows for NiNb5 in an accelerated fashion. Test coupons and mechanical test samples were fabricated on a ProX 200 commercial LPBF system. A series of density, microstructure and mechanical property characterization was conducted to validate the proposed framework.

Near fully-dense parts with more than 99% density were successfully printed using the proposed framework. Furthermore, the mechanical properties of as-printed parts showed low variability, good tensile strength of up to 662 MPa and tensile ductility 51% higher than what has been reported in the literature.

Although many literature studies investigate process optimization for metal AM, there is a lack of a systematic printability assessment framework to determine manufacturing process parameters for newly designed AM materials in an accelerated fashion. Moreover, the majority of existing process optimization approaches involve either time- and cost-intensive experimental campaigns or require the use of proprietary computational materials codes. Through the use of a readily accessible analytical thermal model coupled with statistical calibration and uncertainty quantification techniques, the proposed framework achieves both efficiency and accessibility to the user. Furthermore, this study demonstrates that following this framework results in printed parts with low degrees of variability in their mechanical properties.

The purpose of this paper is to analyze the dependence between the Chinese and Market Integrated Latin America (MILA) stock markets.

The authors adjust the multivariate probability distribution Variance Gamma (VG) on data yields from the Hang Seng Index (HSI) and MILA and they use the estimated parameters under VG to find a robust estimator of the correlation matrix yields.

The degree of dependence between stock indices from China, Peru, Mexico, Colombia and Chile. In addition, the impact of the change in the HSI affects mostly the movements of the selective stock price index (IPSA) and equally affects the index of the Mexican stock exchange (IPC) and Lima Stock Exchange (S&P/BVL). The effect on index of the Colombia Stock Exchange (COLCAP) is not significant.

Over time there are different structural changes so the time has been restricted to the years 2000-2015, but could extend the analysis to other time periods and sectors of listed companies in the indices.

The results can guide policy makers to assess the effect of a random crash on stock markets and measure the level of risk from other markets.

The results can generate a greater understanding of the relationship between the stock markets of China and the emerging countries of Latin America.

The value of this paper is to focus on alternative methodology to calculate the correlation matrix yields and measure the dependence between the Chinese and MILA stock markets.

In treating both sewage and storm runoff, wastewater treatment plants are important to maintaining a healthy environment. If the plant operations managers do not respond correctly to plant conditions, environmental damage resulting in the deterioration of human health may be the result. Unfortunately, there are no formal models to help these managers; they rely upon their own intuition to manage the plants. The purpose of this paper is to investigate the effectiveness of various models, originally used for manufacturing, to detect process conditions in wastewater treatment facilities. We compare and contrast the performance of five statistical models and three neural network architectures. The data used in the research is 527 daily measurements of 38 sensor readings of the process state variables of an urban wastewater treatment plant.

The effects of infiltrant-related factors during post-processing on mechanical performance are fully considered for three-dimensional printing (3DP) technology. The factors contain infiltrant type, infiltrating means, infiltrating frequency and time interval of infiltrating.

A series of printing experiments are conducted and the parts are processed with different conditions by considering the above mentioned four parameters. Then the mechanical performances of the parts are tested from both macroscopic and microscopic papers. In the macroscopic view, the compressive strength of each printed part is measured by the materials testing machine – Instron 3367. In the microscopic view, scanning electron microscope and energy dispersion spectrum are used to obtain microstructure images and element content results. The pore size distributions of the parts are measured further to illustrate that if the particles are bound tightly by infiltrant. Then, partial least square (PLS) is used to conduct the analysis of the influencing factors, which can solve the small-sample problem well. The regression analysis and the influencing degree of each factor are explored further.

The experimental results show that commercial infiltrant has an outstanding performance than other super glues. The infiltrating action will own higher compressive strength than the brushing action. The higher infiltrating frequency and inconsistent infiltrating time interval will contribute to better mechanical performance. The PLS analysis shows that the most important factor is the infiltrating method. When compare the fitted value with the actual value, it is clear that when the compressive strength is higher, the fitting error will be smaller.

The research will have extensive applicability and practical significance for powder-based additive manufacturing.

The impact of the infiltrating-related post-processing on the performance of 3DP technology is easy to be ignored, which is fully taken into consideration in this paper. Both macroscopic and microscopic methods are conducted to explore, which can better explain the mechanical performance of the parts. Furthermore, as a small-sample method, PLS is used for influencing factors analysis. The variable importance in the projection index can explain the influencing degree of each parameter.

The purpose of this paper is to use the wavelet neural network and genetic algorithm to study the effects of polyalphaolefin, TMP108 and OCP0016 on the kinematic viscosity, viscosity index and pour point of lubricating oil.

Wavelet neural network is used to train the known samples, test the unknown samples and compare the obtained results with those obtained with a traditional empirical formula.

It is found that the wavelet neural network prediction value is closer to the experimental value than the traditional empirical formula calculation value.

The results show that the wavelet neural network can be used to study the physical and chemical indexes of lubricating oil.

Generalization ability of genetic programming (GP) models relies highly on the choice of parameter settings chosen and the fitness function used. The purpose of this paper is to conduct critical survey followed by quantitative analysis to determine the appropriate parameter settings and fitness function responsible for evolving the GP models with higher generalization ability.

For having a better understanding about the parameter settings, the present work examines the notion, applications, abilities and the issues of GP in the modelling of machining processes. A gamut of model selection criteria have been used in fitness functions of GP, but, the choice of an appropriate one is unclear. In this work, GP is applied to model the turning process to study the effect of fitness functions on its performance.

The results show that the fitness function, structural risk minimization (SRM) gives better generalization ability of the models than those of other fitness functions.

This study is of its first kind where two main contributions are listed addressing the need of evolving GP models with higher generalization ability. First is the survey study conducted to determine the parameter settings and second, the quantitative analysis for unearthing the best fitness function.

The aim is to explore the potential of a hybrid genetic algorithm‐partial least squares (GA‐PLS) modeling approach to understand the important promotional spending variables that influence physicians' prescribing habits and to help leverage managers' insight to plan better spend on their promotional activities.

A GA was used as a variable‐selecting tool, and PLS analysis was employed for correlating these variables with the observed variation in the volume of prescriptions. This approach is illustrated using database from a marketing consultant on four major brands in the US antibiotic universe.

Good statistical models were derived that permit simpler and faster computational prediction of the effects of physician‐directed promotion. All final models established had r² values ranging from 0.835 to 0.922 and cross‐validated r² (q²) values ranging from 0.791 to 0.911 whereas the mean absolute percentage error (MAPE) values were confined within 5 percent range on averaging all brand models. Further, thorough statistical analyses revealed the usefulness of promotional spending as a variable and the robustness of GA‐PLS as a correlation tool.

Modeling frame was comprised of only antibiotic category, which may limit its general utility.

Managers can become more adept at interpreting the effects of promotion on prescribing behaviors of physicians and are able to build predictive models that would help identify where and how their curious blend of promotional cocktail would yield the highest future returns. Moreover, if the impact of individual promotional spending element can be measured, then this is perhaps a testament to the way the efficacy of interventions to reduce the harmful consequences of pharmaceutical marketing could be validated given a growing number of public beliefs that physician‐directed promotion has grown too heavy‐handed and is undermining medical professionalism.

This area of research has not received much attention in the pharmaceutical marketing literature until recent years, and hopefully this study will stimulate some interest.