The relationship between socioeconomic status, course delivery method, and student success at a state college: A single institution analysis

In an effort to improve instruction and better accommodate the needs of students, community colleges are offering courses delivered in a variety of delivery formats that require students to have some level of technology fluency to be successful in the course. This study was conducted to investigate the relationship between student socioeconomic status (SES), course delivery method, and course type on enrollment, final course grades, course completion status, and course passing status at a state college. ^ A dataset for 20,456 students of low and not low SES enrolled in science, technology, engineering, and mathematics (STEM) course types delivered using traditional, online, blended, and web enhanced course delivery formats at Miami Dade College, a large open access 4-year state college located in Miami-Dade County, Florida, was analyzed. A factorial ANOVA using course type, course delivery method, and student SES found no significant differences in final course grades when used to determine if course delivery methods were equally effective for students of low and not low SES taking STEM course types. Additionally, three chi-square goodness-of-fit tests were used to investigate for differences in enrollment, course completion and course passing status by SES, course type, and course delivery method. The findings of the chi-square tests indicated that: (a) there were significant differences in enrollment by SES and course delivery methods for the Engineering/Technology, Math, and overall course types but not for the Natural Science course type and (b) there were no significant differences in course completion status and course passing status by SES and course types overall and SES and course delivery methods overall. However, there were statistically significant but weak relationships between course passing status, SES and the math course type as well as between course passing status, SES, and online and traditional course delivery methods. ^ The mixed findings in the study indicate that strides have been made in closing the theoretical gap in education and technology skills that may exist for students of different SES levels. MDC's course delivery and student support models may assist other institutions address student success in courses that necessitate students having some level of technology fluency. ^

Determining polarization dependent diffraction coefficients from building edges using FDTD method

Electromagnetic waves in suburban environment encounter multiple obstructions that shadow the signal. These waves are scattered and random in polarization. They take multiple paths that add as vectors at the portable device. Buildings have vertical and horizontal edges. Diffraction from edges has polarization dependent characteristics. In practical case, a signal transmitted from a vertically polarized high antenna will result in a significant fraction of total power in the horizontal polarization at the street level. Signal reception can be improved whenever there is a probability of receiving the signal in at least two independent ways or branches. The Finite-Difference Time-Domain (FDTD) method was applied to obtain the two and three-dimensional dyadic diffraction coefficients (soft and hard) of right-angle perfect electric conductor (PEC) wedges illuminated by a plane wave. The FDTD results were in good agreement with the asymptotic solutions obtained using Uniform Theory of Diffraction (UTD). Further, a material wedge replaced the PEC wedge and the dyadic diffraction coefficient for the same was obtained.

Diatom abundance in surface sediments of the Southern Ocean

Based on the quantitative analysis of diatom assemblages preserved in 274 surface sediment samples recovered in the Pacific, Atlantic and western Indian sectors of the Southern Ocean we have defined a new reference database for quantitative estimation of late-middle Pleistocene Antarctic sea ice fields using the transfer function technique. The Detrended Canonical Analysis (DCA) of the diatom data set points to a unimodal distribution of the diatom assemblages. Canonical Correspondence Analysis (CCA) indicates that winter sea ice (WSI) but also summer sea surface temperature (SSST) represent the most prominent environmental variables that control the spatial species distribution. To test the applicability of transfer functions for sea ice reconstruction in terms of concentration and occurrence probability we applied four different methods, the Imbrie and Kipp Method (IKM), the Modern Analog Technique (MAT), Weighted Averaging (WA), and Weighted Averaging Partial Least Squares (WAPLS), using logarithm-transformed diatom data and satellite-derived (1981-2010) sea ice data as a reference. The best performance for IKM results was obtained using a subset of 172 samples with 28 diatom taxa/taxa groups, quadratic regression and a three-factor model (IKM-D172/28/3q) resulting in root mean square errors of prediction (RMSEP) of 7.27% and 11.4% for WSI and summer sea ice (SSI) concentration, respectively. MAT estimates were calculated with different numbers of analogs (4, 6) using a 274-sample/28-taxa reference data set (MAT-D274/28/4an, -6an) resulting in RMSEP's ranging from 5.52% (4an) to 5.91% (6an) for WSI as well as 8.93% (4an) to 9.05% (6an) for SSI. WA and WAPLS performed less well with the D274 data set, compared to MAT, achieving WSI concentration RMSEP's of 9.91% with WA and 11.29% with WAPLS, recommending the use of IKM and MAT. The application of IKM and MAT to surface sediment data revealed strong relations to the satellite-derived winter and summer sea ice field. Sea ice reconstructions performed on an Atlantic- and a Pacific Southern Ocean sediment core, both documenting sea ice variability over the past 150,000 years (MIS 1 - MIS 6), resulted in similar glacial/interglacial trends of IKM and MAT-based sea-ice estimates. On the average, however, IKM estimates display smaller WSI and slightly higher SSI concentration and probability at lower variability in comparison with MAT. This pattern is a result of different estimation techniques with integration of WSI and SSI signals in one single factor assemblage by applying IKM and selecting specific single samples, thus keeping close to the original diatom database and included variability, by MAT. In contrast to the estimation of WSI, reconstructions of past SSI variability remains weaker. Combined with diatom-based estimates, the abundance and flux pattern of biogenic opal represents an additional indication for the WSI and SSI extent.

Bayesian Inference in Large-scale Problems

Many modern applications fall into the category of "large-scale" statistical problems, in which both the number of observations n and the number of features or parameters p may be large. Many existing methods focus on point estimation, despite the continued relevance of uncertainty quantification in the sciences, where the number of parameters to estimate often exceeds the sample size, despite huge increases in the value of n typically seen in many fields. Thus, the tendency in some areas of industry to dispense with traditional statistical analysis on the basis that "n=all" is of little relevance outside of certain narrow applications. The main result of the Big Data revolution in most fields has instead been to make computation much harder without reducing the importance of uncertainty quantification. Bayesian methods excel at uncertainty quantification, but often scale poorly relative to alternatives. This conflict between the statistical advantages of Bayesian procedures and their substantial computational disadvantages is perhaps the greatest challenge facing modern Bayesian statistics, and is the primary motivation for the work presented here.

Two general strategies for scaling Bayesian inference are considered. The first is the development of methods that lend themselves to faster computation, and the second is design and characterization of computational algorithms that scale better in n or p. In the first instance, the focus is on joint inference outside of the standard problem of multivariate continuous data that has been a major focus of previous theoretical work in this area. In the second area, we pursue strategies for improving the speed of Markov chain Monte Carlo algorithms, and characterizing their performance in large-scale settings. Throughout, the focus is on rigorous theoretical evaluation combined with empirical demonstrations of performance and concordance with the theory.

One topic we consider is modeling the joint distribution of multivariate categorical data, often summarized in a contingency table. Contingency table analysis routinely relies on log-linear models, with latent structure analysis providing a common alternative. Latent structure models lead to a reduced rank tensor factorization of the probability mass function for multivariate categorical data, while log-linear models achieve dimensionality reduction through sparsity. Little is known about the relationship between these notions of dimensionality reduction in the two paradigms. In Chapter 2, we derive several results relating the support of a log-linear model to nonnegative ranks of the associated probability tensor. Motivated by these findings, we propose a new collapsed Tucker class of tensor decompositions, which bridge existing PARAFAC and Tucker decompositions, providing a more flexible framework for parsimoniously characterizing multivariate categorical data. Taking a Bayesian approach to inference, we illustrate empirical advantages of the new decompositions.

Latent class models for the joint distribution of multivariate categorical, such as the PARAFAC decomposition, data play an important role in the analysis of population structure. In this context, the number of latent classes is interpreted as the number of genetically distinct subpopulations of an organism, an important factor in the analysis of evolutionary processes and conservation status. Existing methods focus on point estimates of the number of subpopulations, and lack robust uncertainty quantification. Moreover, whether the number of latent classes in these models is even an identified parameter is an open question. In Chapter 3, we show that when the model is properly specified, the correct number of subpopulations can be recovered almost surely. We then propose an alternative method for estimating the number of latent subpopulations that provides good quantification of uncertainty, and provide a simple procedure for verifying that the proposed method is consistent for the number of subpopulations. The performance of the model in estimating the number of subpopulations and other common population structure inference problems is assessed in simulations and a real data application.

In contingency table analysis, sparse data is frequently encountered for even modest numbers of variables, resulting in non-existence of maximum likelihood estimates. A common solution is to obtain regularized estimates of the parameters of a log-linear model. Bayesian methods provide a coherent approach to regularization, but are often computationally intensive. Conjugate priors ease computational demands, but the conjugate Diaconis--Ylvisaker priors for the parameters of log-linear models do not give rise to closed form credible regions, complicating posterior inference. In Chapter 4 we derive the optimal Gaussian approximation to the posterior for log-linear models with Diaconis--Ylvisaker priors, and provide convergence rate and finite-sample bounds for the Kullback-Leibler divergence between the exact posterior and the optimal Gaussian approximation. We demonstrate empirically in simulations and a real data application that the approximation is highly accurate, even in relatively small samples. The proposed approximation provides a computationally scalable and principled approach to regularized estimation and approximate Bayesian inference for log-linear models.

Another challenging and somewhat non-standard joint modeling problem is inference on tail dependence in stochastic processes. In applications where extreme dependence is of interest, data are almost always time-indexed. Existing methods for inference and modeling in this setting often cluster extreme events or choose window sizes with the goal of preserving temporal information. In Chapter 5, we propose an alternative paradigm for inference on tail dependence in stochastic processes with arbitrary temporal dependence structure in the extremes, based on the idea that the information on strength of tail dependence and the temporal structure in this dependence are both encoded in waiting times between exceedances of high thresholds. We construct a class of time-indexed stochastic processes with tail dependence obtained by endowing the support points in de Haan's spectral representation of max-stable processes with velocities and lifetimes. We extend Smith's model to these max-stable velocity processes and obtain the distribution of waiting times between extreme events at multiple locations. Motivated by this result, a new definition of tail dependence is proposed that is a function of the distribution of waiting times between threshold exceedances, and an inferential framework is constructed for estimating the strength of extremal dependence and quantifying uncertainty in this paradigm. The method is applied to climatological, financial, and electrophysiology data.

The remainder of this thesis focuses on posterior computation by Markov chain Monte Carlo. The Markov Chain Monte Carlo method is the dominant paradigm for posterior computation in Bayesian analysis. It has long been common to control computation time by making approximations to the Markov transition kernel. Comparatively little attention has been paid to convergence and estimation error in these approximating Markov Chains. In Chapter 6, we propose a framework for assessing when to use approximations in MCMC algorithms, and how much error in the transition kernel should be tolerated to obtain optimal estimation performance with respect to a specified loss function and computational budget. The results require only ergodicity of the exact kernel and control of the kernel approximation accuracy. The theoretical framework is applied to approximations based on random subsets of data, low-rank approximations of Gaussian processes, and a novel approximating Markov chain for discrete mixture models.

Data augmentation Gibbs samplers are arguably the most popular class of algorithm for approximately sampling from the posterior distribution for the parameters of generalized linear models. The truncated Normal and Polya-Gamma data augmentation samplers are standard examples for probit and logit links, respectively. Motivated by an important problem in quantitative advertising, in Chapter 7 we consider the application of these algorithms to modeling rare events. We show that when the sample size is large but the observed number of successes is small, these data augmentation samplers mix very slowly, with a spectral gap that converges to zero at a rate at least proportional to the reciprocal of the square root of the sample size up to a log factor. In simulation studies, moderate sample sizes result in high autocorrelations and small effective sample sizes. Similar empirical results are observed for related data augmentation samplers for multinomial logit and probit models. When applied to a real quantitative advertising dataset, the data augmentation samplers mix very poorly. Conversely, Hamiltonian Monte Carlo and a type of independence chain Metropolis algorithm show good mixing on the same dataset.

A new method for resolving uncertainty of energy requirements in large water breathers: the ‘mega-flume’ seagoing swim-tunnel respirometer

Body size is a key determinant of metabolic rate, but logistical constraints have led to a paucity of energetics measurements from large water-breathing animals. As a result, estimating energy requirements of large fish generally relies on extrapolation of metabolic rate from individuals of lower body mass using allometric relationships that are notoriously variable. Swim-tunnel respirometry is the ‘gold standard’ for measuring active metabolic rates in water-breathing animals, yet previous data are entirely derived from body masses <10 kg – at least one order of magnitude lower than the body masses of many top-order marine predators. Here, we describe the design and testing of a new method for measuring metabolic rates of large water-breathing animals: a c. 26 000 L seagoing ‘mega-flume’ swim-tunnel respirometer. We measured the swimming metabolic rate of a 2·1-m, 36-kg zebra shark Stegostoma fasciatum within this new mega-flume and compared the results to data we collected from other S. fasciatum (3·8–47·7 kg body mass) swimming in static respirometers and previously published measurements of active metabolic rate measurements from other shark species. The mega-flume performed well during initial tests, with intra- and interspecific comparisons suggesting accurate metabolic rate measurements can be obtained with this new tool. Inclusion of our data showed that the scaling exponent of active metabolic rate with mass for sharks ranging from 0·13 to 47·7 kg was 0·79; a similar value to previous estimates for resting metabolic rates in smaller fishes. We describe the operation and usefulness of this new method in the context of our current uncertainties surrounding energy requirements of large water-breathing animals. We also highlight the sensitivity of mass-extrapolated energetic estimates in large aquatic animals and discuss the consequences for predicting ecosystem impacts such as trophic cascades.

A new method for resolving uncertainty of energy requirements in large water breathers: the ‘mega-flume’ seagoing swim-tunnel respirometer

Body size is a key determinant of metabolic rate, but logistical constraints have led to a paucity of energetics measurements from large water-breathing animals. As a result, estimating energy requirements of large fish generally relies on extrapolation of metabolic rate from individuals of lower body mass using allometric relationships that are notoriously variable. Swim-tunnel respirometry is the ‘gold standard’ for measuring active metabolic rates in water-breathing animals, yet previous data are entirely derived from body masses <10 kg – at least one order of magnitude lower than the body masses of many top-order marine predators. Here, we describe the design and testing of a new method for measuring metabolic rates of large water-breathing animals: a c. 26 000 L seagoing ‘mega-flume’ swim-tunnel respirometer. We measured the swimming metabolic rate of a 2·1-m, 36-kg zebra shark Stegostoma fasciatum within this new mega-flume and compared the results to data we collected from other S. fasciatum (3·8–47·7 kg body mass) swimming in static respirometers and previously published measurements of active metabolic rate measurements from other shark species. The mega-flume performed well during initial tests, with intra- and interspecific comparisons suggesting accurate metabolic rate measurements can be obtained with this new tool. Inclusion of our data showed that the scaling exponent of active metabolic rate with mass for sharks ranging from 0·13 to 47·7 kg was 0·79; a similar value to previous estimates for resting metabolic rates in smaller fishes. We describe the operation and usefulness of this new method in the context of our current uncertainties surrounding energy requirements of large water-breathing animals. We also highlight the sensitivity of mass-extrapolated energetic estimates in large aquatic animals and discuss the consequences for predicting ecosystem impacts such as trophic cascades.

Building Support Vector Machines in the Context of Regularised Least Squares

This paper formulates a linear kernel support vector machine (SVM) as a regularized least-squares (RLS) problem. By defining a set of indicator variables of the errors, the solution to the RLS problem is represented as an equation that relates the error vector to the indicator variables. Through partitioning the training set, the SVM weights and bias are expressed analytically using the support vectors. It is also shown how this approach naturally extends to Sums with nonlinear kernels whilst avoiding the need to make use of Lagrange multipliers and duality theory. A fast iterative solution algorithm based on Cholesky decomposition with permutation of the support vectors is suggested as a solution method. The properties of our SVM formulation are analyzed and compared with standard SVMs using a simple example that can be illustrated graphically. The correctness and behavior of our solution (merely derived in the primal context of RLS) is demonstrated using a set of public benchmarking problems for both linear and nonlinear SVMs.

Least Squares Approach to Inverse Problems in Musculoskeletal Biomechanics

Inverse simulations of musculoskeletal models computes the internal forces such as muscle and joint reaction forces, which are hard to measure, using the more easily measured motion and external forces as input data. Because of the difficulties of measuring muscle forces and joint reactions, simulations are hard to validate. One way of reducing errors for the simulations is to ensure that the mathematical problem is well-posed. This paper presents a study of regularity aspects for an inverse simulation method, often called forward dynamics or dynamical optimization, that takes into account both measurement errors and muscle dynamics. The simulation method is explained in detail. Regularity is examined for a test problem around the optimum using the approximated quadratic problem. The results shows improved rank by including a regularization term in the objective that handles the mechanical over-determinancy. Using the 3-element Hill muscle model the chosen regularization term is the norm of the activation. To make the problem full-rank only the excitation bounds should be included in the constraints. However, this results in small negative values of the activation which indicates that muscles are pushing and not pulling. Despite this unrealistic behavior the error maybe small enough to be accepted for specific applications. These results is a starting point start for achieving better results of inverse musculoskeletal simulations from a numerical point of view.

Modelling and Verification of a Correction Factor to Evaluate the Efficiency of Solar Thermal Collectors with the Rapid Test Method

A new method for the evaluation of the efficiency of parabolic trough collectors, called Rapid Test Method, is investigated at the Solar Institut Jülich. The basic concept is to carry out measurements under stagnation conditions. This allows a fast and inexpensive process due to the fact that no working fluid is required. With this approach, the temperature reached by the inner wall of the receiver is assumed to be the stagnation temperature and hence the average temperature inside the collector. This leads to a systematic error which can be rectified through the introduction of a correction factor. A model of the collector is simulated with COMSOL Multipyisics to study the size of the correction factor depending on collector geometry and working conditions. The resulting values are compared with experimental data obtained at a test rig at the Solar Institut Jülich. These results do not match with the simulated ones. Consequentially, it was not pos-sible to verify the model. The reliability of both the model with COMSOL Multiphysics and of the measurements are analysed. The influence of the correction factor on the rapid test method is also studied, as well as the possibility of neglecting it by measuring the receiver’s inner wall temperature where it receives the least amount of solar rays. The last two chapters analyse the specific heat capacity as a function of pressure and tem-perature and present some considerations about the uncertainties on the efficiency curve obtained with the Rapid Test Method.

Least Median Of Squares Estimation In Power Systems

Static state estimators currently in use in power systems are prone to masking by multiple bad data. This is mainly because the power system regression model contains many leverage points; typically they have a cluster pattern. As reported recently in the statistical literature, only high breakdown point estimators are robust enough to cope with gross errors corrupting such a model. This paper deals with one such estimator, the least median of squares estimator, developed by Rousseeuw in 1984. The robustness of this method is assessed while applying it to power systems. Resampling methods are developed, and simulation results for IEEE test systems discussed. © 1991 IEEE.

Enhancing SPH using moving least-squares and radial basis functions

In this paper we consider two sources of enhancement for the meshfree Lagrangian particle method smoothed particle hydrodynamics (SPH) by improving the accuracy of the particle approximation. Namely, we will consider shape functions constructed using: moving least-squares approximation (MLS); radial basis functions (RBF). Using MLS approximation is appealing because polynomial consistency of the particle approximation can be enforced. RBFs further appeal as they allow one to dispense with the smoothing-length - the parameter in the SPH method which governs the number of particles within the support of the shape function. Currently, only ad hoc methods for choosing the smoothing-length exist. We ensure that any enhancement retains the conservative and meshfree nature of SPH. In doing so, we derive a new set of variationally-consistent hydrodynamic equations. Finally, we demonstrate the performance of the new equations on the Sod shock tube problem.

Espectroscopia no infravermelho e método quimiométrico para a pesquisa de antimicrobianos em leite da microrregião de Pato Branco - PR

Considering the social and economic importance that the milk has, the objective of this study was to evaluate the incidence and quantifying antimicrobial residues in the food. The samples were collected in dairy industry of southwestern Paraná state and thus they were able to cover all ten municipalities in the region of Pato Branco. The work focused on the development of appropriate models for the identification and quantification of analytes: tetracycline, sulfamethazine, sulfadimethoxine, chloramphenicol and ampicillin, all antimicrobials with health interest. For the calibration procedure and validation of the models was used the Infrared Spectroscopy Fourier Transform associated with chemometric method based on Partial Least Squares regression (PLS - Partial Least Squares). To prepare a work solution antimicrobials, the five analytes of interest were used in increasing doses, namely tetracycline from 0 to 0.60 ppm, sulfamethazine 0 to 0.12 ppm, sulfadimethoxine 0 to 2.40 ppm chloramphenicol 0 1.20 ppm and ampicillin 0 to 1.80 ppm to perform the work with the interest in multiresidues analysis. The performance of the models constructed was evaluated through the figures of merit: mean square error of calibration and cross-validation, correlation coefficients and offset performance ratio. For the purposes of applicability in this work, it is considered that the models generated for Tetracycline, Sulfadimethoxine and Chloramphenicol were considered viable, with the greatest predictive power and efficiency, then were employed to evaluate the quality of raw milk from the region of Pato Branco . Among the analyzed samples by NIR, 70% were in conformity with sanitary legislation, and 5% of these samples had concentrations below the Maximum Residue permitted, and is also satisfactory. However 30% of the sample set showed unsatisfactory results when evaluating the contamination with antimicrobials residues, which is non conformity related to the presence of antimicrobial unauthorized use or concentrations above the permitted limits. With the development of this work can be said that laboratory tests in the food area, using infrared spectroscopy with multivariate calibration was also good, fast in analysis, reduced costs and with minimum generation of laboratory waste. Thus, the alternative method proposed meets the quality concerns and desired efficiency by industrial sectors and society in general.

Desenvolvimento e aplicação de modelo de calibração multivariada para determinação de açúcares e ácidos orgânicos em bebidas comerciais utilizando espectroscopia no infravermelho

The routine analysis for quantization of organic acids and sugars are generally slow methods that involve the use and preparation of several reagents, require trained professional, the availability of special equipment and is expensive. In this context, it has been increasing investment in research whose purpose is the development of substitutive methods to reference, which are faster, cheap and simple, and infrared spectroscopy have been highlighted in this regard. The present study developed multivariate calibration models for the simultaneous and quantitative determination of ascorbic acid, citric, malic and tartaric and sugars sucrose, glucose and fructose, and soluble solids in juices and fruit nectars and classification models for ACP. We used methods of spectroscopy in the near infrared (Near Infrared, NIR) in association with the method regression of partial least squares (PLS). Were used 42 samples between juices and fruit nectars commercially available in local shops. For the construction of the models were performed with reference analysis using high-performance liquid chromatography (HPLC) and refractometry for the analysis of soluble solids. Subsequently, the acquisition of the spectra was done in triplicate, in the spectral range 12500 to 4000 cm-1. The best models were applied to the quantification of analytes in study on natural juices and juice samples produced in the Paraná Southwest Region. The juices used in the application of the models also underwent physical and chemical analysis. Validation of chromatographic methodology has shown satisfactory results, since the external calibration curve obtained R-square value (R2) above 0.98 and coefficient of variation (%CV) for intermediate precision and repeatability below 8.83%. Through the Principal Component Analysis (PCA) was possible to separate samples of juices into two major groups, grape and apple and tangerine and orange, while for nectars groups separated guava and grape, and pineapple and apple. Different validation methods, and pre-processes that were used separately and in combination, were obtained with multivariate calibration models with average forecast square error (RMSEP) and cross validation (RMSECV) errors below 1.33 and 1.53 g.100 mL-1, respectively and R2 above 0.771, except for malic acid. The physicochemical analysis enabled the characterization of drinks, including the pH working range (variation of 2.83 to 5.79) and acidity within the parameters Regulation for each flavor. Regression models have demonstrated the possibility of determining both ascorbic acids, citric, malic and tartaric with successfully, besides sucrose, glucose and fructose by means of only a spectrum, suggesting that the models are economically viable for quality control and product standardization in the fruit juice and nectars processing industry.

High-Throughput Prediction of Acacia and Eucalypt Lignin Syringyl/Guaiacyl Content Using FT-Raman Spectroscopy and Partial Least Squares Modeling

High-throughput techniques are necessary to efficiently screen potential lignocellulosic feedstocks for the production of renewable fuels, chemicals, and bio-based materials, thereby reducing experimental time and expense while supplanting tedious, destructive methods. The ratio of lignin syringyl (S) to guaiacyl (G) monomers has been routinely quantified as a way to probe biomass recalcitrance. Mid-infrared and Raman spectroscopy have been demonstrated to produce robust partial least squares models for the prediction of lignin S/G ratios in a diverse group of Acacia and eucalypt trees. The most accurate Raman model has now been used to predict the S/G ratio from 269 unknown Acacia and eucalypt feedstocks. This study demonstrates the application of a partial least squares model composed of Raman spectral data and lignin S/G ratios measured using pyrolysis/molecular beam mass spectrometry (pyMBMS) for the prediction of S/G ratios in an unknown data set. The predicted S/G ratios calculated by the model were averaged according to plant species, and the means were not found to differ from the pyMBMS ratios when evaluating the mean values of each method within the 95 % confidence interval. Pairwise comparisons within each data set were employed to assess statistical differences between each biomass species. While some pairwise appraisals failed to differentiate between species, Acacias, in both data sets, clearly display significant differences in their S/G composition which distinguish them from eucalypts. This research shows the power of using Raman spectroscopy to supplant tedious, destructive methods for the evaluation of the lignin S/G ratio of diverse plant biomass materials. © 2015, The Author(s).

Inverted decoupling internal model control for square stable multivariable time delay systems

This paper presents a new tuning methodology of the main controller of an internal model control structure for n×n stable multivariable processes with multiple time delays based on the centralized inverted decoupling structure. Independently of the system size, very simple general expressions for the controller elements are obtained. The realizability conditions are provided and the specification of the closed-loop requirements is explained. A diagonal filter is added to the proposed control structure in order to improve the disturbance rejection without modifying the nominal set-point response. The effectiveness of the method is illustrated through different simulation examples in comparison with other works.