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Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG [Abstract] The seas and oceans of the planet provide a wide range of essential resources. However, marine ecosystems are undergoing severe degradation due to the unsustainable exploitation and consumption patterns of the linear economy. On the other hand, many economic activities linked to the sea generate a large amount of waste, leading to negative impacts, such as the cost of treating or disposing of this waste. A case in point is bivalve mollusc production: a purification process is needed to avoid the risk of diseases through faecal contamination. The present work proposes an innovative procedure to convert this waste, calcium carbonate as calcite and aragonite allotropic types, into by-products. These by-products can be used to manufacture green artificial reefs, partially replacing concrete aggregates with a sustainable alternative to the geological sources of CaCO3. By installing these reefs, marine ecosystems could be created in a sustainable way and an innovative approach based on the circular economy could be taken towards protecting them. To this end, different concrete mixtures with bivalve shells are proposed. Although this study had been carried out for Galicia (NW Spain), the methodology followed could also be valid for other regions. A physicochemical characterisation of the waste from purifying the bivalves, including oysters, mussels, clams and scallops, was performed. Statistical and multi-criteria analyses were done in order to select the best dosage. Both have provided justification for using a mixture of shells with a predominance of calcite (oyster, scallop) instead of shells with a predominance of aragonite. The multi-criteria analysis served to identify the two best alternatives with dosages in which the medium aggregates were substituted with shells mainly from oysters, with a predominance of calcite. Finally, the statistical analysis played a role in estimating the compressive strength and water absorption of each mixture from the design parameter values. Xunta de Galicia ; CN-10MMA003CT Ministerio de Ciencia e Innovación ; PID2020-113578RB-100 Xunta de Galicia ; ED431C-2020-14 Xunta de Galicia ; ED431G2019/01

In response to an increased reliance on bigger, Neopanamax-sized vessels for shipping, the Panama Canal has been expanded. Whilst the way that vessels transit the newly expanded canal has stayed the same, maneuvering operations have changed. As a result, transit times have been affected. In accordance with the size and cargo of vessels, canal navigation rules restrict how access channels (Culebra Cut, Gatun Lake, and seaways) and new locks (Cocoli and Agua Clara) are used. Larger sized, Neopanamax vessels will benefit most from the expanded canal. Since the canal opened on 26 June 2016, data on transit times have been gathered. With these data, a thorough statistical analysis should be carried out. The most influential variables on overall time in transit (TET), such as pilot skill and Culebra Cut or Gatun Lake transit times, are identified. Formulae based on multivariate linear regression can be extracted that will make it possible to establish a conductive methodology for estimating the transit time of a Neopanamax vessel. Taking into account the results of the statistical analysis, a proposal is included for improving Neopanamax traffic management, with views to reduce the TET, from a transit policy point of view. This is the first work in scientific literature that analyzes in detail, from qualitative and statistical approaches, the total time in transit through the set of navigable routes and locks that compose the new expanded Panama Canal.

Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG [Abstract] Piloting in the Panama Canal is exceptional as, due to its importance, the functions of the captains of vessels are taken over by pilots. Hence, prior to inauguration of the expanded canal, a limited number of pilots experienced on the existing canal were certified for the transit of Neopanamax vessels by means of planned and innovative individual learning. After this organisational training through operative training, with the implementation of the expanded canal in June 2016, the routine training started. Hence the learning curve in the performance of these manoeuvres will represent the growing skill acquired by both the pilots and the organisation. Given that the learning effect is measurable, this paper has the dual objective of determining two curve models: the organisation operative learning curve model and the routine learning curve model for pilots performing transit manoeuvres in the expanded Panama Canal waterways and the Cocolí and Agua Clara locks. Manoeuvre times in locks and transit in the whole of the canal were followed up continuously in the first 42 months of operation. The research of Salvador Naya and Javier Tarrío has been supported by MINECO grant MTM2017-82724-R, and by the Xunta de Galicia (Grupos de Referencia Competitiva ED431C-2020-14 and Centro de Investigación del Sistema universitario de Galicia ED431G 2019/01), all of them through the ERDF. This work has been funded in part by Project 1-FACINA of the International Maritime University of Panama (UMIP). Xunta de Galicia; ED431C-2020-14 Xunta de Galicia; ED431G 2019/01 Universidad Marítima Internacional de Panamá; Project 1-FACINA

[Abstract] A kinetic model is proposed to fit isothermal thermogravimetric data obtained from cellulose in an inert atmosphere at different temperatures. The method used here to evaluate the model involves two steps: (1) fitting of single time-derivative thermogravimetric curves (DTG) obtained at different temperatures versus time, and (2) fitting of the rate parameter values obtained at different temperatures versus temperature. The first step makes use of derivative of logistic functions. For the second step, the dependence of the rate factor on temperature is evaluated. That separation of the curve fitting from the analysis of the rate factor resulted to be very flexible since it proved to work for previous crystallization studies and now for thermal degradation of cellulose Ministerio de Asuntos Económicos y Transformación Digital; MTM2017-82724-R Xunta de Galicia; ED431C-2020-14 Xunta de Galicia; ED431G 2019/01

In this paper we present an R package with routines to perform Interlaboratory Studies (ILS). The aim of the ILS package is to detect laboratories that provide not consistent results, working simultaneously with different test materials, from the perspective of the Univariate Data Analysis and the Functional Data Analysis (FDA). The ILS package estimates the Mandel's h and k scalar statistics, based on the ASTM E691 and ISO 5725-2 standards, to identify laboratories that provide significantly different results. Cochran and Grubbs tests to evaluate the presence of outliers are also available. In addition, Analysis of Variance (ANOVA) techniques are provided, both for the cases of fixed and random effects, including confidence intervals for the parameters. One of the novelties of this package is the incorporation of tools to perform an ILS from a functional data analysis approach. Accordingly, the functional nature of the data obtained by experimental techniques corresponding to analytical chemistry, applied physics and engineering applications (spectra, thermograms, and sensor signals, among others) is taking into account by implementing the functional extensions of Mandel's h and k statistics. For this purpose, the ILS package also estimates the functional statistics H ( t ) and K ( t ) , as well as the d H and d K test statistic, which are used to evaluate the repeatability and reproducibility hypotheses where the critical c h and c k values are estimated by using a bootstrap algorithm.

Functional data analysis (FDA) alternatives, based on the classical Mandel h and k statistics, are proposed to identify the laboratories that supply inconsistent results in interlaboratory studies (ILS). ILS is the procedure performed by a number of laboratories to test the precision of an analytical method, to measure the proficiency of laboratories in implementing an analytical procedure, to certify reference materials, and to evaluate a new experimental standard. The use of outlier tests, such as h and k Mandel statistics proposed by the ASTM E691, is crucial to assess these aims, estimating inter- and intra-laboratory data position and variability from a univariate point of view. Considering that experimental results obtained in analytical sciences are often functional, the use of FDA techniques can prevent the loss of important data information. The FDA approaches of h and k statistics are presented and point-wise obtained to deal with functional experimental data. Both functional statistics are estimated for each laboratory, their functional critical limits are obtained by bootstrap resampling, and new FDA versions of h and k graphics are presented. Real and synthetic thermogravimetric data are utilized to assess the good performance of the proposed FDA h and k statistics and their advantages with respect to the univariate approach.

The Panama Canal (PC) has recently been in the world spotlight. In August 2014, it celebrated 100 years of uninterrupted service and, in June 2016, the expansion project for the canal was inaugurated. The final project involved building a third set of locks. Once the canal started to operate, it could be seen that the way in which vessels transited the canal remained the same. However, the dimensions of locks and their revised operating procedures have had an effect on vessel size and the manoeuvres for the larger vessels. After the first transit on 26 June 2016, it was possible to have access to data on the new lockage systems for Neopanamax ships. The thorough statistical study of these new datasets (composed of Analysis of Variance (ANOVA), multivariate regression and statistical quality control techniques) has shown the main drivers of transit time across the Cocolí and Agua Clara locks. It has also made it possible to test the learning curve of Panama Canal pilots in the newly expanded canal. The effects of pilot training on the time it takes to transit through the locks, direction of entry in each lock, the type of vessel, vessel dimensions and the use of different types of manoeuvres have been analysed. The results are used to characterise and help optimise the performance of this new and unique lock system.

In this paper, a case study of performing a reliable statistical procedure to evaluate the quality of HVAC systems in buildings using data retrieved from an ad hoc big data web energy platform is presented. The proposed methodology based on statistical quality control (SQC) is used to analyze the real state of thermal comfort and energy efficiency of the offices of the company FRIDAMA (Spain) in a reliable way. Non-conformities or alarms, and the actual assignable causes of these out of control states are detected. The capability to meet specification requirements is also analyzed. Tools and packages implemented in the open-source R software are employed to apply the different procedures. First, this study proposes to fit ARIMA time series models to CTQ variables. Then, the application of Shewhart and EWMA control charts to the time series residuals is proposed to control and monitor thermal comfort and energy consumption in buildings. Once thermal comfort and consumption variability are estimated, the implementation of capability indexes for autocorrelated variables is proposed to calculate the degree to which standards specifications are met. According with case study results, the proposed methodology has detected real anomalies in HVAC installation, helping to detect assignable causes and to make appropriate decisions. One of the goals is to perform and describe step by step this statistical procedure in order to be replicated by practitioners in a better way.

This study proposes a control chart based on functional data to detect anomalies and estimate the normal output of industrial processes and services such as those related to the energy efficiency domain. Companies providing statistical consultancy services in the fields of energy efficiency, heating, ventilation and air conditioning (HVAC), installation and control, and big data for buildings, have been striving to solve the problem of automatic anomaly detection in buildings controlled by sensors. Given the functional nature of the critical to quality (CTQ) variables, this study proposed a new functional data analysis (FDA) control chart method based on the concept of data depth. Specifically, it developed a control methodology, including the Phase I and II control charts. It is based on the calculation of the depth of functional data, the identification of outliers by smooth bootstrap resampling and the customization of nonparametric rank control charts. A comprehensive simulation study, comprising scenarios defined with different degrees of dependence between curves, was conducted to evaluate the control procedure. The proposed statistical process control procedure was also applied to detect energy efficiency anomalies in the stores of a textile company in the Panama City. In this case, energy consumption has been defined as the CTQ variable of the HVAC system. Briefly, the proposed methodology, which combines FDA and multivariate techniques, adapts the concept of the control chart based on a specific case of functional data and thereby presents a novel alternative for controlling facilities in which the data are obtained by continuous monitoring, as is the case with a great deal of process in the framework of Industry 4.0.

The pilotage in the Panama Canal (PC) is unique due to the importance it acquires; in fact, the pilot relieves the captain in his/her duties during the transit. This exceptional nature has meant that prior to the commissioning of the expanded canal; a small group of experienced pilots of the existing Canal was enabled for the transit of Neopanamax ships through a planned and innovative learning.

[Abstract] This work proposes a method based on image analysis and machine and statistical learning to model and estimate osteocyte growth (in type I collagen scaffolds for bone regeneration systems) and the collagen degradation degree due to cellular growth. To achieve these aims, the mass of collagen -subjected to the action of osteocyte growth and differentiation from stem cells- was measured on 3 days during each of 2 months, under conditions simulating a tissue in the human body. In addition, optical microscopy was applied to obtain information about cellular growth, cellular differentiation, and collagen degradation. Our first contribution consists of the application of a supervised classification random forest algorithm to image texture features (the structure tensor and entropy) for estimating the different regions of interest in an image obtained by optical microscopy: the extracellular matrix, collagen, and image background, and nuclei. Then, extracellular-matrix and collagen regions of interest were determined by the extraction of features related to the progression of the cellular growth and collagen degradation (e.g., mean area of objects and the mode of an intensity histogram). Finally, these critical features were statistically modeled depending on time via nonparametric and parametric linear and nonlinear models such as those based on logistic functions. Namely, the parametric logistic mixture models provided a way to identify and model the degradation due to biological activity by estimating the corresponding proportion of mass loss. The relation between osteocyte growth and differentiation from stem cells, on the one hand, and collagen degradation, on the other hand, was determined too and modeled through analysis of image objects’ circularity and area, in addition to collagen mass loss. This set of imaging techniques, machine learning procedures, and statistical tools allowed us to characterize and parameterize type I collagen biodegradation when collagen acts as a scaffold in bone regeneration tasks. Namely, the parametric logistic mixture models provided a way to identify and model the degradation due to biological activity and thus to estimate the corresponding proportion of mass loss. Moreover, the proposed methodology can help to estimate the degradation degree of scaffolds from the information obtained by optical microscopy. Ministerio de Asuntos Económicos y Transformación Digital; MTM2014-52876-R Ministerio de Asuntos Económicos y Transformación Digital; MTM2017-82724-R Xunta de Galicia; ED431C-2016-015 Xunta de Galicia; ED431G/01

In offshore activities, it is necessary for the floating equipment extracting raw materials to reach and maintain a static position along the seabed. Increasingly crucial in these operations is a new type of auxiliary vessel: the anchor handling and supply. While this work is being carried out, the deck gear related to towing and anchor handling – secondary and tugger winches, as well as capstans – play a key role on the anchor handling and supply. To estimate the vessels capacity for taking part in towing and anchor handling tasks, one can turn to the concept of the pull number as the sum total of the traction for this equipment. Linear regression, non-linear and semi-parametric generalised and additive models are adjusted around a broad data base about state-of-the-art vessels. This makes it possible to estimate their value, using as a starting point the vessels main dimensions and power. From this estimate, the traction values for the main and anchor handling winches can be determined. The next step is to propose a way of calculating the traction needed by the secondary, capstan and towing winches assisting the manoeuvre. This study provides procedures and statistical models that can help determine the traction of the towing and anchor handling winches for the anchor handling and supply. These depend on the traction obtained in Bollard Pull testing. The analysis proposed here involves dimensioning the key equipment used on these kinds of vessels so that valuable design information is obtained.

The aim of this study is to statistically identify and distinguish wood samples corresponding to different areas of annual rings in trees of temperate regions, using the corresponding thermogravimetric (TG) and their first TG derivative (DTG) curves, and specifically to verify whether late and early wood chestnut samples are different with statistical significance, taking into account their TG and DTG curves. These significant differences are sought by applying statistical procedures based on functional data analysis (FDA), such as the functional ANOVA and the FDA classification methods. Each TG curve is firstly smoothed using the local polynomial regression estimator, and its first derivative is estimated. Then, functional ANOVA based on random projections (RP) is used to identify significant differences between TG or DTG curves of early and late wood samples. In order to know the extent of the differences between early and late wood samples, they are discriminated (and the correct classification proportion obtained) by employing a kernel nonparametric functional data analysis technique, based on the Bayes' rule, as well as functional generalized linear models and functional generalized additive models, allowing to classify materials using more than one type of thermal curves simultaneously. The results are compared with those obtained using some classical multivariate supervised classification methods: linear discriminant analysis, naive Bayes (NBC) and quadratic classification (QDA). The partial least squares (PLS) dimension reduction procedure was previously applied to the TG curves in order to employ these multivariate methods. The application of RP ANOVA shows significant differences between late and early wood regarding mass loss and mass loss rate under combustion. The use of PLS multivariate methods or FDA classification approaches applied to the TG and DTG curves allows to distinguish very accurately between late and early wood. The proposed method could be applied to other species to identify thermooxidative differences, combined with other experimental methods to find their chemical and physical causes.

En este trabajo, se proponen dos nuevos índices de capacidad robustos para detectar la influencia de los factores que pueden causar grandes desviaciones de las especificaciones técnicas del proceso. El comportamiento de estos nuevos índices se analizó mediante la comparación de su rendimiento con respecto a otras medidas de capacidad ampliamente estudiados en la literatura. El trabajo tiene como objetivo investigar la robustez de estos nuevos índices de capacidad bajo la presencia de valores extremos y de falta de normalidad. Para este propósito, se aplicaron técnicas de remuestreo Bootstrap para detectar la verdadera capacidad potencial de un proceso a través de los métodos de inferencia estadística. La precisión de los índices propuestos es discutida por medio de resultados numéricos con un ejemplo de datos reales. Robustness of process capability measurements is a very important matter in statistical quality control. In this paper, two new classes of capability measurements are studied as robust mechanisms to detect the influence of factors that may cause large departures from the process’ engineering specifications. The behavior of the new indices was analyzed by comparing their performance to other capability measures that have been widely studied in literature. The paper aims to investigate the robustness of the new capability ratios under the presence of outliers and a lack of normality. For this purpose, bootstrap techniques were applied to detect the true potential capability of a process via statistical inference methods. The accuracies of the proposed indices are discussed by means of numerical results from a real data example.

[Abstract] The R package qcr for Statistical Quality Control (SQC) is introduced and described. It includes a comprehensive set of univariate and multivariate SQC tools that completes and increases the SQC techniques available in R. Apart from integrating different R packages devoted to SQC (qcc, MSQC), qcr provides nonparametric tools that are highly useful when Gaussian assumption is not met. This package computes standard univariate control charts for individual measurements, (Formula presented), S, R, p, np, c, u, EWMA, and CUSUM. In addition, it includes functions to perform multivariate control charts such as Hotelling T2, MEWMA and MCUSUM. As representative features, multivariate nonparametric alternatives based on data depth are implemented in this package: r, Q and S control charts. The qcr library also estimates the most complete set of capability indices from first to the fourth generation, covering the nonparametric alternatives, and performing the corresponding capability analysis graphical outputs, including the process capability plots. Moreover, Phase I and II control charts for functional data are included. The work of Salvador Naya, Javier Tarrío-Saavedra, Miguel Flores and Rubén Fernández-Casal has been supported by MINECO grant MTM2017-82724-R, and by the Xunta de Galicia (Grupos de Referencia Competitiva ED431C-2020-14 and Centro de Investigación del Sistema universitario de Galicia ED431G 2019/01), all of them through the ERDF. The research of Miguel Flores has been partially supported by Grant PII-DM-002-2016 of Escuela Politécnica Nacional of Ecuador. In addition, the research of Javier Tarrío-Saavedra has been also founded by the eCOAR project (PC18/03) of CITIC Xunta de Galicia; ED431C-2020-14 Xunta de Galicia; ED431G 2019/01 Escuela Politécnica Nacional de Ecuador; PII-DM-002-2016

A crankshaft is a mechanical component of an engine that performs a conversion of an alternative movement of a piston in a rotational motion of a shaft. It is a critical part and one of the most expensive of an engine. Defects in crankshafts may imply serious failures and, consequently, possible injuries and high costs. Therefore, the manufacture quality is of primordial importance for security and economic reasons. Nowadays, the quality control of crankshafts manufactured by forging in the automotive industry consists, among others, in inspecting them at the final process, using a magnetic particle procedure. This slow and highly stressful technique depends on operators and consumes many human resources, time, and space. This paper presents a methodology to automatically detect defective crankshafts. The proposed procedure is based on digital image analysis techniques, to extract a set of representative features from crankshaft images. Statistical techniques for supervised classification are used to classify the images into defective or not. The experimental results demonstrated the good performance of the proposed method with a classification accuracy over 99%, a 10% higher than the one obtained by manual inspection. Therefore, working time and personnel required for this task can be reduced when using this automated procedure.

[Abstract] This methodology permits to simulate the performance of different Poly(D,L-lactide-co-glycolide) copolymer formulations (PDLGA) in the human body, to identify the more influencing variables on hydrolytic degradation and, thus, to estimate biopolymer degradation level. The PDLGA characteristic degradation trends, caused by hydrolysis processes, have been studied to define their future biomedical applications as dental scaffolds. For this purpose, the mass loss, pH, glass transition temperature (Tg) and absorbed water mass of the different biopolymers have been obtained from samples into a phosphate-buffered saline solution (PBS) with initial pH of 7.4, at 37°C (human body conditions). The mass loss has been defined as the variable that characterize the biopolymer degradation level. Its dependence relationship with respect to time, pH and biopolymer formulation has been modelled using statistical learning tools. Namely, generalized additive models (GAM) and nonlinear mixed-effects regression with logistic and asymptotic functions have been applied. GAM model provides information about the relevant variables and the parametric functions that relate mass loss, pH and time. Mixed effects are introduced to model and estimate the degradation properties, and to compare the PDLGA biopolymer populations. The degradation path for each polymer formulation has been estimated and compared with respect to the others for helping to use the proper polymer for each specific medical application, performing selection criteria. It was found that the mass loss differences in PDLGA copolymers are strongly related with the way the pH decay versus time, due to carboxylic acid groups formation. This may occur in those environments in which the degradation products remain relatively confined with the non degraded mass. This is the case emulated with the present experimental procedure. The results show that PDLGA polymers degradation degree, in terms of half life and degradation rate, is increasing when acid termination is included, when DL-lactide molar ratio is reduced, decreasing the midpoint viscosity, or when glycolide is not included. Xunta de Galicia; ED431G/012016-2019 Xunta de Galicia; ED431C2016-015 Ministerio de Economía y Competitividad; MTM2014-52876-R

The Industry 4.0 concept, also called the fourth industrial revolution, represents a new era for the organization of production. One of the objectives of this fourth industrial revolution is the implementation of the so-called “smart factory,” capable of greater adaptability to the needs and production processes, as well as a more efficient allocation of resources, opening the way to a new industrial era. In the context of naval engineering it presents a great opportunity to improve both the shipyard and the construction process itself of a ship.

[Abstract] The poly(DL-lactide-co-glycolide) (PDLGA) copolymers have been specifically designed and performed as biomaterials, taking into account their biodegradability and biocompatibility properties. One of the applications of statistical degradation models in material engineering is the estimation of the materials degradation level and reliability. In some reliability studies, as the present case, it is possible to measure physical degradation (mass loss, water absorbance, pH) depending on time. To this aim, we propose an expert system able to provide support in collagen degradation analysis through computer vision methods and statistical modelling techniques. On this base, the researchers can determine which statistical model describes in a better way the biomaterial behaviour. The expert system was trained and evaluated with a corpus of 63 images (2D photographs obtained by electron microscopy) of human mesenchymal stem cells (CMMh-3A6) cultivated in a laboratory experiment lasting 44 days. The collagen type-1 sponges were arranged in 3 groups of 21 samples (each image was obtained in intervals of 72 hours).

[Abstract] The Poly(DL-lactide-co-glycolide) copolymers (PDLGA) have designed and performed as biomaterials, taking into account their biodegradability and biocompatibility properties. These materials have a wide range of application in medicine such as orthopedic implants, general surgical implants (suture materials), osteosynthesis, bone cement, among many others. For these reasons, in this paper, we present an intelligent educational environment that can be used for both, researchers and students interested in the analysis of the biomaterial behavior under certain conditions. Our platform includes a Learning Objects (LOs) for MOODLE, and in the same way, implements several digital image processing techniques as well as a decision support module based on a random forest algorithm and statistical modeling. With the aim of determining the real feasibility of this proposal, we have presented the system to 34 Ecuadorian engineering students. After testing the platform, the students answered a survey aimed at determining their perception of the system. The results provide several guidelines to continue with the developing of the platform.

The aim of this study is to propose an alternative methodology to classify wood species using the first (DTG), second (2DTG), and third (3DTG) derivatives of the thermogravimetric curves (TG). Accordingly, the main contribution of this new procedure consists on classifying materials (wood) taking into account the mass loss rate and acceleration with respect to temperature. In our research, each TG curve is firstly smoothed using the local polynomial regression estimator, and the first, second, and third derivatives are estimated. The application of the local polynomial regression estimator provides a reliable way to obtain the TG derivatives, overcoming the noise problem in the TG derivative estimation. Then, using these estimated curves, the different wood classes are discriminated employing a nonparametric functional data analysis (NPFDA) technique, based on the Bayes rule and the Nadaraya-Watson regression estimator, and also novel functional generalized additive models (GAM). The latter allows to classify materials using simultaneously more than one type of thermal curves. The results are compared with those obtained using classical and machine learning multivariate supervised classification methods, such as Linear discriminant analysis, Quadratic classification, Naive Bayes, Logistic regression, \(k\) Nearest neighbors, Neural networks, and Support vector machines. A regression model consisting of the mixture of the first derivatives of four generalized logistic components, one per principal wood constituent (water, hemicellulose, cellulose, and lignin), is applied to fit the DTG curves. The resulting 16 parameters from this fit characterize each curve and are used as datasets to apply the multivariate supervised classification methods. The use of the TG derivatives jointly with the TG curves has proved to be an optimal discriminating feature, when the new functional GAM techniques are employed.

This study presents an alternative to simple estimation of parametric fitting models used in thermal analysis. The addressed problem consists in performing an alternative optimization method to fit thermal analysis curves, specifically TG curves and their first derivatives. This proposal consists in estimating the optimal parameters corresponding to fitting kinetic models applied to thermogravimetric (TG) curves, using evolutionary algorithms: differential evolution (DE), simulated annealing and covariance matrix adapting evolutionary strategy. This procedure does not need to include a vector with the initial values of the parameters, as is currently required. Despite their potential benefits, the application of these methods is by no means usual in the context of thermal analysis curve’s estimation. Simulated TG curves are obtained and fitted using a generalized logistic mixture model, where each logistic component represents a thermal degradation process. The simulation of TG curves in four different scenarios taking into account the extent of processes overlapping allows us to evaluate the final results and thus to validate the proposed procedure: two degradation processes non-overlapped simulated using two generalized logistics, two processes overlapped, four processes non-overlapped and four processes overlapped two by two. The mean square error function is chosen as objective function and the above algorithms have been applied separately and together, i.e., taking the final solution of the DE algorithm is the initial solution of the remaining. The results show that the evolutionary algorithms provide a good solution for adjusting simulated TG curves, better than that provided by traditional methods.

Interest in polymeric materials with dispersed nanotubes has increased in recent years. There are several methods to characterize this kind of dispersions that may be based on evaluating the percolation concentration, the “goodness” of the dispersion, or the matrix-nanotube interphase. Among other techniques, rheology and conductivity are used to this aim. Commonly, the oscillatory rheology measurements are performed within the linear viscoelastic range, which is achieved by operating at small amplitude oscillation shear. Nevertheless, these measurements do not fully describe the behavior of the dispersion structure. In this work, we propose the use of medium amplitude oscillation shear and large amplitude oscillation shear to characterize the dispersion/structure of a thermoplastic polyurethane matrix filled with multiwalled carbon nanotubes. The Ewoldt framework mathematical approach is used to analyze the non-linear stress response. That approach allows obtaining physically grounded magnitudes from the experimental data. These magnitudes allow for a better understanding of the effects of the filler content.

Prediction of polymer properties at short and long observation times is usually performed through time–temperature superposition (TTS) models, which make use of some calculated shift factors. Although TTS principle has been used for many decades, no firm rules have been developed for obtaining the master curves. In the absence of reliable long-term data, it has been a common practice to try to minimize the discrepancy between the individual shifted curves. It was reported that a TTS method is more reliable as that discrepancy is minimized. In this study, a new method for obtaining the shift factors is presented. The optimal shift factors were estimated by minimizing the distance between the single curve derivatives with respect to the derivative of the curve at the reference temperature. That shift factors were tested with some classical models. The data were analyzed by statistical methods, making use of bootstrap resampling and spline estimation. The shift factors obtained from the proposed method allow for obtaining smooth master curves. The accuracy of the estimations was evaluated.

[Resumen] Este trabajo representa una nueva alternativa para el estudio, clasificación y comparación de la producción científica de centros de investigación, utilizando las funciones de tratamiento de datos del paquete Citan del software estadístico R. En particular, se muestra el análisis bibliométrico de las publicaciones de las universidades de A Coruña, Santiago de Compostela y Vigo, en el periodo 2000-2011, recopiladas por la base de datos Scopus. Entre las técnicas usadas se aplicaron modelos de Lotka y Price, modelización no paramétrica y paramétrica de los datos, así como el cálculo y análisis de indicadores de la cantidad y calidad de la producción científica, los índices h y g, y otros menosconocidos como los rp1, lp, Ge1, Ge5 y Slp1. Como novedad, se propone una variante del índice h (hh) que define el grupo de investigadores que forman la élite más productiva de cada universidad y estima su calidad investigadora. [Abstract] This work represents a new alternative for the study, classification and comparison of the scientific production corresponding to research entities. It consists on the application of statistical data processing functions available in the R software’s Citan package. In particular, the bibliometric study of publications of universities of A Coruña, Santiago de Compostela and Vigo, in the period 2000-2011, compiled by the Scopus database. The study was conducted using the statistical analysis of the data, the application of models of Lotka and Price, nonparametric and parametric modeling (Pareto) of the data, and the calculation and analysis of indicators of the scientific production like the h and g indexes, and others lesser known as rp1, lp, Ge1, Ge5 and Slp1. A novelty consists in a variant of the h index (hh) that defines the group of researchers who are the most productive of each university, the elite, and estimates the researching quality of such representative elites.

In this work, functional versions of Mandels’s h and k statistics for outlier laboratory detection in interlaboratory studies (ILS) are presented. The critical values of h and k outlier test are approximated using bootstrap resampling, and their characteristic graphics are obtained. Thermogravimetric data are simulated to study the performance of the proposed d H and d K functional test statistics.

Purpose . This work shows an effective methodology to characterize the creep–recovery behavior of silicones before their application in podiatry. The aim is to characterize, model and compare the creep–recovery properties of different types of silicone used in podiatry orthotics. Methods . Creep–recovery phenomena of silicones used in podiatry orthotics is characterized by dynamic mechanical analysis (DMA). Silicones provided by Herbitas are compared by observing their viscoelastic properties by Functional Data Analysis (FDA) and nonlinear regression. The relationship between strain and time is modeled by fixed and mixed effects nonlinear regression to compare easily and intuitively podiatry silicones. Results . Functional ANOVA and Kohlrausch–Willians–Watts (KWW) model with fixed and mixed effects allows us to compare different silicones observing the values of fitting parameters and their physical meaning. The differences between silicones are related to the variations of breadth of creep–recovery time distribution and instantaneous deformation-permanent strain. Nevertheless, the mean creep-relaxation time is the same for all the studied silicones. Silicones used in palliative orthoses have higher instantaneous deformation-permanent strain and narrower creep–recovery distribution. Conclusions . The proposed methodology based on DMA, FDA and nonlinear regression is an useful tool to characterize and choose the proper silicone for each podiatry application according to their viscoelastic properties.

Wood is one of the most complicated materials to be classified in different classes or species. In this paper, the thermogravimetric (TG) curves of 49 wood samples are used to classify them in 7 predetermined species. Different functional and multivariate statistical supervised classification methods are used for this task: a nonparametric Nadaraya–Watson kernel functional estimator (K-NFDA), using the complete TG curves, and multivariate supervised classification approaches, such as linear discriminant analysis (LDA), k Nearest Neighbors ( k -NN), Naive Bayes (NBC), Neural Networks (NN), and Support Vector Machines (SVM). Before applying the multivariate techniques, the TG curves are discretized using principal component analysis (PCA) or fitting a four-component generalized logistic model. The results show that the classical method of LDA using the logistic parameters had the best performance, although high correct classification percentages were also obtained with the rest of the approaches. The work is completed with a comprehensive simulation study, comparing the classification techniques in different scenarios. Artificial TG curves are generated using the logistic model and additional conclusions on wood classification are established. Due to the heterogeneity of wood, this simulation study is very useful to describe worst-case scenarios and to assess more accurately the proposed classification methodologies.

The present study suggests a new approach, based on the utilization of temperature modulated differential scanning calorimetry (TMDSC) technique, for identifying and characterizing the organic–inorganic interphase of two materials: an epoxy–fumed silica nanocomposite and a thermoplastic polyurethane (TPU)–multiwalled nanotube (MWNT) composite. The approach used here makes use of TMDSC data and basically consists of using the phase angle or the derivative of the reversing heat flow instead of the reversing heat flow curve itself. In the case of epoxy–fumed silica composites, two glass transition regions were identified. The glass transition temperature (Tg) of the composite was observed to vary as a consequence of the filler content. This study shows that the Tg variation is due to the formation of an organic–inorganic interphase, with its own glass transition temperature, which is different from the epoxy matrix Tg. In the case of TPU–MWNT composites, two relaxations and an additional first order transition were observed: the first relaxation corresponds to the hard segment, the second is related to an interaction between filler and matrix and the third process may be connected to the partial melting of the hard segment. The addition of 0.5 wt% MWNT causes a small reduction in Tg of the TPU. A major nanotube addition, 10 wt%, induces the appearance of a new relaxation that may be associated with the existence of an interface. In general, a better separation between the matrix and interphase glass transitions was obtained by the TMDSC phase angle signal.

Poly(l-lactic acid) (PLLA) is one of the most studied biopolymers nowadays. Due to its good performance, it constitutes an alternative to petrochemical-derived polymers. It was largely studied by differential scanning calorimetry (DSC) and temperature-modulated DSC. Nevertheless, there is an ongoing debate of what happens at the overlapping melting processes. In the present work, the experimental setups are discussed. Different modulation conditions are proposed for the study of the glass transition, cold crystallization, and the two reported melting processes. Finally, the experimental results allowed to measure the heat capacity change at the cold crystallization and a correct interpretation of what happens at the reported double melting peak of PLLA, which involves the existence of three crystalline structures.

Thermogravimetry has been widely applied to the study of wood and cellulose materials. There is a general agreement that decomposition of hemicellulose, cellulose, and ligning take place in a relatively narrow range of temperature, partially overlapping. There is no a definitive demonstration of which thermal feature corresponds to each component. In this study, three hardwood and two softwood species were considered: Castannea sativa, Eucaliptus globulus, Quercus robur, Pinus pinaster, and Pinus sylvestris. Thermogravimetric analysis of wood powder, ethanol-extracted wood, holocellulose, and lignin, obtained from those species revealed some important differences between hardwood and softwood holocelluloses and an important role of the ethanol-extractives, which explain the different behavior observed in both kinds of wood. FTIR spectra obtained from the evolved gases helped to clarify some degradation steps.

The principal aim of this study is to classify wood species using scanning electron microscopy (SEM) micrographs obtained with 1500 × magnification and processed by image segmentation. The results show that it is possible to observe differences among species in the wood texture at this magnification. The micrographs have been processed in a simple way using segmentation and object recognition to identify the cross-section tracheids belonging to earlywood of 7 different timber species: Fagus sylvatica , Castanea sativa , Juglans regia , Eucalyptus globulus , Hymenaea courbaril , Pinus silvestris and Pinus radiata . We have analyzed the shape, number and distribution of the tracheids using 5 features: circularity, rectangularity, number of tracheids, distance between tracheids and average area. The extracted features are classified using different statistical methods: Linear Discriminant Analysis (LDA), Quadratic classification, Logistic regression, K Nearest Neighbors (KNN), Naive Bayes, Support Vector Machines (SVM) and Neural Networks. A comparative study using gray level co-occurrence based features is also presented, with the improvement of using the segmentation method. Moreover, some additional results showing the possibility of using fractal analysis in this framework complete the research.