Modeling flow induced crystallization in film casting of polypropylene

Data from iPP film casting experiments served as a basis to model the effect of flow on polymer crystallization kinetics. These data describe the temperature, width, velocity and crystallinity distributions along the drawing direction under conditions permitting crystallization along the draw length. In order to model the effect of flow on crystallization kinetics, a modification of a previously defined quiescent kinetic model was adopted. This modification consisted in using a higher melting temperature than in the original quiescent model. The reason for the modification was to account for an increase of crystallization temperature due to entropy decrease of the flowing melt. This entropy decrease was calculated from the molecular orientation on the basis of rubber elasticity theory applied to the entangled and elongated melt. The evolution of molecular orientation (elongation) during the film casting experiments was calculated using a non-linear dumbbell model which considers the relaxation time, obtained from normal stress difference and viscosity functions, to be a function of the deformation rate. The comparison between experimental distributions and model based crystallinity distributions was satisfactory

Chapter Linear regression pathmox segmentation tree: the case of visitors’ satisfaction to attend a Spanish football match at the stadium

Analysis of a dependency model can be furthered by assessing whether a model and/or the impact of regressors on dependent variables differ if heterogeneity is observed. In other words, it may be interesting to assess differences between a global model estimated for a whole group and models estimated for sub-groups identified on the basis of known categorical variables external to the model, as those variables may identify partitions characterized by dependency structure heterogeneity. This is particularly important in decision-making as policies based on the generic model could yield inaccurate and biased results. In this paper, we propose a procedure, the Pathmox approach that exploits the potential of segmentation trees to identify partitions in an initial set of data characterized by different linear regression patterns. We will apply this new approach to measure the visitors’ satisfaction to attend a Spanish football match at the stadium. Thus, we will analyze the relationship between two significant aspects related to the visitors’ satisfaction: stadium service quality and image of the football team, taking into account five visitors’ background variables as potential sources of heterogeneity: age, gender, if they were tourist (yes or not), if it was the first time at the stadium (yes or not), and level of involvement with the football team. From a decision-making perspective, the paper contributes evidence exemplifying how an apparently representative global model can in fact mask different relationships between variables due to heterogeneous data, underlining the importance of accounting for heterogeneity when defining new policies

Invariance Test: Detecting Difference Between Latent Variables Structure in Partial Least Squares Path Modeling

In the context of heterogeneity, almost all partial least squares path modeling (PLS-PM) approaches focus on differences in the causal relationships between the latent variables. The principal goal is to detect segments that have different path coefficients in the structural model, yet inadequate attention is generally given to the measurement model. Thus, anytime that we define specific sub-models for different groups of individuals, we may wonder if the latent variables are the same in all detected sub-models. Taking this into consideration, the problem of invariance arises, meaning that if the estimation of latent variables are specific in each sub-model, there is reasonable doubt regarding whether we can compare the distinct behavior of individuals who belong to two different segments. In this paper, we present an invariance test as a possible solution, whereby the goal is to verify whether or not the measurement models of each sub-model may be assumed equal among themselves

Uno spazio di Sobolev con la proprietà di Pick completa

Questa tesi si pone come scopi lo studio di un particolare spazio di Sobolev e di una sua proprietà. Tale spazio prevede l’utilizzo della derivata debole che, per essere definita, utilizza la teoria integrale invece di limiti e rapporti incrementali. Le funzioni che ne fanno parte sono funzioni a quadrato sommabile con derivata debole a quadrato sommabile. Gli spazi di Sobolev, molto spesso utilizzati per trovare le soluzioni a equazioni differenziali alle derivati parziali, verranno qui trattati diversamente. Si è infatti deciso di studiare questo particolare spazio, uno tra gli spazi di Sobolev ad essere uno spazio di Hilbert a nucleo riproducente, come tale e non come contenitore di soluzioni a problemi di altra natura. La proprietà studiata è la proprietà di Pick completa, generalizzazione di un problema di interpolazione studiato a inizio del Novecento da Georg Pick. Oltre a questo viene calcolata una distanza sull'insieme [0,1] indotta dallo spazio di Sobolev studiato. Tale calcolo è originale e qui riportato insieme a due grafici che rappresentano tale distanza

Modelling with heterogeneity

When collecting survey data for a specific study it is usual to have some background information, in the form for example, of socio-demographic variables. In our context, these variables may be useful in identifying potential sources of heterogeneity. Resolving the heterogeneity may mean to perform distinct analyses based on the main variables for distinct and homogeneous segments of the data, defined in terms of the segmentation variables. In 2009 Gastón Sánchez proposed an algorithm PATHMOX with the aim to automatic detecting heterogeneous segments within the PLS-PM methodology. This technique, based on recursive partitioning, produces a segmentation tree with a distinct path models in each node. At each node PATHMOX searches among all splits based on the segmentation variables and chooses the one resulting in the maximal difference between the PLS-PM models in the children nodes. Starting from the work of Sanchez the purpose of the thesis is to extend PATHMOX in the following points: 1. Extension to the PATHMOX approach to detect which constructs differentiate segments. The PATHMOX approach uses a F-global test to identify the best split in heterogeneous segments. Following the same approach it is possible to extend the testing to find which the endogenous constructs are and which are the relationships between constructs responsible of the difference between the segments. 2. Extension to the PATHMOX approach to deal with the factor invariance problem. Originally PATHMOX adapted the estimation of constructs to each detected segment, that is, once a split is performed the PLS-PM model is recalculated in every child. This leads to the problem of invariance: if the the estimation of the latent variables are recalculated in each terminal node of the tree, we cannot be sure to compare the distinct behavior of two individuals who belong to two different terminal nodes. To solve this problem we will propose a invariance test based on the X^2 distribution, where the goal of to test whether the measurement models of each terminal node can be considered equal or not among them. 3. Extension to the PATHMOX approach to overcome the parametric hypothesis of F-test. One critic to the PATHMOX approach, applied in the context of partial least square path modeling, is that it utilizes a parametric test based on the hypothesis that the residuals have a normal distribution to compare two structural models. PLS-PM in general, is utilized to model data that come from survey analysis. These data are characterized by an asymmetric distribution. This situation produces skewness in the distribution of data. As we know, PLS-PM methodology, is based in the absence of assumptions about the distribution of data. Hence, the parametric F test used in PATHMOX may represent a limit of the methodology. To overcome this limit, we will extend the test in the context of LAD robust regression. 4. Generalization of PATHMOX algorithm to any type of modeling methodology. The PATHMOX algorithm has been proposed to analyze heterogeneity in the context of the partial least square path modeling. However, this algorithm can be applied to many other kind of methodologies according to the appropriate split criterion. To generalize PATHMOX we will consider three distinct scenarios: Regression analysis (OLS, LAD, GLM regression) and Principal Component Analysis. 5. Implement the methodology, using the R software as specific library.Cuando se realiza un estudio científico, el análisis hace énfasis sobre las variables recogidas para responder a las preguntas que se quieren hallar durante el mismo estudio. Sin embargo en muchos análisis se suele recoger más variables, como por ejemplo variables socio demográfico: sexo, status social, edad. Estas variables son conocidas como variables de segmentación, ya que pueden ser útiles en la identificación de posibles fuentes de heterogeneidad. Analizar la heterogeneidad quiere decir realizar distintas análisis para distintos colectivos homogéneos definidos a partir de las variables de segmentación. Muchas veces, si hay algún conocimiento previo, esta heterogeneidad puede ser controlada mediante la definición de segmentos a priori. Sin embargo no siempre se dispone de conocimiento suficiente para definir a priori los grupos. Por otro lado muchas variables de segmentación podrían ser disponibles para analizar la heterogeneidad de acuerdo con un apropiado algoritmo. Un algoritmo desarrollado con este objetivo fue PATHMOX, propuesto por Gastón Sanchez en 2009. Esta técnica, utilizando particiones recursivas, produce un árbol de segmentación con distintos modelos asociados a cada nodo. Para cada nodo, PATHMOX busca entre todas las variables de segmentación aquella que produce una diferencia máxima entre los modelos de los nodos hijos. Tomando como punto de partida el trabajo de Gastón Sanchez esta tesis se propone: 1. Extender PATHMOX para identificar los constructos responsables de la diferencias. PATHMOX nos permite detectar distintos modelos en un data-set sin identificar grupos a priori. Sin embargo, PATHMOX es un criterio global. Pera identificar las distintas ecuaciones y coeficientes responsables de las particiones, introduciremos los test F-block y F-coefficient. 2. Extender PATHMOX para solucionar el problema de la invariancia. En el contexto del PLS-PM (Partial Least Squares Path Modeling), PATHMOX funciona fijando las relaciones causales entre las variables latentes y el objetivo es identificar modelos con coeficientes path lo más posible distintos sin poner ninguna restricción sobre el modelo de medida. Por lo tanto, cada vez que una diferencia significativa es identificada, y dos nodos hijos vienen definidos, las relaciones causales entre las variables latentes son las mismas en ambos modelos "hijos", pero la estimación de cada variable latente se recalcula y no podemos estar seguros de comparar el comportamiento de dos individuos distintos que pertenecen a dos nodos diferentes. Para resolver este problema propondremos un test de invariancia basado en la distribución X^2, donde el objetivo del test es verificar si los modelos de cada nodo terminales se puede considerar igual o no entre ellos. 3. Extender PATHMOX para superar la hipótesis paramétrica del F-test. Una crítica a PATHMOX, aplicadas en el contexto del PLS-PM, es que el algoritmo utiliza una prueba paramétrica, basada en la hipótesis de que los residuos tienen una distribución normal, para comparar dos modelos estructurales. Para superar este límite, extenderemos el test para comparar dos regresiones robustas LAD en el contexto del PLS. 4. La generalización del algoritmo PATHMOX a cualquier tipo de metodología. El algoritmo PATHMOX ha sido propuesto para analizar la heterogeneidad en el contexto PLS-PM. Sin embargo, este algoritmo se puede aplicar a muchos otros tipos de metodologías de acuerdo con un apropiado criterio de partición. Para generalizar PATHMOX consideraremos tres escenarios distintos: modelos de regresión (modelos OLS, LAD, GLM) y el análisis en componentes principales. 5. Implementar la metodología, utilizando el software R como librería específica

Physiologically Based Pharmacokinetics: A Simple, All Purpose Model

To predict the drug hemeatic levels after administration is a goal of great interest in the design of novel pharmaceutical systems and in therapies management. The most reliable approach in pharmacokinetic modeling consists in analyzing the physiology of the living beings and in describing tissues and organs as different biochemical reactors. These models have been identified as physiologically based pharmacokinetic models (PBPK). They can be very detailed in the description, but, in this case, they also claim for the knowledge of an high number of parameters which are difficult to be determined by experiments. In this work, a review of the most complete PBPK models proposed in literature was performed, and a novel PBPK model was developed and validated by comparison with in vivo data available in the literature. The appeals of the novel model are its simplicity and the limited number of parameters required. Last but not least, it was proved able to predict the hemeatic drug levels after different kinds of administrations (intravenous injection, oral assumption of delayed release tablets)

A phase II, single arm study of CarbopLatin plus Etoposide with Bevacizumab and Atezolizumab in patients with exTEnded-disease small-cell lung cancer (SCLC) – CeLEBrATE trial

Small cell lung cancer (SCLC) is an aggressive neuroendocrine tumor diagnosed at extended disease SCLC (ES-SCLC) stage in about 70% of cases. The new standard of treatment for patients with ES-SCLC is a combination of platinum-etoposide chemotherapy and atezolizumab or durvalumab, two programmed cell death ligand 1 (PD-L1) inhibitory monoclonal antibodies (mAb). However, the benefit derived from the addition of PD-L1 inhibitors to chemotherapy in ES-SCLC was limited and restricted to a subset of patients. The vascular endothelial growth factor (VEGF) is the most important pro-angiogenic factor implicated in cancer angiogenesis, which is abundant in SCLC and associated with poor prognosis. Antiangiogenic agents, such as bevacizumab, a humanized mAb against VEGF, added to platinum-etoposide chemotherapy improved progression-free survival in SCLC in two trials, but it did not translate into a benefit in overall survival. Nevertheless, VEGF has also acts as a mediator of an immunosuppressive microenvironment and its inhibition can revert the immune-suppressive tumor microenvironment and potentially enhance the efficacy of immunotherapies. Based on available preclinical data, we hypothesized that VEGF inhibition by bevacizumab could improve atezolizumab efficacy in a synergistic way and designed a phase II single-arm trial of bevacizumab in combination with carboplatin, etoposide, and atezolizumab as first-line treatment in ES-SCLC. The trial, which is still ongoing, enrolled 53 patients, including those with treated or untreated asymptomatic brain metastases (provided criteria are met), who received atezolizumab, bevacizumab, carboplatin and etoposide for 4-6 cycles (induction phase), followed by maintenance with atezolizumab and bevacizumab for a maximum of 18 total cycles or until disease progression, patient refusal, unacceptable toxicity. The evaluation of efficacy of the experimental combination in terms of 1-year overall survival rate is not yet mature (primary objective of the trial). The combination was feasible and the toxicity profile manageable (secondary objective of the trial)

On the effect of measurementmodel misspecification in PLS Path Modeling: the reflective case

The specification of a measurement model as reflective or formative is the object of a lively debate. Part of the existing literature focuses on measurement model misspecification. This means that a true model is assumed and the impact on the path coefficients of using a wrong model is investigated. The majority of these studies is restricted to Structural Equation Modeling (SEM). Regarding PLS-Path Modeling (PLS-PM), a few authors have carried out simulation studies to investigate the robustness of the estimates, but their focus is the comparison with SEM. The present paper discusses the misspecification problem in the PLSPM context from a novel perspective. First, a real application on Alumni Satisfaction will be used to verify whether different assumptions for the measurements models influence the results. Second, the results of a Monte-Carlo simulation study, in the reflective case, will help to bring some clarity on a complex problem that has not been sufficiently studied yet

Analysis and modeling of swelling and erosion behavior for pure HPMC tablet

This work is focused on the transport phenomena which take place during immersion in water of pure hydroxypropylmethylcellulose tablets. The water uptake, the swelling and the erosion during immersion were investigated in drug-free systems, as a preliminary task before to undertake the study of drug-loaded ones. The tablets, obtained by powder compression, were confined between glass slabs to allow water uptake only by lateral surface and then immersed in distilled water at 37 °C, with simultaneous video-recording. By image analysis the normalized light intensity profiles were obtained and taken as a measure of the water mass fraction. The time evolutions of the total tablet mass, of the water mass and of the erosion radius were measured, too. Thus a novel method to measure polymer and water masses during hydration was pointed out. Then, a model consisting in the transient mass balance, accounting for water diffusion, diffusivity change due to hydration, swelling and erosion, was found able to reproduce all experimental data. Even if the model was already used in literature, the novelty of our approach is to compare model predictions with a complete set of experimental data, confirming that the main phenomena were correctly identified and described

Scaffolds in Tendon Tissue Engineering

Tissue engineering techniques using novel scaffold materials offer potential alternatives for managing tendon disorders. Tissue engineering strategies to improve tendon repair healing include the use of scaffolds, growth factors, cell seeding, or a combination of these approaches. Scaffolds have been the most common strategy investigated to date. Available scaffolds for tendon repair include both biological scaffolds, obtained from mammalian tissues, and synthetic scaffolds, manufactured from chemical compounds. Preliminary studies support the idea that scaffolds can provide an alternative for tendon augmentation with an enormous therapeutic potential. However, available data are lacking to allow definitive conclusion on the use of scaffolds for tendon augmentation. We review the current basic science and clinical understanding in the field of scaffolds and tissue engineering for tendon repair