In-depth analysis and evaluation of diffusive glioma models

Glioma is one of the most aggressive types of brain tumor. Several mathematical models have been developed during the past two decades, toward simulating the mechanisms that govern the development of glioma. The most common models use the diffusion-reaction equation (DRE) for simulating the spatiotemporal variation of tumor cell concentration. Nevertheless, despite the applications presented, there has been little work on studying the details of the mathematical solution and implementation of the 3-D diffusion model and presenting a qualitative analysis of the algorithmic results. This paper presents a complete mathematical framework on the solution of the DRE using different numerical schemes. This framework takes into account all characteristics of the latest models, such as brain tissue heterogeneity, anisotropic tumor cell migration, chemotherapy, and resection modeling. The different numerical schemes presented have been evaluated based upon the degree to which the DRE exact solution is approximated. Experiments have been conducted both on real datasets and a test case for which there is a known algebraic expression of the solution. Thus, it is possible to calculate the accuracy of the different models.     

Correction to: Automatic stress analysis from facial videos based on deep facial action units recognition

Pattern Analysis and Applications - A correction to this paper has been published: 10.1007/s10044-021-01012-9     

A multi-agent platform for content-based image retrieval

Efficient and possibly intelligent image retrieval is an important task, often required in many fields of human activity. While traditional database indexing techniques exhibit a remarkable performance in textual information retrieval current research in content-based image retrieval is focused on developing novel techniques that are biologically motivated and efficient. It is well known that humans have a remarkable ability to process visual information and to handle the volume and complexity of such information quite efficiently. In this paper, we present a content-based image retrieval platform that is based on a multi-agent architecture. Each agent is responsible for assessing the similarity of the query image to each candidate image contained in a collection based on a specific primitive feature and a corresponding similarity criterion. The outputs of various agents are integrated using one of several voting schemes supported by the system. The system’s performance has been evaluated using various collections of images, as well as images obtained in specific application domains such as medical imaging. The initial evaluation has yielded very promising results.

Incineration of a small particle of wet sewage sludge: a numerical comparison between two states of the surrounding atmosphere

The main goal of this paper is to elaborate a mathematical model that represents the physics and chemistry involved when a small particle of wet sewage sludge is incinerated. Compared to existing models, our study includes both drying and heterogenous combustion of the pyrloysis residue of the processed sludge. This model relies on the assumption of homogeneous composition and temperature for the particle under study. It includes drying, pyrolysis (controlled by a four successives steps reaction pathway) and combustion of the resulting char. The ability of the model is illustrated using it in two different process conditions (representing thermogravemetric analysis and fluidized bed conditions) in order to investigate the influence of the surrounding atmosphere. It is found, that fluidized bed conditions reduce the burnout time of a small particle by enhancing the rate at which heat is transferred to that particle. It is also shown that high heating rates enhance the tar yield.     

Modelling of a biomass fired furnace for production of lime

This paper deals with the mathematical modelling of a furnace devoted to the limestone processing. The novelty of this reactor is that the energy required by the endothermic reaction of calcination is provided by the combustion of biomass, making it compatible with sustainable development. The model is based on balance equations completed with phenomenological relations for the estimation of heat and mass transfer inside the reactor and for the estimation of the kinetics of the reaction. In a first section, the equations describing the behaviour of a single reacting particle of limestone are derived. The model relies on the assumption that the reacting particle obeys the shrinking core model. Then this model of a single particle is introduced in a more general one-dimensional model describing the whole furnace, where the different relevant properties depend upon the location within the reactor. Some industrial information is compared to numerical predictions, thus validating the model. Also the results provided by the numerical model allow for a better comprehension of the different phenomena occurring within the reactor.     

Equilibrium model for a travelling bed gasifier

A mathematical model describing a gasification process composed of a dryer section and of a gasification section is presented in this work. This model is based on the equilibrium assumption within both sections. The whole set of assumptions used in the model are presented in the paper as well as the derivation of the corresponding equations. The temperature of both sections is computed using energy balances. The numerical predictions of the model are compared to available data describing the gasification of coal, wood and grass. The influence of two operating parameters of the system is investigated in the study. The first one is the ratio of air supplied to the dryer to the biomass fed to this section. Our computation clearly shows the existence of an optimal value for this parameter. In the same manner, the influence of the ratio of air supplied to the gasifying section to the incoming biomass is investigated. Once again, this parameter shows an optimal value.     

Modelling of thermal removal of tars in a high temperature stage fed by a plasma torch

The thermal degradation of tars in a chamber fed by a non-transferred plasma torch is theoretically examined in this study. The input of this reactor is a product gas coming from a gasification unit with a temperature of about 800 °C. According to literature, naphthalene and toluene are chosen as model compounds to represent the behaviour of their classes. According to this choice and to the data available in the literature, a reaction pathway for the thermal degradation of tars and its associated kinetics are proposed in this study. This mechanism is introduced in a CSTR model in order to check the influence of the operating parameters of the reactor on the degradation efficiency. These computations clearly show that a complete conversion of toluene (>99.9%) and an important conversion of naphthalene (96.7%) can be reached in the reactor, with concentration levels compatible with the further use of gas engines for electricity production. This theoretical study requires to be validated by comparison with experimental results.     

High-grade glioma diffusive modeling using statistical tissue information and diffusion tensors extracted from atlases

Glioma, especially glioblastoma, is a leading cause of brain cancer fatality involving highly invasive and neoplastic growth. Diffusive models of glioma growth use variations of the diffusion-reaction equation in order to simulate the invasive patterns of glioma cells by approximating the spatiotemporal change of glioma cell concentration. The most advanced diffusive models take into consideration the heterogeneous velocity of glioma in gray and white matter, by using two different discrete diffusion coefficients in these areas. Moreover, by using diffusion tensor imaging (DTI), they simulate the anisotropic migration of glioma cells, which is facilitated along white fibers, assuming diffusion tensors with different diffusion coefficients along each candidate direction of growth. Our study extends this concept by fully exploiting the proportions of white and gray matter extracted by normal brain atlases, rather than discretizing diffusion coefficients. Moreover, the proportions of white and gray matter, as well as the diffusion tensors, are extracted by the respective atlases; thus, no DTI processing is needed. Finally, we applied this novel glioma growth model on real data and the results indicate that prognostication rates can be improved.