State estimation and detection of changes in time interval models

This paper presents an estimation approach for Time Event Graphs such as P-Time Event Graphs and Time Stream Event Graphs. It is assumed that the nominal behavior is known and that transitions are partitioned as observable and unobservable transitions. The technique is applied to the detection of changes which are (possibly small) finite variations of dynamic models compared to this nominal behavior. The detected changes provide indications that can be used in future maintenance operations. Using the algebra of dioids, the approach uses a receding-horizon estimation of the greatest state and analyzes the consistency of the data

Multilingual Lexical Semantic Resources for Ontology Translation

We describe the integration of some multilingual language resources in ontological descriptions, with the purpose of providing ontologies, which are normally using concept labels in just one (natural) language, with multilingual facility in their design and use in the context of Semantic Web applications, supporting both the semantic annotation of textual documents with multilingual ontology labels and ontology extraction from multilingual text sources

Trajectory Tracking Control of a Timed Event Graph with Specifications Defined by a P-time Event Graph

The aim of this paper is a trajectory tracking control of Timed Event Graphs with specifications defined by a P-time Event Graph. Two problems are solved on a fixed horizon knowing the current state: The optimal control for favorable past evolution; The prediction of the earliest future evolution of the process. These two parts make up an on-line control which is used on a sliding horizon. Completely defined in (max, +) algebra, the proposed approach is a Model Predictive Control using the componentwise order relation

Bone Marrow Microenvironment and Tumor Progression

The bone marrow constitutes an unique microenvironment for cancer cells in three specific aspects. First, the bone marrow actively recruits circulating tumor cells where they find a sanctuary rich in growth factors and cytokines that promote their proliferation and survival. When in the bone marrow, tumor cells profoundly affect the homeostasis of the bone and the balance between osteogenesis and osteolysis. As a consequence, growth and survival factors normally sequestered into the bone matrix are released, further fueling cancer progression. Second, tumor cells actively recruit bone marrow-derived precursor cells into their own microenvironment. When in the tumors, these bone marrow-derived cells contribute to an inflammatory reaction and to the formation of the tumor vasculature. Third, bone marrow-derived cells can home in distant organs, where they form niches that attract circulating tumor cells. Our understanding of the contribution of the bone marrow microenvironment to cancer progression has therefore dramatically improved over the last few years. The importance of this new knowledge cannot be underestimated considering that the vast majority of cancer treatments such as cytotoxic and myeloablative chemotherapy, bone marrow transplantation and radiation therapy inflict a trauma to the bone marrow microenvironment. How such trauma affects the influence that the bone marrow microenvironment exerts on cancer is still poorly understood. In this article, the reciprocal relationship between the bone marrow microenvironment and tumor cells is reviewed, and its potential impact on cancer therapy is discussed

Predictive control of Timed Event Graphs with specifications defined by P-time Event Graphs

The aim of this paper is the predictive control of Timed Event Graphs with specifications defined by P-time Event Graphs. We propose a fixed-point approach which leads to a pseudo-polynomial algorithm. As the performance of the algorithm is crucial in on-line control, we highlight an important case where the resolution of this first algorithm is efficient. The second technique is a space controller on a horizon leading to a strongly polynomial algorithm