The miniaturization and price reduction of sensors have encouraged the proliferation of smart environments, in which multitudinous sensors detect and describe the activities carried out by inhabitants. In this context, the recognition of activities of daily living has represented one of the most developed research areas in recent years. Its objective is to determine what daily activity is developed by the inhabitants of a smart environment. In this field, many proposals have been presented in the literature, many of them being based on ad hoc ontologies to formalize logical rules, which hinders their reuse in other contexts. In this work, we propose the use of class expression learning (CEL), an ontology-based data mining technique, for the recognition of ADL. This technique is based on combining the entities in the ontology, trying to find the expressions that best describe those activities. As far as we know, it is the first time that this technique is applied to this problem. To evaluate the performance of CEL for the automatic recognition of activities, we have first developed a framework that is able to convert many of the available datasets to all the ontology models we have found in the literature for dealing with ADL. Two different CEL algorithms have been employed for the recognition of eighteen activities in two different datasets. Although all the available ontologies in the literature are focused on the description of the context of the activities, the results show that the sequence of the events produced by the sensors is more relevant for their automatic recognition, in general terms.

Abstract-The study of tumor growth biology with computer-based models is currently an area of active research. Different simulation techniques can be used to describe the complexity of any real tumor behavior, among these, "cellular automata"-based simulations provide an accurate tumor growth graphical representation while, at the same time, keep simpler the implementation of the automata as computer programs. Several authors have recently published relevant proposals, based on the latter approach, to solve tumor growth representation problem through the development of some strategies for accelerating the simulation model. These strategies achieve computational performance of cellular-models representation by the appropriate selection of data types, and the clever use of supporting data structures. However, as of today, multithreaded processing techniques and multicore processors have not been used to program cellular growth models with generality. This paper presents a new model that incorporates parallel programming for multi and manycore processors, and implements any synchronization requirement necessary to implement the solution. The proposed parallel model has been proved using Java and C++ program implementations on two different platforms: chipset Intel i5-4440 and one node of 16-processors cluster of our university. The improvement resulting from the introduction of parallelism into the model is analyzed in this paper, comparing it with the standard sequential simulation model currently used by researchers in mathematical oncology. Keywords- I. INTRODUCCIÓNN la actualidad es de todo punto necesario alcanzar una comprensión profunda y pormenorizada de la biología tumoral con el objetivo de desarrollar nuevas terapias de lucha contra el cáncer. Esta comprensión puede lograrse mediante técnicas de investigación puramente biológicas, es decir, mediante el diseño y control de experimentos en modelos animales o in vitro, o bien mediante el desarrollo de modelos matemáticos e informáticos que permitan simular la biología tumoral, sometiendo dicho modelo a experimentos que permitan observar su respuesta frente a distintas estrategias terapéuticas. Conocer la dinámica de crecimiento tumoral, y cómo puede llegar a modificarse como resultado de la aplicación de terapias citotóxicas, antiangiogénicas, inmunológicas o de radiación, es, sin duda, crucial para alcanzar éxito en la estrategia de lucha contra la enfermedad 1 A. J. Tomeu, University of Cádiz, antonio.tomeu@uca.es A. G. Salguero, University of Cádiz, alberto.salguero@uca.es M. I. Capel, University of Granada, mcapel@ugr.es basada en métodos computacionales.Aunque el modelado por medios computacionales ni puede ni debe sustituir a la investigación biológica, sí es cierto que durante determinadas fases de la investigación puede suponer una importante reducción de costes y de riesgos, frente a la experimentación directa en el laboratorio, que conlleva siempre costes más altos, protocolos de experimentación complejos, y tiempos de experimentación más largos....

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In silico" experimentation allows us to simulate the effect of different therapies by handling model parameters. Although the computational simulation of tumors is currently a well-known technique, it is however possible to contribute to its improvement by parallelizing simulations on computer systems of many and multi-cores. This work presents a proposal to parallelize a tumor growth simulation that is based on cellular automata by partitioning of the data domain and by dynamic load balancing. The initial results of this new approach show that it is possible to successfully accelerate the calculations of a known algorithm for tumor-growth.