Synchronizing automata with finitely many minimal synchronizing words

A synchronizing word for a given synchronizing DFA is called minimal if none of its proper factors is synchronizing. We characterize the class of synchronizing automata having only finitely many minimal synchronizing words (the class of such automata is denoted by FG). Using this characterization we prove that any such automaton possesses a synchronizing word of length at most 3n-5. We also prove that checking whether a given DFA A is in FG is co-NP-hard and provide an algorithm for this problem which is exponential in the number of states A.     

From embedded sensors to sensorial materials—The road to function scale integration

Ubiquitous computing is about to become part of our everyday lives by integrating hundreds of “invisible” to us computing devices in our environment, so that they can unobtrusively and constantly assist us. This will imply more and smaller “invisible” sensors, homogeneously distributed and at the same time densely packed in host materials, responding to various stimuli and immediately delivering information. In order to reach this aim, the embedded sensors should be integrated within the host material, heading towards sensorial materials. The first step is to omit all parts that are not needed for the sensorial task and to find new solutions for a gentle integration. This is what we call function scale integration. The paper discusses sensor embedding in the human hand as an example of integration in nature, new technological applications and main challenges associated with this approach.     

Aggregation in hub location problems

Because of their widespread use in real-world transportation situations, hub location models have been extensively studied in the last two decades. Many types of hub location problems are NP-hard and remain unmanageable when the number of nodes exceeds 200. We present a way to tackle large-sized problems using aggregation, explore the resulting error, and show how to reduce it. Furthermore, we develop a heuristic based on aggregation for k-hub center problems and present computational results.     

Semi-Lagrangian Runge-Kutta Exponential Integrators for Convection Dominated Problems

In this paper we consider the case of nonlinear convection-diffusion problems with a dominating convection term and we propose exponential integrators based on the composition of exact pure convection flows. These methods can be applied to the numerical integration of the considered PDEs in a semi-Lagrangian fashion. Semi-Lagrangian methods perform well on convection dominated problems (Pironneau in Numer. Math. 38:309–332, 1982; Hockney and Eastwood in Computer simulations using particles. McGraw-Hill, New York, 1981; Rees and Morton in SIAM J. Sci. Stat. Comput. 12(3):547–572, 1991; Baines in Moving finite elements. Monographs on numerical analysis. Clarendon Press, Oxford, 1994). In these methods linear convective terms can be integrated exactly by first computing the characteristics corresponding to the gridpoints of the adopted discretization, and then producing the numerical approximation via an interpolation procedure.     

Microfluidic device for continuous magnetophoretic separation of white blood cells

This paper presents a microfluidic device for magnetophoretic separation of red blood cells from blood under continuous flow. The separation method consists of continuous flow of a blood sample (diluted in PBS) through a microfluidic channel which presents on the bottom “dots” of ferromagnetic layer. By applying a magnetic field perpendicular on the flowing direction, the ferromagnetic “dots” generate a gradient of magnetic field which amplifies the magnetic force. As a result, the red blood cells are captured on the bottom of the microfluidic channel while the rest of the blood is collected at the outlet. Experimental results show that an average of 95% of red blood cells is trapped in the device.     

Ordinal regression by a gravitational model in the field of educational data mining

Educational data mining (EDM) is a research area where the goal is to develop data mining methods to examine data critically from educational environments. Traditionally, EDM has addressed the following problems: clustering, classification, regression, anomaly detection and association rule mining. In this paper, the ordinal regression (OR) paradigm, is introduced in the field of EDM. The goal of OR problems is the classification of items in an ordinal scale. For instance, the prediction of students' performance in categories (where the different grades could be ordered according to A ? B ? C ? D) is a classical example of an OR problem. The EDM community has not yet explored this paradigm (despite the importance of these problems in the field of EDM). Furthermore, an amenable and interpretable OR model based on the concept of gravitation is proposed. The model is an extension of a recently proposed gravitational model that tackles imbalanced nominal classification problems. The model is carefully adapted to the ordinal scenario and validated with four EDM datasets. The results obtained were compared with state‐of‐the‐art OR algorithms and nominal classification ones. The proposed models can be used to better understand the learning–teaching process in higher education environments.     

Stability of Local On‐Ramp Metering Control Laws

A framework for stability analysis of local on‐ramp metering control strategies based on the cell transmission model is presented. Within this framework, it is possible to formulate Lyapunov and input‐state stability results for on‐ramp metering control strategies in an open section of highway with on‐ramps. Using this analysis, recommendations for the design of on‐ramp metering control laws set points are derived. Two examples on the use of such analysis are presented. One deals with the stability analysis of a local on‐ramp metering control law and the other with the design of a disturbance observer that, used in combination with the local on‐ramp metering control law, provides a more robust response to traffic regulation. Simulation results are included that confirm the possibility of using this framework to test the impact of local on‐ramp metering control strategies.     

On location observability notions for switching systems

The focus of this paper is on the analysis of initial discrete state distinguishability notions for switching systems, in a discrete time setting. Moreover, the relationship between initial discrete state distinguishability and the problem of reconstructing the current discrete state is addressed.