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.     

Educational recommender systems and their application in lifelong learning

The almost unlimited access to educational information plethora came with a drawback: finding meaningful material is not a straightforward task anymore. Recommender algorithms can be used to make smart decisions in complex information systems and help the users decide upon useful materials; therefore, they become a promising area in academia and industry. The current paper presents a survey on educational recommender systems (RS): a set of analysis criteria are exposed and the technological specifications and challenges of each analysed system are provided, in the context of the main trends in the development of RS. Also, an ontology-based educational recommendation mechanism is proposed and its application to lifelong learning is highlighted, proving that RS can successfully support new learning paradigms.     

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.     

A novel model for arc territory design: promoting Eulerian districts

The problem of district design for the implementation of arc routing activities is addressed. The aim is to partition a road network into a given number of sectors to facilitate the organization of the operations to be implemented within the region. This problem arises in numerous applications such as postal delivery, meter readings, winter gritting, road maintenance, and municipal solid waste collection. An integer linear programming model is proposed where a novel set of node parity constraints to favor Eulerian districts is introduced. Series of instances were solved to assess the impact of these parity constraints on the objective function and deadhead distance. Networks with up to 401 nodes and 764 edges were successfully solved. The model is useful at a tactical level as it can be used to promote workload balance, compactness, deadhead distance reduction and parity in districts.     

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.     

State of the art of frameworks and middleware for facilitating mobile and ubiquitous learning development

The emergence of mobile and ubiquitous technologies as important tools to complement formal learning has been accompanied by a growing interest in their educational benefits and applications. Mobile devices can be used to promote learning anywhere and anytime, to foster social learning and knowledge sharing, or to visualize augmented reality applications for learning purposes. However, the development of these applications is difficult for many researchers because it requires understanding many different protocols; dealing with distributed schemas, processes, platforms, and services; learning new programming languages; and interacting with different hardware sensors and drivers. For that reason, the use of frameworks and middleware that encapsulate part of this complexity appears to be fundamental to the further development of mobile learning projects. This study analyzes the state of the art of frameworks and middleware devoted to simplifying the development of mobile and ubiquitous learning applications. The results can be useful to many researchers involved in the development of projects using these technologies by providing an overview of the features implemented in each of these frameworks.     

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.     

Selfish Bin Packing

Following recent interest in the study of computer science problems in a game theoretic setting, we consider the well known bin packing problem where the items are controlled by selfish agents. Each agent is charged with a cost according to the fraction of the used bin space its item requires. That is, the cost of the bin is split among the agents, proportionally to their sizes. Thus, the selfish agents prefer their items to be packed in a bin that is as full as possible. The social goal is to minimize the number of the bins used. The social cost in this case is therefore the number of bins used in the packing.     

Analyzing Automata with Presburger Arithmetic and Uninterpreted Function Symbols

We study a class of extended automata defined by guarded commands over Presburger arithmetic with uninterpreted functions. On the theoretical side, we show that the bounded reachability problem is decidable in this model. On the practical side, the class is useful for modeling programs with unbounded data structures, and the reachability procedure can be used for symbolic simulation, testing, and verification.