A dialectical invariant for research in mathematics education

Many current problems in research in mathematics education emerge from pairs of contradictory dialectical categories. In effect, these pairs characterize the problems. When an epistemological study is made to determine the object of research in which a problem is immersed, it is possible to find essential pairs of dialectical categories that become more profound and thus provide enough elements for the determination of appropriate didactic actions to solve the problem under research

On Metrics for Location-Aware Games

Metrics are important and well-known tools to measure users’ behavior in games, and gameplay in general. Particularities of location-aware games—a class of games where the player’s location plays a central role-demand specific support in metrics to adequately address the spatio-temporal features such games exhibit. In this article, we analyse and discuss how existing game analytics platforms address the spatio-temporal features of location-aware games. Our analysis reveals that little support is available. Next, based on the analysis, we propose a classification of spatial metrics, embedded in existing literature, and discuss three types of spatial metrics-point-, trajectory- and area-based metrics-, and elaborate examples and difficulties. Finally, we discuss how spatial metrics may be deployed to improve gameplay in location-aware games

N-Glycosylation Regulates Pannexin 2 Localization but Is Not Required for Interacting with Pannexin 1.

Pannexins (Panx1, 2, 3) are channel-forming glycoproteins expressed in mammalian tissues. We previously reported that N-glycosylation acts as a regulator of the localization and intermixing of Panx1 and Panx3, but its effects on Panx2 are currently unknown. Panx1 and Panx2 intermixing can regulate channel properties, and both pannexins have been implicated in neuronal cell death after ischemia. Our objectives were to validate the predicted N-glycosylation site of Panx2 and to study the effects of Panx2 glycosylation on localization and its capacity to interact with Panx1. We used site-directed mutagenesis, enzymatic de-glycosylation, cell-surface biotinylation, co-immunoprecipitation, and confocal microscopy. Our results showed that N86 is the only N-glycosylation site of Panx2. Panx2 and the N86Q mutant are predominantly localized to the endoplasmic reticulum (ER) and cis-Golgi matrix with limited cell surface localization was seen only in the presence of Panx1. The Panx2 N86Q mutant is glycosylation-deficient and tends to aggregate in the ER reducing its cell surface trafficking but it can still interact with Panx1. Our study indicates that N-glycosylation may be important for folding and trafficking of Panx2. We found that the un-glycosylated forms of Panx1 and 2 can readily interact, regulating their localization and potentially their channel function in cells where they are co-expressed

An Analytics Platform for Integrating and Computing Spatio-Temporal Metrics

In large-scale context-aware applications, a central design concern is capturing, managing and acting upon location and context data. The ability to understand the collected data and define meaningful contextual events, based on one or more incoming (contextual) data streams, both for a single and multiple users, is hereby critical for applications to exhibit location- and context-aware behaviour. In this article, we describe a context-aware, data-intensive metrics platform —focusing primarily on its geospatial support—that allows exactly this: to define and execute metrics, which capture meaningful spatio-temporal and contextual events relevant for the application realm. The platform (1) supports metrics definition and execution; (2) provides facilities for real-time, in-application actions upon metrics execution results; (3) allows post-hoc analysis and visualisation of collected data and results. It hereby offers contextual and geospatial data management and analytics as a service, and allow context-aware application developers to focus on their core application logic. We explain the core platform and its ecosystem of supporting applications and tools, elaborate the most important conceptual features, and discuss implementation realised through a distributed, micro-service based cloud architecture. Finally, we highlight possible application fields, and present a real-world case study in the realm of psychological health

A comparative study on VGI and professional noise data

Ponencias, comunicaciones y pósters presentados en el 17th AGILE Conference on Geographic Information Science "Connecting a Digital Europe through Location and Place", celebrado en la Universitat Jaume I del 3 al 6 de junio de 2014.The ubiquitous nature of mobile devices and its growing presence in urban areas, turn them up into low cost environmental monitoring platforms. In this field, several authors made different efforts to provide alternatives to Sensor Networks, to assess noise pollution in cities using crowdsourcing techniques. In this sense, citizens might potentially produce large spatio-temporal datasets using their mobile devices to measure noise levels. There are few attempts of assessing the quality of the mobile noise samples on a real scenario and compare them to commercial data to evaluate if they are reliable enough. This contribution reviews the existing applications to collect or assess the quality of noise samples when they are used as sound level meters. Moreover, it presents the results of our experiment: the volunteer noise dataset generated in a ‘mapping party’ on our campus is compared to professional data. Results show that VGI data might be sufficient for multiple daily situations

Visualization of Sensor Data in Virtual Globes

Ponència presentada en AGILE’2012 International Conference on Geographic Information Science, "Multidisciplinary Research on Geographical Information in Europe and Beyond" celebrat a Avignon, els dies 24-27 d'abril de 2012Virtual Globes have become a common platform for visualizing geographical data. The capability for customization, extensibility and the support of interaction with the visualized elements are some of the aspects to consider when selecting a Virtual Globe for visualization. For visualizing sensor data, aspects such as cardinality, the nature of the data and its temporal and spatial dimensions have to be considered. In this paper we present a prototype application to visualize sensor data retrieved from SOS servers over the NASA World Wind virtual Globe. For implementing the prototype application we relied on a categorization of the sensor data that provides possible visualization methods. The prototype has integrated the SEXTANTE library to enable data analysis over sensor data and include the results as part of the visualizations

Analytical and numerical solutions of the potential and electric field generated by different electrode arrays in a tumor tissue under electrotherapy

<p>Abstract</p> <p>Background</p> <p>Electrotherapy is a relatively well established and efficient method of tumor treatment. In this paper we focus on analytical and numerical calculations of the potential and electric field distributions inside a tumor tissue in a two-dimensional model (2D-model) generated by means of electrode arrays with shapes of different conic sections (ellipse, parabola and hyperbola).</p> <p>Methods</p> <p>Analytical calculations of the potential and electric field distributions based on 2D-models for different electrode arrays are performed by solving the Laplace equation, meanwhile the numerical solution is solved by means of finite element method in two dimensions.</p> <p>Results</p> <p>Both analytical and numerical solutions reveal significant differences between the electric field distributions generated by electrode arrays with shapes of circle and different conic sections (elliptic, parabolic and hyperbolic). Electrode arrays with circular, elliptical and hyperbolic shapes have the advantage of concentrating the electric field lines in the tumor.</p> <p>Conclusion</p> <p>The mathematical approach presented in this study provides a useful tool for the design of electrode arrays with different shapes of conic sections by means of the use of the unifying principle. At the same time, we verify the good correspondence between the analytical and numerical solutions for the potential and electric field distributions generated by the electrode array with different conic sections.</p

From MFN to SFN: Performance Prediction Through Machine Learning

In the last decade, the transition of digital terrestrial television (DTT) systems from multi-frequency networks (MFNs) to single-frequency networks (SFNs) has become a reality. SFN offers multiple advantages concerning MFN, such as more efficient management of the radioelectric spectrum, homogenizing the network parameters, and a potential SFN gain. However, the transition process can be cumbersome for operators due to the multiple measurement campaigns and required finetuning of the final SFN system to ensure the desired quality of service. To avoid time-consuming field measurements and reduce the costs associated with the SFN implementation, this paper aims to predict the performance of an SFN system from the legacy MFN and position data through machine learning (ML) algorithms. It is proposed a ML concatenated structure based on classification and regression to predict SFN electric-field strength, modulation error ratio, and gain. The model's training and test process are performed with a dataset from an SFN/MFN trial in Ghent, Belgium. Multiple algorithms have been tuned and compared to extract the data patterns and select the most accurate algorithms. The best performance to predict the SFN electric-field strength is obtained with a coefficient of determination (R2) of 0.93, modulation error ratio of 0.98, and SFN gain of 0.89 starting from MFN parameters and position data. The proposed method allows classifying the data points according to positive or negative SFN gain with an accuracy of 0.97