A methodology for the identification of glass panes of different origin in a single stained glass window: Application on two 13th century windows

The chemical composition of 11 glass panes originating from two 13th century non-figurative windows were analyzed by means of Scanning Electron Microscopy–Energy Dispersive X-ray system (SEM-EDX). The windows were discovered in the back-wall of the triforium during the restoration of the choir of the cathedral St. Michael and St. Gudule in Brussels (Belgium). In order to determine if these windows were fabricated with glass of different origin or not, the compositional difference between the panes were compared with the variation in composition as a result of the following causes: (1) compositional fluctuation between panes cut from the same sheet of glass, (2) compositional fluctuation caused when panes are cut from different sheets that were made with the same batch, (3) compositional fluctuation caused when the glass is made from different batches at the same production center, and (4) compositional fluctuation as a result of glass produced at different fabrication centers.     

Design of a fast real-time LPV model predictive control system for semi-active suspension control of a full vehicle

This article is concerned with the control of a Semi-Active suspension system of a 7DOF Full Vehicle model, equipped with four Electro Rheological (ER) dampers, taking into account their incipient dissipativity constraints. Herein, a real-time, fast, advanced control structure is presented within the Model Predictive Control framework for Linear Parameter Varying (LPV) systems. The control algorithm is developed to provide a suitable trade-off between comfort and handling performances of the vehicle in a very limited sampling period ($T_s=5\text{ms}$), in view of a possible realtime implementation on a real vehicle. The control structure is tested and compared to other standard fast control approaches. Full nonlinear realistic simulation results illustrate the overall good operation and behaviour of the proposed control approach.     

Learning to rank with (a lot of) word features

In this article we present Supervised Semantic Indexing which defines a class of nonlinear (quadratic) models that are discriminatively trained to directly map from the word content in a query-document or document-document pair to a ranking score. Like Latent Semantic Indexing (LSI), our models take account of correlations between words (synonymy, polysemy). However, unlike LSI our models are trained from a supervised signal directly on the ranking task of interest, which we argue is the reason for our superior results. As the query and target texts are modeled separately, our approach is easily generalized to different retrieval tasks, such as cross-language retrieval or online advertising placement. Dealing with models on all pairs of words features is computationally challenging. We propose several improvements to our basic model for addressing this issue, including low rank (but diagonal preserving) representations, correlated feature hashing and sparsification. We provide an empirical study of all these methods on retrieval tasks based on Wikipedia documents as well as an Internet advertisement task. We obtain state-of-the-art performance while providing realistically scalable methods.     

Complementing subjective with objective data in analysing expertise: A machine-learning approach applied to badminton

ABSTRACT

This study aimed to assess which combination of subjective and empirical data might help to identify the expertise level. A group of 10 expert coaches classified 40 participants in 5 different expertise groups based on the video footage of the rallies. The expertise levels were determined using a typology based on a continuum of 5 conative stages: (1) structural, (2) functional, (3) technical, (4) contextual, and (5) expertise. The video allowed empirical measurement of the duration of the rallies, and tri-axial accelerometers measured the intensity of the player’s involvement. A principal component analysis showed that two dimensions explained 54.9% of the total variance in the data and that conative stage and empirical parameters during rallies (duration, intensity of the game) were correlated with axis 1, whereas duration and acceleration data between rallies were correlated with axis 2. A random forest algorithm showed that among the parameters considered, acceleration, duration of the rallies, and time between rallies could predict conative stages with a prediction accuracy above possibility.

This study suggests that performance analysis benefits from the confrontation of subjective and objective data in order to design training plans according to the expertise level of the participants.     

The Perceived Impact of Eight Systemic Factors on Scientific Capital Accumulation

Minerva - In the global academic capitalist race, academics, institutions and countries’ symbolic power results from the accumulation of scientific capital. This paper relies on the...     

Performance of Spanish universities in technology transfer: An empirical analysis

This paper investigates the performance of universities in the transfer of technology using a unique university-level dataset for Spain. The results suggest that universities with established policies and procedures for the management of technology transfer perform better. Universities with large and experienced technology transfer offices (TTOs) generate higher volumes of contract research, but the TTO characteristics appear to matter less for university performance in terms of licensing and creation of spin-offs. Furthermore, universities with a science park perform better than those without, which suggests that the agglomeration of knowledge close to universities has a positive effect on universities’ technology transfer performance.