US market entry by Spanish pharmaceutical firms

This work explores the factors that spur firms’ propensity to enter in international markets. Among the whole population of Spanish firms active in the pharmaceutical sector (over the period 1995-2004), we identify those firms that have entered the US market by assessing whether they have filed at least a trademark in the US Patents and Trademarks Office. By means of a hazard model, we empirically estimate which firm’s characteristics affect the probability of entry in the US market in a given year. Results show that technological capabilities (breadth and depth of firms’ patent base), and the firm’s cost structure explain the entry in the US market with a branded product. Moreover, our evidence shows that entry strategies based on differentiation advantage (technological diversification) and strategies based on cost advantage (scale economies) are exclusive and do not mix well each otherForeign market entry, Internationalization strategies, Firm-Specific advantages, Competitive advantage, Innovation and R&D, Patents, Trademarks

Blow-up behaviour of a fractional Adams-Moser-Trudinger type inequality in odd dimension

Given a smoothly bounded domain $\Omega\Subset\mathbb{R}^n$ with $n\ge 1$ odd, we study the blow-up of bounded sequences $(u_k)\subset H^\frac{n}{2}_{00}(\Omega)$ of solutions to the non-local equation $$(-\Delta)^\frac n2 u_k=\lambda_k u_ke^{\frac n2 u_k^2}\quad \text{in }\Omega,$$ where $\lambda_k\to\lambda_\infty \in [0,\infty)$, and $H^{\frac n2}_{00}(\Omega)$ denotes the Lions-Magenes spaces of functions $u\in L^2(\mathbb{R}^n)$ which are supported in $\Omega$ and with $(-\Delta)^\frac{n}{4}u\in L^2(\mathbb{R}^n)$. Extending previous works of Druet, Robert-Struwe and the second author, we show that if the sequence $(u_k)$ is not bounded in $L^\infty(\Omega)$, a suitably rescaled subsequence $\eta_k$ converges to the function $\eta_0(x)=\log\left(\frac{2}{1+|x|^2}\right)$, which solves the prescribed non-local $Q$-curvature equation $$(-\Delta)^\frac n2 \eta =(n-1)!e^{n\eta}\quad \text{in }\mathbb{R}^n$$ recently studied by Da Lio-Martinazzi-Rivi\`ere when $n=1$, Jin-Maalaoui-Martinazzi-Xiong when $n=3$, and Hyder when $n\ge 5$ is odd. We infer that blow-up can occur only if $\Lambda:=\limsup_{k\to \infty}\|(-\Delta)^\frac n4 u_k\|_{L^2}^2\ge \Lambda_1:= (n-1)!|S^n|$

Is 0716+714 a superluminal blazar?

We present an analysis of new and old high frequency VLBI data collected during the last 10 years at 5--22 GHz. For the jet components in the mas-VLBI jet, two component identifications are possible. One of them with quasi-stationary components oscillating about their mean positions. Another identification scheme, which formally gives the better expansion fit, yields motion with $\sim 9$ $c$ for $H_0=65$ km s$^{-1}$ Mpc$^{-1}$ and $q_0=0.5$. This model would be in better agreement with the observed rapid IDV and the expected high Lorentz-factor, deduced from IDV.Comment: 2 pages, 3 figures, appears in: Proceedings of the 6th European VLBI Network Symposium held on June 25th-28th in Bonn, Germany. Edited by: E. Ros, R.W. Porcas, A.P. Lobanov, and J.A. Zensu

Analysing the behaviour of robot teams through relational sequential pattern mining

This report outlines the use of a relational representation in a Multi-Agent domain to model the behaviour of the whole system. A desired property in this systems is the ability of the team members to work together to achieve a common goal in a cooperative manner. The aim is to define a systematic method to verify the effective collaboration among the members of a team and comparing the different multi-agent behaviours. Using external observations of a Multi-Agent System to analyse, model, recognize agent behaviour could be very useful to direct team actions. In particular, this report focuses on the challenge of autonomous unsupervised sequential learning of the team's behaviour from observations. Our approach allows to learn a symbolic sequence (a relational representation) to translate raw multi-agent, multi-variate observations of a dynamic, complex environment, into a set of sequential behaviours that are characteristic of the team in question, represented by a set of sequences expressed in first-order logic atoms. We propose to use a relational learning algorithm to mine meaningful frequent patterns among the relational sequences to characterise team behaviours. We compared the performance of two teams in the RoboCup four-legged league environment, that have a very different approach to the game. One uses a Case Based Reasoning approach, the other uses a pure reactive behaviour.Comment: 25 page

Modelling and parameter estimation of diethyl phthalate partitioning behaviour on glass and aluminum surfaces

The knowledge of the partitioning behaviour of semi-volatile organic compounds (SVOCs), such as phthalates, between different materials and their surrounding air is of extreme importance for quantifying levels of human exposure to these compounds, which have been associated with adverse health effects. Phthalates’ partitioning behaviour also represents a key property for modelling and assessing polymer degradation mechanisms associated with plasticiser loss. However, the characterisation of phthalates partitioning behaviour has been reported only for a limited number of compounds, mainly involving di-2-ethylhexyl phthalate (DEHP), di-isononyl phthalate (DINP) and di-isodecyl phtahalate (DIDP), while the characterisation of diethyl phthalate (DEP) partitioning has been overlooked. As one of the first plasticisers employed in the production of semi-synthetic plastics produced industrially in the late 19th and early 20th century, DEP plays an important role for understanding stability issues associated with historically significant artefacts in museum collections and archives. Here we show that the partitioning behaviour of DEP between borosilicate glass and aluminum surfaces and their surrounding air can be described by an exponential function of temperature, presenting a model to describe this relationship for the first time. Model parameters are estimated using nonlinear regression from experimental measurements acquired using 109 samples which have been equilibrated at different temperatures between 20 and 80 °C in sealed environments. Measured partition coefficients have been predicted accurately by our proposed model. The knowledge of DEP equilibrium distribution between adsorptive surfaces and neighbouring environments will be relevant for developing improved mathematical descriptions of degradation mechanisms related to plasticiser loss

Effective Context-Sensitive Memory Dependence Prediction

©2024. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ This document is the Accepted version of a Published Work that appeared in final form in IEEE Computer Society. To access the final edited and published work see https://doi.org/10.1109/HPCA57654.2024.00045Memory dependence prediction is a fundamental technique to increase instruction- and memory-level parallelism in out-of-order processors, which are crucial for high performance. However, over the years, the performance gap of state-of-the-art memory dependence predictors with respect to an ideal predictor has grown due to the increase of the pipeline width, reaching up to 6% for modern architectures. State-of-the-art predictors brace context sensitivity, however, not-well-adjusted history lengths lead to loss of accuracy and high storage requirements. This work proposes PHAST, a novel context-sensitive memory dependence predictor that identifies for each load the minimum history length necessary to provide precise predictions. Our key observation is that for each load, it suffices to identify the youngest conflicting store and the path between them. This observation is proven empirically using an unlimited budget version of PHAST, which performs close to an ideal predictor with a 0.47% gap. Through cycle-accurate simulation of the SPEC CPU 2017 suite, we show that a 14.5KB implementation of PHAST falls 1.50% behind an ideal predictor. Compared to the top-performing state-of-the-art predictors, PHAST achieves average speedups of 5.05% (up to 39.7%), 1.29% (up to 22.0%), and 3.04% (up to 38.2%) with respect to an 18.5KB StoreSets, a 19KB NoSQ, and a 38.6 MDP-TAGE, respectively. This stems from a considerable misprediction reduction, ranging between 62.5% and 70.0%, on average

Human body electromagnetic radiation: the physiological characteristic for post stroke patients / Ros Shilawani S. Abdul Kadir

This thesis presents a novel analysis and classification of human body electromagnetic radiation (EMR) for post stroke patient and non-stroke participant. Presently, some researchers are investigating the human body EMR to characterize the physiological aspect of human being. Some methods include Kirlian photography, gas discharge visualization (GDV) and polycontrast interference photography. However, there are some drawbacks in the current techniques such as the images are not reliable and distorted. Moreover, the relationship between human body EMR and the health condition of post stroke patients has not been found. Thus, this research aims to scientifically establish a fundamental idea and new non-invasive technique to identify and to differentiate human body EMR of post stroke patients and non-stroke participants. The participants' body frequencies are captured at 16 points around the human body and 7 points of Chakra System using a frequency detector. The first part of the analysis concerned with the EMR of the whole body while the second part extended and zooming into specific body segments of chakra, left side, right side, upper body, middle body and lower body. Initially, the characteristics of frequency radiation are examined using statistical analysis to find the correlations between variables, to examine differences of frequency radiation characteristics between samples and to explore the relationship among variables. Next, the classification algorithm of k-nearest neighbor (KNN) and artificial neural network (ANN) are applied to discriminate between the samples and between their body segments. The classifiers are evaluated through analysis of the performance indicators of confusion matrix consisting of accuracy, precision, specificity and sensitivity. The findings of this research show that the EMR characteristics of the samples are different. A successful classification is produced in KNN and ANN classification for post stroke and non-stroke recognition, which is in line with the statistical analysis calculated. In general, the performance measure of training consisting of accuracy, sensitivity, specificity and precision for KNN classification achieved 100% while for ANN achieved 98% - 100%. In addition, for KNN performance measure of testing achieved 77% - 100% while for ANN achieved 75% - 92%. For body segment recognition, the classification results of both classifiers range between 69% - 100% for training accuracy and 64%-92% for testing accuracy. The outcomes of the classifier show that it is able to classify the human body EMR using KNN and ANN analysis. As a conclusion, the KNN classifier exhibit better results compared to ANN. This finding confirmed that the EMR of human body has different characteristics between samples of post stroke and non-stroke and between their body segments