Líneas posibles de avance del modelo de Sraffa

Several historical and ideological considerations have prevented the diffusion of the Sraffa´s masterpiece Production of commodities by means commodities. The Sraffa´s book has been studied by economists such as Garegnani, Abraham-Frois, Pasinetti, Steedman, Kurz, Roncaglia, etc. but these authors comment on some mathematical aspects of the mentioned book without developing his theoretical and economic analysis. This article makes some considerations about the Sraffa´s book trying to develop his same reasoning.Diversas consideraciones históricas e ideológicas han impedido la difusión de la obra fundamental de Sraffa Producción de mercancías por medio de mercancías. La obra de Sraffa ha sido estudiada fundamentalmente en sus aspectos matemáticos por economistas tales como Garegnani, Abraham-Frois, Pasinetti, Steedman, Kurz, Roncaglia, etc. pero sin desarrollar apenas sus aspectos teóricos y económicos. En este artículo se hacen algunas consideraciones sobre posibles desarrollos del libro de Sraffa a partir de su propio esquema de pensamiento

The concept of finite limit of a function at one point as explained by students of non-compulsory secondary education

We review various educational studies of the mathematical concept of limit of a function at a point that indicate how colloquial uses of the terms “to approach,” “to tend toward,” “to reach,” “to exceed” and “limit” influence students’ conceptions of these terms. We then present the results of an exploratory study of this question performed with Spanish students in non-compulsory secondary education and analyze the responses they provide to justify the truth or falsity of statements related to the different characteristics of the concept of finite limit of a function at a point when they use these terms. Finally, we organize their answers according to the kinds of arguments made. Using the response profiles detected, we discuss the influence of everyday usage on the students’ arguments

The Contribution of Global Alliances to Airlines’ Environmental Performance

Global alliances have traditionally been related to improvements in the economic and operational performances of companies, particularly in the airline industry. However, we still do not know the effect of the participation in this kind of multilateral agreement on the environmental performance of airlines. The main aim of this work is to analyze whether the alliance membership of airlines has an effect upon their environmental performance, and if so, whether or not the characteristics of the global alliance, as well as the business model of the airline, may influence this relation to a greater or lesser extent. The results of regression and Analysis of Variance (ANOVA) in a sample of 252 airlines (58 included in one of the three global alliances: Star Alliance, Oneworld, and SkyTeam) show a strong and inverse relationship between environmental performance and belonging to an alliance. The paper also shows empirical evidence of the influence of the business model of the airline on environmental performance. These results suggest important implications for managers facing challenges regarding sustainability

Fusion of Hyperspectral and LiDAR Data Using Sparse and Low-Rank Component Analysis

The availability of diverse data captured over the same region makes it possible to develop multisensor data fusion techniques to further improve the discrimination ability of classifiers. In this paper, a new sparse and low-rank technique is proposed for the fusion of hyperspectral and light detection and ranging (LiDAR)-derived features. The proposed fusion technique consists of two main steps. First, extinction profiles are used to extract spatial and elevation information from hyperspectral and LiDAR data, respectively. Then, the sparse and low-rank technique is utilized to estimate the low-rank fused features from the extracted ones that are eventually used to produce a final classification map. The proposed approach is evaluated over an urban data set captured over Houston, USA, and a rural one captured over Trento, Italy. Experimental results confirm that the proposed fusion technique outperforms the other techniques used in the experiments based on the classification accuracies obtained by random forest and support vector machine classifiers. Moreover, the proposed approach can effectively classify joint LiDAR and hyperspectral data in an ill-posed situation when only a limited number of training samples are available

Adaptación curricular para el área de matemática: dificultades cognitivas (síndrome de Down con retraso mental leve)

Este trabajo incluye una propuesta de adaptación curricular para un alumno/a de 3ºESO para el área de matemáticas.Como en toda adpatación curricular, se proponen recursos,medidas metodológicas, contenidos, objetivos, criterios de evaluación para permitir el acceso al currículo a dicho alumno

Hyperspectral Unmixing Based on Dual-Depth Sparse Probabilistic Latent Semantic Analysis

This paper presents a novel approach for spectral unmixing of remotely sensed hyperspectral data. It exploits probabilistic latent topics in order to take advantage of the semantics pervading the latent topic space when identifying spectral signatures and estimating fractional abundances from hyperspectral images. Despite the contrasted potential of topic models to uncover image semantics, they have been merely used in hyperspectral unmixing as a straightforward data decomposition process. This limits their actual capabilities to provide semantic representations of the spectral data. The proposed model, called dual-depth sparse probabilistic latent semantic analysis (DEpLSA), makes use of two different levels of topics to exploit the semantic patterns extracted from the initial spectral space in order to relieve the ill-posed nature of the unmixing problem. In other words, DEpLSA defines a first level of deep topics to capture the semantic representations of the spectra, and a second level of restricted topics to estimate endmembers and abundances over this semantic space. An experimental comparison in conducted using the two standard topic models and the seven state-of-the-art unmixing methods available in the literature. Our experiments, conducted using four different hyperspectral images, reveal that the proposed approach is able to provide competitive advantages over available unmixing approaches

Data assimilation using sequential Monte Carlo methods in hydrological applications

Hydrologic Data Assimilation concerns the application of state estimation methods to hydrologic models. The hydrologic models used in this dissertation correspond to a lumped conceptual model (rainfall-runoff model) and a distributed physically-based model (land surface-atmosphere transfer scheme). Hydrologic systems are highly nonlinear with complex dynamics. Therefore, nonlinear/non-Gaussian estimation techniques should be used in the inference of the states and/or parameters of the hydrologic models. In this sense, sequential Monte Carlo methods (a.k.a. particle filters) have captured the interest of the scientific community and nowadays are widely utilized in complex state estimation problems. This dissertation introduces two data assimilation methods which are based on Kalman and particle filtering theory: the ensemble Gaussian particle filter and the standard particle filter with parameter resampling. The first hydrologic variable to be assimilated is the discharge of water at the outlet of the Zwalm catchment which is located in East-Flanders. The second variable of interest is the volumetric soil moisture content in the study area located in the Grand Duchy of Luxembourg. These variables are assimilated separately in experiments with different setups and different study areas. Overall, the results indicate an improvement in the estimation of the model output flows when the proposed methods are applied to specific hydrologic data assimilation problems