Economies of Scale and Spatial Scope in the European Airline Industry

In this article we use four different indices to measure cost performance of the European Airline Industry. By using the number of routes as an indicator of Network Size, we are able to estimate indicators of Economies of Scale and Spatial Scope. By estimating total and variable cost functions we are also able to calculate an index of the excess capacity of the firms. For this purpose, we use data from the years 1984 to 1998, a period during which several deregulation measures were imposed on the European airline industry. Some of the implications of this deregulation process for the cost performance of the industry are presented and discussed. Our results suggest that in the year 1998, almost all the firms had Economics of Density in their existing networks, while several of the firms also had Economies of Scale and Economies of Spatial Scope. All of the firms had excess capacity of fixed inputs. These results support our hypothesis that fusion, alliance, and merger strategies followed by the principal European airlines after 1998 are not just explained by marketing strategies, but also by the cost structure of the industry.

On the propagation speed of evanescent modes

The group-velocity of evanescent waves (in undersized waveguides, for instance) was theoretically predicted, and has been experimentally verified, to be Superluminal (v_g > c). By contrast, it is known that the precursor speed in vacuum cannot be larger than c. In this paper, by computer simulations based on Maxwell equations only, we show the existence of both phenomena. In other words, we verify the actual possibility of Superluminal group velocities, without violating the so-called (naive) Einstein causality. (Subject classes: General physics, Classical physics, Optics, Special Relativity; PACS nos.: 73.40Gk, 03.80+z, 03.65Bz; Keywords: evanescent waves; tunnelling photons; Hartman effect; group velocity; Superluminal waves; precursors; transient waves; front velocity; Maxwell equations; electromagnetic waves; computer simulations; Special Relativity; Extended Relativity).Comment: plain LaTeX file (14 pages), plus 15 figures in .jp

An R Package for a General Class of Inverse Gaussian Distributions

The inverse Gaussian distribution is a positively skewed probability model that has received great attention in the last 20 years. Recently, a family that generalizes this model called inverse Gaussian type distributions has been developed. The new R package named ig has been designed to analyze data from inverse Gaussian type distributions. This package contains basic probabilistic functions, lifetime indicators and a random number generator from this model. Also, parameter estimates and diagnostics analysis can be obtained using likelihood methods by means of this package. In addition, goodness-of-fit methods are implemented in order to detect the suitability of the model to the data. The capabilities and features of the ig package are illustrated using simulated and real data sets. Furthermore, some new results related to the inverse Gaussian type distribution are also obtained. Moreover, a simulation study is conducted for evaluating the estimation method implemented in the ig package

Good practices in logistics for SMEs: a strategy for the global marketplace

Thanks to globalization, the environment in which companies operate today has changed. Whereas a few decades ago, the same government generated protectionist policies, today with the opening of markets, competition is not only national but also international, so companies, regardless of their size, must make changes in their business model, aiming at the internationalization of markets both for the acquisition of materials and goods and for the marketing of their products, being more agile in responding to their customers in terms of product quality, expected delivery time and cost. It is in this scenario where logistics plays a fundamental role as a tool for competitiveness and, therefore, the following work aims to highlight the impact of logistics on SMEs and the need to aim for continuous improvement of their logistics processes. By means of a qualitative/descriptive methodology, relevant aspects are reviewed, finding as a final result essential components that SMEs must incorporate to attend a world-class market

Masked Image Modeling for Representation Learning in Earth Observation

Deep learning applied to Earth observation (EO) yields impressive results. However, a significant challenge in EO is the rapidly increasing data volume while limited annotation resources available. Self-supervised representation learning (SSL) employs large amounts of unlabeled data. Recently, Masked Image Modelling (MIM) demonstrated scalability in model and data size. MIM masks a defined ratio of the input image for training a model to predict the masked patches. The learnt encoder is transferred to downstream tasks. In this work, we explore a new approach of MIM for EO combining two state-of-the-art SSL methodologies. One employs the Masked Autoencoder (MAE), which asymmetrically masks and reconstructs the raw input with the aid of an encoder operating on the visible patches followed by a smaller decoder reconstructing. The second methodology utilizes the Masked Feature Prediction (MFP), where image feature descriptors get reconstructed. We test our approach on the (SSL4E0-s12) dataset reconstructing Histogram Oriented Gradients (HOG). We evaluate the pre-trained model on a multi-class classification for Eurosat. Experimental results indicate stable performance with more than 90% accuracy down to 10% of labeled data. An ablation study on data normalization reveals that linear classification downstream task accuracy benefits from normalization by up to 6%. In contrast, fine tuning accuracies are robust to data normalization