El rompecabezas monetario y financiero cubano

La crisis de la balanza de pagos en 2008-2009 y un grupo de errores de política económica han generado nuevas complicaciones monetarias y financieras en la economía cubana, que se añaden a los costes y distorsiones de la dualidad monetaria. Actualmente la economía cubana opera con dos monedas locales –peso cubano y peso convertible, ambas con problemas de convertibilidad, y con tipos de cambios múltiples y sobrevaluados– y desde 2009 sufre una crisis bancaria. Es un verdadero rompecabezas monetario y financiero. Para poder eliminar la doble moneda y superar los desequilibrios financieros, la política monetaria necesita devaluar las dos monedas domésticas. Los bancos cubanos se encuentran en una crisis de liquidez sistémica sin un prestamista de última instancia que les ayude a sobrepasarla. El país no puede contar con un préstamo de última instancia desde el FMI, Banco Mundial o el BID por no ser miembro de estas instituciones. El gobierno viene aplicando una fuerte política de ajuste que ha permitido reducir el déficit fiscal y disponer de un superávit en la balanza de pagos, con el cual se empezaron a pagar deudas y a descongelar gradualmente cuentas bancarias, aunque todavía el asunto está lejos de resolverse completamente

Ressenyes

Index de les obres ressenyades: Girolamo SANTOCONO, Rue des italien

Advanced tomographic image reconstruction algorithms for Diffuse Optical Imaging

Diffuse Optical Imaging is relatively new set of imaging modality that use infrared and near infrared light to characterize the optical properties of biological tissue. The technology used is less expensive than other imaging modalities such as X-ray mammography, it is portable and can be used to monitor brain activation and cancer diagnosis, besides to aid to other imaging modalities and therapy treatments in the characterization of diseased tissue, i. e. X-ray, Magnetic Resonance Imaging and Radio Frequency Ablation. Due the optical properties of biological tissue near-infrared light is highly scattered, as a consequence, a limited amount of light is propagated thus making the image reconstruction process very challenging. Typically, diffuse optical image reconstructions require from several minutes to hours to produce an accurate image from the interaction of the photons and the chormophores of the studied medium. To this day, this limitation is still under investigation and there are several approaches that are close to the real-time image reconstruction operation. Diffuse Optical Imaging includes a variety of techniques such as functional Near-Infrared Spectroscopy (fNIRS), Diffuse Optical Tomography (DOT), Fluorescence Diffuse Optical Tomography (FDOT) and Spatial Frequency Domain imaging (SFDI). These emerging image reconstruction modalities aim to become routine modalities for clinical applications. Each technique presents their own advantages and limitations, but they have been successfully used in clinical trials such as brain activation analysis and breast cancer diagnosis by mapping the response of the vascularity within the tissue through the use of models that relate the interaction between the tissue and the path followed by the photons. One way to perform the image reconstruction process is by separating it in two stages: the forward problem and the inverse problem; the former is used to describe light propagation inside a medium and the latter is related to the reconstruction of the spatio-temporal distribution of the photons through the tissue. Iterative methods are used to solve both problems but the intrinsic complexity of photon transport in biological tissue makes the problem time-consuming and computationally expensive. The aim of this research is to apply a fast-forward solver based on reduced order models to Fluorescence Diffuse Optical Tomography and Spatial Frequency Domain Imaging to contribute to these modalities in their application of clinical trials. Previous work showed the capabilities of the reduced order models for real-time reconstruction of the absorption parameters in the brain of mice. Results demonstrated insignificant loss of quantitative and qualitative accuracy and the reconstruction was performed in a fraction of the time normally required on this kind of studies. The forward models proposed in this work, offer the capability to run three-dimensional image reconstructions in CPU-based computational systems in a fraction of the time required by image reconstructions methods that use meshes generated using the Finite Element Method. In the case of SFMI, the proposed approach is fused with the approach of the virtual sensor for CCD cameras to reduce the computational burden and to generate a three-dimensional map of the distribution of tissue optical properties. In this work, the use case of FDOT focused on the thorax of a mouse model with tumors in the lungs as the medium under investigation. The mouse model was studied under two- and three- dimension conditions. The two-dimensional case is presented to explain the process of creating the Reduced-Order Models. In this case, there is not a significant improvement in the reconstruction considering NIRFAST as the reference. The proposed approach reduced the reconstruction time to a quarter of the time required by NIRFAST, but the last one performed it in a couple of seconds. In contrast, the three-dimensional case exploited the capabilities of the Reduced-Order Models by reducing the time of the reconstruction from a couple of hours to several seconds, thus allowing a closer real-time reconstruction of the fluorescent properties of the interrogated medium. In the case of Spatial Frequency Domain Imaging, the use case considered a three-dimensional section of a human head that is analysed using a CCD camera and a spatially modulated light source that illuminates the mentioned head section. Using the principle of the virtual sensor, different regions of the CCD camera are clustered and then Reduced Order Models are generated to perform the image reconstruction of the absorption distribution in a fraction of the time required by the algorithm implemented on NIRFAST. The ultimate goal of this research is to contribute to the field of Diffuse Optical Imaging and propose an alternative solution to be used in the reconstruction process to those models already used in three-dimensional reconstructions of Fluorescence Diffuse Optical Tomography and Spatial Frequency Domain Imaging, thus offering the possibility to continuously monitor tissue obtaining results in a matter of seconds

Optimal risk in marketing resource allocation

Marketing resource allocation is increasingly based on the optimization of expected returns on investment. If the investment is implemented in a large number of repetitive and relatively independent simple decisions, it is an acceptable method, but risk must be considered otherwise. The Markowitz classical mean-deviation approach to value marketing activities is of limited use when the probability distributions of the returns are asymmetric (a common case in marketing). In this paper we consider a unifying treatment for optimal marketing resource allocation and valuation of marketing investments in risky markets where returns can be asymmetric, using coherent risk measures recently developed in finance. We propose a set of first order conditions for the solution, and present a numerical algorithm for the computation of the optimal plan. We use this approach to design optimal advertisement investments in sales response managementResource allocation, Coherent risk measures, Optimization, Sales response models

Quantification of 3D network geometry in collagen hydrogels

Cancer is one of the principal causes of death in the world. Despite of the fact that there are ways of fighting the disease, full understanding about cancer cells behaviour is still distant. Extracellular matrix (ECM) is the three-dimensional environment where cells live, and its arrangement is known to modulate cell fate. Study of how ECMcancer cell interactions affect tumour progression is key for developing more effective drugs to heal cancer. As collagen is the most abundant ECM component, collagen hydrogels can be used as ECM models for research. This bachelor thesis is focused on proposing a processing pipeline for the extraction and characterization of fibre network from collagen hydrogel’s reflection microscopy images. This pipeline is composed by an image binarization procedure and a fibre network extraction algorithm from which the network parameters (i.e., fibre length, fibre persistence length, and crosslink density) are computed. The whole pipeline was tested in order to ensure its consistency, and eventually used for the characterization of real collagen hydrogel reflection microscopy images at different gel concentrations introduced in two types of in vitro platforms, culture wells and microfluidic devices. The study showed that fibre length and persistence length are similar for the different concentrations, while the network pore size decreases as the collagen concentration of the hydrogel increases. The future final step of the project would be to introduce cancer cells along with collagen hydrogels in the two platforms, and observe how collagen (ECM) disposition and characteristics direct cancer cell behaviour.Ingeniería Biomédic

Single-atom control of the optoelectronic response in sub-nanometric cavities

By means of ab-initio time dependent density functional theory calculations carried out on an prototypical hybrid plasmonic device (two metallic nanoparticles bridged by a one-atom junction), we demonstrate the strong interplay between photoinduced excitation of localized surface plasmons and electron transport through the single atom. Such an interplay is remarkably sensitive to the atomic orbitals of the junction. Therefore, we show the possibility of a twofold tuning (plasmonic response and photoinduced current across the juntion) just by changing a single atom in the device.Comment: 5 pages, 5 figure