La ecología socio ambiental del sur

El objetivo de este artículo es iniciar un debate en torno al tema de la sustentabilidad urbana, a través del análisis de experiencias del norte y del sur. Experiencias actuales, diversas y diferente s, pero ambas igualmente afectadas por las condiciones impuestas por la economía de mercado. Condiciones que también constituyen las causas estructurales de la actual crisis civilizatoria, donde la crisis urba na ocupa un lugar privilegiado. Un debate que señaliza la necesidad de sumar culturas , experiencias y propuestas incompletas de ambas latitudes, pero sustentadas en la base común de una nueva economía ecológica y solidaria, que nos permita caminar hacia la construcción de un nuevo paradigma de convivencia social . En esta segunda parte, abo rdamos las experiencias del sur, especialmente de Latinoamérica

Exploring the potential application of Matrimid® and ZIFs-based membranes for hydrogen recovery: a review

Hydrogen recovery is at the center of the energy transition guidelines promoted by governments, owing to its applicability as an energy resource, but calls for energetically nonintensive recovery methods. The employment of polymeric membranes in selective gas separations has arisen as a potential alternative, as its established commercial availability demonstrates. However, enhanced features need to be developed to achieve adequate mechanical properties and the membrane performance that allows the obtention of hydrogen with the required industrial purity. Matrimid®, as a polyimide, is an attractive material providing relatively good performance to selectively recover hydrogen. As a consequence, this review aims to study and summarize the main results, mechanisms involved and advances in the use of Matrimid® as a selective material for hydrogen separation to date, delving into membrane fabrication procedures that increase the effectiveness of hydrogen recovery, i.e., the addition of fillers (within which ZIFs have acquired extraordinary importance), chemical crosslinking or polymeric blending, among others.This research was funded by Agencia Estatal de Investigación (PID2019-104369RB-I00/ AEI/10.13039/501100011033). This work was also partially funded by European Regional Development Fund (“HYLANTIC”-EAPA_204/2016) in the framework of the Interreg Atlantic program

El plan director de la vega baja de Toledo, España: paisaje patrimonial, ecológico y urbano.

La Vega Baja de Toledo constituye un gran vacío urbano que, por avatares históricos, se ha mantenido al margen del crecimiento de la ciudad, rodeada por el casco histórico de Toledo, los barrios del ensanche norte y el río Tajo. Su localización privilegiada, junto a la riqueza patrimonial y ecológica del espacio, han sido las bases de la propuesta del Plan Director de la Vega Baja (PDVB). El objetivo del PDVB ha sido articular este vacío y abrirlo a la población, a la vez que proteger y regenerar sus valores ecológicos y culturales. Para ello ha sido necesario integrar distintos elementos: la fachada urbana de Toledo, el río Tajo con su vegetación de ribera y sus bienes patrimoniales que testimonian la sucesión de aprovechamientos históricos, y como cuerpo central del ámbito, el yacimiento arqueológico de lo que puede ser una gran ciudad visigoda. El planteamiento general del PDVB ha sido tratar el espacio como un continuo abierto, una sucesión de paisajes con su propio carácter, que alberguen distintos usos y funciones: Desde el jardín clásico que rodearía al circo romano, llegando hasta el río, con una vegetación, mobiliario y recorridos acordes con las ruinas existentes; pasando por el jardín patrimonial del yacimiento, para el que se proponen plantaciones e itinerarios efímeros que cambien a la par que avanzan las excavaciones; hasta el paisaje más puramente agrícola del vivero o paisaje de ribera, de gran valor ecológico en relación con la fauna aviar

HYDI-DSI revisited: Constrained non-parametric EAP imaging without q-space re-gridding

Producción CientíficaHybrid Diffusion Imaging (HYDI) was one of the first attempts to use multi-shell samplings of the q-space to infer diffusion properties beyond Diffusion Tensor Imaging (DTI) or High Angular Resolution Diffusion Imaging (HARDI). HYDI was intended as a flexible protocol embedding both DTI (for lower -values) and HARDI (for higher -values) processing, as well as Diffusion Spectrum Imaging (DSI) when the entire data set was exploited. In the latter case, the spherical sampling of the q-space is re-gridded by interpolation to a Cartesian lattice whose extent covers the range of acquired b-values, hence being acquisition-dependent. The Discrete Fourier Transform (DFT) is afterwards used to compute the corresponding Cartesian sampling of the Ensemble Average Propagator (EAP) in an entirely non-parametric way. From this lattice, diffusion markers such as the Return To Origin Probability (RTOP) or the Mean Squared Displacement (MSD) can be numerically estimated. We aim at re-formulating this scheme by means of a Fourier Transform encoding matrix that eliminates the need for q-space re-gridding at the same time it preserves the non-parametric nature of HYDI-DSI. The encoding matrix is adaptively designed at each voxel according to the underlying DTI approximation, so that an optimal sampling of the EAP can be pursued without being conditioned by the particular acquisition protocol. The estimation of the EAP is afterwards carried out as a regularized Quadratic Programming (QP) problem, which allows to impose positivity constraints that cannot be trivially embedded within the conventional HYDI-DSI. We demonstrate that the definition of the encoding matrix in the adaptive space allows to analytically (as opposed to numerically) compute several popular descriptors of diffusion with the unique source of error being the cropping of high frequency harmonics in the Fourier analysis of the attenuation signal. They include not only RTOP and MSD, but also Return to Axis/Plane Probabilities (RTAP/RTPP), which are defined in terms of specific spatial directions and are not available with the former HYDI-DSI. We report extensive experiments that suggest the benefits of our proposal in terms of accuracy, robustness and computational efficiency, especially when only standard, non-dedicated q-space samplings are available.Ministerio de Ciencia e Innovación (PID2021-124407NB-I00 and TED2021-130758B-I00)Ministry of Science and Higher Education (Poland) (PPN/BEK/ 2019/1/00421

Automatic, fast and robust characterization of noise distributions for diffusion MRI

Knowledge of the noise distribution in magnitude diffusion MRI images is the centerpiece to quantify uncertainties arising from the acquisition process. The use of parallel imaging methods, the number of receiver coils and imaging filters applied by the scanner, amongst other factors, dictate the resulting signal distribution. Accurate estimation beyond textbook Rician or noncentral chi distributions often requires information about the acquisition process (e.g. coils sensitivity maps or reconstruction coefficients), which is not usually available. We introduce a new method where a change of variable naturally gives rise to a particular form of the gamma distribution for background signals. The first moments and maximum likelihood estimators of this gamma distribution explicitly depend on the number of coils, making it possible to estimate all unknown parameters using only the magnitude data. A rejection step is used to make the method automatic and robust to artifacts. Experiments on synthetic datasets show that the proposed method can reliably estimate both the degrees of freedom and the standard deviation. The worst case errors range from below 2% (spatially uniform noise) to approximately 10% (spatially variable noise). Repeated acquisitions of in vivo datasets show that the estimated parameters are stable and have lower variances than compared methods.Comment: v2: added publisher DOI statement, fixed text typo in appendix A

Anisotropic Diffusion Filter with Memory based on Speckle Statistics for Ultrasound Images

Ultrasound imaging exhibits considerable difficulties for medical visual inspection and for the development of automatic analysis methods due to speckle, which negatively affects the perception of tissue boundaries and the performance of automatic segmentation methods. With the aim of alleviating the effect of speckle, many filtering techniques are usually considered as a preprocessing step prior to automatic analysis methods or visual inspection. Most of the state-of-the-art filters try to reduce the speckle effect without considering its relevance for the characterization of tissue nature. However, the speckle phenomenon is the inherent response of echo signals in tissues and can provide important features for clinical purposes. This loss of information is even magnified due to the iterative process of some speckle filters, e.g., diffusion filters, which tend to produce over-filtering because of the progressive loss of relevant information for diagnostic purposes during the diffusion process. In this work, we propose an anisotropic diffusion filter with a probabilistic-driven memory mechanism to overcome the over-filtering problem by following a tissue selective philosophy. Specifically, we formulate the memory mechanism as a delay differential equation for the diffusion tensor whose behavior depends on the statistics of the tissues, by accelerating the diffusion process in meaningless regions and including the memory effect in regions where relevant details should be preserved. Results both in synthetic and real US images support the inclusion of the probabilistic memory mechanism for maintaining clinical relevant structures, which are removed by the state-of-the-art filters

Spatially-variant noise filtering in magnetic resonance imaging : a consensus-based approach

In order to accelerate the acquisition process in multiple-coil Magnetic Resonance scanners, parallel techniques were developed. These techniques reduce the acquisition time via a sub-sampling of the k-space and a reconstruction process. From a signal and noise perspective, the use of a acceleration techniques modify the structure of the noise within the image. In the most common algorithms, like SENSE, the final magnitude image after the reconstruction is known to follow a Rician distribution for each pixel, just like single coil systems. However, the noise is spatially non-stationary, i.e. the variance of noise becomes x-dependent. This effect can also be found in magnitude images due to other processing inside the scanner. In this work we propose a method to adapt well-known noise filtering techniques initially designed to deal with stationary noise to the case of spatially variant Rician noise. The method copes with inaccurate estimates of variant noise patterns in the image, showing its robustness in realistic cases. The method employs a consensus strategy in conjunction with a set of aggregation functions and a penalty function. Multiple possible outputs are generated for each pixel assuming different unknown input parameters. The consensus approach merges them into a unique filtered image. As a filtering technique, we have selected the Linear Minimum Mean Square Error (LMMSE) estimator for Rician data, which has been used to test our methodology due to its simplicity and robustness. Results with synthetic and in vivo data confirm the good behavior of our approach