Programa 2000: A modo de plebiscito

Sin resume

Invirtiendo en capital natural: un marco para integrar la sostenibilidad ambiental en las políticas de cooperación

Tomando en cuenta la magnitud de la crisis ambiental que afecta al planeta y los estrechos vínculos existentes entre la conservación de los ecosistemas y la lucha contra la pobreza, cabría preguntarse por qué los temas de protección del medio ambiente continúan teniendo un peso relativo tan bajo en las agendas y prioridades de las agencias de cooperación internacional. En este artículo, se analizan las razones de este desequilibrio y se propone un marco conceptual con base socio-ecológica para facilitar una verdadera integración de la sostenibilidad ambiental como prioridad estratégica en las políticas y herramientas de ayuda oficial al desarrollo. Varios paradigmas y principios fundamentales emanan de este nuevo marco conceptual, que considera a los ecosistemas funcionales como un capital natural que, adecuadamente gestionado, es capaz de producir un rico y variado flujo de servicios sobre los cuales es posible construir un proceso de desarrollo social, económica y ambientalmente sostenible, además de justo en términos de equidad intra e intergeneracional

The complexity of soil biological sustainability

Additions of organic amendments to soil not only compensate for decreased soil C, but also contribute to energy requirements for conserving biological activity levels. The soil microbial biomass displays some highly conserved, and possibly unique, characteristics that do not permit a classic interpretation of microbial metabolic parameter data. The resilience of soil microbial biomass and the role of soil organic matter in sustaining microbial biomass under practically zero C inputs were assessed in two long term incubation experiments using soils from the Broadbalk experiment at Rothamsted (UK). Soils with low organic C contents, showed the greatest decline in biomass C during the first 30 d of incubation. The ATP concentration of this rapidly declining microbial biomass did not change during the prolonged incubation period, confirming this peculiar character of the soil microbial biomass. Specific respiration rate did not depend upon substrate availability, being higher in soils that had received the lowest C inputs. Qualitative and quantitative changes observed in humic fractions suggest that humified soil organic matter is a much more dynamic soil fraction than is normally considered and provides a utilizable energy reserve for soil microorganisms. Carbon levels can be successfully restored in soils through practices such as incorporation of crop residues, re\u2010vegetation and application of manures, biosolids and composts. Some amendments, such as olive mill waste compost, promote incorporation of altered lignin structures, N\u2010containing compounds and carbohydrates into humic acids. The mineral\u2010bound fraction of humic C also increases, after their addition, and contributes to the accumulation of the most inert soil C pools

Integración de los principios de cuidados paliativos en cuidados intensivos

En la literatura científica se constata un reciente interés por integrar los principios de la medicina paliativa en el trabajo cotidiano de la unidad de cuidados intensivos (UCI). El artículo revisa este planteamiento con la idea de fondo de que su aplicación pueda aportar luz en la resolución de ciertos problemas éticos presentes. Los pacientes con procesos avanzados y en situación de final de vida ingresados en cuidados intensivos se encuentran con un nivel de sufrimiento y vulnerabilidad que sólo una atención comprensiva y holística puede dar un alivio adecuado. Sin embargo, la realidad clínica del cuidado de estos pacientes en UCI, por el momento, está lejana a ese ideal. La mejora de la atención clínica en este sentido, especialmente en el fallecimiento, es el punto de interés que nos ocupa. Avanzar en este aspecto es complejo pero se hace necesario un esfuerzo. La propuesta es recurrir a la medicina paliativa como modelo de referencia en los cuidados del final de vida y en la atención holística, e introducir sus principios de tratamiento en la UCI. El objetivo del artículo es exponer una estrategia práctica para llevarlo a cabo y que pueda ser útil en la mejora de la atención clínica y ética de los pacientesRecent scientific literature has shown a growing interest to integrate palliative medicine principles into the daily workflow in the intensive care unit (ICU). This article reviews this trend with the goal that its application might provide more understanding in the resolution of some current ethical issues. Patients with an advanced disease process and at the end of life who are admitted in the intensive care unit are in such a profound level of suffering and vulnerability that only an holistic and comprehensive approach can provide adequate relief to them. Nevertheless, the reality of the clinical care of these patients in the ICU is far beyond that ideal. Our primary end point is the improvement in the clinical care provided, especially when the patient is dying. It is indeed very complex to make progress in this field, but an effort has to be made. The project is to turn to palliative medicine as a role model for end of life care and as an holistic approach, and introduce palliative medicine principles in the ICU. The goal of this article is to reveal a practical approach to accomplish this, and make it functional in order to improve our patients’ clinical and ethical car

Metrics to guide a multi-objective evolutionary algorithm for ordinal classification

Ordinal classification or ordinal regression is a classification problem in which the labels have an ordered arrangement between them. Due to this order, alternative performance evaluation metrics are need to be used in order to consider the magnitude of errors. This paper presents a study of the use of a multi-objective optimization approach in the context of ordinal classification. We contribute a study of ordinal classification performance metrics, and propose a new performance metric, the maximum mean absolute error (MMAE). MMAE considers per-class distribution of patterns and the magnitude of the errors, both issues being crucial for ordinal regression problems. In addition, we empirically show that some of the performance metrics are competitive objectives, which justify the use of multi-objective optimization strategies. In our case, a multi-objective evolutionary algorithm optimizes an artificial neural network ordinal model with different pairs of metric combinations, and we conclude that the pair of the mean absolute error (MAE) and the proposed MMAE is the most favourable. A study of the relationship between the metrics of this proposal is performed, and the graphical representation in the two-dimensional space where the search of the evolutionary algorithm takes place is analysed. The results obtained show a good classification performance, opening new lines of research in the evaluation and model selection of ordinal classifiers

A study on multi-scale kernel optimisation via centered kernel-target alignment

Kernel mapping is one of the most widespread approaches to intrinsically deriving nonlinear classifiers. With the aim of better suiting a given dataset, different kernels have been proposed and different bounds and methodologies have been studied to optimise them. We focus on the optimisation of a multi-scale kernel, where a different width is chosen for each feature. This idea has been barely studied in the literature, although it has been shown to achieve better performance in the presence of heterogeneous attributes. The large number of parameters in multi-scale kernels makes it computationally unaffordable to optimise them by applying traditional cross-validation. Instead, an analytical measure known as centered kernel-target alignment (CKTA) can be used to align the kernel to the so-called ideal kernel matrix. This paper analyses and compares this and other alternatives, providing a review of the literature in kernel optimisation and some insights into the usefulness of multi-scale kernel optimisation via CKTA. When applied to the binary support vector machine paradigm (SVM), the results using 24 datasets show that CKTA with a multi-scale kernel leads to the construction of a well-defined feature space and simpler SVM models, provides an implicit filtering of non-informative features and achieves robust and comparable performance to other methods even when using random initialisations. Finally, we derive some considerations about when a multi-scale approach could be, in general, useful and propose a distance-based initialisation technique for the gradient-ascent method, which shows promising results