Ecoturismo en comunidades rurales: análisis de los impactos negativos para la población local. Un estudio de caso

En los últimos años han aparecido nuevas formas de turismo más sostenibles, donde existen elementos relacionados con el descanso, el disfrute y la protección del medio ambiente o el conocimiento de la cultura local, a través de políticas que favorecen la sostenibilidad del destino. En este sentido, el ecoturismo se configura como una tipología turística que se desarrolla en contacto con la naturaleza. Este turismo ayuda a mejorar el desarrollo socioeconómico de las comunidades locales, a la vez, que fomenta la conservación de los recursos naturales y el respeto hacia el medio ambiente. Aunque, esta tipología de turismo también genera importantes impactos negativos. El objetivo principal de esta investigación es analizar los impactos socioeconómicos, culturales y medioambientales percibidos por el ecoturismo por parte de los residentes de comunidades rurales de República Dominicana. La técnica de recolección de datos utilizada ha consistido en un cuestionario. Entre las principales conclusiones, cabe destacar que actualmente no se perciben impactos negativos, pero si hay una serie de elementos que hay que ir considerando, con la finalidad de que no se conviertan en un impacto a corto plazo para la comunidad rural.

Local and semilocal density functional computations for crystals of 1-alkyl-3-methyl-imidazolium salts

The accuracy and reliability of popular density functional approximations for the compounds giving origin to room temperature ionic liquids have been assessed by computing the T=0 K crystal structure of several 1-alkyl-3-methyl-imidazolium salts. Two prototypical exchange-correlation approximations have been considered, i.e., the local density approximation (LDA) and one gradient corrected scheme [PBE-GGA, Phys. Rev. Lett. 77, 3865 (1996)]. Comparison with low-temperature x-ray diffraction data shows that the equilibrium volume predicted by either approximations is affected by large errors, nearly equal in magnitude (~10%), and of opposite sign. In both cases the error can be traced to a poor description of the intermolecular interactions, while the intramolecular structure is fairly well reproduced by LDA and PBE-GGA. The PBE-GGA optimization of atomic positions within the experimental unit cell provides results in good agreement with the x-ray structure. The correct system volume can also be restored by supplementing PBE-GGA with empirical dispersion terms reproducing the r-6 attractive tail of the van der Waals interactions.

Nonlinear PCA with Local Approach for Diesel Engine Fault Detection and Diagnosis

This brief examines the application of nonlinear statistical process control to the detection and diagnosis of faults in automotive engines. In this statistical framework, the computed score variables may have a complicated nonparametric distri- bution function, which hampers statistical inference, notably for fault detection and diagnosis. This brief shows that introducing the statistical local approach into nonlinear statistical process control produces statistics that follow a normal distribution, thereby enabling a simple statistical inference for fault detection. Further, for fault diagnosis, this brief introduces a compensation scheme that approximates the fault condition signature. Experimental results from a Volkswagen 1.9-L turbo-charged diesel engine are included.

Genetic algorithms for local controller network construction

Local Controller Networks (LCNs) provide nonlinear control by interpolating between a set of locally valid, subcontrollers covering the operating range of the plant. Constructing such networks typically requires knowledge of valid local models. This paper describes a new genetic learning approach to the construction of LCNs directly from the dynamic equations of the plant, or from modelling data. The advantage is that a priori knowledge about valid local models is not needed. In addition to allowing simultaneous optimisation of both the controller and validation function parameters, the approach aids transparency by ensuring that each local controller acts independently of the rest at its operating point. It thus is valuable for simultaneous design of the LCNs and identification of the operating regimes of an unknown plant. Application results from a highly nonlinear pH neutralisation process and its associated neural network representation are utilised to illustrate these issues.

On velocity-based local model networks for nonlinear identification

This paper exposes the strengths and weaknesses of the recently proposed velocity-based local model (LM) network. The global dynamics of the velocity-based blended representation are directly related to the dynamics of the underlying local models, an important property in the design of local controller networks. Furthermore, the sub-models are continuous-time and linear providing continuity with established linear theory and methods. This is not true for the conventional LM framework, where the global dynamics are only weakly related to the affine sub-models. In this paper, a velocity-based multiple model network is identified for a highly nonlinear dynamical system. The results show excellent dynamical modelling performances, highlighting the value of the velocity-based approach for the design and analysis of LM based control. Three important practical issues are also addressed. These relate to the blending of the velocity-based local models, the use of normalised Gaussian basis functions and the requirement of an input derivative.