Challenges for Participatory Conservation in Times of Global Change: Lessons from a Comparative Analysis and New Developments

Participatory approaches to conservation have been applied worldwide by governments and non-governmental organisations. However, results from a comparative analysis of the impacts of global change on management issues in 13 protected areas in Africa, Latin America, Asia, and Europe show that in many cases the involvement of local people has remained limited, and economic gains for local livelihoods have been limited or non-existent. Viewed from a ‘new institutionalist’ perspective and focusing on power relations and ideologies, the results of this study carried out within the framework of the Swiss National Centre of Competence in Research (NCCR) North-South show that in African cases local people do not feel part of the process and, therefore, become disengaged. In Asia, and even more so in Latin America, local indigenous peoples and their leaders support protected areas as a means to gain political rights over areas threatened by immigration. The European (Swiss) case is the only one where political rights and economic incentives present a context in which participation is of direct interest to local people. Meanwhile, recent debates on new global conservation developments in the context of climate change policy indicate a growing tendency to treat conservation as a commodity. We argue that this can have problematical effects on efforts to devolve power to the local level in the context of conservation.

Auditory verbal hallucinations: imaging, analysis, and intervention

In this article, we will link neuroimaging, data analysis, and intervention methods in an important psychiatric condition: auditory verbal hallucinations (AVH). The clinical and phenomenological background as well as neurophysiological findings will be covered and discussed with respect to noninvasive brain stimulation. Additionally, methods of noninvasive brain stimulation will be presented as ways to intervene with AVH. Finally, preliminary conclusions and possible future perspectives will be proposed.

Analysis and Prediction of Transient Opacity Spikes Using Dimensional Modeling

Dimensional modeling, GT-Power in particular, has been used for two related purposes-to quantify and understand the inaccuracies of transient engine flow estimates that cause transient smoke spikes and to improve empirical models of opacity or particulate matter used for engine calibration. It has been proposed by dimensional modeling that exhaust gas recirculation flow rate was significantly underestimated and volumetric efficiency was overestimated by the electronic control module during the turbocharger lag period of an electronically controlled heavy duty diesel engine. Factoring in cylinder-to-cylinder variation, it has been shown that the electronic control module estimated fuel-Oxygen ratio was lower than actual by up to 35% during the turbocharger lag period but within 2% of actual elsewhere, thus hindering fuel-Oxygen ratio limit-based smoke control. The dimensional modeling of transient flow was enabled with a new method of simulating transient data in which the manifold pressures and exhaust gas recirculation system flow resistance, characterized as a function of exhaust gas recirculation valve position at each measured transient data point, were replicated by quasi-static or transient simulation to predict engine flows. Dimensional modeling was also used to transform the engine operating parameter model input space to a more fundamental lower dimensional space so that a nearest neighbor approach could be used to predict smoke emissions. This new approach, intended for engine calibration and control modeling, was termed the "nonparametric reduced dimensionality" approach. It was used to predict federal test procedure cumulative particulate matter within 7% of measured value, based solely on steady-state training data. Very little correlation between the model inputs in the transformed space was observed as compared to the engine operating parameter space. This more uniform, smaller, shrunken model input space might explain how the nonparametric reduced dimensionality approach model could successfully predict federal test procedure emissions when roughly 40% of all transient points were classified as outliers as per the steady-state training data.