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Stochastic resonance aided tactile sensingKondo, Shingo, Ohka, Masahiro 07 1900 (has links)
No description available.
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Scaling-up Impact : Knowledge-based Organizations Working Toward SustainabilityAdema, David, Blenkhorn, Sara, Houseman, Sarah January 2009 (has links)
Human society faces serious environmental and social threats as a result of systemic unsustainable actions and values. This is a time of cultural self-evaluation and profound change. This study examines how 13 social and environmental change-based organizations, through network partnerships and a robust organizational knowledge-base, are responding to these challenges. A systems perspective was used as a guide to promote a holistic understanding of the actors within the system, their perceptions of success, strategies, actions and tools used to guide them toward sustainability. The results indicate that organizations seeking to scale-up their impact toward sustainability might benefit from the following success factors: 1. Collaboration with diverse partners to contribute to more effective interventions in complex systems, 2. Integrating comprehensive definitions of sustainability with organizational vision to facilitate success, 3. The application of leverage points and transparent prioritization processes to asure congruence between organizational purpose and actions, 4. Practices of dialogue and deep listening build rewarding partnerships and, 5. Metrics that support partnerships and gauge progress toward sustainability. In conclusion network partnerships have the potential to generate widespread sustained change and can be supported by complex systems science and a Framework for Strategic Sustainable Development.
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The prevalence of complexity in flammable ecosystems and the application of complex systems theory to the simulation of fire spreadKatan, Jeffrey 08 1900 (has links)
Les forêts sont une ressource naturelle importante sur le plan écologique, culturel et économique, et sont confrontées à des défis croissants en raison des changements climatiques. Ces défis sont difficiles à prédire en raison de la nature complexe des interactions entre le climat et la végétation, dont une le feu. Compte tenu de l’importance des écosystèmes forestiers, des dangers potentiels des feux de forêt et de la complexité de leurs interactions, il est primordial d'acquérir une compréhension de ces systèmes à travers le prisme de la science des systèmes complexes. La science des systèmes complexes et ses techniques de modélisation associées peuvent fournir des informations sur de tels systèmes que les techniques de modélisation traditionnelles ne peuvent pas. Là où les techniques statistiques et basées sur équations cherchent à contourner la dynamique non-linéaire, auto-organisée et émergente des systèmes complexes, les approches de modélisation telles que les automates cellulaires et les modèles à base d'agents (MBA) embrassent cette complexité en cherchant à reproduire les interactions clés de ces systèmes. Bien qu'il existe de nombreux modèles de comportement du feu qui tiennent compte de la complexité, les MBA offrent un terrain d'entente entre les modèles de simulation empiriques et physiques qui peut fournir de nouvelles informations sur le comportement et la simulation du feu. Cette étude vise à améliorer notre compréhension du feu dans le contexte de la science des systèmes complexes en développant un tel MBA de propagation du feu. Le modèle utilise des données de type de carburant, de terrain et de météo pour créer l'environnement des agents. Le modèle est évalué à l'aide d’une étude de cas d'un incendie naturel qui s'est produit en 2001 dans le sud-ouest de l'Alberta, au Canada. Les résultats de cette étude confirment la valeur de la prise en compte de la complexité lors de la simulation d'incendies de forêt et démontrent l'utilité de la modélisation à base d'agents pour une telle tâche. / Forests are an ecologically, culturally, and economically important natural resource that face growing challenges due to climate change. These challenges are difficult to predict due to the complex nature of the interactions between climate and vegetation. Furthermore, fire is intrinsically linked to both climate and vegetation and is, itself, complex. Given the importance of forest ecosystems, the potential dangers of forest fires, and the complexity of their interactions, it is paramount to gain an understanding of these systems through the lens of complex systems science. Complex systems science and its attendant modeling techniques can provide insights on such systems that traditional modelling techniques cannot. Where statistical and equation-based techniques seek to work around the non-linear, self-organized, and emergent dynamics of complex systems, modelling approaches such as Cellular Automata and Agent-Based Models (ABM) embrace this complexity by seeking to reproduce the key interactions of these systems. While there exist numerous models of fire behaviour that account for complexity, ABM offers a middle ground between empirical and physical simulation models that may provide new insights into fire behaviour and simulation. This study seeks to add to our understanding of fire within the context of complex systems science by developing such an ABM of fire spread. The model uses fuel-type, terrain, and weather data to create the agent environment. The model is evaluated with a case study of a natural fire that occurred in 2001 in southwestern Alberta, Canada. Results of this study support the value of considering complexity when simulating forest fires and demonstrate the utility of ABM for such a task.
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