Changes in vegetation cover due to increasing frequencies of extreme climate events and anthropogenic pressure are already underway; so, predicting the impacts of the near‐future climate will be ...essential for developing mitigation strategies. We modelled the responses of Brazilian biomes to a future scenario (2070) of steady increases in atmospheric CO levels, adding soil data to better represent the multidimensional space of the environmental suitability of each biome. We also assessed the effects of changes in environmental suitability on the Brazilian network of protected areas and projected those effects on 1 km resolution maps. The area predicted to be affected by future climate change in Brazil and the consequent loss of suitable habitat surface is 2.59 Mkm2 – larger than the combined areas of Central America and Mexico – leading the current vegetation to a progressive replacement. We project major changes in the vegetation of the Amazon basin, with the replacement of rainforest by dryer vegetation in the southern and eastern regions of that basin, and the opening of a dry corridor in Pará State. We also project an expansion of 41% of the current caatinga cover in the Brazilian semiarid region, with large losses of suitable habitat surface of the current deciduous forest. Approximately, 37% of the coverage of protected areas in Brazil will be affected – with greater damage to indigenous lands. The speed of current environmental change is now unprecedented for the post‐glacial era, and will almost certainly lead to increased rates of extinction and the collapse of transition ecosystems. We propose the urgent creation of protected areas in regions designed without significant impacts, but contiguous to those that will be more seriously affected by climate change. Those areas will act as refugia preserving biodiversity, ecosystem services, and the cultural heritages of traditional populations.
Resumo
As mudanças na cobertura vegetal devido ao aumento da frequência de eventos climáticos extremos e à pressão antropogênica já estão em andamento; portanto, prever os impactos do clima no futuro próximo será essencial para o desenvolvimento de estratégias de mitigação. Modelamos as respostas dos biomas brasileiros a um cenário futuro (2070) de aumentos constantes nos níveis de CO2 atmosférico, acrescentando dados do solo para representar melhorar o espaço multidimensional da adequação ambiental de cada bioma. Também avaliamos os efeitos das mudanças na adequação ambiental sobre a rede brasileira de áreas protegidas e projetamos esses efeitos em mapas com resolução de 1 km. No Brasil, a área prevista para ser afetada pelas futuras mudanças climáticas e a consequente perda de superfície de adequabilidade de habitat é de 2,59 Mkm2 – maior do que as áreas combinadas da América Central e do México – levando a uma substituição progressiva da vegetação atual. Estimamos grandes mudanças na vegetação da bacia amazônica, com a substituição da floresta tropical por uma vegetação mais seca nas regiões sul e leste da bacia e a abertura de um corredor seco no Estado do Pará. Também projetamos uma expansão de 41% da atual cobertura de caatinga na região semiárida brasileira, com grandes perdas de superfície de adequabilidade de habitat para a atual floresta decídua. Aproximadamente 37% da cobertura das áreas protegidas no Brasil será afetada, com maiores danos às terras indígenas. A velocidade da mudança ambiental atual não tem precedentes na era pós‐glacial e quase certamente levará ao aumento das taxas de extinção e ao colapso dos ecossistemas de transição. Propomos a urgente criação de áreas protegidas em regiões sem previsão de impactos significativos, mas contíguas àquelas que serão mais seriamente afetadas pelas mudanças climáticas. Essas áreas atuarão como refúgios, preservando a biodiversidade, os serviços ecossistêmicos e o patrimônio cultural das populações tradicionais.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Typical models of learning assume incremental estimation of continuously-varying decision variables like expected rewards. However, this class of models fails to capture more idiosyncratic, discrete ...heuristics and strategies that people and animals appear to exhibit. Despite recent advances in strategy discovery using tools like recurrent networks that generalize the classic models, the resulting strategies are often onerous to interpret, making connections to cognition difficult to establish. We use Bayesian program induction to discover strategies implemented by programs, letting the simplicity of strategies trade off against their effectiveness. Focusing on bandit tasks, we find strategies that are difficult or unexpected with classical incremental learning, like asymmetric learning from rewarded and unrewarded trials, adaptive horizon-dependent random exploration, and discrete state switching.
How are people able to plan so efficiently despite limited cognitive resources? We aimed to answer this question by extending an existing model of human task decomposition that can explain a wide ...range of simple planning problems by adding structure information to the task to facilitate planning in more complex tasks. The extended model was then applied to a more complex planning domain of spatial navigation. Our results suggest that our framework can correctly predict the navigation strategies of the majority of the participants in an online experiment.
This paper presents two universal active power filter (UAPF) configurations for reactive power and harmonic compensation without series isolation transformer. The proposed systems combine both series ...and shunt compensation, by using converters with one (configuration I) or two (configuration II) dc-links. They are suitable for applications in which size, weight and cost associated with transformers becomes a critical issue. The PWM techniques and complete control of the systems are developed. Simulation and experimental results are shown for validation purposes.
This article represents the proceedings of a symposium at the 2004 annual meeting of the Research Society on Alcoholism in Vancouver, Canada. The symposium was organized by Etienne Quertemont and ...chaired by Kathleen A. Grant. The presentations were (1) Behavioral stimulant effects of intracranial injections of ethanol and acetaldehyde in rats, by Mercè Correa, Maria N. Arizzi and John D. Salamone; (2) Behavioral characterization of acetaldehyde in mice, by Etienne Quertemont and Sophie Tambour; (3) Role of brain catalase and central formed acetaldehyde in ethanol's behavioral effects, by Carlos M.G. Aragon; (4) Contrasting the reinforcing actions of acetaldehyde and ethanol within the ventral tegmental area (VTA) of alcohol‐preferring (P) rats, by William J. McBride, Zachary A. Rodd, Avram Goldstein, Alejandro Zaffaroni and Ting‐Kai Li; and (5) Acetaldehyde increases dopaminergic transmission in the limbic system, by Milena Pisano and Marco Diana.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Human behavior is inherently hierarchical, resulting from the decomposition of a task into subtasks or an abstract action into concrete actions. However, behavior is typically measured as a sequence ...of actions, which makes it difficult to infer its hierarchical structure. In this paper, we explore how people form hierarchically-structured plans, using an experimental paradigm that makes hierarchical representations observable: participants create programs that produce sequences of actions in a language with explicit hierarchical structure. This task lets us test two well-established principles of human behavior: utility maximization (i.e. using fewer actions) and minimum description length (MDL; i.e. having a shorter program). We find that humans are sensitive to both metrics, but that both accounts fail to predict a qualitative feature of human-created programs, namely that people prefer programs with reuse over and above the predictions of MDL. We formalize this preference for reuse by extending the MDL account into a generative model over programs, modeling hierarchy choice as the induction of a grammar over actions. Our account can explain the preference for reuse and provides the best prediction of human behavior, going beyond simple accounts of compressibility to highlight a principle that guides hierarchical planning.
How are people able to plan so efficiently despite limited cognitive resources? We aimed to answer this question by extending an existing model of human task decomposition that can explain a wide ...range of simple planning problems by adding structure information to the task to facilitate planning in more complex tasks. The extended model was then applied to a more complex planning domain of spatial navigation. Our results suggest that our framework can correctly predict the navigation strategies of the majority of the participants in an online experiment.
Human behavior emerges from planning over elaborate decompositions of tasks into goals, subgoals, and low-level actions. How are these decompositions created and used? Here, we propose and evaluate a ...normative framework for task decomposition based on the simple idea that people decompose tasks to reduce the overall cost of planning while maintaining task performance. Analyzing 11,117 distinct graph-structured planning tasks, we find that our framework justifies several existing heuristics for task decomposition and makes predictions that can be distinguished from two alternative normative accounts. We report a behavioral study of task decomposition (\(N=806\)) that uses 30 randomly sampled graphs, a larger and more diverse set than that of any previous behavioral study on this topic. We find that human responses are more consistent with our framework for task decomposition than alternative normative accounts and are most consistent with a heuristic -- betweenness centrality -- that is justified by our approach. Taken together, our results provide new theoretical insight into the computational principles underlying the intelligent structuring of goal-directed behavior.
Lead is a nonphysiological metal that has been implicated in toxic processes that affect several organ systems in humans and other animals. Although the brain generally has stronger protective ...mechanisms against toxic substances than other organs have, exposure to lead results in several neurophysiological and behavioral symptoms. The administration of a single injection (i.p.) of lead acetate in mice is a model of acute Pb2 + toxicity. In the present study, this model was used to explore the magnitude of the effect of different doses, time intervals and mice strains on several biobehavioral parameters. We investigated the effects of acute lead acetate administration on body and brain weight, brain lead acetate accumulation and specially, spontaneous locomotion and brain catalase activity. Lead acetate was injected i.p. in outbred (Swiss or CD1) and inbred (BALB/c, C57BL/J6 or DBA/2) mice at doses of 0, 50, 100, 150 or 200 mg/kg. At different time intervals following this acute treatment, several biochemical, physiological and behavioral responses were recorded. Results indicated that acute lead acetate has deleterious dose-dependent effects on brain and body weight. The effect on body weight in the present study was transient, although lead acetate was detected in neural tissues for several days after administration. Spontaneous locomotor activity only was reduced up until 24 hours. The effect of lead on body weight was strain-dependent, with Swiss mice showing greater resistance compared to the other strains. Total brain catalase activity in lead-pretreated Swiss mice showed a significant induction. This enzymatic upregulation could provide a protective mechanism for oxidative stress in these mice.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
One of the most striking features of human cognition is the capacity to plan. Two aspects of human planning stand out: its efficiency and flexibility. Efficiency is especially impressive because ...plans must often be made in complex environments, and yet people successfully plan solutions to myriad everyday problems despite having limited cognitive resources. Standard accounts in psychology, economics, and artificial intelligence have suggested human planning succeeds because people have a complete representation of a task and then use heuristics to plan future actions in that representation. However, this approach generally assumes that task representations are fixed. Here, we propose that task representations can be controlled and that such control provides opportunities to quickly simplify problems and more easily reason about them. We propose a computational account of this simplification process and, in a series of pre-registered behavioral experiments, show that it is subject to online cognitive control and that people optimally balance the complexity of a task representation and its utility for planning and acting. These results demonstrate how strategically perceiving and conceiving problems facilitates the effective use of limited cognitive resources.
