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The environmental problems with climate change have become the focus of social attention. Based on STIRPAT model, ridge regression analysis is used to analyze the impact of five factors, such as resident population, per capita GDP, energy intensity, energy structure and urbanization rate, on the carbon emissions of Anhui Province. Scenario-analysis is used to set up three scenarios to forecast the carbon emissions of Anhui Province from 2020 to 2035. The results show that when other economic conditions remain unchanged, the energy structure has the most significant impact on carbon emissions, followed by the number of permanent residents. Under the high carbon scenario, there is no peak. Under the medium carbon scenario, Anhui's carbon emissions will peak in 2027, and under the low carbon scenario, the peak time will be advanced to 2025. Finally, the policy recommendations are put forward from the energy structure and urbanization development. [ABSTRACT FROM AUTHOR]

With the accelerating process of urbanization, carbon emission reduction plays an indispensable role in the development of low-carbon economy. The paper puts forward the research topic of carbon emission in Anhui Province under low-carbon development. According to the relevant energy consumption data of Anhui Province from 2010 to 2019, the carbon emission was calculated. Based on the GM (1,1) model, the growth trend of carbon emission in the next ten years (2020-2029) is predicted, and the main influencing factors of carbon emission in Anhui Province were analyzed by using the grey correlation analysis method. The results show that the annual carbon emissions in Anhui Province will continue to grow, but the growth rate will slow down; Energy efficiency, urbanization rate, GDP of the secondary industry, GDP per capita and GDP of the primary industry had a decreasing effect on carbon emissions. Based on the research results, the paper proposes the following viewpoints to reverse the carbon emission dilemma of Anhui Province, namely, optimizing the energy consumption structure, improving the energy utilization efficiency, promoting the upgrading of industrial structure, creating a low-carbon industrial system of new urbanization pattern, and promoting the green sustainable development. [ABSTRACT FROM AUTHOR]

Abstract Neuroscience seeks to understand how neural circuits lead to behavior. However, the gap between circuits and behavior is too wide. To explore how the microscopic neural activities leads to macroscopic behavioral control strategy, we considered the behaviors that humans and animals exhibit following some basic control rules, which can be described by simple logic language. The pulse mode of biological neurons is viewed as an expression of logical propositions, and a circuit constructed by biological neurons is seen as an equivalent achievement of logical operations (McCulloch & Pitts, 1943). And we know that any one complex logical operation can be equivalent to a logical expression that only contains three basic operations. We assume the logic-like operation as the one kind of “canonical computations” in the brain. This paper first designed the functional circuits to achieve the basic logic-like operations (And-like, Or-like, Negative-like) by spiking neuron model based on the known neurophysiological properties, and then, using the functional networks constructed a possible neural circuit for decision logic of animal’s behavior. Finally, we simulated a decision-making process from both microcosmic neural activities and macro behavior. The contributions of this study is that we extend our understanding that treating the neurons, constituting neural circuits and its working principle from a logical perspective under the premise of strict compliance with the neural electrophysiological characteristics and anatomical facts. In addition, this study provides a general approach for constructing the neural circuits to implement the behavioral control logic. This study may be useful for us to understand how the microscopic activities of the nervous system lead to the macroscopic animal behavior. [ABSTRACT FROM AUTHOR]