A wavelet analysis of multiday extreme ozone and its precursors in Mexico city during 2015–2016

Aguilar-Velázquez, Daniel; Reyes-Ramírez, Israel

doi:10.1016/j.atmosenv.2018.06.017

Cited by 13 publications

(6 citation statements)

References 39 publications

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“…These values correspond to cross-over points found for supercritical regimes at intermediate excitatory/inhibitory ratio: η = 0.5 ( Figure 5). Cross-over points were found using the method in [49]. White grids represent critical and near to critical exponents.…”

Section: Critical Neural Network Minimize Metabolic Costmentioning

confidence: 99%

Critical Neural Networks Minimize Metabolic Cost

Aguilar-Velázquez

2021

Physics

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Brain dynamics show a rich spatiotemporal behavior whose stability is neither ordered nor chaotic, indicating that neural networks operate at intermediate stability regimes including critical dynamics represented by a negative power-law distribution of avalanche sizes with exponent α=−1.5. However, it is unknown which stability regimen allows global and local information transmission with reduced metabolic costs, which are measured in terms of synaptic potentials and action potentials. In this work, using a hierarchical neuron model with rich-club organization, we measure the average number of action potentials required to activate n different neurons (avalanche size). Besides, we develop a mathematical formula to represent the metabolic synaptic potential cost. We develop simulations variating the synaptic amplitude, synaptic time course (ms), and hub excitatory/inhibitory ratio. We compare different dynamic regimes in terms of avalanche sizes vs. metabolic cost. We also implement the dynamic model in a Drosophila and Erdos–Renyi networks to computer dynamics and metabolic costs. The results show that the synaptic amplitude and time course play a key role in information propagation. They can drive the system from subcritical to supercritical regimes. The later result promotes the coexistence of critical regimes with a wide range of excitation/inhibition hub ratios. Moreover, subcritical or silent regimes minimize metabolic cost for local avalanche sizes, whereas critical and intermediate stability regimes show the best compromise between information propagation and reduced metabolic consumption, also minimizing metabolic cost for a wide range of avalanche sizes.

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Section: Critical Neural Network Minimize Metabolic Costmentioning

confidence: 99%

Critical Neural Networks Minimize Metabolic Cost

Aguilar-Velázquez

2021

Physics

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“…一氧化碳(CO)不仅易与血红蛋白结合, 严重危害人体健康, 还是地面臭氧的重要前驱体, 可以与氮氧化物(NO x )和挥发性有机化合物(VOCs)发生光化学反应产生臭氧 [1,2] . 化石燃料驱动的发动机所产生的尾气是CO主要的人为排放源 [3,4] , 包括道路机动车辆、非道路发动机和交通工具; 由于尾气处理装置中的催化剂需要一定的温度(通常在300℃以上)才能发挥最大效率, 因此在冷启动阶段, 燃料不完全燃烧往往会导致 CO的过度排放 [5] .…”

Section: 引言unclassified

Enhanced effect of H2 pretreatment on CuO<italic>x</italic>/CeO2 catalyst for the CO plasma catalytic oxidation

Zhang¹,

Hu²,

Liu³

et al. 2021

Sci. Sin.-Chim

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摘要一氧化碳(CO)对公众健康和环境都会造成严重危害, 低温工况下CO的催化氧化是目前的研究热点之一. 低温等离子体(non-thermal plasma, NTP)催化技术在该领域具备独特优势. 本研究采用等体积浸渍法制备了 CuO x /CeO 2 催化剂, 并对催化剂进行了H 2 预处理, 比较研究了不同湿度条件下NTP催化体系中各类铜铈催化剂催化氧化CO的性能, 并探讨了氢气预处理对催化剂的影响及作用机制. 结果表明, H 2 预处理可以增加CuO x /CeO 2 催化剂NTP氧化CO的活性, 同时抑制副产物O 3 和NO x 的排放; 其作用机制与催化剂表面Cu + 活性位点与氧空位的增加有关. 本研究揭示了氢气预处理对NTP协同CuO x /CeO 2 催化氧化CO的影响, 对开发低温等离子体催化系统下多功能催化剂具有一定的参考价值.

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“…These articles analyse the characteristics of air pollutants in 14 locations in Egypt, and makes a correlation between air pollutants and meteorology [7][8]. In order to further grasp the correlation model between meteorological factors and pollutants [9], this paper uses the observation database of daily meteorological elements and pollutants in the past 10 years [10]. Through detailed comparison, it is proved that their long-term cross-correlation behavior in rural areas is more obvious than that in urban areas.…”

Section: Introductionmentioning

confidence: 99%

Correlation Analysis between Meteorological Factors and Pollutants Based on Copula Theory

Wang¹,

Li²

2022

J. Phys.: Conf. Ser.

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In recent years, the continuous acceleration of industrial and agricultural production has aggravated the deterioration of the environment and seriously affected people’s health. Gas pollutant index has become an important indicator to measure air quality. Taking a region of China as the study subject, this paper firstly uses the monitoring data of air pollutants in 5 years and meteorological data (rainfall, temperature and wind speed) of meteorological observation stations in the same period. Then, the autocorrelation function is selected to analyze the autocorrelation of pollutants, and on this basis, the significant correlation level between meteorological factors and pollutants is analyzed by Copula theory. The results show that the ACF values of pollutants are all higher than 0.6, which means that the autocorrelation of pollutants within 2 hours is relatively significant. PM2.5 has a significant negative correlation with wind speed in four seasons, but a low correlation with rainfall. SO2 is negatively correlated with most of the three meteorological factors. In addition, the study also finds that there are significant differences in the impact of meteorological factors on pollutants at different time scales. This paper proposes a correlation analysis method between meteorological factors and pollutants, which provides corresponding support and guarantee for China’s economic and environmental protection policies.

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A wavelet analysis of multiday extreme ozone and its precursors in Mexico city during 2015–2016

Cited by 13 publications

References 39 publications

Critical Neural Networks Minimize Metabolic Cost

Critical Neural Networks Minimize Metabolic Cost

Enhanced effect of H<sub>2</sub> pretreatment on CuO<sub><italic>x</italic></sub>/CeO<sub>2</sub> catalyst for the CO plasma catalytic oxidation

Correlation Analysis between Meteorological Factors and Pollutants Based on Copula Theory

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A wavelet analysis of multiday extreme ozone and its precursors in Mexico city during 2015–2016

Cited by 13 publications

References 39 publications

Critical Neural Networks Minimize Metabolic Cost

Critical Neural Networks Minimize Metabolic Cost

Enhanced effect of H&lt;sub&gt;2&lt;/sub&gt; pretreatment on CuO&lt;sub&gt;&lt;italic&gt;x&lt;/italic&gt;&lt;/sub&gt;/CeO&lt;sub&gt;2&lt;/sub&gt; catalyst for the CO plasma catalytic oxidation

Correlation Analysis between Meteorological Factors and Pollutants Based on Copula Theory

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Enhanced effect of H<sub>2</sub> pretreatment on CuO<sub><italic>x</italic></sub>/CeO<sub>2</sub> catalyst for the CO plasma catalytic oxidation