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ALM Fluid Treatment Shifts Supportive Behavioral for you to Parasympathetic Dominance inside the Rat Model of Non-Compressible Hemorrhagic Distress.
The emission of CO2 from major sectors and key industries are the predominant sources of regional CO2 emissions. It is the prerequisite to promote sectoral carbon emissions reduction, to cla-rify their influencing factors and investigate their relationship with regional economic growth. It is also of great significance for the implementation of regional total carbon emissions control. Using the Logarithmic mean Divisia index method (LMDI) and the Tapio decoupling model, we analyzed the driving factors, and decoupling status with economic growth of 13 major carbon emissions industries in Fujian Province from 1997 to 2017. The results showed that the electricity and heat production and supply industry was the major source of CO2 emissions in Fujian Province, with an increase of 101.74 Mt (from 18.89 Mt to 120.63 Mt) during the period 1997 to 2017. The top three industries with the fastest annual growth rate in CO2 emissions were non-ferrous metal smelting and rolling processing industry (18.1%), textile industrs industries. The industrial structure effect had a smaller impact on the decoupling with economic growth, while the population scale effect had almost no impact.Non-point source pollution risk assessment and zonation research are of great significance for the eco-environmental protection and optimization of land use structure. We identified the "source" and "sink" landscape using the "source-sink" landscape pattern theory based on the two phases of land use data in the lower reaches of Zijiang River in 2010 and 2018. We comprehensively considered the non-point source pollution occurrence and migration factors, and used location-weighted landscape contrast index (LCI) and non-point source pollution load index (NPPRI) to analyze non-point source pollution risk spatio-temporal characteristics in the study area. Zonation on non-point source pollution in the lower reaches of Zijiang River was studied by identifying the key factors of non-point source pollution risk. The results showed that the overall risk of non-point source pollution was relatively low. The sub-basin with "sink" landscape was the main type, accounting for 61.2%. Non-point source pollution risk was low in the southwest and was high along the banks of Zhixi River, Taohua River and main stream of Zijiang River, as well as plain in the northeast of the lower Zijiang River. The risk of non-point source pollution from 2010 to 2018 showed an increasing trend. The changes in landscape pattern, especially the expansion of rural settlement, arable land and the shrinkage of forest land had positive and negative responses to the risk of non-point source pollution, respectively. LCI, slope, and distance were the key factors affecting the change of the risk index of non-point source pollution. The lower reaches of the Zijiang River could be divided into four control regions pollution treatment area near river, low slope pollution control area, ecological restoration-risk prevention and control area, and ecological priority protection area.We explored the application of different feature mining methods combined with genera-lized boosted regression models in digital soil mapping. Environmental covariates were selected by two feature selection methods i.e., recursive feature elimination and selection by filtering. Using the original environmental covariates and the selected optimal variable combination as independent varia-bles, soil pH prediction model of Anhui Province was established and mapped based on the genera-lized boosted regression model and random forest model. The results showed that both kinds of feature mining methods could effectively improve the accuracy of soil pH prediction by generalized boosted regression models and random forest model, and could reduce dimensionality. Compared with the random forest model, the prediction accuracy of the validation set of the generalized boosted regression model was slightly lower. In the training set, the accuracy of the generalized boosted regression models was much higher than that of the random forest model, with higher interpretation and better overall effect. KPT-8602 supplier The main parameters of the random forest model, ntree and mtry, had limi-ted effect on the model. Different parameters and their combination could affect the prediction accuracy of the generalized boosted regression models, and thus should be tuned before modeling. The results of spatial mapping showed that soil pH in Anhui Province showed a pattern of "south acid and north alkali".Wetlands are one of the most productive ecosystems in the world, with functions of water purification, climate regulation, and carbon sinks. Due to the stresses caused by human social development and changes of natural conditions, wetlands have been seriously damaged. We examined the evolutionary law of landscape pattern of wetland along the Yellow River, and acquainted the current situation of wetland resources and dynamic change. Based on satellite images of year 2000, 2009, and 2018 from Landsat, we used landscape indices and geographic detectors to quantitatively analyze the characteristics and driving forces of wetland landscape pattern evolution of the city belt along the Yellow River in Ningxia from 2000 to 2018. The results showed that the wetland area of the city belt along the Yellow River in Ningxia enlarged first and then decreased from 2000 to 2018. The wetland area increased by 52.2 km2 in the early stage of the study with an increasing rate of 8.2%, and decreased by 26.8 km2 with a reduction rate of 3.9% in the later stage. The wetland was mainly transformed to construction land and unused land, with transfer out area being 166.7 and 158.4 km2 respectively. New wetland was mainly transformed from unused land, forest, and grassland, with an area of 543.1 km2. The fragmentation degree of wetland landscape in city belt was increasing, the balanced distribution of all kinds of wetlands was gradually strengthened, the landscape diversity was increasing, and the dominant landscape types were gradually weakening. Natural factors and socio-economic factors jointly affected the evolution of wetland landscape pattern in city belt. Among all socio-economic factors population was the most important one. Among natural factors, precipitation and temperature were important. Other driving factors were relatively weak, but could not be ignored.
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