Presently, China is the largest CO 2 emitting country in the world, which accounts for 28% of the CO 2 emissions globally. China's CO 2 emission reduction has a direct impact on global trends. Therefore, accurate forecasting of CO 2 emissions is crucial to China's emission reduction policy formulating and global action on climate change. In order to forecast the CO 2 emissions in China accurately, considering population, the CO 2 emission forecasting model using GM(1,1) (Grey Model) and least squares support vector machine (LSSVM) optimized by the modified shuffled frog leaping algorithm (MSFLA) (MSFLA-LSSVM) is put forward in this paper. First of all, considering population, per capita GDP, urbanization rate, industrial structure, energy consumption structure, energy intensity, total coal consumption, carbon emission intensity, total imports and exports and other influencing factors of CO 2 emissions, the main driving factors are screened according to the sorting of grey correlation degrees to realize feature dimension reduction. Then, the GM(1,1) model is used to forecast the main influencing factors of CO 2 emissions. Finally, taking the forecasting value of the CO 2 emissions influencing factors as the model input, the MSFLA-LSSVM model is adopted to forecast the CO 2 emissions in China from 2018 to 2025.
Abstract:In recent years, the construction of China's power grid has experienced rapid development, and its scale has leaped into the first place in the world. Accurate and effective prediction of power grid investment can not only help pool funds and rationally arrange investment in power grid construction, but also reduce capital costs and economic risks, which plays a crucial role in promoting power grid investment planning and construction process. In order to forecast the power grid investment of China accurately, firstly on the basis of analyzing the influencing factors of power grid investment, the influencing factors system for China's power grid investment forecasting is constructed in this article. The method of grey relational analysis is used for screening the main influencing factors as the prediction model input. Then, a novel power grid investment prediction model based on DE-GWO-SVM (support vector machine optimized by differential evolution and grey wolf optimization) algorithm is proposed. Next, two cases are taken for empirical analysis to prove that the DE-GWO-SVM model has strong generalization capacity and has achieved a good prediction effect for power grid investment forecasting in China. Finally, the DE-GWO-SVM model is adopted to forecast power grid investment in China from 2018 to 2022.
Alzheimer’s disease (AD) is a neurodegenerative disorder that affects approximately 35 million people worldwide, and diet has been reported to influence the prevalence/incidence of AD. Colorectal cancer is among the most common cancers in Western populations, and the correlation between constipation and the occurrence of colorectal cancer has been identified in a number of studies, which show that a Westernized diet is a mutual risk factor. Constipation is a growing health problem, particularly in middle-aged and older adults. As the most common gastrointestinal disorder in adults, constipation affects 2–20% of the world population, and it is associated with several diseases, such as diabetes, Parkinson’s disease, and others. Comparing the epidemiological data on colorectal cancer and AD, we find that colorectal cancer and AD have similar epidemiologic feature, which is both disease correlate with high prevalence of constipation. Therefore, we hypothesized that constipation may influence Alzheimer’s disease in a similar way that it contributes to colorectal cancer. This review aimed to systemically elucidate the evidence that constipation contributes to Alzheimer’s disease progression.
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