Search

As global warming continues to worsen, the balance between carbon dioxide emissions and economic growth has received increasing attention and carbon-reduction comes to be an urgent task in many countries. In literature, various regression models have been developed to investigate the relationship between carbon dioxide emissions and economic growth, such as the inverted U-shaped EKC model, inverted N-shaped model, etc., which play critical roles in analyzing the relationships. Existing studies suggest that some countries follow similar models to describe the relationships, while others employ different ones. Regarding the interplay between carbon dioxide emissions and economic growth, there lacks a cluster analysis to systematically uncover the similarity of countries that employ models more similar to each other than to the countries in other clusters. In this paper, a novel clustering approach is proposed to identify clusters of 67 countries from the spatial, temporal, and descriptive dimensions. Unlike the traditional clustering technique, in which clusters are determined by geometric distances, the clusters in this research are obtained based on the differences between the fitting models of the countries in each cluster. The first step of the approach is to find clusters of countries sharing similar models in a given year based on the symbolic regression method, and the second step is to determine the countries that frequently cooccur in the same cluster by the Apriori algorithm. The results present two high-order clusters with different dynamic features during the period between 1971 and 2010. One high-order cluster mainly consists of countries in higher level of income while the other contains lower income level countries, and the carbon intensities of the two high-order clusters have distinct differences. The findings suggest that the countries within the same cluster could learn more lessons from each other, while countries belonging to different clusters should not be examined together indiscriminately. Several policy implications are provided, which may inform decision-making for policymakers when choosing proper learning objects and help researchers with designing optimal models for specific countries.

Balancing the contradiction between electricity demand and supply is the fundamental issue in demand side management (DSM). To address it, time-of-use (TOU) tariff has been studied extensively. In the TOU tariff, different prices are assigned to different periods of electricity consumption. The customers are implicitly encouraged to shift the consumption from peak to non-peak periods, resulting in the decrease of electricity supply cost and the increase of customer benefits. In this paper, the TOU tariff for a real-world thermal electricity company under dynamic electricity demand is studied. Specifically, a flexible TOU (FTOU) tariff model is proposed to optimize the electricity prices and their allocations to different time periods simultaneously, constrained by the dynamic demand of customers. A mixed artificial bee colony (mABC) approach is proposed to deal with the continuous prices and discrete allocations simultaneously, embedded with a transferred memory scheme (TMS) to achieve the flexible and smooth tariff design with dynamic demand. The experimental studies via the real-world scenarios are conducted to assess the performance of the proposal in comparison with various state-of-the-art approaches, including the standard and advanced variants. The effectiveness and applicability of TMS are also demonstrated by integrating into other advanced optimizers, such as L-SHADE and HCLPSO.

Given the fast-paced world economy, increasing carbon dioxide (CO2) emissions have caused serious concern regarding global warming. Many studies have examined the nexus between CO2 emissions and economic growth and inconsistent results are provided. Limited evidence suggests that different time periods under investigation could be a source of inconsistency, but there lacks of a thorough analysis. This paper systematically focuses on the evolutionary characteristics of the nexus to explore the influence of time periods. The panel data are reorganized by considering different time periods in various countries, and an intelligent data-driven approach, symbolic regression, is applied to discover models for each period without predefined structure and parameters. Three issues (Does the time period matter? What are the trends in different time periods? What are the applicable scopes of the models?) have been addressed in this paper. The results reveal the evolutionary nature of the nexus and it is verified that an intrinsic time period distinction does generate extrinsic inconsistency. The detailed dynamics of models indicate that the optimal models tend to be more complex as the time periods are extended. For countries with different locations and income levels, the applicable scopes of the four superior models (monotonically increasing, inverted U-shaped, inverted N-shaped, and M-shaped) vary in different time periods. The findings highlight that the effect of time periods cannot be ignored and the models should not be applied unconditionally when conducting empirical research. Finally, several policy implications are provided, which are critical for the balance of economic growth and CO2 emissions, particularly in some developing countries.

In recent years, ozone (O3) pollution in China has shown a worsening trend. Due to the vast territory of China, O3 pollution is a widespread and complex problem. It is vital to understand the current spatiotemporal distribution of O3 pollution in China. In this study, we collected hourly data on O3 concentrations in 338 cities from January 1, 2016, to February 28, 2019, to analyze O3 pollution in China from a spatiotemporal perspective. The spatial analysis showed that the O3 concentrations exceeded the limit in seven geographical regions of China to some extent, with more serious pollution in North, East, and Central China. The O3 concentrations in the eastern areas were usually higher than those in the western areas. The temporal analysis showed seasonal variations in O3 concentration, with the highest O3 concentration in the summer and the lowest in the winter. The weekend effect, which occurs in other countries (such as the USA), was found only in some cities in China. We also found that the highest O3 concentration usually occurred in the afternoon and the lowest was in the early morning. The comprehensive analysis in this paper could improve our understanding of the severity of O3 pollution in China.

As one of the most important research branches of evolutionary computation (EC), learning classifier system (LCS) is dedicated to discover decision making classifiers (“IF-THEN” type rules) via evolution and learning. Recent advances in LCS have shown distinguished generalization property over traditional approaches. In this paper, a novel LCS named niching genetic network programming with rule accumulation (nGNP-RA) is proposed. The unique features of the proposal arise from the following three points: First, it utilizes an advanced graph-based EC named GNP as the rule generator, resulting higher knowledge representation ability than traditional genetic algorithm (GA)-based LCSs; Second, a novel niching mechanism is developed in GNP to encourage the discovery of high-quality diverse rules; Third, a novel reinforcement learning (RL)-based mechanism is embedded to assign accurate credits to the discovered rules. To verify the effectiveness and robustness of nGNP-RA over traditional systems, two decision making testbeds are applied, including the benchmark tileworld problem and the real mobile robot control application.

Genetic network programming (GNP) is a relatively new type of graph-based evolutionary algorithm, which designs a directed graph structure for its individual representation. A number of studies have demonstrated its expressive ability to model complicated problems/systems and explored it from the perspectives of methodologies and applications. However, the unique features of its directed graph are relatively unexplored, which cause unnecessary dilemma for the further usage and promotion. This paper is dedicated to uncover this issue systematically and theoretically. It is proved that the traditional GNP with uniform genetic operators does not consider the ``transition by necessity”feature of the directed graph, which brings the unnecessary difficulty of evolution to cause invalid/negative evolution problems. Consequently, simplified genetic operators are developed to address these problems. Experimental results on two benchmark testbeds of the agent control problems are carried out to demonstrate its superiority over the traditional GNP and the state-of-the-art algorithms in terms of fitness results, search speed, and computation time.

China has been suffering from severe PM2.5 pollution in recent years. Significant effort has been devoted to analyzing the chemical characteristics and emission sources of PM2.5 pollution. However, because air pollution is a complex problem with a broad scope, macro-level analysis is also required to systematically address this issue. In this paper, we analyze the spatial-temporal features of PM2.5 pollution and discover sequential patterns in cities located in three important zones in China, namely, the Bohai Sea, Yangtze River Delta, and Pearl River Delta regions. The sequential patterns reveal hidden associative relationships that could provide evidence to support united policy-making in different areas. Our results also reveal significant heterogeneities among the underlying relationships, while certain homogeneities do exist in some seasons in the three regions. Moreover, the relations to the monsoon systems and Huai River policy are further discussed to draw policy implications.

Escherichia coli (E. coli) commonly reside in human intestine and most E. coli strains are harmless, but some serotypes cause serious food poisoning. This study identified and molecularly characterized blaSHV genes from 490 E. coli strains with multi-drug resistance in a hospital population. PCR and molecular cloning and southern blot were performed to assess functions and localizations of this resistant E. coli gene and the pulsed-field gel electrophoresis (PFGE) was utilized to demonstrate the clonal relatedness of the positive E. coli strains. The data showed that 4 of these 490 E. coli strains (4/499, 0.8%) carried blaSHV genes that included EC D2485 (blaSHV-5), EC D2487 (blaSHV-5), EC D2684 (blaSHV-11) and EC D2616 (blaSHV-195, a novel blaSHV). Analysis of blaSHV open-reading frame showed that blaSHV-5 had a high hydrolysis activity to the broad-spectrum penicillin (ampicillin or piperacillin), ceftazidime, ceftriaxone, cefotaxime and aztreonam. blaSHV-195 and blaSHV-11 had similar resistant characteristics with high hydrolysis activities to ampicillin and piperacillin, but low activities to cephalosporins. Moreover, the two blaSHV-5 genes were located on a transferable plasmid (23kb), whereas the other two blaSHV variants (blaSHV-11 and blaSHV-195) seemed to be located in the chromosomal material. Both EC D2485 and EC D2487 clones isolated in 2010 had the same DNA finger printing profile and they might be the siblings of clonal dissemination. The data from the current study suggest that the novel blaSHV and clonal dissemination may be developed, although blaSHV genes were infrequently identified in this hospital population. The results of the work demonstrate the necessity for molecular surveillance in tracking blaSHV-producing strains in large teaching hospital settings and emphasize the need for epidemiological monitoring.

Since 2013, the severe haze has occurred in China and caused great harm; thus, public concern regarding PM2.5 pollution has been aroused. Evidence has proven that public concern over the haze influences the decision-making of the government; however, a comprehensive analysis has not been conducted of the correlation between the public concern over haze and PM2.5 concentration. Based on the daily data of Baidu Index and daily PM2.5 concentration, this paper investigates the correlation between haze and its public concern from two dimensions: keyword and temporal. The methodology of this paper consists of Spearman rank correlation, the time-lag correlation, and the regression analysis method. Various observation keywords are mined through a quantitative selection process to reflect different aspects of public concern over haze. The daily, monthly, seasonal, and annual characteristics of the public concern and PM2.5 pollution are examined to investigate the evolutionary trends over the past 5 years. The regression analysis is conducted to model the relationship between PM2.5 pollution and its public concern, and the regional differences and the trends of related respiratory diseases are further discussed. The results show that most of the observation keywords have a strong positive correlation (ρ > 0.6, P

Junrong Wang, Fenqin Liu, Peiran Ao, Xianneng Li, Haixiao Zheng, Di Wu, Nina Zhang, Junping She, Junhui Yuan, Xiuying Wu Department of Laboratory Medicine, Wenling Maternal and Child Health Care Hospital, Wenling, Zhejiang Province, People’s Republic of China Objective: To explore the clinical significance of 3-phosphoinositide-dependent protein kinase-1 (PDK1) expression in hepatocellular carcinoma (HCC) and its association with clinicopathologic features and prognosis in HCC patients.Materials and methods: A total of 128 HCC patients who received radical resection were enrolled from Wenling Maternal and Child Health Care Hospital between May 2005 and December 2008, and tumor and adjacent tissue samples were collected. Expression of PDK1 was detected by immunohistochemistry method. Correlation of PDK1 expression with clinicopathological features and prognosis was determined by Spearman’s correlation analysis. Impact of expression of PDK1 on overall survival and recurrence was determined by Kaplan–Meier analysis.Results: Immunohistochemistry results showed that PDK1 expression in HCC tissues was significantly higher than that in the corresponding adjacent cancer tissues. Univariate analysis showed that PDK1 messenger RNA expression can predict time to recurrence with diagnostic significance (P=0.001). Univariate analysis showed that alpha-fetoprotein level, tumor number, tumor encapsulation, microvascular invasion, and tumor–node–metastasis stage were also unfavorable prognostic variables for recurrence (P