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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.

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.

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

The hypothalamus plays key roles in regulating reproduction and energy balance. We determined the transcriptional profile of the hypothalamus (F0 and F1) to identify its relationship with the reproductive function and metabolic phenotypes of rats under BPA exposure and the paternal contribution. Male rats received 35 μg/kg bw/day BPA, 5 μg/kg bw/day ethinylestradiol (EE2) or vehicle via diet. At PND 170, the sexual behaviour of male F0 rats was assessed before mating. The metabolic and reproductive phenotypes and the transcriptional profiles of the testis (F0) and hypothalamus were tested in F0 and F1 rats. Male rats exposed to BPA exhibited no changes in fertility, metabolic phenotypes, sperm quality or serum sex hormones, but none of them ejaculated during the test period. Four differentially expressed genes (DEGs) in the F0 hypothalamus and 51 DEGs in the F0 testis were identified in the BPA group. Paternal BPA and EE2 exposure increased the body weight and food intake of male offspring, and 164 and 3433 DEGs were identified in the hypothalamus of BPA- and EE2-treated rats (F1, male), respectively. The highest-ranked function impaired by BPA and EE2 in the F1 hypothalamus was immune function. BPA and EE2 exposure induced alterations in the metabolic phenotype in F1-generation males, and this effect may be related to the altered inflammatory-related signalling pathways in the hypothalamus but neither treatment affected the F0 hypothalamus. Hundreds of genes in the F1 hypothalamus showed changes upon BPA and EE2 treatment. Concern regarding paternal environmental endocrine disruptor exposure should be raised.

Automobile exhaust pollution is one of the major contributors to the severe air pollution in China, and policies have been issued by the Chinese government to mitigate this problem. In this paper, we evaluate the impact of a recently implemented policy to upgrade the quality of gasoline on reducing the fine particulate matter (PM) concentration. Based on regression discontinuity (RD) design, we systematically analyze the policy effect on the PM10 and PM2.5 concentrations at three scales, namely, the macro-, meso-, and microscales. Macroscale analysis measures the impact on all cities, and mesoscale analysis measures it on all monitoring stations, while microscale analysis focuses on the rush hours at each station. Our results show that the policy has little effect on reducing particulate matter pollution at the macro- or mesoscale, while positive evidence does exist at the microscale when focusing on pollution reduction during rush hours, especially for areas near main roads or junctions with heavy traffic. Our research suggests that the policy impact is affected by the heterogeneous pollution sources, and the potential of mitigating air pollution by continually upgrading the quality of gasoline will be limited after five stages of upgrades. In addition, one single policy can hardly completely solve particulate matter pollution in China, and a systematic design consisting of a series of policies is needed to mitigate this problem.

Serious PM2.5 air pollution has persistently plagued and endangered most urban areas in China in recent years, and targeted policies are necessary to improve urban air quality ranging from macro policy (national level) to medium policy (city level) to micro policy (site specific). However, the macro-pattern study of air pollution between Chinese cities is inadequate, and not conducive to the formulation of macro-policy. To bridge this gap, we applied a sequential pattern mining algorithm to analyze the spatial-temporal patterns of PM2.5 pollution across Chinese cities during the period 2015 to 2018. The sequential patterns were collected from three levels of granularity on geographic areas and ten temporal scenarios covering time intervals from 10 to 100 h. Many underlying associative relationships were revealed between different cities by the mined patterns. The patterns were heterogeneous and presented five characteristics (i.e., clustering, symmetry, imbalance, decay, and stability). Each of the urban areas under investigation at different granularities was analyzed to identify the occurrence of associative relationships between it and other urban areas; moreover, we determined the degree of severity of such relationships. Our research results provide solid data that can be used as a reference by the various levels of Chinese governments for decision-making; overall, they can be used to improve the design of joint policies to prevent and control PM2.5 pollution in Chinese urban areas.

Artificial bee colony (ABC) algorithm has attracted growing interest for the continuous global optimization problems (CGOPs), where numerous algorithmic extensions have been developed. However, existing studies generally employ identical population size to perform the comparison among different ABC variants, regardless a fact that the generally suitable population size should be algorithm-dependent. Here we focus on the analysis of population size. This study is conducted in several well-known ABC variants under a set of benchmark CGOPs. We demonstrate that i) with the independently optimal population size, standard ABC can perform competitively comparing with its advanced variants, and ii) the most remunerative population size is related to the algorithmic exploitation/exploration ability. We anticipate that this study will provide useful insights to guide the appropriate usage of ABC, as well as its further enhancements.

The effects of economic growth on the environment have received increased attention as global warming and other environmental problems become more serious. Many empirical studies explain the nexus between carbon dioxide emissions and economic growth with such models as the environmental Kuznets curve (EKC) theory. However, the assumptions of these models have never received strict verification with a large available data set and therefore may not be appropriate to describe the relationship. In this study, the nexus is modeled for 67 countries from 1971 to 2010 using a novel symbolic regression method. From the experimental results, several conclusions as follows could be reached. Firstly, there is no universal model fitting every country, and symbolic regression could discover a set of reasonable models for a specific country or region. Secondly, four models, including the inverted N-shaped, M-shaped, inverted U-shaped and monotonically increasing, are frequently found without domain experts’ intervention in these countries, whereas the M-shaped model has received little attention in previous studies but exhibits promising performance. Thirdly, the relationship is diversified due to the difference of regions and economic development, where developed countries generally follow the inverted N-shaped and M-shaped models to explain the relationship, whereas developing countries are more likely to refer to the inverted N-shaped, inverted U-shaped and monotonically increasing models. Finally, several policy suggestions are presented.

Numerous models have been proposed to forecast the future trends of oil production and almost all of them are based on some predefined assumptions with various uncertainties. In this study, we propose a novel data-driven approach that uses symbolic regression to model oil production. We validate our approach on both synthetic and real data, and the results prove that symbolic regression could effectively identify the true models beneath the oil production data and also make reliable predictions. Symbolic regression indicates that world oil production will peak in 2021, which broadly agrees with other techniques used by researchers. Our results also show that the rate of decline after the peak is almost half the rate of increase before the peak, and it takes nearly 12 years to drop 4% from the peak. These predictions are more optimistic than those in several other reports, and the smoother decline will provide the world, especially the developing countries, with more time to orchestrate mitigation plans.

As the new form of online reviews, supplemental reviews have attracted the attention of many scholars. Considering current studies do not take full consideration on the content of reviews, the study goes better maturely and thoroughly on all information in supplemental reviews. In detail, sentiments of supplemental reviews in terms of different features corresponding to product, service and logistics are quantitatively analyzed. Except for the sentiments of reviews, other important factors including the volume of supplemental reviews, price, and ratings are introduced into the designed log-linear regression model for estimation. To explore the impact of supplemental reviews on product sales, an empirical study is conducted. The selected product with high involvement is mobile phone covering 32 products. The related sales as well as initial reviews and supplemental reviews are crawled from tmall.com for experiments. The period of data is from July 5, 2018 to July 24, 2018. By regression analysis, the results show that the sentiments of product features in both initial reviews and supplemental reviews and the sentiments of logistics features in supplemental reviews have significant positive impact on product sales. The volume of supplemental reviews has a negative impact on product sales. Compared with initial reviews, the impact of sentiments of product features on sales in supplemental reviews is greater.

Objective To analyze the serotype distribution and drug resistance of Streptococcus pneumoniae isolated from pediatric patients in a Chinese hospital. Methods From October 2011 to May 2014, we collected 284 isolates (including 217 noninvasive and 67 invasive strains). We tested the antimicrobial susceptibility of these specimens using the Epsilomer test and disk diffusion method, and the expression of macrolide resistant genes ermB and mefA by polymerase chain reaction. Results The most common serotype for 217 noninvasive strains was 19F (31.3%). The coverage rates of the 7-, 10-, and 13-valent pneumococcal conjugate vaccines (PCV7, PCV10, and PCV13) were 53.9%, 53.9%, and 86.2%, respectively. For 67 invasive strains, the most common serotype was 23F (22.4%). The coverage rates of PCV7, PCV10, and PCV13 for invasive strains were 77.6%, 82.1%, and 91.0%, respectively. The susceptibility of strains isolated from nonmeningitis specimens to penicillin (PEN) was 97.2%. The rate of multidrug resistance in 284 isolates was 98.7%. All of these isolates were resistant to erythromycin (ERY) and had the ermB gene; 38.6% of those isolates had the mefA gene. Conclusions Compared with PCV7 and PCV10, the coverage rate of PCV13 was relatively higher for the S. pneumoniae isolates from pediatric patients. This finding suggests that PCV13 probably plays the strongest role in prevention of pneumococcal diseases and control of multidrug resistance. Because pneumococci were sensitive to PEN, this drug is still the preferred choice for clinical treatment of pediatric pneumococcal diseases.

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 bla

With the popularity of on-line shopping increasing, on-line products diffusion becomes the growth of importance for marketing decision. Bass diffusion theory is a classical method of forecasting products sales. In this paper, we introduce symbolic regression method to describe the trend of on-line products diffusion and verify whether Bass model in on-line condition performs good as before. Almost all products exhibit seasonality in their sales pattern. Considering the particularity of on-line shopping, we define monthly, weekly and daily period as the division of on-line season. The models perform differently when frequency of data varies.

There have been extensive studies exploring the relationship between agglomeration economies and economic growth. However, far less attention is paid to the nonlinear relationship of urbanization economies–firm productivity nexus, especially in developing countries. This paper aims to explore how urbanization economies impact the total factor productivity (TFP) of research and development (R&D) performers using a large sample of Chinese manufacturing firms. The generalized propensity score matching methodology is employed to not only address the selection-bias and endogenous issues, but also quantitatively investigate how firm TFP responds to urbanization economies at each urbanization level. Based on the full data sample, our results show that there exists an S-shaped relationship between urbanization economies and TFP of Chinese R&D performers. Importantly, there is an optimal interval in which urbanization economies’ impact on firm TFP can be maximized. There is also a threshold value, only beyond which a further increase in urbanization economies improves firm TFP, and a saturation point, beyond which a further increase in urbanization economies does not increase and even decrease firm TFP. The relationship between urbanization economies and TFP of Chinese R&D performers presents a consistent pattern for firms in high- and medium–low-technology industries, as well as in Eastern and North East China. However, for firms in Central and Western China, there is a much smaller and even no range of optimal interval of urbanization economies, respectively. Our research helps provide insights into policy makers to maximize urbanization economies’ positive impact on firm TFP.