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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.

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.

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.

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

Graphical abstractDisplay Omitted HighlightsArtificial bee colony with memory algorithm (ABCM) is proposed.ABCM introduces the memory ability of natural honeybees to ABC.ABCM is designed as simply as possible for easy implementation.Experiments on the benchmark functions show the superiority of ABCM.It bridges the gap between ABC and the neuroscience research of real honeybees. Artificial bee colony algorithm (ABC) is a new type of swarm intelligence methods which imitates the foraging behavior of honeybees. Due to its simple implementation with very small number of control parameters, many efforts have been done to explore ABC research in both algorithms and applications. In this paper, a new ABC variant named ABC with memory algorithm (ABCM) is described, which imitates a memory mechanism to the artificial bees to memorize their previous successful experiences of foraging behavior. The memory mechanism is applied to guide the further foraging of the artificial bees. Essentially, ABCM is inspired by the biological study of natural honeybees, rather than most of the other ABC variants that integrate existing algorithms into ABC framework. The superiority of ABCM is analyzed on a set of benchmark problems in comparison with ABC, quick ABC and several state-of-the-art algorithms.

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.

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.

Graphical abstractDisplay Omitted HighlightsThis paper proposes a novel continuous estimation of distribution algorithm (EDA).A recent EDA named PMBGNP is extended from discrete domain to continuous domain.Reinforcement Learning (RL) is applied to construct the probabilistic model.Experiments on real mobile robot control show the superiority of the proposed algorithm.It bridges the gap between EDA and RL. Recently, a novel probabilistic model-building evolutionary algorithm (so called estimation of distribution algorithm, or EDA), named probabilistic model building genetic network programming (PMBGNP), has been proposed. PMBGNP uses graph structures for its individual representation, which shows higher expression ability than the classical EDAs. Hence, it extends EDAs to solve a range of problems, such as data mining and agent control. This paper is dedicated to propose a continuous version of PMBGNP for continuous optimization in agent control problems. Different from the other continuous EDAs, the proposed algorithm evolves the continuous variables by reinforcement learning (RL). We compare the performance with several state-of-the-art algorithms on a real mobile robot control problem. The results show that the proposed algorithm outperforms the others with statistically significant differences.