Search

Abstract Forecasting all components in complex systems is an open and challenging task, possibly due to high dimensionality and undesirable predictors. We bridge this gap by proposing a data-driven and model-free framework, namely, feature-and-reconstructed manifold mapping (FRMM), which is a combination of feature embedding and delay embedding. For a high-dimensional dynamical system, FRMM finds its topologically equivalent manifolds with low dimensions from feature embedding and delay embedding and then sets the low-dimensional feature manifold as a generalized predictor to achieve predictions of all components. The substantial potential of FRMM is shown for both representative models and real-world data involving Indian monsoon, electroencephalogram (EEG) signals, foreign exchange market, and traffic speed in Los Angeles Country. FRMM overcomes the curse of dimensionality and finds a generalized predictor, and thus has potential for applications in many other real-world systems.

Introduction: Human basic fibroblast growth factor (hbFGF) is a highly valuable multifunctional protein that plays a crucial role in various biological processes. In this study, we aim to accomplish the scaling-up production of mature hbFGF (146aa) by implementing a high cell-density fermentation and purification process on a 500-L scale, thereby satisfying the escalating demands for both experimental research and clinical applications.Methods: The hbFGF DNA fragment was cloned into a mpET-3c vector containing a kanamycin resistance gene and then inserted into Escherichia coli BL21 (DE3) plysS strain. To optimize the yield of hbFGF protein, various fermentation parameters were systematically optimized using BOX-Behnken design and further validated in large-scale fermentation (500-L). Additionally, a three-step purification protocol involving CM-Sepharose, heparin affinity, and SP-Sepharose column chromatography was developed to separate and purify the hbFGF protein. Isoelectric focusing electrophoresis, MALDI-TOF/MS analysis, amino acid sequencing, CD spectroscopy, and Western blotting were performed to authenticate its identity. The biological efficacy of purified hbFGF was evaluated using an MTT assay as well as in a diabetic deep second-degree scald model.Results: The engineered strain was successfully constructed, exhibiting high expression of hbFGF and excellent stability. Under the optimized fermentation conditions, an impressive bacterial yield of 46.8 ± 0.3 g/L culture with an expression level of hbFGF reaching 28.2% ± 0.2% was achieved in 500-L scale fermentation. Subsequently, during pilot-scale purification, the final yield of purified hbFGF protein was 114.6 ± 5.9 mg/L culture with RP-HPLC, SEC-HPLC, and SDS-PAGE purity exceeding 98%. The properties of purified hbFGF including its molecular weight, isoelectric point (pI), amino sequence, and secondary structure were found to be consistent with theoretical values. Furthermore, the purified hbFGF exhibited potent mitogenic activity with a specific value of 1.05 ± 0.94 × 106 AU/mg and significantly enhanced wound healing in a deep second-degree scald wound diabetic rat model.Conclusion: This study successfully established a stable and efficient large-scale production process of hbFGF, providing a solid foundation for future industrial production.

This study aims to employ a causal network model based on transfer entropy for the early warning of systemic risk in commodity markets. We analyzed the dynamic causal relationships of prices for 25 commodities related to China (including futures and spot prices of energy, industrial metals, precious metals, and agricultural products), validating the effect of the causal network structure among commodity markets on systemic risk. Our research results identified commodities and categories playing significant roles, revealing that industry and precious metal markets possess stronger market information transmission capabilities, with price fluctuations impacting a broader range and with greater force on other commodity markets. Under the influence of different types of crisis events, such as economic crises and the Russia–Ukraine conflict, the causal network structure among commodity markets exhibited distinct characteristics. The results of the effect of external shocks to the causal network structure of commodity markets on the entropy of systemic risk suggest that network structure indicators can warn of systemic risk. This article can assist investors and policymakers in managing systemic risk to avoid unexpected losses.

Abstract Although China has been the main manufacturer and consumer of phthalate esters (PAEs), human health ambient water quality criteria (AWQCs) have not been proposed for these chemicals. In this study, the distribution and bioaccumulation of six PAEs (dimethyl phthalate (DMP), diethyl phthalate (DEP), di-n-butyl phthalate (DBP), butyl benzyl phthalate (BBP), bis (2-ethylhexyl) phthalate (DEHP), and di-n-octyl phthalate (DnOP)) were investigated in 11 edible fish species collected from Poyang Lake, China. The results showed that the total concentrations of the six PAEs in the fish ranged between 118.63 and 819.84 μg/kg wet weight (mean of 327.50 ± 190.44 μg/kg). DMP, DEP, DBP, and DEHP were detected in all samples, of which DEHP and DBP were two of the most predominant phthalates, accounting for more than 90% of the total PAEs. The DEHP concentrations in fish with different habitat preferences were different, demersal species were significantly higher than pelagic species (p

This paper provides the concept of import dependence between countries, which is a relative quantity. In order to reveal the evolutionary characteristics of the import dependence between countries in energy trade and its influencing factors, firstly, based on the network analysis method, this paper constructs a model of energy import dependence network (EIDN) among countries and regions along the Belt and Road (B&R countries). Crude oil and natural gas are taken as empirical objects, and the evolution characteristics of the two kinds of EIDNs are analysed. The result showed that most of the B&R countries had a small number of crude oil and natural gas trade partners. However, the import dependence in crude oil and natural gas trade between countries is relatively large, indicating that the risk of oil and gas security in B&R countries is high. Moreover, based on the QAP method, spatial distance, economic differences, the signing of free trade agreements, and the differences in energy consumption between countries have a significant impact on the import dependence in crude oil and natural gas trade among B&R countries. First published online 30 November 2021

Financial crisis, rooted in a lack of system resilience and robustness, is a particular type of critical transition that may cause grievous economic and social losses and should be warned against as early as possible. Regarding the financial system as a time-varying network, researchers have identified early warning signals from the changing dynamics of network motifs. In addition, network motifs have many different morphologies that unveil high-order correlation patterns of a financial system, whose synchronous change represents the dramatic shift in the financial system’s functionality and may indicate a financial crisis; however, it is less studied. This paper proposes motif transition intensity as a novel method that quantifies the synchronous change of network motifs in detail. Applying this method to stock networks, we developed three early warning indicators. Empirically, we conducted a horse race to predict ten global crises during 1991–2020. The results show evidence that the proposed indicators are more efficient than the VIX and the other 39 network-based indicators. In a detailed analysis, the proposed indicators send sensitive and comprehensible warning signals, especially for the U.S. subprime mortgage crisis and the European sovereign debt crisis. Furthermore, the proposed method provides a new perspective to detect critical signals and may be extended to predict other crisis events in natural and social systems.

Effectively reducing carbon emissions is a serious issue faced by many countries. Identifying key sectors in terms of carbon emissions in production activities is vital for policy-making regarding energy related carbon emission reductions. This paper built a carbon-flow-based production chain network and proposed five indicators, including direct outflows, direct inflows, self-flows, indirect outflows and indirect inflows of carbon emissions, to quantify the importance of sectors to carbon emissions. The results showed that there were no strong correlations among the different roles of sectors. This independence of sectoral roles indicated that it was necessary to measure the importance of sectors from different perspectives. The results also showed that a few sectors played important roles in carbon emission flows. It indicated that the growth of carbon emissions in China was direct and indirectly driven by a few key sectors. Finally, the results of clustering analysis showed the clusters exhibited significantly different features. Commonly, industries processing primary products played core roles in production chains. Most industries processing deep-processing products had strong inflows of carbon emissions. Such information provides a valuable reference to make carbon reduction policies according to the roles of sectors in the production chains.

To mitigate environmental pressure, many studies have calculated the CO2 emissions of a sector from the perspectives of production and consumption. However, determining a sector's CO2 reduction tasks based on these two indicators may underestimate the contribution of that sector to CO2 reductions in the entire economic system. The total CO2 emission reduction in the entire economic system cannot be regarded as a simple sum of CO2 emission reductions in various sectors. The situation that the reduction of CO2 emissions in one sector can promote the reduction of CO2 emissions in other sectors through a series of chain reactions in the industrial chain needs to be considered. Therefore, the purpose of this paper is to develop new indicators to assess the impact of sectoral CO2 reduction in the industrial chain, that is, when CO2 emissions are reduced in one sector, how much it promotes to the reductions in other sectors. We take China as a case and gain additional insights that are not available from existing indicators. The results reveal when the CO2 emissions of the Electricity and hot water production and supply sector change by 10%, its PCIC-CE and CCIC-CE are 4.13% and 3.83%, respectively. This indicate that the direct reduction of CO2 emissions in the sector has an additional impact on the CO2 emissions of the entire economic system. Hence, the new environmental indicators proposed in this paper can complement the existing indicators and guide sector-level environmental pressure mitigation strategies from a holistic perspective.

Ammonia has been of concern for its high toxicity to aquatic species and frequent detection in waters worldwide. This study calculated the national aquatic life criteria for ammonia in China. The temporal and spatial distributions were investigated and the multi-tier ecological risks were assessed for ammonia and un-ionized ammonia (NH3) during 2014–2018 based on a total of 18989 ammonia monitoring data from 110 monitoring sites in seven river basins. The sensitivity comparison of different species taxa to ammonia showed that Perciformes fish should be listed as a priority protected species in the derivation of ammonia criteria. The participation of introduced aquaculture species have no significant impact on the final criteria values (t-test, p > 0.05). The final criterion maximum concentration (CMC) and criterion continuous concentration (CCC) were 10.24 and 3.31 mg/L for ammonia (pH 7.0 and 20 °C). The interannual variation showed that decreasing trends were observed for ammonia and NH3 pollutions in the past five years. However, the increasing trends were observed for ammonia in Liao River basin, for NH3 in Yangtze River and Pearl River basins (2014–2018). The significant seasonal and geographical differences of ammonia and NH3 pollution were found. Moreover, the pollutions of ammonia and NH3 in some monitoring points of Huai River, Yellow River and Songhua River basins at the provincial borders were significant. The result of ecological risk assessment showed that the average exceedance probability for 5% affected species by NH3 in long-term exposure was 28.96% in the past five years.

Time series processing and analyzing is one of the major challenges in big data research, especially in inferring the dynamical mechanism of time series. In this paper, we constructed a complex network from time series via exploring the evolutionary relationship among the volatility patterns. We introduced the symbolic method and sliding window to describe the volatility patterns of time series that contains multidimensional information. Meanwhile, we explored the evolutionary mechanism of these volatility patterns based on the topological characteristics in network. In our research, we selected six stock indices around the world as sample data. Interestingly, for the six networks, they all showed a “petal-shaped”structure which consists of a core and loops. Moreover, through analyzing the topological characteristics of the six networks, we discovered distinguished results of their overall characteristics and loop length distributions. Furthermore, we uncovered the media patterns which trigger the structure of network changing among core and loops. In a word, this paper described the volatility patterns and explored the topological characteristics in networks constructed from time series data, which provides a novel perspective to understand the evolutionary dynamic mechanism.

Microscopic factors are the basis of macroscopic phenomena. We proposed a network analysis paradigm to study the macroscopic financial system from a microstructure perspective. We built the cointegration network model and the Granger causality network model based on econometrics and complex network theory and chose stock price time series of the real estate industry and its upstream and downstream industries as empirical sample data. Then, we analysed the cointegration network for understanding the steady long-term equilibrium relationships and analysed the Granger causality network for identifying the diffusion paths of the potential risks in the system. The results showed that the influence from a few key stocks can spread conveniently in the system. The cointegration network and Granger causality network are helpful to detect the diffusion path between the industries. We can also identify and intervene in the transmission medium to curb risk diffusion.

In the gathered multiplex systems, nodes inherit a part of their original system’s topological features, as in the world economic system, national policies and resource distribution bring industry advantages and resource advantages to the domestic industry. Although they represent one of the important original topological features of nodes, the inherited topological features of nodes have not received sufficient attention and have hardly been analyzed by existing network models. In our research, we defined the inherited topological features of nodes as ‘cluster-based topological features. To accurately calculate the cluster-based topological features of nodes in multiplex networks, we first provide a network model to summarize the multiplex networks into a calculable network of networks (NoN). Based on our network model, we propose a series of algorithms for calculating industries’ cluster-based topological features. Our calculating process contains 2 steps: ‘abstracting’ the NoN into one-layer calculable network; ‘inheriting’ subnetworks’ topological features into the inner nodes. Our network model and calculation algorithms are applied in a series of theoretical and empirical multiplex networks. The results not only confirm the realizability of our model but also produce several interesting findings, the most important of which is that some unremarkable nodes in multiplex network may have a very high contributory value from NoN perspective.

Moth search (MS) algorithm, originally proposed to solve continuous optimization problems, is a novel bio-inspired metaheuristic algorithm. At present, there seems to be little concern about using MS to solve discrete optimization problems. One of the most common and efficient ways to discretize MS is to use a transfer function, which is in charge of mapping a continuous search space to a discrete search space. In this paper, twelve transfer functions divided into three families, S-shaped (named S1, S2, S3, and S4), V-shaped (named V1, V2, V3, and V4), and other shapes (named O1, O2, O3, and O4), are combined with MS, and then twelve discrete versions MS algorithms are proposed for solving set-union knapsack problem (SUKP). Three groups of fifteen SUKP instances are employed to evaluate the importance of these transfer functions. The results show that O4 is the best transfer function when combined with MS to solve SUKP. Meanwhile, the importance of the transfer function in terms of improving the quality of solutions and convergence rate is demonstrated as well.

In this paper, a network model is constructed using real trading data from the EU carbon market. Metric indicators are then introduced to measure the network, and the economic meanings of the indicators are discussed. By integrating time windows with the network model, three types of network features are examined: growth features, structural features, and scale-free features. The growth pattern of the carbon trading network is then analyzed. As the market grow, the geodesic distances become shorter and the clustering coefficients become larger. The trends of these two indicators suggest that the market is evolving towards efficiency; however, their tiny changes are insufficient to have significant impact. By modeling the heterogeneity of the carbon trading network, we find that the trading relationships between firms obey a broken power law model, which consists of two power law models. The broken power law model can be approximately defined as a traditional power law but with a longer tail in distribution. Furthermore, we find that the model is valid for most of the time of both phases, the model only invalid when the market approaches a high growth rate.

What are the features of the correlation structure of price indices? To answer this question, 5 types of price indices, including 195 specific price indices from 2003 to 2011, were selected as sample data. To build a weighted network of price indices each price index is represented by a vertex, and a positive correlation between two price indices is represented by an edge. We studied the features of the weighted network structure by applying economic theory to the analysis of complex network parameters. We found that the frequency of the price indices follows a normal distribution by counting the weighted degrees of the nodes, and we identified the price indices which have an important impact on the network's structure. We found out small groups in the weighted network by the methods of k-core and k-plex. We discovered structure holes in the network by calculating the hierarchy of the nodes. Finally, we found that the price indices weighted network has a small-world effect by calculating the shortest path. These results provide a scientific basis for macroeconomic control policies.