Microstructural Characteristics of the Weighted and Directed International Crop Trade Networks.

With increasing global demand for food, international food trade is playing a critical role in balancing the food supply and demand across different regions. Here, using trade datasets of four crops that provide more than 50% of the calories consumed globally, we constructed four international crop trade networks (iCTNs). We observed the increasing globalization in the international crop trade and different trade patterns in different iCTNs. The distributions of node degrees deviate from power laws, and the distributions of link weights follow power laws. We also found that the in-degree is positively correlated with the out-degree, but negatively correlated with the clustering coefficient. This indicates that the numbers of trade partners affect the tendency of economies to form clusters. In addition, each iCTN exhibits a unique topology which is different from the whole food network studied by many researchers. Our analysis on the microstructural characteristics of different iCTNs provides highly valuable insights into distinctive features of specific crop trades and has potential implications for model construction and food security.

Highway Freight Transportation Diversity of Cities Based on Radiation Models.

Using a unique data set containing about 15.06 million truck transportation records in five months, we investigate the highway freight transportation diversity of 338 Chinese cities based on the truck transportation probability pij from one city to another. The transportation probabilities are calculated from the radiation model based on the geographic distance and its cost-based version based on the driving distance as the proxy of cost. For each model, we consider both the population and the gross domestic product (GDP), and find quantitatively very similar results. We find that the transportation probabilities have nice power-law tails with the tail exponents close to 0.5 for all the models. The two transportation probabilities in each model fall around the diagonal pij=pji but are often not the same. In addition, the corresponding transportation probabilities calculated from the raw radiation model and the cost-based radiation model also fluctuate around the diagonal pijgeo=pijcost. We calculate four sets of highway truck transportation diversity according to the four sets of transportation probabilities that are found to be close to each other for each city pair. It is found that the population, the gross domestic product, the in-flux, and the out-flux scale as power laws with respect to the transportation diversity in the raw and cost-based radiation models. It implies that a more developed city usually has higher diversity in highway truck transportation, which reflects the fact that a more developed city usually has a more diverse economic structure.

Identifying risk factors for chronic kidney disease stage 3 in adults with acquired solitary kidney from unilateral nephrectomy: a retrospective cohort study.

BACKGROUND: We aimed to examine the risk factors for chronic kidney disease (CKD) stage 3 among adults with ASK from unilateral nephrectomy. METHODS: We retrospectively collected data from adult patients with ASK between January, 2009 and January, 2019, identified from a tertiary hospital in China. The clinical data were compared between patients who developed CKD stage 3 and those who did not develop CKD stage 3 during follow-up. RESULTS: In total, 172 patients with ASK (110 men; median 58.0 years) were enrolled, with a median follow-up duration of 5.0 years. During follow-up, 91 (52.9%) and 24 (14.0%) patients developed CKD stage 3 and end-stage renal disease, respectively. Multiple regression analyses showed that age (odds ratio [OR] 1.076, 95% confidence interval [CI] 1.039-1.115, p < 0.001), diabetes (OR 4.401, 95% CI 1.693-11.44, p = 0.002), hyperuricemia (OR 2.733, 95% CI 1.104-6.764, p = 0.03), a history of cardiovascular disease (CVD) (OR 5.583, 95% CI 1.884-18.068, p = 0.002), and ASK due to renal tuberculosis (OR 8.816, 95% CI 2.92-26.62, p < 0.001) were independent risk factors for developing CKD stage 3 among patients with ASK. CONCLUSIONS: Regular follow-up of renal function is needed among adult patients with ASK. Optimal management of diabetes, hyperuricemia, and CVD may reduce their risk of CKD stage 3, especially among those that undergo unilateral nephrectomy for renal tuberculosis.

Information Transfer between Stock Market Sectors: A Comparison between the USA and China.

Information diffusion within financial markets plays a crucial role in the process of price formation and the propagation of sentiment and risk. We perform a comparative analysis of information transfer between industry sectors of the Chinese and the USA stock markets, using daily sector indices for the period from 2000 to 2017. The information flow from one sector to another is measured by the transfer entropy of the daily returns of the two sector indices. We find that the most active sector in information exchange (i.e., the largest total information inflow and outflow) is the non-bank financial sector in the Chinese market and the technology sector in the USA market. This is consistent with the role of the non-bank sector in corporate financing in China and the impact of technological innovation in the USA. In each market, the most active sector is also the largest information sink that has the largest information inflow (i.e., inflow minus outflow). In contrast, we identify that the main information source is the bank sector in the Chinese market and the energy sector in the USA market. In the case of China, this is due to the importance of net bank lending as a signal of corporate activity and the role of energy pricing in affecting corporate profitability. There are sectors such as the real estate sector that could be an information sink in one market but an information source in the other, showing the complex behavior of different markets. Overall, these findings show that stock markets are more synchronized, or ordered, during periods of turmoil than during periods of stability.

Multifractal analysis of financial markets: a review.

Multifractality is ubiquitously observed in complex natural and socioeconomic systems. Multifractal analysis provides powerful tools to understand the complex nonlinear nature of time series in diverse fields. Inspired by its striking analogy with hydrodynamic turbulence, from which the idea of multifractality originated, multifractal analysis of financial markets has bloomed, forming one of the main directions of econophysics. We review the multifractal analysis methods and multifractal models adopted in or invented for financial time series and their subtle properties, which are applicable to time series in other disciplines. We survey the cumulating evidence for the presence of multifractality in financial time series in different markets and at different time periods and discuss the sources of multifractality. The usefulness of multifractal analysis in quantifying market inefficiency, in supporting risk management and in developing other applications is presented. We finally discuss open problems and further directions of multifractal analysis.

Calling patterns in human communication dynamics.

Modern technologies not only provide a variety of communication modes (e.g., texting, cell phone conversation, and online instant messaging), but also detailed electronic traces of these communications between individuals. These electronic traces indicate that the interactions occur in temporal bursts. Here, we study intercall duration of communications of the 100,000 most active cell phone users of a Chinese mobile phone operator. We confirm that the intercall durations follow a power-law distribution with an exponential cutoff at the population level but find differences when focusing on individual users. We apply statistical tests at the individual level and find that the intercall durations follow a power-law distribution for only 3,460 individuals (3.46%). The intercall durations for the majority (73.34%) follow a Weibull distribution. We quantify individual users using three measures: out-degree, percentage of outgoing calls, and communication diversity. We find that the cell phone users with a power-law duration distribution fall into three anomalous clusters: robot-based callers, telecom fraud, and telephone sales. This information is of interest to both academics and practitioners, mobile telecom operators in particular. In contrast, the individual users with a Weibull duration distribution form the fourth cluster of ordinary cell phone users. We also discover more information about the calling patterns of these four clusters (e.g., the probability that a user will call the c(r)-th most contact and the probability distribution of burst sizes). Our findings may enable a more detailed analysis of the huge body of data contained in the logs of massive users.

Impact of the Russia-Ukraine Conflict on International Staple Agrifood Trade Networks.

The Russia-Ukraine conflict is a growing concern worldwide and poses serious threats to regional and global food security. Using monthly trade data for maize, rice, and wheat from 2016/1 to 2023/12, this paper constructs three international crop trade networks and an aggregate international food trade network. We aim to examine the structural changes following the occurrence of the Russia-Ukraine conflict. We find significant shifts in the number of edges, average in-degree, density, and efficiency in the third quarter of 2022, particularly in the international wheat trade network. Additionally, we have shown that political reasons have caused more pronounced changes in the trade connections between the economies of the North Atlantic Treaty Organization and Russia than with Ukraine. This paper could provide insights into the negative impact of geopolitical conflicts on the global food system and encourage a series of effective strategies to mitigate the negative impact of the conflict on global food trade.

Temporal rich club phenomenon and its formation mechanisms.

The temporal rich club (TRC) phenomenon is widespread in real systems, forming a tight and continuous collection of the prominent nodes that control the system. However, there is still a lack of sufficient understanding of the mechanisms of TRC formation. Here we use the international N-nutrient trade network as an example of an in-depth identification, analysis, and modeling of its TRC phenomenon. The system exhibits a statistically significant TRC phenomenon, with eight economies forming the cornerstone club. Our analysis reveals that node degree is the most influential factor in TRC formation compared to other variables. The mathematical evolution models we constructed propose that the TRC in the N-nutrient trade network arises from the coexistence of degree-homophily and path-dependence mechanisms. By comprehending these mechanisms, we introduce a different perspective on TRC formation. Although our analysis is limited to the international trade system, the methodology can be extended to analyze the mechanisms underlying TRC emergence in other systems.

Prediction of drug-target interactions and drug repositioning via network-based inference.

Drug-target interaction (DTI) is the basis of drug discovery and design. It is time consuming and costly to determine DTI experimentally. Hence, it is necessary to develop computational methods for the prediction of potential DTI. Based on complex network theory, three supervised inference methods were developed here to predict DTI and used for drug repositioning, namely drug-based similarity inference (DBSI), target-based similarity inference (TBSI) and network-based inference (NBI). Among them, NBI performed best on four benchmark data sets. Then a drug-target network was created with NBI based on 12,483 FDA-approved and experimental drug-target binary links, and some new DTIs were further predicted. In vitro assays confirmed that five old drugs, namely montelukast, diclofenac, simvastatin, ketoconazole, and itraconazole, showed polypharmacological features on estrogen receptors or dipeptidyl peptidase-IV with half maximal inhibitory or effective concentration ranged from 0.2 to 10 µM. Moreover, simvastatin and ketoconazole showed potent antiproliferative activities on human MDA-MB-231 breast cancer cell line in MTT assays. The results indicated that these methods could be powerful tools in prediction of DTIs and drug repositioning.

Experimental and Modeling Investigation on the Pyrolysis of n-Decane Initiated by Nitropropane. Part I: 1-Nitropropane.

Initiators can accelerate the pyrolysis of hydrocarbon fuels, thereby reducing the required reaction temperature in the hypersonic vehicle heat exchanger/reactor. Nitro-alkanes are considered as efficient initiators due to their lower energy barrier of the C-N bond cleavage reaction. To research the mechanism of the initiation effect of nitro-alkanes on the decomposition of hydrocarbon fuel, synchrotron radiation vacuum ultraviolet photoionization-mass spectrometry (SVUV-PIMS) was employed to experimentally study the pyrolysis of n-C10H22, 1-C3H7NO2, and their binary mixtures in a flow tube under pressures of 30 and 760 Torr. The species identified and measured in the experiments included alkanes, alkenes, dialkenes, alkynes, nitrogen oxides, benzene, and free radicals, which revealed the mechanism of n-decane and 1-C3H7NO2 pyrolysis, as well as the interactions of the two fuels. Experiments show that the presence of 1-C3H7NO2 reduces the initial decomposition temperature of n-C10H22, and the increased pressures could achieve a stronger promoting effect on the conversion of n-C10H22. A detailed kinetic model containing 1769 reactions and 278 species was established and validated based on the mole fraction distributions of n-C10H22, major pyrolysis species, and important intermediates measured in pure fuel and initiated pyrolysis. The kinetic model can accurately predict the experimental data, and the mechanism of 1-C3H7NO2-initiated pyrolysis of n-C10H22 is analyzed with the model. The effect of 1-C3H7NO2 on the consumption of n-C10H22 and selectivity of cracked products is highlighted.

Evolving community structure in the international pesticide trade networks.

The statistical properties of the international trade networks of all commodities as a whole have been extensively studied. However, the international trade networks of individual commodities often behave differently. Due to the importance of pesticides in agricultural production and food security, we investigated the evolving community structure in the international pesticide trade networks (iPTNs) of five categories from 2007 to 2018. We reveal that the community structures in the undirected and directed iPTNs exhibit regional patterns. However, the regional patterns are very different for undirected and directed networks and for different categories of pesticides. Moreover, the community structure is more stable in the directed iPTNs than in the undirected iPTNs. We also extract the intrinsic community blocks for the directed international trade networks of each pesticide category. It is found that the largest intrinsic community block is the most stable, appears in every pesticide category, and contains important economies (Belgium, Germany, Spain, France, the United Kingdom, Italy, the Netherlands, and Portugal) in Europe. Other important and stable intrinsic community blocks are Canada and the United States in North America, Argentina and Brazil in South America, and Australia and New Zealand in Oceania. These results suggest that, in the international trade of pesticides, geographic distance and the complementarity of important and adjacent economies are significant factors.

The health risk of acetochlor metabolite CMEPA is associated with lipid accumulation induced liver injury.

Liver injury may cause many diseases, such as non-alcoholic fatty liver disease (NAFLD). Acetochlor is one of the representative chloroacetamide herbicides, and its metabolite 2-chloro-N-(2-ethyl-6-methyl phenyl) acetamide (CMEPA) is the main form of exposure in the environment. It has been shown that acetochlor can cause mitochondrial damage of HepG2 cells and induce apoptosis by activating Bcl/Bax pathway (Wang et al., 2021). But there has been less research on CMEPA. we explored the possibility of CMEPA and liver injury through biological experiments. In vivo, CMEPA (0-16 mg/L) induced liver damage in zebrafish larvae, including increased lipid droplets, changes in liver morphology (>1.3-fold) and increased TC/TG content (>2.5-fold). In vitro, we selected L02 (human normal liver cells) as the model, and explored its molecular mechanism. We found that CMEPA (0-160 mg/L) induced apoptosis (similar to 40%), mitochondrial damage and oxidative stress in L02 cells. CMEPA induced intracellular lipid accumulation by inhibiting AMPK/ACC/CPT-1A signaling pathway and activating SREBP-1c/FAS signaling pathway. Our study provides evidence of a link between CMEPA and liver injury. This raises concerns regarding the health risks of pesticide metabolites to liver health.

Quantifying interconnectedness and centrality ranking among financial institutions with TVP-VAR framework.

Financial risk is spread and amplified through the interconnectedness among financial institutions. We apply a time-varying parameter vector autoregression model to analyze the dynamic spillover effects in the Chinese financial system. We find that the 2017 house price control policies have significantly increased the risk of China's financial system. Before 2017, with the prosperity of the real estate market, the interconnectedness of the Chinese financial system continued to decline, while after 2017, with the slowdown of house price growth and the downturn of the real estate market, the interconnectedness turned to increase. For different sectors, the trends and the magnitudes of the spillover effects are diverse, and any sector can contribute to systemic risk in a dynamic way. Finally, we rank 20 systemically important financial institutions according to two centrality measures. The stable institution ranking provides less noisy information for regulators to formulate a policy and intervene in the market effectively.

Robustness and efficiency of international pesticide trade networks subject to link removal strategies.

The international pesticide trade network (iPTN) is a key factor affecting global food production and food security. The trade relationship is a key component in iPTNs. In a complex international trade environment, we model the impacts of uncertain factors such as trade wars, economic blockades and local wars, as removing vital relationships in the trade network. There are many complex network studies on node centrality, but few on link centrality or link importance. We propose a new method for computing network link centrality. The main innovation of the method is in converting the original network into a dual graph, the nodes in the dual graph corresponding to the links of the original network. Through the dual graph, the node centrality indicators can measure the centrality of the links in the original network. We verify the effectiveness of the network link centrality indicator based on the dual graph in the iPTN, analyze the relationship between the existing network link centrality indicators and the indicator proposed in this paper, and compare their differences. It is found that the trade relationships with larger indicators (hub, outcloseness, outdegree) based on the dual graph have a greater impact on network efficiency than those based on the original pesticide trade networks.

Modeling aggressive market order placements with Hawkes factor models.

Price changes are induced by aggressive market orders in stock market. We introduce a bivariate marked Hawkes process to model aggressive market order arrivals at the microstructural level. The order arrival intensity is marked by an exogenous part and two endogenous processes reflecting the self-excitation and cross-excitation respectively. We calibrate the model for a Shenzhen Stock Exchange stock. We find that the exponential kernel with a smooth cut-off (i.e. the subtraction of two exponentials) produces much better calibration than the monotonous exponential kernel (i.e. the sum of two exponentials). The exogenous baseline intensity explains the U-shaped intraday pattern. Our empirical results show that the endogenous submission clustering is mainly caused by self-excitation rather than cross-excitation.

Predicting highway freight transportation networks using radiation models.

Highway freight transportation (HFT) plays an important role in the economic activities. Predicting HFT networks is not only scientifically significant in the understanding of the mechanism governing the formation and dynamics of these networks, but also of practical significance in highway planning and design for policymakers and truck allocation and route planning for logistic companies. In this work we apply parameter-free radiation models to predict the HFT network in mainland China and assess their predictive performance using metrics based on links and fluxes, which can be done in reference to the real directed and weighted HFT network between 338 Chinese cities constructed from about 15.06 million truck transportation records in five months. It is found that the radiation models exhibit relatively high accuracy in predicting links but low accuracy in predicting fluxes on links. Similar to gravity models, radiation models also suffer difficulty in predicting long-distance links and the fluxes on them. Nevertheless, the radiation models perform well in reproducing several scaling laws of the HFT network. The adoption of population or gross domestic product in the model has a minor impact on the results, and replacing the geographic distance by the path length taken by most truck drivers does not improve the results.

Multifractal detrended cross-correlation analysis for two nonstationary signals.

We propose a method called multifractal detrended cross-correlation analysis to investigate the multifractal behaviors in the power-law cross-correlations between two time series or higher-dimensional quantities recorded simultaneously, which can be applied to diverse complex systems such as turbulence, finance, ecology, physiology, geophysics, and so on. The method is validated with cross-correlated one- and two-dimensional binomial measures and multifractal random walks. As an example, we illustrate the method by analyzing two financial time series.

Forecasting extreme atmospheric events with a recurrence-interval-analysis-based autoregressive conditional duration model.

With most city dwellers in China subjected to air pollution, forecasting extreme air pollution spells is of paramount significance in both scheduling outdoor activities and ameliorating air pollution. In this paper, we integrate the autoregressive conditional duration model (ACD) with the recurrence interval analysis (RIA) and also extend the ACD model to a spatially autoregressive conditional duration (SACD) model by adding a spatially reviewed term to quantitatively explain and predict extreme air pollution recurrence intervals. Using the hourly data of six pollutants and the air quality index (AQI) during 2013-2016 collected from 12 national air quality monitoring stations in Beijing as our test samples, we attest that the spatially reviewed recurrence intervals have some general explanatory power over the recurrence intervals in the neighbouring air quality monitoring stations. We also conduct a one-step forecast using the RIA-ACD(1,1) and RIA-SACD(1,1,1) models and find that 90% of the predicted recurrence intervals are smaller than 72 hours, which justifies the predictive power of the proposed models. When applied to more time lags and neighbouring stations, the models are found to yield results that are consistent with reality, which evinces the feasibility of predicting extreme air pollution events through a recurrence-interval-analysis-based autoregressive conditional duration model. Moreover, the addition of a spatial term has proved effective in enhancing the predictive power.

Temporal and spatial correlation patterns of air pollutants in Chinese cities.

As a huge threat to the public health, China's air pollution has attracted extensive attention and continues to grow in tandem with the economy. Although the real-time air quality report can be utilized to update our knowledge on air quality, questions about how pollutants evolve across time and how pollutants are spatially correlated still remain a puzzle. In view of this point, we adopt the PMFG network method to analyze the six pollutants' hourly data in 350 Chinese cities in an attempt to find out how these pollutants are correlated temporally and spatially. In terms of time dimension, the results indicate that, except for O3, the pollutants have a common feature of the strong intraday patterns of which the daily variations are composed of two contraction periods and two expansion periods. Besides, all the time series of the six pollutants possess strong long-term correlations, and this temporal memory effect helps to explain why smoggy days are always followed by one after another. In terms of space dimension, the correlation structure shows that O3 is characterized by the highest spatial connections. The PMFGs reveal the relationship between this spatial correlation and provincial administrative divisions by filtering the hierarchical structure in the correlation matrix and refining the cliques as the tinny spatial clusters. Finally, we check the stability of the correlation structure and conclude that, except for PM10 and O3, the other pollutants have an overall stable correlation, and all pollutants have a slight trend to become more divergent in space. These results not only enhance our understanding of the air pollutants' evolutionary process, but also shed lights on the application of complex network methods into geographic issues.

Direct determination approach for the multifractal detrending moving average analysis.

In the canonical framework, we propose an alternative approach for the multifractal analysis based on the detrending moving average method (MF-DMA). We define a canonical measure such that the multifractal mass exponent τ(q) is related to the partition function and the multifractal spectrum f(α) can be directly determined. The performances of the direct determination approach and the traditional approach of the MF-DMA are compared based on three synthetic multifractal and monofractal measures generated from the one-dimensional p-model, the two-dimensional p-model, and the fractional Brownian motions. We find that both approaches have comparable performances to unveil the fractal and multifractal nature. In other words, without loss of accuracy, the multifractal spectrum f(α) can be directly determined using the new approach with less computation cost. We also apply the new MF-DMA approach to the volatility time series of stock prices and confirm the presence of multifractality.