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As a type of coronavirus, COVID-19 has quickly spread around the majority of countries worldwide, and seriously threatens human health and security. This paper aims to depict cumulative numbers of COVID-19 infections in China using the growth model chosen by cross validation. The residual plot does not look like a null plot, so we can not find a distribution function for the disturbance term that is close enough to the true frequency. Therefore, the disturbance term can not be characterized as random variables, and stochastic regression analysis is invalid in this case. To better describe this pandemic automatically, this paper first employs uncertain growth models with the help of uncertain hypothesis tests to detect and modify outliers in data. The forecast value and confidence interval for the cumulative number of COVID-19 infections in China are provided. [ABSTRACT FROM AUTHOR]

Due to their practicability, simple and data-driven empirical models have been extensively developed and applied in practical engineering to predict the run-out distance of landslides. However, the definition of the most appropriate empirical model for specific landslide data is rarely discussed. Moreover, the empirical model is subjected to the high variability of landslide data, which should be quantified into the model to provide more reliable predictions. As such, we propose in this paper, a simple, practical, and probabilistic run-out distance prediction method based on an empirical model and Bayesian method. This method was implemented with a regional landslide inventory compiled from 34 loess landslides in Heifangtai Terrace, Gansu Province, China. In this method, we performed a Bayesian model selection to determine the most appropriate empirical model for the compiled database among the possible candidate models adapted from previous literature. Considering the high variability of data, unknown parameters of the empirical model are regarded as random variables, and their posterior distributions are obtained by Bayesian updating with the compiled database. Then, we developed the probabilistic run-out prediction model to evaluate the run-out distance exceedance probability of landslides based on the most appropriate model and its associated posterior random variable information. We utilized data from two recent landslides that occurred in Heifangtai Terrace to validate the performance of the proposed model. In addition, we produced a run-out distance exceedance probability curve using the proposed method for a potential landslide in Heifangtai Terrace, in which the sliding volume interval is estimated using the sloping local base level (SLBL) method. In general, this study presents a practical method for landslide run-out distance analyses within a probabilistic framework, aiming to provide support for risk-based decisions. [ABSTRACT FROM AUTHOR]

This paper first proposes a European option pricing method for deposit insurance based on triangular intuitionistic fuzzy numbers. In the proposed method, we take into account the randomness and fuzziness of bank asset value simultaneously, and hence, the method can adequately reflect the high uncertainty of bank asset value. This method fuzzifies the value of bank asset, resubmits it into the original deposit insurance option pricing model as a fuzzy random variable, and then gives an analytic formula of deposit insurance rates using a risk-neutral method. After this, we have also conducted a numerical analysis. In specific, we have obtained the premium interval and presented the static analysis of key parameters. Finally, seven small- and middle-sized banks in Hunan Province in China are used as examples to validate the proposed interval pricing model. The Black-Scholes option pricing model and Yoshida's triangular fuzzy model are also employed for comparison. The research results show that the interval rates obtained from the proposed European option pricing method for deposit insurance can better reflect the uncertainty of bank asset evaluation than the fixed rates obtained from the Black-Scholes option pricing model. Moreover, the model proposed in this paper is also superior to Yoshida's model in practice. [ABSTRACT FROM AUTHOR]

This paper presents an uncertain time series model to analyse and predict the evolution of confirmed COVID-19 cases in China, excluding imported cases. Compared with the results of the classical time series model, the uncertain time series model could better describe the COVID-19 epidemic by using an uncertain hypothesis test to filter out outliers. This improvement is reflected in the two observations. One is that the estimated variance of the disturbance term in the uncertain time series model is more appropriate and acceptable than that in the classical time series model, and the other is that the disturbance term of the classical time series model cannot be regarded as a random variable but as an uncertain variable. [ABSTRACT FROM AUTHOR]

The research objects of this paper were the prefabricated concrete components produced by four enterprises in China, and the dimension deviation data of more than 1400 prefabricated concrete components are measured with high-precision 3D photogrammetry technology. The nonparametric Kruskal–Wallis test was carried out for the size deviation of the same type of components produced by different enterprises. The distribution characteristics of geometric parameters of typical components of prefabricated structures in China, such as beams, columns, wall boards, and composite slabs, were analyzed by using the probability statistical method. The Kolmogorov–Smirnov goodness-of-fit method was used to test the cumulative distribution function of dimension deviation, and the size distribution of fabricated components was studied. The results showed that there was no significant difference in the size deviation of the same-type component produced by different enterprises, and the range of geometric parameter uncertainty random variables was small, which was between 0.99 and 1.02. Also, the fluctuation was small, the coefficient of variation was below 0.0093, and the variability of component size deviation was small. The transverse dimension of the component shows a positive deviation, the vertical dimension of component shows a negative deviation, and the dimension deviation of prefabricated concrete components follows the normal distribution. [ABSTRACT FROM AUTHOR]

Currently, the increasing wind power penetration, with consequent randomness and variability, presents great challenges to power system planning and operation. Probabilistic power flow (PPF) has been developed to calculate the power flow under uncertain circumstances. However, the current wind power models are subject to specific probability distributions, limiting their accuracies in wider applications. Additionally, the cumulant method (CM)-based PPF, if nonlinear relationship is considered in, would face an impractically high computational complexity. To address these problems in modeling and cumulant calculation, this article proposes a novel generalized density/distribution fitting method (GDFM) combining with the Copula function to establish a joint probability model for wind power generation. A special impulse- mixed probability density (IMPD) integration method is also introduced to derive the input cumulants from the model. Finally, a fast cumulant method (FCM) is proposed to reduce the computational burden of output cumulant calculation while retaining a high accuracy in a nonlinear context. Case study on the IEEE-118 test system validates the effectiveness of the proposed methods, and a real application to a provincial power grid in China provides some useful power flow risk information for decision making. The whole FCM-based PPF scheme can be helpful for future power flow examination in power system planning and operation. [ABSTRACT FROM AUTHOR]

The paper introduces a three-dimensional numerical technique to assess typhoon hazards in China coastal regions based on a series of full-set numerical meteorology simulations. The boundary and initial conditions of the simulations are provided by adding pseudorandom fluctuations, which represent the localized, short-term meteorological variations, to synoptic fields, which show the large-scale, long-term meteorological patterns. A series of bogus typhoons are inserted into the initial field to provide the "seeds" from which the artificial typhoons could grow. The initial positions and intensities of the bogus typhoons are drawn from the random variables whose statistics agree with those derived from historical typhoon track data. In the present study, 1503 full-set meteorology simulations of artificial typhoons are conducted. The extreme wind speeds versus return periods calculated from the simulation results are compared to not only the specifications in the load code, but also the results from the previous studies. It is found that the extreme wind speeds in the Pearl-River Delta are, contradicting to the common expectation, higher than at the mainland side of the Taiwan Strait, which imply that the typhoons hitting Guangdong are, on average, more intense than those influencing Fujian. Given the possibility to improve the three-dimensional meteorology model in the future, the simulation technique proposed in the present study provides a novel direction to assess the meteorological hazards, including threads posted by typhoons. [ABSTRACT FROM AUTHOR]

For stocks traded on the Hong Kong Exchange, the median of five prices taken over the last minute of trading is currently chosen as the closing price. We introduce a stochastic control formulation to target such a median benchmark in an empirically justified model which takes the key microstructural features into account. We solve this problem by providing an explicit and efficient algorithm which even has applications beyond this paper as it can be used for the dynamic linear approximation of any square-integrable random variable. Implementing the algorithm on the stocks of the Hang Seng Index, we find an average improvement of around 6% in standard deviation of slippage compared to an average trader’s execution. We conclude by providing a novel decomposition of the trading risk into that which is intrinsic to the median benchmark and that due to execution. [ABSTRACT FROM AUTHOR]

• A pollution control problem is introduced in uncertain random environments. • An equivalent mathematical programming for the problem is provided. • Algorithms are proposed for solving the mathematical programming. • The optimal distribution strategies of pollution control fund are obtained. This paper describes the allocation of a wastewater treatment fund within a region based on a dynamic input-output model. Considering the complexity of the input-output process, many indeterminate factors must be included in the model. For example, with the aging of machines, an unexpected loss will be caused by the retention of raw materials during an operation; this can be realistically considered as a random variable, because of the sufficiently large amount of historical data. By contrast, actions such as a temporary transfer or inexperienced operators can only be regard as uncertain variables, because of a lack of historical data. First, the pollution control model is formulated in an uncertain environment by including both human uncertainty and objective randomness. Second, an optimal control model subject to an uncertain random singular system is established; this model can be transformed into an equivalent optimization problem. To solve such a problem, recurrence equations are presented based on Bellman's principle, and these were successfully applied to address the optimal control problem in two special cases. Moreover, two algorithms are formulated for solving the pollution control problem. Finally, the optimal distribution strategies of the pollution control fund used to control the emissions of COD and NH 3 -H, which are two indicators of wastewater in China, were obtained through the proposed algorithms. [ABSTRACT FROM AUTHOR]

Proper description of the return distribution is crucial for investment practitioners. The underestimation of the tail risk may lead to severe consequences, even for assets with moderate fluctuations. However, many empirical studies found that the distribution tails of many financial assets drop off more slowly than the Gaussian distributions. Therefore, we intend to model and calibrate the heavy tails observed in financial fluctuations in this study. By maximizing the Varma entropy with value-at-risk and expected shortfall constraints, we obtain the probability distribution of stock return and observe that the tail of stock return distribution is a power law. Since the variance of the real stock portfolio may be a random variable, using the mean-VaR-ES constraints to maximize the Varma entropy effectively avoids the problem of assuming that the variance is a constant value under the traditional mean-variance constraint. Therefore, the deduced theoretical model would be more consistent with the real market. Using high-frequency data from China's stock markets, we calibrate our theoretical model and give the concrete form of probability density distribution p(x) for different time intervals. The calibration results show that the tail of the stock return distribution is a power law with most of the power-law orders between −2 and −7. We prove the robustness of our results by calibrating the Varma entropy for S&P 500 of the USA stock market and different stock market indices in China's A-share market. Our research's findings not only offer a theoretical perspective for researchers but also give investing professionals a theoretical foundation on which to base their decisions. [ABSTRACT FROM AUTHOR]

Abstract Systems for monitoring the deformation and stress conditions of excavated high rock slopes are usually implemented for safety reasons and to predict the stability of future works. This paper adopts Bayesian methods for updating important geomechanical parameters namely: E (III 1), E (III 2), E (IV 2), E (f 8), c (III 1), c (III 2), c (V 1) and c (f 8) in these types of cases. The proposed method utilizes parametric sensitivity analysis, the BP neural network (back propagation neural network), and Bayesian updating to effectively reduce the number of variables, improve the computational efficiency and gradually update the random variables by using progressive monitoring information. The high rock slope excavation on the left bank at the Lianghekou Hydropower Station in China is illustrated as a detailed case study. Initially, only one type of measurement is first used for Bayesian updating (measured displacements or anchorage forces), and then both types of measurements are used. Compared to using only one type of measurement, the parameter uncertainty is reduced and the model accuracy is improved when both types of measurements are employed. Highlights • A Bayesian updating framework for rock slopes with multiple responses is proposed. • Random variables are gradually updated by using progressive monitoring information. • The effectiveness of three groups of data in Bayesian updating is discussed. • The proposed framework can be used for the prediction of subsequent excavation step. [ABSTRACT FROM AUTHOR]

Understanding the complex network response to nodes failure is important to protect them and guarantee their functioning. Most existing studies considered binary networks where the intensity (weight) of the links is not accounted for. However, in real world networks the intensity of the links widely differ so that a realistic description of complex networks is only possible if the heterogeneity present in edge weights is taken into account. Further, the vast majority of the existing studies investigates the efficacy of the attack strategies when all the nodes are removed. Despite it is important to study the network behavior in such a circumstance, real-world networks are more likely to experience local failures involving a small amount of nodes with a decrease in the system functioning not causing an overall collapse. This is the case of crossroad closing in complex road networks. For this reason it is interesting to investigate the networks response focusing to small node removals. In this paper we analyze the response of the real-world road networks of Beijing, the capital of China, to the removal of 1 and 2 nodes and accounting the weighted structure of the network. We use both classic binary node properties and network functioning measurement, introduced a weighted rank for node importance (node strength or page rank), and used a measurement for network functioning that accounts for the weight of the links (weighted efficiency). We find that the weighted node attack strategy Str (i.e. strength attack), which removes nodes according to their strength, is the most efficient to blemish the system when evaluating the damage with a weighted efficiency. Differently, betweenness attack strategy Bet , which removes nodes according to their centrality is the most efficient when using the binary efficiency to compute the network damage. These results indicate that adopting weighted or binary efficiency to measure networks functioning changed the efficacy of the attack strategies to damage the system. Last, we found a non-negligible fraction of random removals to be more efficient than the attack strategies; given that the main goal of the attack strategies is to individuate important nodes in the system, i.e. nodes which removal triggers highest damage, our results would indicate how in order to find the most important nodes may be more efficient to simulate an higher number of random nodes removal. [ABSTRACT FROM AUTHOR]

We examine the different effects of monetary policy actions and central bank communication on China’s stock market bubbles with a Time-varying Parameter SVAR model. We find that with negative responses of fundamental component and positive responses of bubble component of asset prices, contractionary monetary policy induces the observed stock prices to rise during periods of large bubbles. By contrast, central bank communication acts on the market through expectation guidance and has more significant effects on stock prices in the long run, which implies that central bank communication be used as an effective long-term instrument for the central bank’s policymaking. [ABSTRACT FROM AUTHOR]

The facing is a key component for the design of soil nail walls. However, current design specifications of soil nail walls in China do not specify methods for estimation of facing tensile forces, which has posed challenges for engineers to produce consistent, safe, and cost-effective facing designs. This study first constructs the facing tensile force as the product of the maximum nail load and a facing factor. Then a database containing 91 measured facing tensile force data collected from the literature is developed and used to calibrate the facing factor matching six maximum nail load models that are commonly adopted in China, namely, the simplified incremental calculation models based on the Rankine and Coulomb earth pressure theories, the China Academy of Building Research model, and the China Association for Engineering Construction Standardization model. Based on the developed database, the accuracies of the six proposed models for facing tensile forces are demonstrated to be unbiased on average and the dispersions in the prediction accuracy are ranked from low to medium. Furthermore, the influences of three wall working conditions on prediction accuracy of the developed models are investigated and shown to be practically insignificant. Last, the model biases are characterised as lognormal random variables. [ABSTRACT FROM AUTHOR]

Air temperature (Ta) is an essential parameter for science research and engineering practice. While the traditional site-based approach is only able to obtain observations in limited and discrete locations, satellite remote sensing is promising to retrieve some environmental variables with spatially continuous coverage. Nowadays, land surface temperature (Ts) measurements can be obtained from some satellite sensors (e.g., MODIS), further enabling us to estimate Ta in view of the relationship between Ta and Ts. In this article, we proposed a two-phase integrated framework to estimate daily mean Ta nationwide. In the first phase, multivariate linear regression models were fitted between site-based observations of daily mean air temperature (Ta-mean) and MODIS land surface temperature products (including Terra day: TMOD-day, Terra night: TMOD-night, Aqua day: TMYD-day, and Aqua night: TMYD-night) conditional on some covariates of environmental factors. The fitted models were then used to predict Ta-mean from those covariates at unobserved locations. The predicted Ta-mean were looked on as stochastic variables, and their distributions were also obtained. In the second phase, Bayesian maximum entropy (BME) methods were used to produce spatially continuous maps of Ta-mean taking the meteorological station observations as hard data and the predicted Ta-mean in the first phase as soft data. It is shown that the proposed approach is promising to improve the interpolation accuracy significantly, comprehensively considering the prior knowledge and the context of space variability and correlation, which will enable it to compile spatially continuous air temperature products with higher accuracy. [ABSTRACT FROM AUTHOR]

Land use classification is fundamental both for monitoring and predicting regional development patterns and for planning and regulating land use. This research proposed a joint probability-based classifier for land use classification of multispectral remote sensing data and applied it to the Lake Baiyangdian region of North China. This classifier, based on the vine copula method, was suitable for dealing with the uncertainties of land classification and its random variables that did not necessarily obey predefined distributions. Comparison of the results obtained using the proposed classifier with those derived using the widely used maximum likelihood classifier indicated that the accuracy of land use classification of multispectral remote sensing data was higher with the proposed classifier. Compared with the contingency matrix of the maximum likelihood classifier, that of the vine copula classifier showed an increase in the producer's (user's) accuracy of rural land (shallow water) of 29.4% (30.0%). The proposed classifier increased the shallow water area and significantly reduced the area of rural land. The main reason was the maximum likelihood classifier had poor classification performance, misclassifying pixels of shallow water as rural land. The findings of this study demonstrated that the vine copula classifier performs better than the traditional maximum likelihood classifier and that its application could promote full utilization of remotely sensed data. [ABSTRACT FROM AUTHOR]

In this paper, a two-stage coordinated scheduling model for dealing with the optimization problem of multiple-energy (power, heating, and cooling) synergistic supply in the micro energy grid (MEG) is proposed. To overcome the uncertainty impact of wind power and photovoltaic power, the scheduling cycle was divided into a day-ahead phase and a real-time phase. Then, the day-ahead forecasting results were taken as random variables for the upper-layer model. The actual value was taken as the realization of random variables for the lower-layer model, including the energy storage revised model and demand response (DR) scheduling model. The cell membrane and chaotic search algorithms were employed to improve the particle swarm algorithm. The "international low carbon park" in China was selected for a case study. The case study showed the following results: (1)The two-stage optimization model and solution algorithm achieved the optimal synergistic supply of multiple energy forms. (2) Power-to-gas could convert the surplus electricity into natural gas, realizing multi-directional energy cascade conversion. (3)MEG could flexibly interact with the upper-grade energy network for obtaining more economic benefits. (4)The price-based DR could smooth the energy load curve and maximize the operation revenue of the MEG by utilizing complementary characteristics of energy prices. • Designed a novel structure for micro energy gird considering energy transmission chain. • Proposed a bi-level coordinated scheduling optimization model for micro energy grid. • Construct a chaotic cell membrane-particle swarm optimization (C2–PSO) algorithm. • Analyzed the influence of different energy conversion strategies on the optimal strategy. • Discussed the strategy difference of micro energy gird in different operation scenarios. [ABSTRACT FROM AUTHOR]

Air pollution is well-known as a major risk to public health, causing various diseases including pulmonary and cardiovascular diseases. As social concern increases, the amount of air pollution data is increasing rapidly. The purpose of this study is to statistically characterize dependence between major cities in China based on a measure of directional dependence estimated from PM2.5 measurements. As a measure of the directional dependence, we propose the so-called copula directional dependence (CDD) using beta regression models. An advantage of the CDD is that it does not rely on strict assumptions of specific probability distributions or linearity. We used hourly PM2.5 measurement data collected at four major cities in China: Beijing, Chengdu, Guangzhou, and Shanghai, from 2013 to 2017. After accounting for autocorrelation in the PM2.5 time series via nonlinear autoregressive models, CDDs between the four cities were estimated to produce directed network structures of statistical dependence. In addition, a statistical method was proposed to test the directionality of dependence between each pair of cities. From the PM2.5 data, we could discover that Chengdu and Guangzhou are the most closely related cities and that the directionality between them has changed once during 2013 to 2017, which implies a major economic or environmental change in these Chinese regions. [ABSTRACT FROM AUTHOR]

Although the item count technique is useful in surveys with sensitive questions, privacy of those respondents who possess the sensitive characteristic of interest may not be well protected due to a defect in its original design. In this article, we propose two new survey designs (namely the Poisson item count technique and negative binomial item count technique) which replace several independent Bernoulli random variables required by the original item count technique with a single Poisson or negative binomial random variable, respectively. The proposed models not only provide closed form variance estimate and confidence interval within [0, 1] for the sensitive proportion, but also simplify the survey design of the original item count technique. Most importantly, the new designs do not leak respondents' privacy. Empirical results show that the proposed techniques perform satisfactorily in the sense that it yields accurate parameter estimate and confidence interval. [ABSTRACT FROM AUTHOR]