Search

Long-distance high-speed train localization based on distributed optical fiber sensors (DOFS) has been a challenging issue due to the large-scale heterogeneous sensor nodes. It requires a competent localization algorithm to be capable of strong generalization and quick response. This paper proposes a cooperative multi-classifier network (CMCN) for locating HSTs based on heterogeneous DOFS signals by adaptive modeling of the local characteristics. The proposed CMCN is composed of adaptive feature extraction, lightweight base classifiers and spatial boostrap aggregating (SBA). First, the heterogeneous signals are adaptively transformed to an optimal intrinsic mode function for extracting the statistical features of base classifiers. The base classifiers are constructed based on dynamic soft-margin support vector machine to model local characteristics without computationally burdensome kernel functions by introducing a dynamic penalty factor. The factor is automatically initialized by evaluating the regional consistency before training. Furthermore, the SBA estimates the location of HSTs based on the local states of nodes. It can cooperate with base classifiers for enhanced accuracy by searching for the interval with maximum regional consistency. Finally, a trial is conducted in a high-speed railway in China in long-term running of 92 days. The results prove feasibility and accuracy of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Reasonable prediction of natural gas consumption and production in China is helpful for decision-makers formulate policies to solve the increasingly prominent contradiction between supply and demand of natural gas in China. Taking into account the data environment of China's small sample, this paper considers using the grey prediction model with characteristic of small sample modeling to study the consumption and production of natural gas in China. To achieve accurate prediction, this paper developed a novel discrete fractional nonlinear grey Bernoulli model with power term (DFNGBM(1,1, α)), and used the latest equilibrium optimizer to solve the hyper-parameters of the model. The proposed model has the advantages of most existing grey prediction models, such as fractional-order accumulation operation and time power term. In addition, the proposed model is a unified expression form of many other grey forecasting models. The consumption and production of natural gas in China from 2003 to 2020 are used as examples to verify the feasibility and validity of the proposed model compared with other competitive models that have been used to study natural gas. It turns out that the prediction ability of the proposed model is better than other models. Therefore, the feasibility and effectiveness of the proposed model have been confirmed. Finally, the DFNGBM (1,1, α) model is used to predict China's natural gas consumption and production in the next few years, and some corresponding recommendations are given based on the prediction results. [ABSTRACT FROM AUTHOR]

An accurate estimation of tourism demand is of great significance to tourism management. The seasonal and non-stationary features present a significant challenge in developing tourism demand estimation. An effective tourism demand forecasting model is important to address this problem. This paper proposes a novel model for tourism demand forecasting based on developed mechanism-guided attention. The model consists of three sections: the first section defines the degree of stationarity of the demand time series, the second section develops a guided attention mechanism for improving the feature recognition of neural networks; the third section generates forecasting results of tourism demand, in the meanwhile, the developed fully connected recurrent neural network is adopted to identify complex features of tourism demand time series. The proposed model is helpful in identifying the features of seasonality and non-linearity in tourism demand data, the model maintains good forecasting accuracy with the appropriate training process. Case studies show that the proposed method can forecast the daily tourism demand of Macau, China accurately compared with other traditional forecasting methods. • A novel model basing a guided attention mechanism is proposed to forecast tourism demand. • Identify non-stationary feature in tourism demand time series using Fully connected recurrent neural network. • Use adaptive evolutionary algorithms to evaluate the tourism demand forecasting results in different scenarios. • Adopt NSGA-II to optimize the mapping matrix of guided attention mechanism. • Validate the proposed model using open data sets with complex characteristics. [ABSTRACT FROM AUTHOR]

• A 14-layer model with high predictive performance is proposed. • The model fuses three types of multi-source and multi-frequency features. • The model addresses three limitations of LSTM. • The model is validated through four comparison experiments on four stock indices. • Each component of the model improves the predictive performance. The most widely used model in stock price forecasting is the long short-term memory network (LSTM). However, LSTM has its limitations, as it does not recognize and extract features well and has a representational bottleneck. Furthermore, the factors affecting stock prices are multi-source and multi-frequency information, making neural network models difficult to handle. In this paper, we introduce a feature fusion residual LSTM (FFRL) model to answer these two questions – how to compensate for the three limitations of LSTM and how to fuse the multi-source and multi-frequency information. FFRL consists of three modules to improve the three limitations of LSTM, namely the feature selection module, feature extraction module, and residual module. To learn features from multi-source and multi-frequency information, FFRL applies the feature selection module to emphasize important features and the feature extraction module to extract deeper features. We demonstrate significant performance improvements of FFRL over comparison models, ablation networks, and visualization methods on a variety of Chinese stocks. [ABSTRACT FROM AUTHOR]

• A 14-layer model with high predictive performance is proposed. • The model fuses three types of multi-source and multi-frequency features. • The model addresses three limitations of LSTM. • The model is validated through four comparison experiments on four stock indices. • Each component of the model improves the predictive performance. The most widely used model in stock price forecasting is the long short-term memory network (LSTM). However, LSTM has its limitations, as it does not recognize and extract features well and has a representational bottleneck. Furthermore, the factors affecting stock prices are multi-source and multi-frequency information, making neural network models difficult to handle. In this paper, we introduce a feature fusion residual LSTM (FFRL) model to answer these two questions – how to compensate for the three limitations of LSTM and how to fuse the multi-source and multi-frequency information. FFRL consists of three modules to improve the three limitations of LSTM, namely the feature selection module, feature extraction module, and residual module. To learn features from multi-source and multi-frequency information, FFRL applies the feature selection module to emphasize important features and the feature extraction module to extract deeper features. We demonstrate significant performance improvements of FFRL over comparison models, ablation networks, and visualization methods on a variety of Chinese stocks. [ABSTRACT FROM AUTHOR]

Pine wilt disease is one of the most serious forest pest and disease in China, which seriously influences the realization of the dual carbon goal. In this paper, considering susceptible longhorns and infected longhorns, we study a delayed reaction–diffusion pine wilt disease model both with memory diffusion and nonlocal effect. We analyze the dynamic properties for with and without memory diffusion and nonlocal effect, respectively. Especially, for this model, we find that memory diffusion plays a leading role in spatial dynamics. Memory diffusion can induce spatially inhomogeneous periodic solutions and steady-state solutions. Nonlocal effect mainly affects the amplitude and period of spatially inhomogeneous periodic solutions of the system. In addition, we give some biological explanations for the different phenomena in this paper. • Diffusive PWD model with memory diffusion and nonlocal effect is proposed. • Dynamics of the system with and without memory diffusion and nonlocal effect are compared. • Memory diffusion can induce spatially inhomogeneous periodic and steady-state solutions. • Nonlocal effect has impact on the amplitude and period of spatially inhomogeneous periodic solutions. [ABSTRACT FROM AUTHOR]

• In the proposed IVIF-BWM, we build weight-determining models concerning expert's attitudes. • A bi-objective programming model is proposed to derive experts' dynamic weights. • Develop an IVIF-DBWM to determine the global weights of all criteria. • IVIF-SPA is proposed to evaluate the overall resumption risk of each alternative. Currently, China has achieved a remarkable achievement on the containment of COVID-19, which creates a favorable condition for the gradual resumption of normal life. However, COVID-19 infections continue to rise in many nations and some sporadic cases occur from time to time in China, which still poses some risks to the resumption. Hence, it is imperative to develop some reasonable techniques to assess the resumption risk. This paper aims to investigate an integrated interval-valued intuitionistic fuzzy (IVIF) technique to adroitly assess the resumption risk based on DEMATEL (decision making trial and evaluation laboratory), BWM (best-worst method) and SPA (set pair analysis). This integrated technique is called IVIF-DBWM-SPA, where the IVIF-DBWM (combined by the IVIF-DEMATEL and IVIF-BWM) is used to determine the global criteria weights and the IVIF-SPA is employed to generate the ranking order of the alternatives. The IVIF-DEMATEL and IVIF-BWM are used to determine the weights of dimensions and the weights of criteria under each dimension, respectively. In this IVIF-BWM, two bi-objective programming models are constructed by regarding experts' pessimistic and optimistic attitudes, respectively. Combined experts' intrapersonal and interpersonal uncertainties simultaneously, a bi-objective programming model is proposed to derive the dynamic weights of experts. Based on the determined weights of experts and criteria, an IVIF-SPA is developed to assess the risk levels of all alternatives. The validity of the proposed technique is demonstrated with a real case of college resumption risk assessment amid COVID-19. Some sensitivity and comparison analyses are provided to show the merits of the proposed technique. [ABSTRACT FROM AUTHOR]

Accurate and timely crop yield prediction is difficult to achieve due to the nonlinear and dynamic spatial–temporal correlations included during the crop growth process. The latest approaches utilize deep learning techniques to implicitly capture sequential dependencies but fail to consider prior knowledge. In this paper, a novel Knowledge-guided Spatial–Temporal Attention Graph Network (KSTAGE), which incorporates prior knowledge to model dynamic temporal relationships and geospatial topology structures, is proposed. The proposed KSTAGE first utilizes a 3D convolutional neural network (CNN) to embed the initial spectral feature. Following its development, a new Knowledge-guided Temporal Multi-head Attention Algorithm (KTMA), the core of KSTAGE, is applied to automatically generate temporal attention weights from a self-attention mechanism under the guidance of prior knowledge. Furthermore, a new strategy for aligning self-attention scores with the prior distribution is developed. Finally, a location-aware Spatial Attention Graph Network utilizing geospatial knowledge is proposed to aggregate the spatial neighborhood features for the final yield prediction. The experimental results obtained on county-level yield prediction tasks in China and the contiguous United States (CONUS) demonstrate that the proposed KSTAGE achieves obvious improvements over the baselines. [ABSTRACT FROM AUTHOR]

Pawlak conflict analysis focuses on three-valued ratings of a set of agents on a set of issues, in which the three values +, 0, and − indicate, respectively, that an agent is positive, neutral, and negative about an issue. According to their shared ratings, we can form different types of agent coalitions and, similarly, different types of issue bundles (i.e., families of issues to be considered together). The main objective of this paper is to introduce and investigate connections of agent coalitions and issue bundles from Wille formal concept analysis perspectives. By interpreting a three-valued rating table as four different formal contexts, we introduce four types of agent coalitions, namely, support, non-opposition, opposition, and non-support coalitions, the corresponding four types of issue bundles, and four lattices of coalition-bundle couplings. The lattices reveal structural information of agents and issues in a conflict situation. To demonstrate the usefulness of the proposed model, we analyze the problem of development planning of the Gansu Province of China. [ABSTRACT FROM AUTHOR]

Changes in stock markets impact the optimal policies for governments, businesses, and even individual households. Causal networks summarizing pairwise directed dependencies between stocks can be used to characterize these changes, as well as to identify influential stocks whose variations have unusually strong effects on the movements of other stocks. In this paper, we apply Granger causality tests in sliding windows to build directed causal networks representing the interactions of stock markets in the world's second largest economy, China, during recent economic, trade and epidemiological crises. We propose identifying influential stocks by using distance correlation and surrogate tests, rather than linear Pearson correlation and asymptotic distributions, to quantify the strength of general nonlinear dependencies between network centrality and financial indicators. We find that, at the onset of a crisis, influential stocks gain their position by themselves being attentive to the stock market. As the crisis progresses, the stock market steadily adjusts to give a stronger leadership role to influential stocks at the expense of non-influential stocks. Interestingly, the industries to which influential stocks belong are closely related to the nature of crisis events. Finally, we show that industries which contained influential stocks represented better investments. [ABSTRACT FROM AUTHOR]

• A novel multi-objective scheduling model is developed for the emergency rescue planning problem. • The proposed model minimizes the fire extinguishing time, delay time, and number of fire rescue vehicles. • The factor of fire spreading speed is firstly considered in our optimization model. • A revised multi-objective discrete gravitational search slgorithm is designed to produce Pareto solutions. • An empirical case study in Heilongjiang Province, China, is applied. The implementation of emergency scheduling of priority-based rescue vehicles for extinguishing forest fires is a complex optimization problem facing with many challenges and difficulties to optimize the operational costs and to improve the efficiency to make robust decisions. The main challenges are to minimize the number of fire engines while minimizing firefighting time and firefighting delay time, simultaneously. The main difficulties to make these decisions are to consider the severity of each fire point with regards to the limited resources of vehicles. Hence, this paper motivates to develop a new multi-objective scheduling model for extinguishing the fire of forests considering rescue priority with the limited rescue resources. To make an efficient and robust solution for the proposed problem, another novelty is to propose a hybrid optimization algorithm which is a modified discrete gravitational search algorithm. To confirm the applicability of the proposed problem, an actual forest fire emergency scheduling in Heilongjiang Province, China, is simulated. The proposed hybrid optimization algorithm is tested to check the feasibility the Pareto solutions and it is compared with a set of well-known and recent algorithms to show its efficiency. Finally, after a comprehensive discussion, the results prove that this work provides an accurate and effective tool for performing the emergency scheduling for extinguishing the forest fire. [ABSTRACT FROM AUTHOR]

As grade differences between concentrates increase, long-term concentrate ingredient planning (LCIP) becomes a crucial issue in ensuring continuous production and maximizing the concentrate utilization. Comparing with the existing studies, the number of LCIP ingredient stages is not deterministic and the decision variables and constraints are also will also change in accordance with the preceding ingredient list. Given these features of LCIP, this paper presents the concept of unpredictable multistage dynamic optimization (UMDO) and establishes an LCIP model that comprehensively considers production constraints, ingredient list duration, and concentrates inventories. A multistage stochastic object coding (MSOC) that establishes a mapping relationship between the coding sequence and the feasible solution space at each scheduling stage is further proposed. A multi-agent differential evolution (DE) algorithm based on the application of sequential simulation is proposed to optimize the high-dimensional population of the MSOC, enabling a globally optimal scheme in which the feasibility of the ingredient plan at each stage is ensured. Finally, the actual inventory concentration data collected from a copper industry in China are used to validate the effectiveness of the proposed planning methodology. [ABSTRACT FROM AUTHOR]

The global health crisis caused by SARS-CoV-2 since 2019 has emphasized the critical significance of effective disease detection and treatment in minimizing infection rates and fatalities, as well as halting the spread of pandemics. During an outbreak, individuals suspected of being infected require a significant amount of testing resources, while those confirmed to be infected demand substantial treatment resources. Hence, this paper is dedicated to presenting a new pandemic model that enables joint forecasting and allocation of resources for testing and treatment. The proposed model in this paper is an innovative agent-based epidemic compartmental model, which also incorporates a mixed integer model. It integrates novel features based on crucial disease characteristics, such as self-healing for asymptomatic or mild-symptomatic cases, varying infection risk levels among different groups, and the inclusion of secondary infections. Moreover, the solutions of the joint allocation model are compared with those of the independent allocation model, which entails considering resource interactions rather than allocating each resource independently. Furthermore, the validity of this model was confirmed through real-world data obtained during the SARS-CoV-2 outbreak in China. The findings offer valuable insights into the impact of intervention levels and duration, joint allocation schemes, as well as optimal allocation of test and treatment resources on cross-regional transmission of the pandemic. [ABSTRACT FROM AUTHOR]

• A novel fuzzy c-means algorithm is proposed for attribute-associated data clustering. • The parameters of proposed algorithms are fully investigated through synthetic datasets. • The proposed algorithm performs better compared with others on synthetic, real-world, and tunnel boring machine datasets. Partitioning data into internally homogeneous parts is an important problem when mining in situ engineering data. In this paper, a polynomial regression-based fuzzy c-means (PR-FCM) clustering algorithm that utilizes the functional relationships among the attributes of the input dataset is proposed. In this algorithm, a polynomial regression equation is taken as the center of each cluster instead of the cluster prototype used in conventional FCM, and the difference between a sample and a cluster prototype is defined as the distance between the actual value of one attribute and the corresponding predicted value provided by its own polynomial regression equation. An alternating optimization method is designed to optimize the new clustering objective function of the proposed algorithm. A series of experiments on synthetic and real-world datasets are conducted to evaluate the performance of the PR-FCM algorithm, which exhibits higher effectiveness and possesses more advantages than the original FCM algorithm. The PR-FCM algorithm is applied to tunnel boring machine (TBM) operation data from a TBM project in China. The experimental results show that the proposed algorithm can effectively cluster TBM operation data. [ABSTRACT FROM AUTHOR]

To achieve sustainable management of municipal solid waste, selecting an appropriate waste to energy (WTE) technology is pivotal. The WTE technology selection is a complicated issue because it involves a range of conflicting criteria and a rich diversity of stakeholders. Moreover, the complexity will be multiplied when considering that the preference information provided by different people with respect to different criteria takes different forms and granularities. To tackle the WTE technology selection problems with multi-granular hybrid information, this paper proposes a new group decision-making framework. The proposed framework provides a reasonable solution to the WTE technology selection problems through four stages. In the first stage, three information transformation mechanisms are established to transform information in different forms and granularities into unified belief structures. In the second stage, a symmetrical cross-entropy measure-based weight determination model is developed to calculate the criteria weights. In the third stage, by combining information transformation mechanisms and criteria weights, the analytical evidential reasoning algorithm is extended to generate group opinions. Finally, according to the generated group opinions, the expected utilities of alternatives are calculated to compare and rank the alternatives. To illustrate the implementation process of the proposed decision-making framework, an application about the WTE technology selection in China is performed. Besides, a comparative experiment is conducted to show the flexibility and reliability of our proposal. [ABSTRACT FROM AUTHOR]

• Integrate the impact of uncertain unit adjustment cost and risk preference of the moderator on RMCCM. • To achieve stability, reliability and robustness, some general robust RMCCM are proposed in three cases by using robust optimization. • Compared with other methods, the proposed method can get better decision results. • That's because the proposed method can well describe the MCC problem under uncertain conditions. Due to the uncertain circumstances in which decisions are made, the moderator may be unable to provide an accurate unit adjustment cost to each decision maker, making the unit adjustment cost uncertain. This paper formulates a consensus approach to reduce the adverse effects of parameter uncertainty and estimation errors. First, the mean–variance (MV) theory is employed to build the risk minimum cost consensus model (RMCCM) to reflect the risk preference of the moderator in the consensus progress. Second, due to existing estimation errors of the mean and covariance matrix of unit adjustment cost in RMCCM, new RMCCM variants are proposed using robust optimization. To identify a solution that performs best in the worst case, the robust counterpart RMCCM problem is equivalent to a tractable problem. Finally, a case study of urban housing demolition in China is used to verify the feasibility and applicability of the proposed method, and then sensitivity analysis and comparative analysis are presented. [ABSTRACT FROM AUTHOR]

The energy industry is a major source of greenhouse gas emissions, and energy investment is an important regulatory tool to encourage the energy industry to actively respond to climate change and achieve low-carbon development. Therefore, it is of great practical significance to correctly understand the important role of the energy industry, to predict energy investments objectively and accurately, to achieve scientific and rational investment, and make policy recommendations for the energy production and consumption revolution. In this paper, the Haavelmo model of economic growth is introduced into the energy system, using the characteristics of the continuous form of the model to establish the differential equations for the dynamics of fixed asset investment in the energy industry, and Haavelmo's grey prediction model using the grey difference information principle. Meanwhile, the Python program is used to solve the parameters of the new model, and the mathematical transformation is used to find the time response equation of the new model, and the modeling steps and the modeling flow chart of the model are obtained. Finally, the new model will be applied to two types of energy investments in China: total energy industry investment and investment in electricity, steam, hot water production, and supply industry. Both types of energy use the same modeling object and forecast object, and six cases are compared with three grey forecasting models from different perspectives, and their results show that they are much better than the other three grey forecasting models, demonstrating the effectiveness of the new model to effectively forecast energy investments and improve the efficiency of energy industry investments, cultivate healthy and environmentally friendly energy consumption habits. [ABSTRACT FROM AUTHOR]

In this paper, we present a stochastic hepatitis B virus (HBV) infection model and the dynamic behaviors of the model are investigated. When the fraction of vertical transmission μ ω ν C is not considered to be new infections, the existence and ergodicity of the stationary distribution of the model are obtained by constructing a suitable Lyapunov function, which determines a critical value ρ 0 s corresponding to the basic reproduction number of ODE system. This implies the persistence of the diseases when ρ 0 s > 1. Meanwhile, the sufficient conditions for the extinction of the diseases are derived when ρ 0 T < 0. What is more, we give the specific expression of the probability density function of the stochastic model around the unique endemic quasi-equilibrium by solving the Fokker–Planck equation. Finally, the numerical simulations are illustrated to verify the theoretical results and match the HBV epidemic data in China. • A stochastic HBV infection model is proposed and studied. • Existence and ergodicity of stationary distribution are proved. • Critical value ρ 0 s corresponding to basic reproduction number ρ 0 is obtained. • Density function of stochastic model near quasi-equilibrium is given. • Simulations match the HBV epidemic data in China. [ABSTRACT FROM AUTHOR]

• Context-Dependent DEASort is proposed based on BWM for multi-criteria sorting problem. • Categories can be simply obtained without predefined classification categorical rules. • A common set of weight model is introduced for evaluating attractiveness of each DMU. • Case study concerning risk assessment of Yangtze River Economic Zone is provided. • Sensitivity analysis is conducted to illustrate the stability of the proposed method. With the increasing urban economic development and awareness of environment protection, ecological risk assessment (ERA), as a management mode combined with ecological research and risk assessment, has become a highly relevant topic in environment sustainable development. In this paper, we propose a novel sorting model based on Data Envelopment Analysis (DEA) and Best Worst method (BWM) to solve multi-criteria sorting problems and apply it to ERA. The proposed Context-Dependent DEASort method based on the idea of Context-Dependent DEA to position decision-making units (DMUs) into diverse categories in uncertain circumstance. It also takes experts' preference into full account as well, which makes the sorting solution more flexible and reasonable. Besides, a common set of weight-based model is introduced to deal with the situation when evaluating attractiveness and progress of each DMU. The common weight set model can ensure to provide an overall evaluation context compared to the original Context-Dependent DEA model, which only distinct the DMU from a single virtual DMU. Furthermore, a case study concerning ecological risk assessment of Yangtze River Economic Zone in China is provided to illustrate the applicability of the developed environment. Finally, comparative analysis and sensitivity analysis are carried out to demonstrate the practicality and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Automatic understanding of floor plan images is a key component of various applications. Due to the style diversity of rural housing design, the latest learning-based approaches cannot achieve satisfactory recognition results. In this paper, we present a new framework for parsing floor plans of rural residence that combines semantic neural networks with a post-processed room segmentation. First, we take case studies from typical residential buildings in China's rural areas and provide a novel image dataset, called RuralHomeData , containing 800 rural residence floor plans with accurate man-made annotations. Based on the dataset, we propose a new deep learning-based recognition framework using a joint neural network to predict the geometric elements and text information on the floor plan simultaneously. Our insight is that walls and openings (doors and windows) are the basic elements corresponding to the room boundary that a closed 1D loop must form a certain room. Then the semantic information (e.g., the room function) of room regions can be obtained through text detection and identification. Furthermore, we use the MIQP algorithm to divide the area containing multiple room type texts into multiple room areas. Finally, the input floor plan can be transformed into a room layout graph with room attributes and adjacent relationships. The proposed algorithm has been tested on both urban and rural datasets, and the experimental results demonstrate our efficiency and robustness compared with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In the Pawlak model of conflict analysis, a rating of −1, 0, and +1 indicates that an agent is negative, neutral, and positive towards an issue. One defines measures of alliance and conflict of agents based on their three-valued ratings on a set of issues. Existing studies make some restrictive assumptions. One assumption is that a single distance function determines alliance, conflict, and neutrality. Another assumption is that all issues are equally important. The third assumption is that different pairs, such as (+ 1 , + 1) , (0 , 0) , and (− 1 , − 1) , contribute equally to alliance and conflict. Intuitively, different issues may have different importances and a (+ 1 , + 1) agreement may be stronger than a (0 , 0) agreement. To address these weaknesses, in this paper we use a pair of an alliance measure and a conflict measure, rather than a single distance measure. We propose and study a new class of additive measures of alliance and conflict by assigning different weights to different issues and assigning different values to different pairs of ratings. Existing measures are special cases of the new measures. By using the new measures, we construct a general model of three-way conflict analysis. Finally, we give an application of the new alliance and conflict measures in making the development plan of Gansu Province in China. [ABSTRACT FROM AUTHOR]

The spread of online games has become more and more serious in recent years, which not only endangers the healthy growth of young people, but also has a certain potential harm to society. Therefore, how to effectively control the spread of games has become an urgent problem to be solved. To solve this problem, a new fractional model of online game addiction is developed in this paper. In the aspect of qualitative analysis, the non-negativity and boundedness of the model solutions are demonstrated. All equilibria and basic reproduction number are solved. The global asymptotic stability of all equilibria are proved by constructing ingenious Lyapunov functions. In terms of quantitative analysis, different from previous fractional order literature, we use a new method to fit the real data of game users in China from 2010 to 2021, and obtain the optimal fractional order α = 0. 98942 and model parameters. The estimated results show that the value of the basic reproduction number during this period is R 0 = 7. 1045. By setting different values of the parameters, the effects of all parameters on the model are shown. With the Latin hypercube sampling (LHS) and partial rank correlation coefficients (PRCC) techniques, the influence of all parameters in the basic reproduction number R 0 are quantified and the means to control the spread of the game were generalized: isolation and treatment. Finally, the Fractional Euler Method (FEM) and the fractional Forward Backward Sweep Method (FBSM) are used together to solve the fractional optimal control problem and the optimal control strategy is determined. The results of this paper provide a new research idea for parameter estimation of fractional equation, and a more reliable result for controlling the spread of online games. • In Caputo sense, a new fractional model of online game addiction is developed. • The nonlinear dynamics of the new model are investigated. • Through a new parameter estimation method, the estimates are much closer to the real data are obtained. • An efficient algorithm combining Fractional Euler Method (FEM) and Forward Backward Sweep Method (FBSM) is shown. [ABSTRACT FROM AUTHOR]

• An efficient scheme, TrackU, is designed to accurately track the mobility behaviours without any GPS data. • A drift adjusting algorithm is designed to achieve accurate positioning results through WiFi positioning. • An optimization model is designed to minimize power consumption while maintaining the tracking accuracy. • An activity detection algorithm is proposed to help TrackU to infer user's daily activities by merging adjacent segments. • A trace inferring algorithm is proposed to infer to the user's travel mode (hovering, walking, or vehicle). The concerns about location privacy has received considerable attention with the development of Location-based Services (LBSs) over the recent years. Most smartphone users ignore the fact that Apps can infer their locations through accessing WiFi list, although they carefully set location-related permissions to preserve their privacy. Therefore, it is crucial to the public to investigate severe such consequence of WiFi list leakage. In this paper, we develop a tracking scheme for Android, called TrackU, which validate that it is possible to obtain user's location data as well as their location-related behaviors, just by monitoring the WiFi list without any GPS data. Firstly, it periodically scans available Access Points (APs) nearby and queries the geo-location of the device from LBS providers. Secondly, a drift adjusting algorithm proposed obtains the exact locations considering a set of factors, such as historical location information, average signal strength and changing of WiFi list. To preserve the battery life, an optimization is made to dynamically adjust the positioning interval. Based on the obtained data, we design an activity detection algorithm precisely to infer users' daily activities, and identify their travel modes, i.e., hovering, walking, and vehicles. Finally, we implement TrackU and carry out a series of experiments with 39 volunteers from seven cities of China. The experiment results show that our design can detect 91.6% of activities by monitoring the WiFi list, and accurately recognize 94.6% of user's travel mode. [ABSTRACT FROM AUTHOR]

Haze has become a frequent disastrous weather condition in China. Its formation consists of evolution process of several pollutants in certain meteorological conditions with complex relationships among these factors of haze formation. How to explore the complex relationships among these multi-dimensional factors as well as effectively predict them have become a key issue in research community. The research in this paper presents a method to quantitatively reveal causality in the formation of haze, which can be used to effectively predict haze pollution. In order to make the complicated relationship among different factors be interpretable, an PS-FCM (Primary Sub-Fuzzy Cognitive Maps) model is proposed and its multi-dimensional causality solution is demonstrated. By considering the formation of haze as an evolving process with time, we explore and discover the causality based on time series data of haze pollution with PS-FCM. Thus, a multi-dimensional time series data mining method based on the PS-FCM is developed to investigate the formation of haze. We validate our model by comparing with other machine learning method via experimental data and discuss the performance of PS-FCM under different transformation functions. The results explicitly show the quantitative causality among the different factors in haze formation. [ABSTRACT FROM AUTHOR]

Traditional grey forecasting models cannot achieve satisfactory predictive accuracy on approximate non-homogeneous exponential sequence with acceleration, velocity and constant perturbation terms. Hence, it is hard to forecast the developing trend of non-homogeneous exponential data sequences widespread in the real world. It is for this reason that an extended intelligent grey forecasting model with variable speed and adaptive structure (VSSGM for short) is provided in this paper. The proposed model possesses three advantages, namely, strong adaptability to perturbation sequence, self-changing structure, and outstanding accuracy in simulation and prediction. One prominent property is that it can simulate/forecast any given inhomogeneous sequence with acceleration, velocity and constant disturbances, which are the most common perturbations widespread in nature and society. And the proposed VSSGM model can be reducible to DGM(1,1), NDGM, SAIGM and VCGM models in theory. Therefore, the proposed model outperforms the traditional grey models. In order to verify the effectiveness, universality, practicality and feasibility of the model, we conducted an experiment to analyze the precision of the model and employ it to simulate China's industrial added value from 2006 to 2015, then utilize it to forecast the industrial added values from 2019 to 2022. Comparing with the real statistic data, it shows that the proposed model has better prediction performance than traditional grey models. • VSSGM model has characteristics of varistructure and speed adaption. • Possess alterable structure and strong adaptability to perturbation sequence. • Simulate and forecast linear autoregressive sequence unbiasedly. • Proposed model has better performance than traditional GM(1,1) derived models. • China's industrial added value has been predicted effectively. [ABSTRACT FROM AUTHOR]

In order to manage the railway dangerous goods transportation system (RDNGTS) successfully, an explicit and effective previous accident analysis and accident control approach is essential and necessary. In this study a Fault Tree and Fuzzy D-S Evidential Reasoning combined approach is proposed to analyze the RDNGTS accident, which can solve the uncertainty modeling and information fusion problems existing in RDNGTS accident analysis. The approach has six steps: (i) identify causes of accident and calculate their weights based on Fault Tree, (ii) establish the Fuzzy Belief Structure model of causes of accident, (iii) handle initial qualitative and quantitative data, (iv) fuse the pre-processing data based on Fuzzy D-S Evidential Reasoning algorithm, (v) allocate Confidence Level of fuzzy intersection and, (vi) rank the final Fuzzy Belief Structure of each component based on trapezoidal fuzzy numbers and triangular fuzzy numbers. A historical lithium battery railway transportation accident happened in 2016 in China is applied as the background to examine the approach mentioned in this paper. The results show that professional skills and attitudes of transportation staffs are the weakest component in this lithium battery railway transportation accident. Managers of RDNGTS in China should pay more attentions to the professional skills and attitudes of transportation staffs. Some measures such as improve the awareness of safety and protection, train and examine the professional skills of transportation staffs, may be helpful in curbing the negligent working attitude of transportation staffs. Furthermore, the results also show that D-S Evidential Reasoning could provide a unified modeling framework for uncertain, incomplete, inaccurate and even ignorant information. It could solve the limitations in probability reasoning processes effectively. [ABSTRACT FROM AUTHOR]

In many countries, wind power is being developed as a primary source of renewable energy. However, it is difficult to describe and forecast wind speed features, which are stochastic and intermittently complex. Moreover, it is important both to obtain precise forecasts of wind speed for wind power generation and to determine their contribution to regional economies. In this paper, we propose a novel approach for wind speed forecasting. The proposed model is a hybrid that uses the wavelet analysis technique (WAT) for denoising, a negative correlation learning neural network (NCL-NN) ensemble, and an ensemble structure optimized using particle-swarm optimization (PSO). We name the approach WAT-NCL-PSO. We define a novel fitness function to optimize the performance of the NCL-NN ensemble. Then, we rebuild the new NCL-NN ensemble using the contribution rates (CRs) by applying the PSO algorithm. We compiled wind speed datasets from six wind power generator sites in western China and used them to test the performance of the proposed model. Further, we analyzed the model's performance in terms of its robustness and time complexity. Finally, to illustrate the effectiveness of the proposed approach, we compared its performance with that of the back-propagation neural network (BPNN), support vector machine (SVM), bagging, AdaBoost, random forest (RF), long short-term memory (LSTM), seasonal autoregressive integrated moving average (SARIMA), SVM with ensemble empirical mode decomposition (EEMD-SVM), and NCL-NN with wavelet denoising (WAT-NCL) models. The simulation results demonstrate that the performance of the WAT-NCL-PSO model is superior to other methods in terms of forecasting accuracy for short-term wind speeds. [ABSTRACT FROM AUTHOR]

Due to the unknown system structure of the froth flotation process and frequent fluctuations in production conditions, design of control strategy is a challenging problem. As a result, manual operation is still widely applied in practice by observing froth image features. However, since the manual observation is subjective and the production conditions are time-varying, the manual operation cannot make decisions quickly and accurately. In this paper, a data-driven-based adaptive fuzzy neural network control strategy is developed to implement the automatic control of the antimony flotation process. The strategy is composed of fuzzy neural network (FNN) controllers, a data-driven model, and an on-line adaptive algorithm. The FNN is constructed to derive the control laws of the reagent dosages. The parameters of the FNN controllers are tuned by gradient descent algorithm. To obtain the real-time error feedback information, the data-driven model is established, which integrates the long short term memory (LSTM) network and radial basis function neural network (RBFNN). The LSTM network is utilized as a primary model, and the RBFNN is used as an error compensation model. To handle the challenges of the frequent fluctuations in the production conditions, the on-line adaptive algorithm is proposed to tune the parameters of the FNN controllers. Simulations and experiments are carried out in a real-world antimony flotation plant in China. The results demonstrate that the proposed adaptive fuzzy neural network control strategy produces better control performance than the other two existing methods. [ABSTRACT FROM AUTHOR]

As the density of railway operations increases annually, train delays typically result in a greater degree of economic loss. If we can accurately assess the current evolution of delayed trains, railway authorities will be able to make timely decisions and reduce travel anxiety among passengers. In this paper, a deep learning model integrating wavelet transform (WT), fully connected neural network (FCNN), and long short-term memory (LSTM) network is proposed to address the short-term delay prediction problem for trains. Its predictive performance is enhanced by introducing two front train selection schemes, two methods for converting train operating time features into time-series data, and five commonly used wavelet functions. For validation purposes, the model is tested using real-world operational data from two distinct high-speed railway lines in China, and common machine learning algorithms are compared, including random forest (RF), gradient boosting regression tree (GBRT), XGBoosting (XGB), and support vector regression (SVR). The final experimental results demonstrate that our proposed model outperforms the benchmark models and can be effectively applied to practical prediction problems. • We investigate the spatio-temporal effects of delay propagation, categorizing delay data attributes into front trains and target trains. Two feature selection schemes are proposed to assess the impact of front train features on prediction performance. • Wavelet Transform (WT) is employed for the first time in train delay prediction to eliminate random interference. The model's prediction performance is enhanced through denoising and expanding feature dimensionality. • Real data from two Chinese high-speed railways validate the model's accuracy, providing practical guidance for its application. [ABSTRACT FROM AUTHOR]

Massive and complex longitudinal data processing of extended target-effective observations highlights a major challenge: existing methods are concerned with the difficulty of improving prediction robustness and ensuring relatively low computing costs. This paper proposes a novel hierarchical tensor processing error correction gated recurrent unit-effective observation prediction lower- and upper-bound estimation ensemble model. First, we introduce a continuous-time tensor value function to integrate time and context information and create a dynamic variable coefficient embedding layer of continuous-time tensor decomposition, including time granularity, observation, Gaussian component, and context information through a B-spline function approximation. The main advantages of this method include accurate normalized distance squared, effective threshold delimitation, and effective prediction boundary estimation. Second, to solve the error interference caused by the physical factors of multivariate observations, the lower and upper interval boundaries of prediction interval (PI) are deeply integrated into the proposed tensor model through the error factor correction method. The experimental results for aviation datasets from Central and Southern China demonstrate that the proposed model is better than other benchmark models in terms of PI output quality, prediction performance, and computing resource consumption. [ABSTRACT FROM AUTHOR]

Energy consumption can effectively reflect not only the regional economic and industrial structure but also the regional energy utilization efficiency. Effective analysis of energy consumption structures and further scientific understanding of energy consumption are crucial to the formulation of energy development and utilization policies. This paper establishes a nonlinear differential equation for the diversified development of oil-gas, coal, and other energy sources for energy conservation, consumption reduction, and optimization of energy structure and establishes a nonlinear grey multivariate prediction model for energy structure using the differential equation and grey difference information. Specifically, the relationships among several differential functions in the system are analyzed, and specific modeling steps are given. By solving the parameter estimates and time response functions of the model, the consumption of oil-gas and other energy sources can be predicted simultaneously, and the novel model's effectiveness is demonstrated from three different perspectives. The results show that the novel model can effectively predict the consumption of oil-gas and other energy sources as well as the price and consumption of coal in China from 2021 to 2026, and its prediction results can provide effective information for maintaining a sustainable state of the energy system and formulating reasonable and effective control measures. [ABSTRACT FROM AUTHOR]

In these days, as the recycling of household appliances becomes increasingly popular, the recycling network tends to be perfect in television industry. This paper focuses on the game among two recyclers and a processor in a Duopoly market of color TV recycling. We find that if the adjustment coefficients of the decision variables are changed abruptly, the system will fall into chaotic state. In order to avoid hazard of falling into a chaotic state, we adopt the method of delay control, providing the manufacturers with effective measures about chaos control. This paper analyzes the system's reactions to government decision, finding that when the parameters become beneficial for manufacturers, consumers and the environment, the system will fall into chaos and system's regional stability will reduce. Resulting from our analysis, this paper gives advice on the improvement of the environment and enhance in social welfare. Tested through the data we collected, this study is practical in both its theory and its applicability. [ABSTRACT FROM AUTHOR]

Whether the pension system transition is successful is closely related to the accurately accounted IPD amount and rationally solved scheme. China faces the problem of IPD with no exception. This paper uses individual cost method theory, combining Chinese pension system and its operation, builds up the implicit pension debt calculation model, then it measures the Chinese IPD quantity by statistical data. The paper finds out that the average IPD per-year is 39.404 billion Yuan in 2013–2050, the maximum is 185.053 in 2022, the minimum is 0.150 in 2050, and the accumulative IPD will sustain growth with annual growth rate of 7.06% in 2013–2050, from 119.787 billion Yuan to 1497.337 billion Yuan. Finally, this paper proposes the government to raise the legal retirement age, reduce the pension substitution rate, expand the coverage of endowment insurance, improve the investment yield of the pension fund, and so on, to compensate the IPD in China. [ABSTRACT FROM AUTHOR]

Highlights • We construct the volatility spillover networks by VAR-MGARCH-BEKK model. • We calculate five network centralities to analyze systemic importance of financial institutions. • We use the principal component analysis method to obtain comprehensive information about systemic importance. • We identify the factors affecting systemic importance of the financial institutions by using of panel data regression. Abstract From the perspective of volatility spillover, this paper investigates systemic importance and its influential factors of Chinese financial institutions by complex network modeling method. We first construct the volatility spillover networks by vector autoregressive models-multivariate generalized autoregressive conditional heteroscedastic models (VAR-MGARCH) in a BEKK form, and then construct a comprehensive network centrality index based on five network centralities (degree centrality, closeness centrality, betweenness centrality, modified Katz centrality and information centrality) to measure the financial institutions' systemic importance. The results indicate that the larger comprehensive network centrality index is, the higher corresponding ranking for the node of networks is and the greater systemic importance of financial institution will be. Finally, we identify the major factors which affect systemic importance of the financial institutions with panel data regression analysis. We find that compared with the market factors, the accounting factors are more advantageous to identify important financial institutions. Specifically, financial institutions with lager size and higher assets growth rate tend to be associated with greater systemic importance. [ABSTRACT FROM AUTHOR]

A pair of fractal gravity models can be derived from spatial interaction models based on the entropy maximizing principle and the allometric scaling law. The models can be expressed in dual form in mathematics and are important for analyzing and predicting spatial flows in networks of cities. However, the dual relationship of urban gravity parameters has remained an empirical relationship for a long time and lacks theoretical proof. This paper is devoted to proving the duality of fractal gravity models by means of ideas from scaling invariance. The results show that a fractal gravity model can be derived from its dual form. The observational data of interurban spatial flows of the Beijing-Tianjin-Hebei region of China are employed to make a case study, lending further support for the theoretical derivation. It appears that the duality of gravity models rests with the scaling symmetry of fractal structure. This work may be helpful for understanding the theoretical essence and application direction of spatial interaction modeling. • Fractal gravity models include inflow model and outflow model with dual relationships. • There is a dual relation between two scaling exponents of fractal gravity models. • The duality indicates commutative law of scaling exponents of fractal gravity models. [ABSTRACT FROM AUTHOR]

Firefly algorithm (FA) is an effective optimization technique based on swarm intelligence, which has been successfully applied to various practical engineering problems. In this paper, a new dynamic FA (called NDFA) is proposed for demand estimation of water resources in Nanchang city of China. First, a dynamic parameter strategy is used to avoid manually adjusting the step factor. Second, three estimation models in different forms (linear, exponential and hybrid) are developed in terms of the historical water use and local economic structure. Third, normalization method is utilized to eliminate the influences of different units of data. In the experiments, water use in Nanchang city from 2003 to 2015 is considered as a case study. The data from 2003 to 2012 are used for finding the optimal weights of the models, and the rest of data (2013–2015) are applied to test the models. Computational results show that all five FA variants can achieve promising solutions. The proposed NDFA obtains better performance than four other FA variants, and its prediction accuracy is up to 97.91%. Finally, the water demand in Nanchang city from 2017 to 2020 is predicted. [ABSTRACT FROM AUTHOR]

In this paper, we assume that under the balanced and optimal economic growth path, the economic growth rate is equal to the consumption growth rate, from which we can obtain the ordinary differential equation governing the consumption level by solving an optimal control problem. Then, a novel numerical method, namely a so-called discontinuous Galerkin method, is applied to solve the ordinary differential equation. The error estimation and the superconvergence estimation of this method are also performed. The model’s mechanism, which makes our assumption coherent, is that once the energy intensity is given, the economic growth is determined, followed by the GDP, the energy demand and the emissions. By applying this model to China, we obtain the conclusion that under the balanced and optimal economic growth path the CO 2 emission will reach its peak in 2030 in China, which is consistent with the U.S.-China Joint Announcement on Climate Change and with other previous scientific results. [ABSTRACT FROM AUTHOR]

Chinese heavy-polluting industrial enterprises, especially petrochemical or chemical industry, labeled low carbon efficiency and high emission load, are facing the tremendous pressure of emission reduction under the background of global shortage of energy supply and constrain of carbon emission. However, due to the limited amount of theoretic and practical research in this field, problems like lacking prediction indicators or models, and the quantified standard of carbon risk remain unsolved. In this paper, the connotation of carbon risk and an assessment index system for Chinese heavy-polluting industrial enterprises (eg. coal enterprise, petrochemical enterprises, chemical enterprises et al.) based on support vector machine are presented. By using several heavy-polluting industrial enterprises’ related data, SVM model is trained to predict the carbon risk level of a specific enterprise, which allows the enterprise to identify and manage its carbon risks. The result shows that this method can predict enterprise’s carbon risk level in an efficient, accurate way with high practical application and generalization value. [ABSTRACT FROM AUTHOR]

Antiretroviral therapy and long-range diffusion of HIV-infected individuals in heterogeneous environments can greatly impact the transmission and distribution of HIV/AIDS in a population. In this paper, we present a nonlocal dispersal HIV/AIDS epidemic model incorporating spatial heterogeneity as well as antiretroviral therapy to study the spatial and temporal dynamics of HIV/AIDS in China. To overcome the difficulty about a lack of compactness of the semiflow and the challenge about the existence and uniqueness of the principal eigenvalue of an eigenvalue problem without obvious compactness, we apply the operator semigroup and dissipative system theories. We establish the well-posedness and the existence of the global attractor for the system. Then we derive the basic reproduction number R 0 which is defined as the spectral radius of the next generation operator by the renewal equation. Afterwards, we obtain the global dynamics of the system in terms of R 0. Our numerical simulations suggest that: (1) the dynamics of HIV/AIDS transmission largely depends on the nonlocal dispersal kernel function; (2) it is necessary to monitor and design/refine rules for the mobility of HIV-infected population between remote and metropolitan areas to control the HIV/AIDS transmission; (3) antiretroviral therapy benefits not only each treated individual but also the entire community, and increasing the therapy coverage rate is one of the most effective ways to prolong the longevity of HIV-infected population. Moreover, spatial dispersal is also an important factor that cannot be ignored for designing antiretroviral therapy strategies. [ABSTRACT FROM AUTHOR]

Brucellosis is a zoonotic disease, which has gave rise to massive financial burden to animal husbandry in China. Jilin province is one of the worst-hit areas of brucellosis and thus understanding the transmission mechanism of brucellosis is of great significance for successful containment. In this paper, we leverage a discrete-time human-sheep coupled model constructed by employing backward Euler method to investigate the effects of different control measures on the brucellosis transmission in Jilin province. Firstly, we introduce the basic reproduction number R 0 which completely determines the threshold dynamics. Secondly, the parameters values of the transmission dynamics are estimated based on the confirmed human brucellosis cases and the trend of newly infected human brucellosis cases are simulated for the next few years in Jilin province, and the simulated results elucidate that brucellosis will not disappear in a short time. Lastly, we reveal that enlarging the frequency and effectiveness of disinfection, the efficacy of propaganda and education, and the elimination rate can reduce the prevalent time and final scale of brucellosis in Jilin Province. Specifically speaking, when the disinfection frequency τ = 0. 0666 and the effective disinfection rate δ = 0. 3 , the incidence of brucellosis can be dropped to less than 1 per 100,000 eleven years ahead of baseline model. When the effectiveness of propaganda and education is 60%, the control target will be attained eight years in advance. When the elimination rate μ increases by 20%, brucellosis can reach the control target ten years ahead. However, when the new born number of sheep exceeds 12075 or the elimination rate is below 0.00234, the brucellosis will outbreak promptly. Overall, the obtained results may be conductive to suppress the spread of brucellosis. • A discrete model is posed to investigate the dynamics of brucellosis transmission. • Threshold dynamics is completely proved for the discrete model. • We evaluate the effectiveness of control measures based on the model and data. [ABSTRACT FROM AUTHOR]

Abstract: The integration of high dimensional geo-visualization, geo-data management, geo-process modeling and computation, geospatial analysis, and geo-collaboration is a trend in GIScience. The technical platform that matches the trend forms a new framework unlike that of GIS and is conceptualized in this paper as a collaborative virtual geographic environment (CVGE). This paper focuses on two key issues. One is scientific research on CVGE including the concept definition and the conceptual and system framework development. The other is a prototype system development according to CVGE frameworks for air pollution simulation in the Pearl River Delta. The prototype system integrates air pollution source data, air pollution dispersion models, air pollution distribution/dispersion visualization in geographically referenced environments, geospatial analysis, and geo-collaboration. Using the prototype system, participants from geographically distributed locations can join in the shared virtual geographic environment to conduct collaborative simulation of air pollution dispersion. The collaborations supporting this simulation happen on air pollution source editing, air pollution dispersion modeling, geo-visualization of the output of the modeling, and geo-analysis. [Copyright &y& Elsevier]

Abstract: As a newly introduced interconnection network for parallel computing, the locally twisted cube possesses many desirable properties. In this paper, mesh embeddings in locally twisted cubes are studied. Let LTQ n (V, E) denote the n-dimensional locally twisted cube. We present three major results in this paper: (1) For any integer n ⩾1, a 2×2 n−1 mesh can be embedded in LTQ n with dilation 1 and expansion 1. (2) For any integer n ⩾4, two node-disjoint 4×2 n−3 meshes can be embedded in LTQ n with dilation 1 and expansion 2. (3) For any integer n ⩾3, a 4 ×(2 n−2 −1) mesh can be embedded in LTQ n with dilation 2. The first two results are optimal in the sense that the dilations of all embeddings are 1. The embedding of the 2×2 n−1 mesh is also optimal in terms of expansion. We also present the analysis of 2p ×2q mesh embedding in locally twisted cubes. [Copyright &y& Elsevier]

Abstract: At first, the paper described a brief history of communication in China. In 2008, the third generation (3G) telecommunication technologies began to develop. In Chinese 3G telecommunication market, there are three oligopolistic competitors. In order to study competing process of the triopoly, the paper considered a Bertrand model with bounded rational. A triopoly game is modeled by three nonlinear difference equations. By using the theory of bifurcations of dynamical systems, the existence and stability for the equilibria of this system were obtained. Numerical simulations were used to show bifurcations diagrams, the maximum Lyapunov exponent and sensitive dependence on initial conditions. We observed that increasing the speed of adjustment of bounded rational player might change the stability of Nash equilibrium point and cause bifurcation and chaos to occur. Thus, it will be difficult to make strategy and to forecast for each enterprise. It might cause the telecom markets to be irregular. The analysis and results in this paper are important for Chinese telecommunication markets, the same to mathematics. [Copyright &y& Elsevier]

Abstract: An integrated ecological evaluation of the Beijing economy was presented in the paper based on the emergy accounting with the data in 2004. Through calculating environmental and economic inputs within and outside the Beijing economy, this paper discusses the Beijing’s resource structure, economic situation and trade status based on a series of emergy indicators. Through the comparison of the systematic indicators of Beijing with those of the selected Chinese cities, the general status of the Beijing economy in China is identified. The results also show that most indicators of Beijing are located at middle levels among the selected Chinese cities. Particularly, the environmental impacts, expressed by the ratio of waste to the renewable emergy, and the ratio of waste to the total emergy use, are 84.2 and 0.26, respectively in Beijing in 2004, which indicate that the Beijing economy is greatly reliant on the imported intensive fuels, goods and services with high empower density and environmental loading. [Copyright &y& Elsevier]

Abstract: Starting from the delay Liénard and van der Pol equations with time delay, this paper represents an attempt to give an introductory presentation of dynamics for a class of nonlinear systems with time delay and emphasizes the recent development on this field in china. It is clearly seen that the time delay occurred in systems can lead to the rich dynamical behaviors such as death island, Hopf bifurcation, double Hopf bifurcation, period-doubling bifurcation, phase locked (periodic) and phase shifting solutions, co-existing motions, quasi-periodic motion and even chaos. In the quantitative and qualitative treatment of the analytical method, a new method, called perturbation-incremental scheme (PIS), is introduced by two steps, namely perturbation and increment for continuation to the critical value at a simple Hopf bifurcation. This paper shows that time delay may be used as a simple but efficient “switch” to control motions of a system: either from order to complex motions or from complex to order motions for different applications. As well as the PIS can bypass and avoid the tedious calculation of the center manifold reduction (CMR) and normal form. [Copyright &y& Elsevier]

Abstract: This paper provides an integrated study on the ecological account for the Chinese economy in 2004 based on emergy synthesis theory. The detailed flows of the Chinese economy is diagramed, accounted and analyzed in categories using the biophysically based ecological accounting. Through calculating environmental and economic inputs within and outside the Chinese economy, this paper discusses the Chinese international exchange, describes the resource structure, and assesses its sustainability as a whole. Also, the comparison of systematic indicators, such as emergy/dollar ratio, environmental load ratio, and emergy self-support ratio, with those of the other countries is tabled and explored to illustrate the general status of the Chinese economy in the world. Take, for example, the environmental load ratio, which was 9.29 in China 2004, it reveals that the Chinese economy put high pressure on the local environment compared with those of the environment-benign countries, such as Brazil (0.75), Australia (0.86) and New Zealand (0.81). In addition, in this paper, the accounting method of tourism is adjusted based on the previous researches. [Copyright &y& Elsevier]

Abstract: A risk assessment model for water shortage is constructed using a risk analysis method based on the information diffusion theory. The application of this model is demonstrated in the city of Yiwu in Zhejiang Province, China. Based on the analytical results from a small sample, this study indicates that the present model is more stable and effective than the traditional model. Risk assessment results are used to analyze the carrying capacity of water resources from an ecological angle. For this study, the carrying capacity of water resources is defined as the maximum volume of water suitable for supporting human activity in certain stages of social development that can be borne by water resources under favorable ecological conditions. Further study on Yiwu is also performed, with results indicating that water shortages in this city are not related to types of water source, but can be classified in terms of water quality and conservation. To verify the results of theoretical investigation in this paper, the authors simulate changes in the carrying capacity of water resources under the conditions of future water management policies. This simulation uses the system dynamics (SD) model, based on the historical data collected by the city over the past 20 years and governmental plans to raise inhabitants’ living standards between the present and 2020. The paper simultaneously indicates that both singularly pursuing fast economic development at the expense of the environment and promoting environmental protection via reduced economic development are undesirable for Yiwu. Simultaneously giving consideration to both the economic development and environmental protection is likely to produce better overall results. However, if the present water supply level is maintained but does not increase in the near future, Yiwu''s water supply will be unable to satisfy requirements even under this scheme. In this case, the carrying capacity of water resources in the region can only be effectively improved by promoting more efficient use of water and water conservation schemes, as well as strengthening long-term investment in environmental protection. [Copyright &y& Elsevier]

The fast and steady economic growth in China during the 1990s has attracted much international attention. Using the three most recent Chinese input–output tables, this paper investigates industry structure and inter-industry relationships and the relationship of both to economic growth. The input–output tables contain intermediate demand and final demand for six broad industries, namely, Agriculture, Industry, Construction, Transportation, Post and Telecommunications, Services, and Others, for 1992, 1995 and 1997, which enables computing of input–output coefficients for three time periods. As direct and indirect input–output coefficients characterise industry structure during a particular time period, changes over time reflect the patterns in industry structure evolvement. Furthermore, output growth in a particular industry can be analysed from two different sources, namely the changes in input–output coefficients that reflect technological change, and the change in final demand. This paper sheds light on four different issues over the five-year period from 1992 to 1997: (1) Was growth driven by technological changes or final demand increases? (2) As a result of the interdependence of industries, how did an increase in final demand in one industry affect growth in another? (3) How has the bottleneck of an insufficient capability in the transportation, post and telecommunications sectors to cope with demands from other sectors been affected during this period? (4) Has the industry structure of the economy been shifting in conformity with traditional growth theory, namely, with a decline in the agricultural sector and a rise in the modern industrial sector? [Copyright &y& Elsevier]

• This paper presents a new method for dynamic prediction of COVID-19 spread by considering time-dependent model parameters. • This method discretises the susceptible-exposed-infected-recovered-dead (SEIRD) epidemiological model in time domain to construct the nonlinear state-space equation for dynamic estimation of COVID-19 spread. • A maximum likelihood estimation theory is established to online estimate time-dependent model parameters. • An extended Kalman filter is developed to estimate dynamic COVID-19 spread based on the online estimated model parameters. Prediction of COVID-19 spread plays a significant role in the epidemiology study and government battles against the epidemic. However, the existing studies on COVID-19 prediction are dominated by constant model parameters, unable to reflect the actual situation of COVID-19 spread. This paper presents a new method for dynamic prediction of COVID-19 spread by considering time-dependent model parameters. This method discretises the susceptible-exposed-infected-recovered-dead (SEIRD) epidemiological model in time domain to construct the nonlinear state-space equation for dynamic estimation of COVID-19 spread. A maximum likelihood estimation theory is established to online estimate time-dependent model parameters. Subsequently, an extended Kalman filter is developed to estimate dynamic COVID-19 spread based on the online estimated model parameters. The proposed method is applied to simulate and analyse the COVID-19 pandemics in China and the United States based on daily reported cases, demonstrating its efficacy in modelling and prediction of COVID-19 spread. [ABSTRACT FROM AUTHOR]

Denoeux proposed an operator called the cautious conjunctive rule (CCR) to combine non-dogmatic belief functions (i.e., the frame is considered a focal element) from reliable dependent sources, which can occur in practice. However, in cases such as uncertain multiple attribute and group decision analyses, belief functions may denote data that vary in importance. This paper extends CCR as a weighted CCR (WCCR) to combine belief functions in consideration of their relative weights in such situations. Properties of WCCR are analyzed, proven, and demonstrated using numerical examples. In particular, the consideration of relative weights enables WCCR to combine dogmatic belief functions. The normalized WCCR is further constructed and used to assess the trustworthiness of a hospital information system employed in many hospitals in Anhui Province, China. [ABSTRACT FROM AUTHOR]