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Urbanization and the release of carbon emission are two significant features of contemporary economic and social progress in China. However, there are few studies that consider how urbanization policy goals affect carbon emission. This paper aims to study the nonlinear effects of urbanization policy goals on carbon emission in 13 cities of the Beijing-Tianjin-Hebei region. In this paper, a multi-dimensional spatial disturbance grey model is proposed. Based on matrix perturbation theory, this paper explores the spatial perturbation bounds of the novel model and verifies that the spatial property of the model is consistent with the viewpoint of the First Law of Geography. Then, the novel model is used to predict the urban carbon emission under different urbanization policy goal scenarios. The results showed that the novel model has spatial regional correlation in spatial region prediction. With the development of urbanization level, carbon emissions show a downward trend in more than 50% of cities from 2022 to 2025. In addition, the improvement of urbanization policy goals will promote the carbon peak time of most cities. The carbon emission intensity will exhibit an inverted U-shaped trend distribution in space. The research results are conducive to providing scientific guidance for the formulation of new-type urbanization policies and helping to achieve the dual carbon goals. • A multi-dimensional spatial disturbance grey model is proposed. • The novel grey model is consistent with the viewpoint of the First Law of Geography. • The spatial distribution of carbon intensity showed an inverted U-shaped pattern. [ABSTRACT FROM AUTHOR]

With large volume of passengers boarding and alighting through subway platforms, the stations are getting crowded, resulting in drops in the level of service and safety concerns, especially for subway systems operating at capacity during peak hours. Thus, it is crucial for subway agencies to sense changes in travel demand and adjust their management schemes accordingly. In this paper we propose a statistical approach to estimate dynamic passenger flows with automated data. First, we develop a dynamic logistic model for calculating passenger tap-out times, which can be employed to infer passenger flow characteristics at the aggregate level. In addition, a new passenger-to-train assignment model for any subway route is derived based on the dynamic model. Subsequently, we apply an expectation-maximization algorithm to estimate the model parameters with automated fare collection and automated vehicle location data. Finally, a cross-validation method is employed to validate our approach with data obtained from several routes in Beijing subway system in China. Results of 95% prediction intervals indicate the effectiveness of the models and the proposed estimation methods. • Propose a dynamic logistic model for inferring and predicting subway passenger tap-out times. • Apply an expectation-maximization algorithm for model parameters estimation. • Develop a novel passenger-to-train assignment strategy. • No prior distributions of parameters or additional information required in the proposed methods. • Results show good effectiveness of the models and acceptable accuracy of the prediction methods. [ABSTRACT FROM AUTHOR]

• The model for multiple horizontal wells considers heterogeneous fracture network. • The number of frac hits is determined by pressure differentials and derivatives. • Higher conductivity promotes inter-well pressure equilibrium. • Lower conductivity is beneficial to extended interference testing. Frac hits can significantly impact the final development results. Therefore, accurately and quickly understanding the frac-hit interference between wells is crucial for efficient development. At present, traditional pressure/production data analysis methods and numerical simulation methods can characterize the frac-hit interference. However, challenges such as quantitatively assess interference, high uncertainty in modeling parameters and computational time still exist. A quantitative evaluation method for the interference between wells with complex fracture networks has not been reported yet. This paper takes shale oil reservoir with three production wells as an example and establishes a semi-analytical model (frac-hit semi-analytical model, FSM) based on the linear flow characteristics. The model accurately characterizes the non-uniform distribution of complex fracture networks in the stimulated reservoir volume by introducing fractal theory. A quantitative evaluation index, interference coefficient, is defined to assess the interference. The coupled flow model is applied to evaluate the effects of interference in three typical wells in a continental shale oil field in China. We can see from the actual data fitting of the FSM model that the interference coefficients between target well A and adjacent wells B and C are 0.1 and 0.05, respectively, which means there are about 8–10 and 3–5 frac hits, respectively. Comprehensive analysis for the effects of interference shows that the current inter-well interference is beneficial for production in low-pressure areas but detrimental to new well production. Based on this, sensitivity analysis is generated to illustrate the relationship between the conductivity of combined fractures arising from hits and the interference coefficient. As the interference increases, the propagation of inter-well pressure accelerates, significantly reducing the time required for inter-well pressure equilibrium. The research findings have important guiding implications for the quantitative evaluation of inter-well interference in field operations of horizontal shale oil and gas wells and optimization of fracturing operations. [ABSTRACT FROM AUTHOR]

• An improved singular spectrum decomposition is proposed. • High complexity components are secondarily decomposed to reduce its complexity. • An improved prediction model of optimization algorithm is proposed. • Intelligent weighting strategy is introduced to overcome traditional weighting problem. • A combined prediction model for carbon emission is proposed. Accurate prediction of carbon emission is critical for the development of low-carbon economy. However, most carbon emission prediction studies use a single model with low prediction accuracy, and do not consider the instability of carbon emission. Therefore, this paper proposes a combined prediction model of carbon emission. Firstly, the original data is decomposed by singular spectrum decomposition to obtain a limited amount of singular spectrum components. Secondly, high complexity components are secondarily decomposed by variational mode decomposition. Then, chameleon swarm algorithm and carnivorous plant algorithm are used to train the regularization coefficients and kernel parameters of kernel extreme learning machine and least squares support vector machine respectively, and the trained model is used to predict the decomposition components. Finally, induced ordered weighted averaging operator is used to calculate the weight of single model, and error correction is introduced to further promote the prediction accuracy. The carbon emission data of China and the United States is used to make a prediction experiment. The results indicate that the proposed model is superior to other comparative models in different indexes, which provides a new idea for carbon emission prediction. [ABSTRACT FROM AUTHOR]

• A non-linear programming model to design full-day demand-adapted timetables is proposed. • The time-dependent maximum sectional passenger flow is used as input data. • A new algorithm is proposed to match demand and services' supply. • The algorithm uses a rolling-horizon mechanism to iteratively compute departure times. • The effectiveness of the proposed algorithm is illustrated using real data of Chengdu Metro line 1. This paper focuses on the problem of designing the schedule of urban rail transit systems under dynamic passenger demand considering limited train capacity and congestion. From the point of view of operators, the goal of timetable optimization consists of using as few as possible service trains to transport the arriving passengers to their destinations securely and quickly. On the other hand, passengers want to spend the minimum time travelling to their destinations, including the time they wait on platforms until they board the first and successive trains in case of transferring between lines. Due to the variability of passenger flows and the difficulty in exactly solving full-day timetabling problems in short computation times, it is difficult in practice to use the approaches proposed in the literature. Moreover, when the capacity of trains is considered and oversaturation conditions emerge, the problem becomes even more complex, converting into a challenge the design of schedules from an operational point of view. By considering fixed train dwell times at stations and train running times between stations, we first propose a non-linear programming formulation to model the problem of determining the most convenient interdeparture times of services at the first station of a line, aiming at minimizing the waiting time of passengers. Since the dynamic behaviour of passenger demand and the maximum sectional passenger flow are approximately synchronous, an innovative method, the spatiotemporal synchronization coupling algorithm, based on working with the time-varying maximum sectional passenger flow instead of the demand patterns is proposed to quickly solve in an approximate way the demand-driven timetabling problem. The proposed approach is applied to a real case of Chengdu Metro Line 1 in China. The results show the effectiveness of the algorithm, both in terms of computational effort and in terms of reducing the waiting time currently experienced by passengers. [ABSTRACT FROM AUTHOR]

• An interval sequence prediction method for power generation is proposed. • The new matrixed grey model can be applied to different interval sequences. • Parameters are estimated by combining Particle swarm optimization algorithm and Ordinary Least Square method. • The new model has higher prediction accuracy compared with four competing models. • Intervals of different types of power generation in China are predicted. Development trends of power generation sequences have nonlinearity and uncertainty. The nonlinearity can be reflected by the nonlinear grey Bernoulli model. The uncertainty can be reflected by the interval sequence. A matrixed nonlinear grey Bernoulli model based on interval sequences is proposed in this paper. In order to make the model directly applicable to interval sequence, the traditional grey Bernoulli model is linearized by power function transformation, then the parameters are matrixized, and finally the recursive prediction formula is obtained by Cramer's law. Particle swarm optimization algorithm is used to estimate the nonlinear parameter. By adjusting the parameter, the new model can be used to predict exponential, saturated and parabolic interval sequences. The new model is used to predict the intervals of China's thermal power, hydro power, nuclear power, wind power, national power generation and power generation in east China. The prediction results show that the new model has higher prediction accuracy than the competing models. Finally, the power generation of four modes and national power generation from 2022 to 2025 are predicted and analyzed. [ABSTRACT FROM AUTHOR]

• A dengue model is proposed with parameters linked to climate and human factors. • Both local and global stability of the disease-free equilibrium are analyzed. • Sensitive analysis reveals a decisive role of temperature in dengue spread. • Sensitive analysis discloses a local role of precipitation and human population. • Climate changes aggregate dengue burden more than human population. Dengue fever (DF) is a re-emerging disease spread by mosquitoes. It is primarily found in the tropics and subtropics, and its prevalence is heavily influenced by global warming. A comprehensive understanding of how the changes in climate and human population affect future dengue epidemics is crucial for effective prevention and control. In this paper we extend a mechanistic compartmental model for mosquito dynamics of immature and mature stages, coupled with human population dynamics. Stability analysis of the disease-free equilibrium is carried out analytically, and the basic reproduction number is derived. Model parameters are explicitly linked to climatic variables and human population density. Model validation suggests that the basic reproduction number acts as a reliable measure of the spread ability of DF in response to future scenarios of climate and human population change. Our model predicts that the risk area of DF will expand considerably in mainland China, and the expansion fronts are mainly extended in the center and east. Most importantly, sensitivity analysis and model prediction both show that while temperature is critical in determining the threshold suitability for DF transmission, precipitation and human population density act locally to regulate the spreading DF patterns, where high values of these two factors may significantly accelerate the spreading process. Our results indicate a strong call for increased attention in the areas that are currently safe from DF, but climatically suitable for DF spread as consequence of the fast-economic growth and rapid urbanization. [ABSTRACT FROM AUTHOR]

• A multi-stage Malmquist-Luenberger index model is constructed for the multiple production of iron and steel industry. • The proposed model can evaluate environmental total factor productivity (ETFP) growth of whole system and sub-process. • Technical regression of steel-making sub-process is the main source of restricting system ETFP growth. • The change of system ETFP varies greatly, being highest in the eastern region, and lowest in the western region. • Compared with the single-stage conventional MLPI, the multi-stage MLPI is smaller and has more discriminatory power. This paper develops a multi-stage Malmquist-Luenberger index (MLPI) model by considering the joint production of desirable and undesirable outputs in multiple production processes. The proposed approach not only evaluates the environmental total factor productivity (ETFP) growth of the whole system but also investigate the progress and regress of each sub-process, i.e., the iron-making sub-process and the steel-making sub-process. Driving factors of ETFP change are identified through multi-stage MLPI decomposition. With an application of the proposed model in China's 48 iron and steel enterprises over the period of 2009–2013, the validity of the proposed models has been demonstrated. Moreover, this study explores the regional difference analysis of system and multi-stage ETFP among 31 provinces in eastern, central and western regions. Results show that the ETFP of system, iron-making sub-process and steel-making sub-process experienced increase annually, the most important finding is that the environmental efficiency increase is the most critical driving factor in promoting ETFP growth. Technical regression of steel-making sub-process is the main source of restricting system ETFP growth. In addition, the change of system ETFP varies greatly in different regions, being highest in the eastern region, and lowest in the western region. Compared with the single-stage conventional MLPI, the multi-stage MLPI is smaller and has more discriminatory power. [ABSTRACT FROM AUTHOR]

• Propose a new nonsmooth two-stage stochastic equilibrium model of medical supplies in epidemic management. • Transform to a monotone two-stage stochastic variational inequality model that is computationally tractable. • Decisions with fairness, including storage and dynamic distribution by competitions among hospitals. • Efficient and convergent progressive hedging method enrolling semismooth Newton method for subproblems. • Case study in Wuhan suffered from the COVID-19 pandemic. The storage and distribution of medical supplies are important parts of epidemic prevention and control. This paper first proposes a new nonsmooth two-stage stochastic equilibrium model of medical supplies in epidemic management. The first stage addresses the storage in the pre-disaster phase, and the second stage focuses on the dynamic distribution by enrolling competitions among multiple hospitals over a period of time in the post-disaster phase. The uncertainties are the numbers of infected people treated in multiple hospitals during the period of time, which are time-varying around a nominal distribution predicted by historical experience. The two-stage stochastic equilibrium model is further approximated and transformed to a monotone two-stage stochastic variational inequality (SVI) model that is computationally tractable, with the aid of a smooth approximation technique. We employ the progressive hedging method (PHM) to solve a case study in the city of Wuhan in China suffered from the COVID-19 pandemic. Numerical results are presented to demonstrate the effectiveness of the proposed model in planning the storage and dynamic distribution of medical supplies in epidemic management. [ABSTRACT FROM AUTHOR]

• The principle of difference information and the principle of minimum information are discussed. • The proposed method can deal with the environment problem in other regions. • The unequal adjacent accumulation multivariate grey model is used to forecast the public concern about air pollution. In grey theory, the principle of difference information holds that difference is information. To mine the difference information between data, the unequal accumulation is proposed in this paper. The unequal accumulation reduces the loss of difference information and improves the forecasting performance of the grey model. Based on the unequal accumulation, the unequal adjacent discrete multivariable grey model is proposed. According to the comparison with other models, the proposed model has good forecast performance and algorithm efficiency. To further prove the practical significance of the model, it is used to predict the public concern about air pollution in three cities of China. The results show that the public concern about air pollution in cities with different air pollution conditions is quite different. Environmental protection departments could pay attention to the public concern about air pollution to better control air pollution. [ABSTRACT FROM AUTHOR]

• An improved box-plot is used to modify the outliers in the original carbon price data. • The optimal parameter structure of the individual model is determined by a novel comprehensive evaluation criterion. • The adaptability between the optimum forecasting model and the predicted subseries is ensured by an original matching strategy. • A new hybrid optimization algorithm is employed to optimize the ensemble models parameters. Accurate carbon price forecast plays a vital role in energy conservation, emission reduction and environmental protection. In previous studies, more attention was focused on the prediction accuracy and stability, while the problem of disharmony between the prediction model and the data pattern is usually ignored. Considering the matching utility with deeper understanding of data and model, this paper proposes a novel approach to forecast carbon price, which combines the data preprocessing mechanism, decomposition technology, forecast module with selection and matching strategy and ensemble model based on an original hybrid optimization algorithm. According to a comprehensive evaluation index in consideration of several evaluation perspectives, the optimal parameter structures of the three forecast models are selected in this framework. Then, the data components decomposed by variational mode decomposition are reconstructed into three novel range entropy series with different levels of complexity by range entropy. As a result, the matching relation between the three forecasting models and the three range entropy series is correspondingly established. Additionally, a feedback neural network optimized by hybrid optimization algorithm, which persists more superiorities of reasonable weight assignment than the usual ensemble method, is initially used to synthesize three forecasting results of range entropy series. The carbon price data from four different trading markets in China is used to test the novel approach and the experimental results indicate that it does enhance the performance of carbon price forecasting, and provide a convincing tool for the operation and investment of the carbon markets. [ABSTRACT FROM AUTHOR]

• Probability density prediction can measure uncertainties of runoff. • B-spline quantile regression is proposed to predict the probability density of runoff. • Different evaluation criteria of probabilistic predictions is adopted. • The new method is used in Shigu station in Jinsha River of China. Exact and dependable runoff forecasting plays a vital role in water resources management and utilization. This paper proposes a B-spline quantile regression probability density prediction method to predict future runoff and quantify the uncertainty of prediction. The method includes three steps. First, the B-spline function is used to perform spline processing on the runoff data. Secondly, the spline-processed training data is input into the quantile regression model to calculate the parameters of the B-spline quantile regression model, and the successfully constructed B-spline quantile regression model is combined with kernel density estimation to construct a probability density prediction method. Finally, the constructed B-spline quantile regression probability density prediction method is used to forecast future runoff flow, and quantitatively analyze the relevant prediction uncertainty. Six evaluation indicators are constructed, among which the root mean square error, the deterministic coefficient, the pass rate are the evaluation metrics of point predictions based on probability mean, median and mode; the prediction interval coverage probability, prediction interval normalized average width and continuous ranked probability score are the evaluation criteria of interval predictions and probabilistic forecasting. The presented method is able to depict the probability density curve of future runoff flow, and obtain more comprehensive information than point forecasting and interval predictions. As a case study, this method is applicable to the Shigu station of the Jinsha River in China. The results show that the results of the proposed method are better than that of existing some state-of-the-art methods. From the perspective of application, the pass rate and the deterministic coefficient of the method have reached the grade A of accuracy. Therefore, the B-spline quantile regression model provides an alternative scheme to runoff prediction. [ABSTRACT FROM AUTHOR]

• Four time-delay weights structural types are designed by the grey incidence model. • The novel multivariable grey prediction model considering the time-delay periods and time-delay weights is proposed. • The accumulating impact of influencing factors on CO 2 emissions of China's transport sector is measured. CO 2 emissions from the transportation sector occupy an increasingly important proportion in China's carbon dioxide emissions. Measuring the accumulative impact of factors on carbon emissions over time is of great significance for formulating corresponding policies. This paper aims to propose a novel time-delay multivariate grey model to measure the CO 2 emissions' accumulating impact of China's transportation sector. Firstly, the grey incidence model is used to identify time lags between the input and output variables, and also analyze the structure type of time-delay weights. Then, an accumulative time-delay multivariate grey prediction model is developed. In this model, a Gaussian formula is used to discretize the convolution integral of the time response function, and the particle swarm optimization algorithm is employed to determine the optimal weight coefficients. Finally, a case for CO 2 emissions prediction is adopted to test the effectiveness and practicality of this model compared with the alternative models. The results show that the proposed model outperforms other six competing models in accordance with two measuring indices, and suggestions on emissions mitigation are proposed based on the prediction results. [ABSTRACT FROM AUTHOR]

Aiming to problems of high drug shortage possibility and rupture of liquidity caused by serious liquidity occupied in drug supply chain of public hospitals in China, this paper discusses effectiveness about application of Inventory Pooling theory in this section. Employing Vensim software of system dynamic, the paper establishes 2 simulation models of whole process from drug demand to supply involved by patients, hospitals, distributors and manufacturers in traditional mode and the Pooling mode respectively. By quantitative comparison and analysis, it is proved that Inventory Pooling could be an effective method to improve performance of drug supply chain in China, especially in cutting down shortage possibility and reducing liquidity occupied. [ABSTRACT FROM AUTHOR]

• Simultaneous product and service delivery vehicle routing problem with time windows and release dates. • Mathematical formulation provided and improved. • Designed an adaptive large neighborhood search algorithm(ALNS) with three new customized removal operators. • Conducted numerical experiments and validated the effectiveness of the algorithm. • Compared simultaneous and separate modes of delivery and identify favorable conditions for choosing one mode over the other. In this paper, a new variant of the well-known vehicle routing problem with time windows (VRPTW) called as the simultaneous product and service delivery problem with order release dates is studied. The problem is motivated by the operations of a 3PL service provider who manages the last mile delivery of electrical and electronic home appliances of a large retailing company based out of China. Apart from the usual constraints on time windows and vehicle capacities, an interesting characteristic of this problem is that the product and service delivery has to happen simultaneously with the explicit consideration of order release dates. This situation necessitates the presence of a skilled service technician at the time of product delivery itself. Being a proven NP -hard problem, we propose a mixed-integer linear programming (MILP) formulation for the problem. For solving large problem instances, an adaptive large neighborhood search (ALNS) algorithm is developed with three new customized removal operators. The proposed approaches are validated using real-life data as well as benchmark problem instances available in the literature. The computational results are quite encouraging and demonstrate the superior performance of the proposed algorithm in terms of computational time as well as the quality of the solutions generated. We also analyze the effect of length of time windows and compare simultaneous and separate modes of delivery and identify favorable conditions for choosing one mode over the other. [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]

• An optimization method for coal-fired power plant desulfurization systems is proposed. • The optimization method can effectively reduce the desulfurization cost. • An enhanced FCM clustering method is developed. Desulfurization systems in coal-fired power stations often suffer the problem of high operating costs caused by a rule-of-thumb control strategy, which implies great potential for optimization of the operation. Due to the complex desulfurization mechanism, frequently fluctuating unit load, and severe disturbance, it is challenging to determine the optimal operating parameters based on the traditional mechanistic models, and the operating parameters are closely related to the operational efficiency of the flue gas desulfurization system. In this paper, an operation strategy optimization method for the desulfurization process is proposed based on a data mining framework, which is able to determine online the optimal operating parameter settings from a large amount of historical data. First, Principal Component Analysis (PCA) is used to reduce data redundancy by mapping the data into a new vector space. Based on the new vector space, an enhanced fuzzy C-means clustering (Enhanced-FCM) is developed to cluster the historical data into groups sharing similar characteristics. Taking sulfur dioxide emission concentration as a constraint condition, the system is optimized with economic benefits and desulfurization efficiency as the objective function. When performing optimization, the group that current operating conditions belong to is determined first, then the operating parameters of the best performance are searched within the group and provided as the optimization results. The method is validated and tested based on the data from a wet flue gas desulfurization (WFGD) system of a 1000 MWe supercritical coal-fired power plant in China. The results indicate that the proposed operation strategy can appropriately obtain operating parameter settings at different conditions, and effectively reduce the desulfurization cost under the constraint of meeting emission requirements. [ABSTRACT FROM AUTHOR]

• We fixed the exponential function to describe the progression rate of TB. • The threshold value of disease persistent or not is given. • The existence and global stability of disease free equilibrium and endemic equilibrium both are proved. • The existence of global attractor, and persistence of disease are studied. This paper deals with the global dynamic analysis for a tuberculosis transmission model with age-structure and the exponential progress rate. The time delay in the progression from the latent individuals to becoming the infectious individuals is also considered in our model. The basic reproduction number R 0 of the model is defined. It is proved that R 0 = 1 is a threshold to determine the disease extinction or persistence. The disease free equilibrium is globally stable (unstable) if R 0 < 1 (if R 0 > 1). There exists an endemic equilibrium and the system is uniformly persistent if R 0 > 1. The global stability of the endemic equilibrium is given when R 0 > 1. At end, the model is applied to describe tuberculosis transmission in China. The number of total population, the number of the annual new TB cases, the annual PPD positive rate, and the prevalent rate all match the statistical data well. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a nonlinear piecewise smooth dynamical system of the trajectory of deviated wells according to engineering background. An optimal control model is established and the necessary conditions for optimality are proved via maximum principle. The optimal control problem is solved by a revised Hooke–Jeeves algorithm. The uniform design technique has been incorporated into the revised Hooke–Jeeves algorithm to handle the multimodal function. Computer simulation is used for this paper, and the numerical example illustrates the validity and efficiency of the algorithm. The procedure demonstrates its advantages in practical applications in Liaohe Oil Field. [Copyright &y& Elsevier]

Abstract: With the start up of West–East Natural Gas Transmission Project, the construction of natural gas-pipeline will enter on a new era in China. The development tendency will be towards large-diameter, high-pressure and long-distance for natural gas-pipelines. Correspondingly, the life cycle cost of natural gas-pipeline networks is increasing gradually. The mainline system is a vital part of natural gas network systems. The investment required for the mainline system is enormous, usually accounting for 80% of the total investment for this system. In general, the investment required for a gas-pipeline depends on its operating parameters. Therefore, based on the characteristics of gas networks, optimization for investment becomes indispensable to gas networks design. A comprehensive and optimal mathematic model of a gas networks system is established in this paper which considers all the factors influencing the total investment of a gas networks system (e.g. pipe diameter, thickness, pressure, length, compression ratio, etc). From the standpoint of the characteristics of a model comprising both continuous and discrete variables, a new methodology, rank-optimization, is presented. On the basis of this model, a simple and visual high-pressure networks optimization program has been compiled. Furthermore, the developed optimization program has been applied to a practical project and the effects of operating parameters on the total investment have been analyzed. The simulation model in this paper is shown to be an effective method to solve optimization on mainline system in high-pressure gas networks. [Copyright &y& Elsevier]

Abstract: Some forecasting models have been developed, each has its own application condition. The grey model is used for small sample forecasting, but until now there is no reasonable explanation for the reason why it is not used for large sample. Therefore, in this paper, matrix perturbation theory is employed to explain the reason. The results of practical numerical examples from previous works demonstrate that the small sample usually has more accuracy than the large sample when establishing grey model in theory. Furthermore, we used the grey model with small samples to analyse the trend of syphilis incidence in China. [Copyright &y& Elsevier]

Abstract: Grey theory is one approach that can be used to construct a model with limited samples to provide better forecasting advantage for short-term problems. Generally, the GM (1,1) and Discrete GM (1,1) models are two typical grey forecasting models in grey theory. However, there are two shortcomings in the above grey models respectively, i.e., the homogeneous-exponent simulative deviation in GM (1,1) model, and the unequal conversion between the original and white equations in DGM (1,1) model. In this paper, we firstly propose a novel Generalized GM (1,1) model termed GGM (1,1) model, based on GM (1,1) and DGM (1,1) models, to overcome the above shortcomings. Then, we detailedly study four important properties in this new grey model. Four estimative approaches of stepwise ratio in GGM (1,1) model context is also covered. In the end, we simulate and forecast the fuel production in China during the period 2003–2010 using three GM (1,1) models. The empirical results show that GGM (1,1) model has higher simulative and predictive accuracy than GM (1,1) and DGM (1,1) models. This work contributes significantly to improve grey forecasting theory and proposes a optimized GM (1,1) model. [Copyright &y& Elsevier]

Abstract: Flood disasters are one of the most common and destructive natural hazards all over the world. In this paper, improved interior-outer-set model (IIOSM) based on information diffusion theory is introduced in detail to assess flood risk in an effort to obtain accurate analytical results that represent the actual situation. Then fuzzy α-cut technique is applied to calculate the fuzzy expected values under the possibility–probability distribution (PPD) calculated by IIOSM. Taking the value of α throughout the interval (0,1], we correspondingly get access to the conservative risk value (R C ) and venture risk value (R V ). Selection of α, R C and R V is dependent on present technical conditions and risk preference of different people. To illustrate the procedure of IIOSM and fuzzy α-cut technique, we employ them respectively to analyze the flood risk in Sanshui District, located in the center of Guangdong province in China. The results, such as risk value estimations, as well as fuzzy expected values, i.e. R C and R V under the given α-cut level, can reflect the flood risk quite accurately. The outcomes of this research based on IIOSM and fuzzy α-cut technique offer new insights to carry out an efficient way for various flood protection strategies. [Copyright &y& Elsevier]

Abstract: The Nonlinear Grey Bernoulli Model NGBM(1,1) performs well in the simulation and forecasting of series having non-linear variations. To improve the simulation and forecasting accuracy, the parameters optimization of an NGBM(1,1) model is formulated as a combinatorial optimization problem and is solved collectively using LINGO (an Operational Research software) in this paper. The optimized result has been verified by a numerical example of a fluctuating sequence and a case study of opto-electronics industry in Taiwan. Comparisons of the obtained simulation results from the optimized combinatorial NGBM(1,1) model with the traditional one demonstrates that the optimal algorithm is a good alternative for parameters optimization of the NGBM(1,1) model. The optimized NGBM(1,1) model is used to simulate and forecast the annual qualified discharge rate of industrial wastewater in 31 provinces of China for the period from 2001 to 2011. The modeling results can assist the government in developing future policies regarding environmental management. [Copyright &y& Elsevier]

Abstract: This paper presents an epidemic model aiming at the prevalence of HIV/AIDS in Yunnan, China. The total population in the model is restricted within high risk population. By the epidemic characteristics of HIV/AIDS in Yunnan province, the population is divided into two groups: injecting drug users (IDUs) and people engaged in commercial sex (PECS) which includes female sex workers (FSWs), and clients of female sex workers (C). For a better understanding of HIV/AIDS transmission dynamics, we do some necessary mathematical analysis. The conditions and thresholds for the existence of four equilibria are established. We compute the reproduction number for each group independently, and show that when both the reproduction numbers are less than unity, the disease-free equilibrium is globally stable. The local stabilities for other equilibria including two boundary equilibria and one positive equilibrium are figured out. When we omit the infectivity of AIDS patients, global stability of these equilibria are obtained. For the simulation, parameters are chosen to fit as much as possible prevalence data publicly available for Yunnan. Increasing strength of the control measure on high risk population is necessary to reduce the HIV/AIDS in Yunnan. [Copyright &y& Elsevier]

Abstract: This paper introduced a stochastic programming model to address the air freight hub location and flight routes planning under seasonal demand variations. Most existing approaches to airline network design problems are restricted to a deterministic environment. However, the demand in the air freight market usually varies seasonally. The model is separated into two decision stages. The first stage, which is the decision not affected by randomness, determines the number and the location of hubs. The second stage, which is the decision affected by randomness, determines the flight routes to transport flows from origins to destinations based upon the hub location and realized uncertain scenario. Finally, the real data based on the air freight market in Taiwan and China is used to test the proposed model. [Copyright &y& Elsevier]

Abstract: The goal of planning a horizontal well path is to obtain a trajectory that arrives at a given target subject to various constraints. In this paper, the optimal control problem subject to a nonlinear multistage dynamical system (NMDS) for horizontal well paths is investigated. Some properties of the multistage system are proved. In order to derive the optimality conditions, we transform the optimal control problem into one with control constraints and inequality-constrained trajectories by defining some functions. The properties of these functions are then discussed and optimality conditions for optimal control problem are also given. Finally, an improved simplex method is developed and applied to the optimal design for well Ci-16-Cp146 in Oil Field of Liaohe, and the numerical results illustrate the validity of both the model and the algorithm. [Copyright &y& Elsevier]

Abstract: In this paper, we consider the growth of densities of two kinds of typical HAB algae: diatom and dianoflagellate on some coasts of China’s mainland. Since there exist many random factors that cause the change of the algae densities, we shall develop a new nonlinear dynamical model with stochastic excitations on the algae densities. Applying a stochastic averaging method on the model, we obtain a two-dimensional diffusion process of averaged amplitude and phase. Then we investigate the stability and the Hopf bifurcation of the stochastic system with FPK (Fokker Planck–Kolmogorov) theory and obtain the stationary transition probability density of the process. We obtain the critical values of parameters for the occurrences of Hopf bifurcation in terms of probability. We also investigate numerically the effects of various parameters on the stationary transition probability density of the occurrences of Hopf bifurcation. The numerical results are in good correlation with the analysis. We draw the conclusion that if the Hopf bifurcation occurs with a radius large enough, i.e., if the densities of the HAB algae reach a high value, the HAB will take place with comparatively high probability. [Copyright &y& Elsevier]