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The security of data transmission is one of the focus issues in vehicular ad hoc networks (VANETs). The timely and effective dissemination of emergency messages can help drivers make intelligent decisions and improve road safety and traffic efficiency. However, malicious nodes in the network seriously interfere with the proper judgement of vehicles via data tampering or spreading false messages, which brings significant risks to traffic safety. In order to guarantee the security and reliability of data transmission, this paper proposes a hybrid-trust-based emergency message dissemination (HTEMD) model that effectively integrates entity trust into the data consistency evaluation. In the proposed model, for emergency messages from different sources, the receiver first calculates the probability of the event based on the sender's entity trust and event status. Then, it uses the principle of information entropy to conduct consistent analysis of the results to help the vehicle make correct decisions. Afterwards, a series of simulations are conducted in four typical scenarios. The results show that the HTEMD model has strong environmental adaptability and robustness. Moreover, compared with other classical message dissemination models, the correct decision rate of the HTEMD model always remains above 80%, while other models drop to 75.5%, 63.2%, and 40.2%. The model proposed in this paper significantly outperforms the baseline model with a higher correct decision rate and resistance to attack. [ABSTRACT FROM AUTHOR]

• Identified driving predictors for streamflow forecasts using information entropy. • Introduced LSTM and GARCH models to precisely refine forecast equation and errors characterization. • Forecast model optimized hierarchically for accuracy, reliability, and stability. Accurate, reliable, and stable streamflow forecasts are essential for risk assessment and decision making in water resources management. Owing to the limited value of deterministic forecasting, probabilistic forecasting incorporating probability and confidence intervals is achieved by optimizing the predictors and forecast equation while identifying forecast error features. The combination of deterministic forecasts and error identification can facilitate water resources management. This study improved the traditional forecast model's inputs selection, structure designation, and error parameters calibration to propose a long-term probabilistic streamflow forecast model with hierarchical optimization of the predictors, forecast equation, and errors characteristics. The model develops information entropy theory to screen the driving predictor set, the long short-term memory (LSTM) model to conduct deterministic forecasts, and the generalized autoregressive conditional heteroskedasticity (GARCH) model to identify time-varying errors. The proposed model, used in some case studies to forecast the monthly streamflow of two lakes, Hongze and Luoma lakes in China, was evaluated using the multidimensional index method considering "accuracy–reliability–stability" performance. The results revealed the following: (1) Predictor screening based on information entropy investigates the statistical characteristics of predictors, thereby improving the reliability of streamflow forecasts. (2) The LSTM model exploits the response between the driving predictors and streamflow, whereas GARCH model identifies the time-varying features of forecast errors effectively, which reduces the probability of forecast failure and increases the accuracy and stability of probabilistic forecasts. (3) Under current meteorological observation conditions and forecasting capability, the proposed forecasts can extend the maximum forecast lead time from 1 month to 3 months. (4) The proposed model improves the accuracy (root mean square error: 6.7%–34.8%), reliability (Brier score: 15.3%–27.9%), and stability (mistaken distance: 36.4–52.6%) in comparison with the benchmark of the two case studies, indicating that "inputs–structure–parameters" hierarchical optimization provides effective forecast information for water resources management. [ABSTRACT FROM AUTHOR]

When analyzing photovoltaic plant’s output characteristics, the time interval and the time length of data need to be considered. At present, there is no exact theoretical basis of the time length of data. In the case of a certain time interval, the selection of the time length of data is of great significance for the analysis of extracting photovoltaic characteristics. This paper takes a feature mining of fluctuation degree and generated energy according to cluster analysis. On the basis of this, a definition of “daily performance coefficient” is given to characterize data characteristics. The optimum sample size estimation of daily performance coefficient based on principles of probability and statistics is taken to obtain the time length of data under different permissible errors. As a criterion that best meets the objective situation for selecting the statistical properties of the random variable, maximum entropy principle from information entropy theory provides the time length of data-determining method with no other constraints. Based on information entropy theory, taking the issue of storage capacity configuration in Photovoltaic (PV)-storage system as an example, this paper studies the relation between storage capacity demand and the time length of data and the relation between information entropy and the time length of data. In this way, the time length of data for PV-storage system’s operating characteristic analysis is determined. [ABSTRACT FROM AUTHOR]

The goal of this paper is to evaluate the performance of public transit systems based on a combined evaluation method (CEM) consisting of information entropy theory and super efficiency data envelopment analysis (SE-DEA). Taking 13 transit operators in Yangtze Delta Region of China as the research object, we integrate the public transit industry regulations, transit operation and passenger requirements to construct an evaluation indicator system based on satisfaction and efficiency. The CEM is used to evaluate the performance. The results show significant differences in the efficiency scores between CEM and SE-DEA. The CEM can reduce the risks of SE-DEA affected by the dimensions of indicators, and improve its discrimination capability. The evaluation outcomes of the CEM seem to be more objective, therefore, provide a more suitable basis for decision-making related to public transit service performance, as well as for the study of operation and management. [ABSTRACT FROM AUTHOR]

Environmental capacity paves the foundation for sustainable economic development. As the vital growth pole in the Rise Strategy of Central China, the environmental and economic construction of Hunan province proves to have enormous demonstration effect. In order to better understand the environmental condition in Hunan province, this study combines the information entropy theory and ecological comprehensive index to measure the environmental capacity of 14 administration divisions in Hunan province and 30 provinces across China in 2013. The results indicate that, first of all, the average environmental capacity of 14 administration divisions in Hunan province is relatively lower, i.e., 0.39. Second, Chang-Zhu-Tan city cluster in Hunan province exerts limited radiative and guiding effect on its surroundings; in particular, Xiangtan possesses a notably lower environmental capacity than other two cities, which hampers the integration process. Third, among the four types of environmental capacities concerned, i.e., population capacity, land capacity, resource capacity and waste assimilative capacity, waste assimilative capacity takes up the biggest weight, i.e., 0.31, indicating the most important role in environmental improvement. Finally, the environmental capacity of Hunan province ranks the 24th among the 30 provinces in China and the last in central China. Hunan should pay special attention to the expansion of waste assimilate capacity, so as to promote the overall environmental capacity. [ABSTRACT FROM AUTHOR]

Abstract: Mixing behavior in a magnetic mixer is investigated using an experimental approach. The concept of the residence time distribution (RTD) was applied to characterize a mixing process under the action of a rotating magnetic field (RMF). The main idea of the proposed approach is the utilization of experimental RTD measurements to determine the information entropy. This paper discusses a method to compute an information degree of mixing as a measure of mixing performance for a continuous flow system. The quality of mixing in the tested equipment is analyzed by means of a proposed mixing capacity. Different operating conditions with respect to liquid flow rates and a magnetic field strength are used to study corresponding effects on a recommended parameter. It is observed that mixing capacity increases with increasing magnetic field strength. This mixing parameter is analytically described as a function of operating flow rates and the applied magnetic field. [Copyright &y& Elsevier]

Based on information entropy theory, a novel feature extraction algorithm based on improved polynomial entropy (IPE) is set up. Firstly, the concepts and their properties of information entropy and cross entropy (CE) are analysed and studied. On this foundation, we prove that symmetrical cross entropy (SCE) proposed here based on CE satisfies three axioms of the distance, i.e. nonnegativity, symmetry and triangle inequation. So SCE is a kind of distance measure, which can be used to measure the degree of variation between two random variables. Secondly, for convenience, we propose a new concept of improved polynomial entropy (IPE) based on polynomial entropy (PE), and explain that IPE is equivalent to SCE. Thirdly, we make IPE separability criterion of the classes for feature extraction, and design a novel feature extraction algorithm based on IPE. Fianlly, the experimental results demonstrate that the algorithm proposed here is valid and reliable, and it provides a new research approach for feature extraction, data mining and pattern recognition. [ABSTRACT FROM AUTHOR]

• The dynamic energy management of hybrid energy system is studied. • Uncertainty of renewable energy, demand side and price are considered. • A data-driven deep reinforcement learning method is applied. • The flexibility adjustment of battery and water tank is achieved. Significant dependence on fossil fuels and freshwater shortage are common problems in remote and arid regions. In this context, the operation of a wind-solar-diesel-battery-reverse osmosis hybrid energy system has become a suitable option to solve this problem. However, owing to the uncertainties of renewable energy availability and load demand, it is a challenge for operators to develop an energy management scheme for such a system. This study aims to determine a real-time dynamic energy management strategy considering the uncertainties of the system. To this end, the energy management of a hybrid energy system is presented as an optimal control objective, and multi-targets are considered along with constraints. The information entropy theory is introduced to calculate the weight factor for the trade-off between different targets. Then, a deep reinforcement learning algorithm is adopted to solve this problem and obtain the optimal control policy. Finally, the proposed method is applied to a typical hybrid energy system, and numerous data are applied to train an agent to obtain the optimal energy management policy. Simulation results demonstrate that a well-trained agent can provide a better control policy and reduce costs by up to 14.17% in comparison with other methods. [ABSTRACT FROM AUTHOR]

In recent years, the individualized demand of customers brings small batches and diversification of orders towards enterprises. The application of enabling technologies in the factory, such as the industrial Internet of things (IIoT) and cloud manufacturing (CMfg), enhances the ability of customer requirement automatic elicitation and the manufacturing process control. The job shop scheduling problem with a random job arrival time dramatically increases the difficulty in process management. Thus, how to collaboratively schedule the production and logistics resources in the shop floor is very challenging, and it has a fundamental and practical significance of achieving the competitiveness for an enterprise. To address this issue, the real-time model of production and logistics resources is built firstly. Then, the task entropy model is built based on the task information. Finally, the real-time self-adaption collaboration of production and logistics resources is realized. The proposed algorithm is carried out based on a practical case to evaluate its effectiveness. Experimental results show that our proposed algorithm outperforms three existing algorithms. [ABSTRACT FROM AUTHOR]

• A new generalized plane model is proposed based on entropy and large deviation. • The modified method overcomes tedious data preprocessing in large deviation theory. • The model can detect complex behaviors of time series and capture main features. • Stock markets of different areas are distinguished using our method. Complexity-entropy causality plane analysis and large deviations spectrums theory are proposed to study time series. The entropy plane analysis depicts the complexity of a system in two-dimensional plane, while large deviations theory shows the spectral structure of time series in the way of multifractal. In this paper, we combine the characteristics of these two popular methods and propose a generalized entropy plane model based on the large deviation theory. The methodology is applied to both synthetic data and financial markets. We discuss the impact of the parameters on the results in detail. Besides, the modified model can distinguish different time series. Meanwhile, we compare our results with the original complexity-entropy causality plane and large deviations spectrums, and the consistency of these results is confirmed. The method can provide abundant dynamical properties of complex systems. [ABSTRACT FROM AUTHOR]