The train-set circulation plan problem belongs to the rolling stock scheduling problem and is similar to the aircraft routing problem in airline operations and the vehicle routing problem in the logistics field. However, TCPP involves additional complexity due to the maintenance constraint of train-sets: train-sets must conduct maintenance tasks after running for a certain time and distance. The TCPP is nondeterministic polynomial hard. There is no available algorithm that can obtain the optimal global solution, and many factors such as the (...) utilization mode and the maintenance mode impact the solution of the TCPP. This paper proposes a train-set circulation optimization model to minimize the total connection time and maintenance costs and describes the design of an efficient multiple-population genetic algorithm to solve this model. A realistic high-speed railway case is selected to verify our model and algorithm, and, then, a comparison of different algorithms is carried out. Furthermore, a new maintenance mode is proposed, and related implementation requirements are discussed. (shrink)

In the era of the Internet, image encryption plays an important role in information security. Chaotic systems and DNA operations have been proven to be powerful for image encryption. To further enhance the security of image, in this paper, we propose a novel algorithm that combines the fractional-order hyperchaotic Lorenz system and DNA computing for image encryption. Specifically, the algorithm consists of four parts: firstly, we use a fractional-order hyperchaotic Lorenz system to generate a pseudorandom sequence that will be utilized (...) during the whole encryption process; secondly, a simple but effective diffusion scheme is performed to spread the little change in one pixel to all the other pixels; thirdly, the plain image is encoded by DNA rules and corresponding DNA operations are performed; finally, global permutation and 2D and 3D permutation are performed on pixels, bits, and acid bases. The extensive experimental results on eight publicly available testing images demonstrate that the encryption algorithm can achieve state-of-the-art performance in terms of security and robustness when compared with some existing methods, showing that the FOHCLDNA is promising for image encryption. (shrink)

The introduction of the notion of family resemblance represented a major shift in Wittgenstein’s thoughts on the meaning of words, moving away from a belief that words were well defined, to a view that words denoted less well defined categories of meaning. This paper presents the use of the notion of family resemblance in the area of machine learning as an example of the benefits that can accrue from adopting the kind of paradigm shift taken by Wittgenstein. The paper presents (...) a model capable of learning exemplars using the principle of family resemblance and adopting Bayesian networks for a representation of exemplars. An empirical evaluation is presented on three data sets and shows promising results that suggest that previous assumptions about the way we categories need reopening. (shrink)

As one of the most widely used media types, images play an important role in the era of the Internet. And hence how to enhance the security of images has become a hot topic in the field of information security. However, due to some intrinsic characteristics of images, image security is still a challenging task. For the purpose of coping with this issue, in this paper, we propose a novel algorithm that combines a hyperchaotic system, dynamic filtering, and bit cuboid (...) operations, namely, DFBC, for image encryption. Specifically, the proposed DFBC consists of four steps: firstly, a 7D Lorenz hyperchaotic system is utilized to generate a pseudorandom sequence; secondly, variable 1D filters are derived from the pseudorandom sequence, and dynamic filtering is conducted on each pixel of an image; thirdly, a diffusion scheme is performed and then the image is transformed to a bit cuboid; and, finally, various types of permutation are performed on the bit cuboid. The experiments on several testing images demonstrate that the DFBC achieves state-of-the-art results in terms of several evaluation criteria, showing that the DFBC is promising for image encryption. (shrink)

In opportunistic complex networks, information transmission between nodes is inevitable through broadcast. The purpose of broadcasting is to distribute data from source nodes to all nodes in the network. In opportunistic complex networks, it is mainly used for routing discovery and releasing important notifications. However, when a large number of nodes in the opportunistic complex networks are transmitting information at the same time, signal interference will inevitably occur. Therefore, we propose a low-latency broadcast algorithm for opportunistic complex networks based on (...) successive interference cancellation techniques to improve propagation delay. With this kind of algorithm, when the social network is broadcasting, this algorithm analyzes whether the conditions for successive interference cancellation are satisfied between the broadcast links on the assigned transmission time slice. If the conditions are met, they are scheduled at the same time slice, and interference avoidance scheduling is performed when conditions are not met. Through comparison experiments with other classic algorithms of opportunistic complex networks, this method has outstanding performance in reducing energy consumption and improving information transmission efficiency. (shrink)

A novel adaptive tracking control scheme is proposed for a class of uncertain nonlinear switched systems with perturbations in this paper. The common Lyapunov function method is introduced to handle the switched system in the design process of the desired adaptive controller. In addition, a dynamic surface control method is proposed by employing a nonlinear filter such that the “explosion of complexity” problem existing in the conventional backstepping design can be overcome. Under the presented adaptive controller, all the closed-loop signals (...) are semiglobally bounded, and especially the output signal of the controlled system can follow the given reference signal asymptotically. To show the availability of the presented control scheme, a simulation is given in this paper. (shrink)

Game experiments designed to test the effectiveness of reward and/or punishment incentives in promoting cooperative behavior among their participants are quite common. Results from two such recent experiments conducted in Beijing, based on the Prisoner’s Dilemma (PD) game and Public Goods Game respectively, are summarized here. The unexpected empirical outcomes for the repeated PD game, that cooperation actually decreased when the participants had the option of using a costly punishment strategy and that participants who used costly punishment in some round (...) of the repeated game often did so in the first round, are discussed in terms of differences in attitudes toward reputation in Chinese culture compared to other locations (mostly in Western society) where similar experiments have been conducted. The second experiment models an institution providing incentives to increase contribution levels (i.e., cooperation) to the public good. The results show that combined institutional reward and punishment is the most effective means to increase cooperation, followed by a scheme using only punishment. It is shown how these empirical results are related to the theoretical predictions that assume players play rationally by optimizing their personal payoff given their opponents’ actions in these multi-player games. (shrink)

Previous urban traffic network-based studies have been based mostly on single-layer networks. Based on their shortcomings, starting from the perspective of a multilayer urban traffic network, this paper takes the different anticongestion abilities and network characteristics of various network structures under the condition of traffic congestion as the research object. Then, a comparative experiment is performed via simulation, and the optimal multilayer urban traffic network topology is obtained under different conditions. It is found that these scale-free related multilayer networks have (...) relatively strong ability to support more traffic flows and have higher anticongestion abilities, regardless of whether it is a lower-layer or upper-layer network. The research results are helpful to deepen our understanding of the characteristics of traffic network structures, help scholars further cognize the structural properties of multilayer urban traffic networks, practically help urban traffic network planners to further optimize the urban traffic network, and broaden the study of multilayer traffic networks. (shrink)

This paper examines approximation-based fixed-time adaptive tracking control for a class of uncertain nonlinear pure-feedback systems. Novel virtual and actual controllers are designed that resolve the meaninglessness of virtual and actual controllers at the origin and in the negative domain, and the sufficient condition for the system to have semiglobal fixed-time stability is also provided. Radial basis function neural networks are introduced to approximate unknown functions for solving the fixed-time control problem of unknown nonlinear pure-feedback systems, and the mean value (...) theorem is used to solve the problem of nonaffine structure in nonlinear pure-feedback systems. The controllers designed in this paper ensure that all signals in the closed-loop system are semiglobally uniform and ultimately bounded in a fixed time. Two simulation results show that appropriate design parameters can limit the tracking error within a region of the origin in a fixed time. (shrink)

Host-microbiome interactions (HMIs) are critical for the modulation of biological processes and are associated with several diseases, and extensive HMI studies have generated large amounts of data. We propose that the logical representation of the knowledge derived from these data and the standardized representation of experimental variables and processes can foster integration of data and reproducibility of experiments and thereby further HMI knowledge discovery. A community-based Ontology of Host-Microbiome Interactions (OHMI) was developed following the OBO Foundry principles. OHMI leverages established (...) ontologies to create logically structured representations of microbiomes, microbial taxonomy, host species, host anatomical entities, and HMIs under different conditions and associated study protocols and types of data analysis and experimental results. (shrink)

A glimpse of the new application of Buddhist logic in the seventeenth century leads us to reflect about our approach to logic in a given religious tradition: Should we isolate a logical system from the very context that has given rise to the genesis and development of such an intellectual apparatus? Methodologically, we do have the legitimate right to approach Buddhist logic from a purely logical point of view. However, when we study the actual use of Buddhist logic in the (...) seventeenth-century anti-Christian polemic, an analysis of its intentional application allows us to conclude that Buddhist logic in the context of controversy is primarily apologetic. Therefore, with a methodological concern, I suggest that philosophers and logicians should reconsider the apologetic nature of logic in any given religious tradition. (shrink)

Fracturing is a key factor for shale oil and gas enrichment and high production. An accurate fracture model can effectively guide shale oil and gas exploration and development. The establishment of a natural fracture model must address the challenges of difficult data acquisition and poor representativeness of data points. To solve these problems, we have developed a method of shale-reservoir natural fracture modeling based on microseismic monitoring data. This method includes three steps. First, we establish an initial natural fracture model (...) based on scale classification, vertical stratification, and genetic classification. Second, the shape and density of the hydraulic fractures are interpreted by microseismic monitoring data to calibrate the initial model of the shale reservoir natural fractures. Third, we verify the rationality of the model by assessment of the fracture porosity and permeability values. The results show that it is possible to calibrate the natural fracture density model using the fracture shape and density as determined by microseismic monitoring. And we predict that an ideal hydraulic fracture network can be formed when the body density of the natural fracturing is greater than 0.3 m2/m3. The crude production of wells is negatively correlated with the development of large-scale structural fractures and positively correlated with small-scale structural fractures. The well trajectory should run through small-scale fracture development sections as much as possible, avoiding large-scale, high-angle fracture areas. This method provides a new approach to model natural fractures in shale reservoirs that has wide applicability and can be used for modeling shale oil and gas reservoirs. (shrink)

Data stream classification becomes a promising prediction work with relevance to many practical environments. However, under the environment of concept drift and noise, the research of data stream classification faces lots of challenges. Hence, a new incremental ensemble model is presented for classifying nonstationary data streams with noise. Our approach integrates three strategies: incremental learning to monitor and adapt to concept drift; ensemble learning to improve model stability; and a microclustering procedure that distinguishes drift from noise and predicts the labels (...) of incoming instances via majority vote. Experiments with two synthetic datasets designed to test for both gradual and abrupt drift show that our method provides more accurate classification in nonstationary data streams with noise than the two popular baselines. (shrink)

In this paper, we first present a generalization of the Cauchy-Schwarz inequality. As an application of our result, we obtain a new sufficient condition for the stability of a class of nonlinear impulsive control systems. We end up this note with a numerical example which shows the effectiveness of our method.

The purpose of this paper is to discuss modelling and synchronization of nonlinear supply chain system. Firstly, we present a new supply chain system which is sensitive to various uncertainties along with exogenous disturbances. Synchronization is an important method to reduce the negative impact of uncertainties and disturbances on the supply chain. Since impulsive control can reduce control cost and the amount of transmitted information drastically, we discuss impulsive synchronization behavior of two supply chain systems with the same structure. Finally, (...) simulation experiments are given to show the effectiveness of our analytical results. (shrink)

The aerial manipulator is a complex system with high coupling and instability. The motion of the robotic arm will affect the self-stabilizing accuracy of the unmanned aerial vehicles. To enhance the stability of the aerial manipulator, a composite controller combining conventional proportion integration differentiation control, fuzzy theory, and neural network algorithm is proposed. By blurring the attitude error signal of UAV as the input of the neural network, the anti-interference ability and stability of UAV is improved. At the same time, (...) a neural network model identifier based on Maxout activation function is built to realize accurate recognition of the controlled model. The simulation results show that, compared with the conventional PID controller, the composite controller combined with fuzzy neural network can improve the anti-interference ability and stability of UAV greatly. (shrink)

To benefit from users’ co-creation knowledge in online communities, enterprises need effective methods to discover and manage co-creation users and knowledge as suggested in existing research. However, the existing methods still have their own limitations when analysing different relationships and networks. Therefore, first, this paper proposes a co-creation cyberspace super-network model for product innovation from a “user-knowledge-product” perspective to integrate the co-creation information of users, knowledge, and products. Second, to increase the accuracy of discovering, managing, and using the co-creation knowledge, (...) three subnetworks including co-creation user network, co-creation knowledge network, and co-creation product network as well as the relationships among them are constructed and improved. Third, a well-known co-creation community in China is selected as a case to verify the feasibility and validity of the proposed model for enterprise’s product innovation. Three subnetworks and the mapping relationships among them are constructed and visualized based on the introduced User-Knowledge-Product Cyberspace model. The results from the case study validate the co-creation cyberspace constructed in this paper and provide enterprises with a comprehensive and detailed integration framework analysis model. Hence, the enterprises can use this model for user discovery, product innovation knowledge mining, and dynamic innovation knowledge basement and forecast hotspot trend in co-creation communities. (shrink)

Chairman Hua emphasized in his speech last year at the opening ceremony of the Central Party School that: "Chairman Mao instructed us over and over that ‘the integration of theory and practice is the most fundamental principle of Marxism.’ Chairman Mao fought all his life against the evil work style of boasting and of separating theory from practice. … Political swindlers such as Lin Biao, Chen Boda, and the "gang of four" have messed up many basic theoretical issues and damaged (...) our Party's good learning style. We must make a tremendous effort to correct that.". (shrink)

The fluctuations of economic and financial time series are influenced by various kinds of factors and usually demonstrate strong nonstationary and high complexity. Therefore, accurately forecasting economic and financial time series is always a challenging research topic. In this study, a novel multidecomposition and self-optimizing hybrid approach integrating multiple improved complete ensemble empirical mode decompositions with adaptive noise, whale optimization algorithm, and random vector functional link neural networks, namely, MICEEMDAN-WOA-RVFL, is developed to predict economic and financial time series. First, we (...) employ ICEEMDAN with random parameters to separate the original time series into a group of comparatively simple subseries multiple times. Second, we construct RVFL networks to individually forecast each subseries. Considering the complex parameter settings of RVFL networks, we utilize WOA to search the optimal parameters for RVFL networks simultaneously. Then, we aggregate the prediction results of individual decomposed subseries as the prediction results of each decomposition, respectively, and finally integrate these prediction results of all the decompositions as the final ensemble prediction results. The proposed MICEEMDAN-WOA-RVFL remarkably outperforms the compared single and ensemble benchmark models in terms of forecasting accuracy and stability, as demonstrated by the experiments conducted using various economic and financial time series, including West Texas Intermediate crude oil prices, US dollar/Euro foreign exchange rate, US industrial production, and Shanghai stock exchange composite index. (shrink)

This article studies the evolution of altruism. We consider a model in which a population of agents are assortatively matched to play some asymmetric two-player game, and evolution operates at the level of behavior rules. We find that the relationship between the evolutionarily stable level of altruism and the index of assortativity of matching is determined by two novel features: whether the total payoff function of the game exhibits complementarity or substitutability; whether the two players’ strategies affect each other’s fitness (...) in the same direction or the opposite. These two features combined generalize the stability conditions related to Hamilton’s rule to a class of asymmetric games. (shrink)

What is the criterion of truth? This is a question that was resolved long ago by the revolutionary teachers of the proletariat. But as a result of damage done by the "Gang of Four" and a mass of distorted propaganda in the media under their control, it has become muddled beyond compare in recent years. In order to deepen the criticism of the "Gang of Four" and eradicate the remnants of their poison and influence, it is very important to clear (...) this up and sort it out. (shrink)

This article presents an approach that combines the active global Kriging method and multidisciplinary strategy to investigate the problem of evidence-based multidisciplinary design optimization. The global Kriging model is constructed by introducing a so-called learning function and using actively selected samples in the entire optimization space. With the Kriging model, the plausibility, Pl, of failure is obtained with evidence theory. The multidisciplinary feasible and collaborative optimization strategies of multidisciplinary design optimization are combined with the evidence-based reliability analysis. Numerical examples are (...) provided to illustrate the efficiency and accuracy of the proposed method. The numerical results show that the proposed algorithm is effective and valuable, which is valuable in engineering application. (shrink)

The problem of finite-time tracking control is discussed for a class of uncertain nonstrict-feedback time-varying state delay nonlinear systems with full-state constraints and unmodeled dynamics. Different from traditional finite-control methods, a C 1 smooth finite-time adaptive control framework is introduced by employing a smooth switch between the fractional and cubic form state feedback, so that the desired fast finite-time control performance can be guaranteed. By constructing appropriate Lyapunov-Krasovskii functionals, the uncertain terms produced by time-varying state delays are compensated for and (...) unmodeled dynamics is coped with by introducing a dynamical signal. In order to avoid the inherent problem of “complexity of explosion” in the backstepping-design process, the DSC technology with a novel nonlinear filter is introduced to simplify the structure of the controller. Furthermore, the results show that all the internal error signals are driven to converge into small regions in a finite time, and the full-state constraints are not violated. Simulation results verify the effectiveness of the proposed method. (shrink)

This paper is concerned with the adaptive tracking control design for a class of uncertain switched systems subject to input delay. Unlike the existing results on uncertain switched systems, the new proposed control scheme ensures that the tracking error converges to the accuracy given a priori according to the requirement. To achieve this aim, some nonnegative switching functions are introduced to replace the conventional Lyapunov function. In addition, neural networks are used to approximate the unknown simultaneous domination functions. By combining (...) the backstepping technique and some common nonnegative switching functions, a stable adaptive neural controller is established. It can be shown that the closed-loop system is semiglobally uniformly ultimately bounded and the tracking error satisfies the predefined accuracy. The effectiveness of the proposed control scheme is verified by a simulation example. (shrink)