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Particulate matter (PM)2.5 is a common air pollutant known to induce damages in the respiratory, cardiovascular, and nervous systems. Previous study has shown that acute and high-level PM insult could significantly aggravate the severity of LPS-induced acute lung injury (ALI). However, humans typically experience more chronic and low-level PM, of which the effect on ALI is yet unclear. Here, we varied the concentration of PM from low, medium, to high, which was given to mice via intratracheal instillation for a short period of time. Compared to the saline-treated mice, mice with medium or high PM treatment presented significantly higher mortality rate, weight reduction, and bronchoalveolar lavage (BAL) protein concentration during ALI, while mice with low PM treatment did not demonstrate significant differences from saline-treated mice. However, when the PM was given for an elongated period of time, PM, even at the low level, significantly aggravated ALI severity. Furthermore, the PM-mediated changes were sustained even after PM withdrawal. We also examined the CD4 T cells in saline- or PM-treated mice. We found that, although PM did not significantly change the number of lung-infiltrating CD4 T cells, it significantly altered the composition of lung-infiltrating CD4 T cells, characterized by having a higher T-bet/Foxp3 ratio in the PM-treated group compared to the saline-treated group. Additionally, the Treg-mediated suppression was reduced in PM-treated mice. The effect of PM on CD4 T cells depended on the concentration of PM and the duration of the treatment, and was independent of the PM withdrawal. Overall, these results demonstrated that chronic and low-level PM was sufficient at aggravating ALI and altering pulmonary CD4 T cells, and the effect could be sustained even after PM withdrawal.

The paper established a conventional bus traffic network and a subway network by using the method of space R based on the existing public traffic network model. Regarding these two networks as the ...

Artificial bee colony (ABC) algorithm is a relatively new biologically-inspired optimization algorithm. According to its solution search equation, it can be seen that ABC is good at exploration but poor at exploitation. Inspired by the gravity model, an attractive force model is proposed for choosing a better neighbor of a current individual to improve the exploitation ability of ABC. Then we propose a novel solution search equation, in which the chosen neighbor plays an important role in guiding the searching process in the employed bee phase. Next, a random guiding search is introduced in the onlooker bee phase to balance the foregoing exploitation. Subsequently, multiple solution search equations, a scheme of perturbation frequency, and a multiple scouts search strategy in view of opposition-based learning are also incorporated into the proposed algorithm, called ABCG, to further reach a good compromise between the exploitation and the exploration. Finally, ABCG is tested on a great number of benchmark functions . The experimental results show that ABCG is effective for solving the complex benchmark problems and it can be considered as a competitive approach.

Artificial bee colony (ABC) is a very popular and powerful optimization tool. However, there still exists an insufficiency of slow convergence in ABC. To further improve the convergence rate of ABC, a novel ABC (CosABC for short) is proposed based on the cosine similarity, which is employed to choose a better neighbor individual. Under the guidance of the chosen neighbor individual, a new solution search equation is introduced to reduce the weakness of undirected search of ABC. Furthermore, in the employed bees phase, a solution search equation with the guidance of global best individual is also integrated, and the frequency of parameters perturbation is also employed to further increase the information share between different individuals. In the onlooker bees phase, ABC/rand/1/ is used to enhance the exploitation ability, yet an opposition-based learning technique is also used to balance the exploitation of ABC/rand/1. All these modifications together with ABC form the proposed CosABC algorithm. To demonstrate the effectiveness of CosABC, a comprehensive experimental research is conducted on a test suite composed of twenty-four benchmark functions. What is more, it is further compared with a few state-of-the-art algorithms to validate the superiority of CosABC. The related comparison results show that CosABC is effective and competitive.

Urban public transport is an effective way to solve urban traffic problems and promote sustainable development of urban traffic. A scientific operation scheduling system has an important guiding significance for optimizing the configuration of urban public transport capacity resources, improving the level of operation organization and management, and providing for the sustainability of the transportation system. According to the inhomogeneous distribution of passenger flow along transit lines, this study develops a combinational scheduling model in which the enterprise supplies zonal service based on regular service. The objective function minimizes the sum of passenger travel cost and operation cost, and the simulated annealing algorithm is designed to solve the optimization model. This paper abstracts an ideal example by taking a real-world case of Bus Line 131 in Lanzhou, China. The numerical example is used to verify the validity of the model and algorithm. Results show that the combinational operation scheme can effectively satisfy passengers&rsquo, demand and reduce the total cost by 7.03% in comparison with the regular operation system. The optimal combinational system with the lowest total cost can increase the vehicle load factor and improve the utilization ratio.

For deep learning cloud computing platforms, file system is a fundamental and critical component. Hadoop distributed file system (HDFS) is widely used in large scale clusters due to its high performance and high availability. However, in deep learning datasets, the number of files is huge but the file size is small, making HDFS suffer a severe performance penalty. Although there have been many optimizing methods for addressing the small file problem, none of them take the file correlation in deep learning datasets into consideration. To address such problem, this paper proposes a Pile-HDFS (PHDFS) based on a new file aggregation approach. Pile is designed as the I/O unit merging a group of small files according to their correlation. In order to effectively access small files, we design a two-layer manager and add the inner organization information to data blocks. Experimental results demonstrate that, compared with the original HDFS, PHDFS can dramatically decrease the latency when accessing small files and improve the FPS (Frames Per Second) of typical deep learning models by 40%.

Artificial bee colony (ABC) has a good exploration ability against its exploitation ability. For enhancing its comprehensive performance, we proposed a multistrategy artificial bee colony (ABCVNS for short) based on the variable neighborhood search method. First, a search strategy candidate pool composed of two search strategies, i.e., ABC/best/1 and ABC/rand/1, is proposed and employed in the employed bee phase and onlooker bee phase. Second, we present another search strategy candidate pool which consists of the original random search strategy and the opposition-based learning method. Then, it is used to further balance the exploration and exploitation abilities in the scout bee phase. Last but not least, motivated by the scheme of neighborhood change of variable neighborhood search, a simple yet efficient choice mechanism of search strategies is presented. Subsequently, the effectiveness of ABCVNS is carried out on two test suites composed of fifty-eight problems. Furthermore, comparisons among ABCVNS and several famous methods are also carried out. The related experimental results clearly demonstrate the effectiveness and the superiority of ABCVNS.

An urban public transportation super-network model is established in this article. Compared with three existing complicated urban public transportation network models (the C-space network, L-space network, and P-space network), our urban public transportation super-network model first incorporated both station networks and bus line networks explicitly, which connects two non-homogeneous nodes (station and line nodes) through the notion of super networks. The proposed model reflects the interaction and mutual influences between the internal features and internal structures of the urban public transportation super network. And then the function projective synchronization of the dynamic public transportation super-network model is proven, and the high stability of the public transportation super network is confirmed by the Lyapunov stability theorem. Finally, the classical Lorenz chaotic system is applied in a numerical example to illustrate the proposed model and algorithm. The impact of departure frequency, line crowdedness, passenger transport density, and station dwell time on synchronous ability of the urban public transportation super-network model is analyzed. So, this study is a discussion on the urban public transportation super-network balance in order to offer theoretical basis for people to research bus dispatching and bus lines optimization.

On the basis of traditional weighted network, we study a new complex network model with multi-weights, which has one or several different types of weights between any two nodes. According to the method of network split, we split the complex network with multi-weights into several different complex networks with single weight, and study its global synchronization. Taking bus lines as the network nodes, a new public traffic roads network model with multi-weights is established by the proposed network model and space R modeling approach. Then based on the Lyapunov stability theory, the criteria is designed for the global synchronization of the public traffic roads networks with multi-weights. By changing the different weights and taking the Lorenz chaotic system for example, some numerical examples are given to discuss the balance of the whole public traffic roads network.

Traffic assignment is a key step in urban traffic planning, and efficient path set significantly affects stochastic user equilibrium (SUE) traffic assignment. Thereby, the main allocation algorithms of the Logit model are analyzed and some disadvantages of them are point out. The conception of efficient path set should consider the path's character and traffic demand. Thus, the conception of path effect degree is defined according to the degree of one path affecting traffic volumes on other paths, and the conception of efficient path set is proposed considering the traffic demand. Moreover, efficient path set searching method is designed by combining the improved Dial algorithm. At last, a numerical example is given and the results verify the efficiency of efficient path set conception

Based on the Lyapunov stabilization theorem, a new method of global chaos synchronization among three different structures chaotic systems is proposed under the framework of drive-response systems. Then this method is applied to secure communication through chaotic masking. Using three coupled identical systems, proposes a novel method of chaos encryption, after encrypting in the first two coupled systems, encrypt again in the later two coupled systems. In consideration of interfering Gauss white noise, the results of encoding and decoding are simulated and analyzed. The characteristics of noise are analyzed and then a de-noising method using wavelet transform is presented. With Matlab, the effects of signal de-noising could be demonstrated.

In order to study the dynamic characteristics of urban public traffic network, this paper establishes the conventional bus traffic network and the urban rail traffic network based on the spaceRmodeling method. Then regarding these two networks as the subnetwork, the paper presents a new bilayer coupled public traffic network through the transfer relationship between subway and bus, and this model well reflects the connection between the passengers and bus operating vehicles. Based on the synchronization theory of coupling network with time-varying delay and taking “Lorenz system” as the network node, the paper studies the synchronization of bilayer coupled public traffic network. Finally, numerical results are given to show the impact of public traffic dispatching, delayed departure, the number of public bus stops between bus lines, and the number of transfer stations between two traffic modes on the bilayer coupled public traffic network balance through Matlab simulation.

Transport planners often need to forecast the influence of the transport policies on travel demand, e.g., construction of a new transport alternative, changing public transit fares, or imposing road pricing schemes. Stated preference (SP) surveys are frequently adopted by transport planners for the analysis of the impact of transport policies on travel demand. However, evidence suggests that preferences derived from SP surveys are contingent on context, which indicates that trip makers may show asymmetric preferences for different levels of services. The objective of this article is to propose an alternative conceptual framework for travel demand analysis. It is expected that travel behavior may be heterogeneous across trip makers; response (or parameter of) to the level of service is defined as a function of the attributes of the trip makers. It is also aimed to propose some urban transportation policies based on a new type of discrete choice model. In an empirical demonstration, the panel data obtained from commuters in Lanzhou, China, are used, and support is obtained for several predictions on why behavioral intentions are, or are not, implemented.

The paper studies the coopetition of the downstream different carriers by providing complementary transport services in intermodal freight transport chain. Considering different information structure, a two-stage dynamic game model with simultaneous actions on investment and price is first formulated. Equilibria show both parties have motivation to select coopetition even if the agreement for cooperation investment is reached in advance. When both firms agree on the specific allocation, the new coopetition with higher efficiency would be emerged. Moreover, we analyze the complexity and evolution of coopetition by repeated pricing game with finitely and infinitely time horizon. In the finitely repeated pricing game, both firms have incentive to reach a tacit understanding to alternate choosing price cooperation and competition after setting suitable allocation scheme; the repeated periodstare then going to be an issue. In the infinitely repeated pricing game, the perfect cooperation is realized by designing the suitable trigger strategy.

In this paper, we present a new approach which is spanning tree-based genetic algorithm for bicriteria transportation problem. The transportation problem have the special data structure in solution characterized as a spanning tree. In encoding transportation problem, we introduce one of node encoding which is adopted as it is capable of equally and uniquely representing all possible basic solutions. The crossover and mutation was designed based on this encoding. And we designed the criterion that chromosome always feasibility converted to a transportation tree. In the evolutionary process, the mixed strategy and roulette wheel selection is used. Numerical experiments will be shown the effectiveness and efficiency of the proposed algorithm.

In this paper, we present improved genetic algorithm for solving the fuzzy multiobjective solid transportation problem in which the coefficients of objective function are represented as fuzzy numbers. The ranking fuzzy numbers with integral value are used in the evaluation and selection. The proposed algorithm is incorporated with problem-specific knowledge and conductive to find out the set of nondominated points in the criteria space based on decision maker degree of optimism.

Design of vertical section of rolling down zone on hump affects rolling condition of car, and efficiency, ability and safety of shunting operating. Taking three factors, say, minimizing rolling time difference between front car and the latter, height of hump and the number of retarders as objectives, considering engineering technology criterion that should be satisfied, a Multi-objective optimization model is established and NSGA-II multi-objective genetic algorithm is adopted to obtain Patero Set in this paper. The model and algorithm are shown by a cast and both of them are reasonable.

Design of vertical section of hump pushing zone affects disconnecting capacity of marshalling yard. There are 2 factors being chosen to be the objective function, one is height difference between reception yard and hump crest, the other is the distance between the position of disconnecting the coupler of cars. Multi-objective optimization model is established. Meanwhile, Random weight multi-objective genetic algorithm is proposed. Pareto set method and reserving elite strategy is introduced as well. The model and algorithm are shown by a concrete example. As a result, both of them are reasonable.

The transportation of hazardous materials may result in significant social risks. The route planning is one of the most effective measures to reduce the risk and the cost. A new probability model based on time dependent of links is developed. The routing algorithm based on the model is also put forward to find the optimal route whose sum of weighted risk-cost is the least. An example is introduced to verify its usability. The model is convenient for carrier to select the optimal route by adjusting the probability distribution in different time slots of links according to the real situation.

The transportation of containers is an important part of the modern logistics industry. As a result of unbalance freight volume between different regions, the empty container allocation has already become a very important and valuable research project. Taking into account the limitations of capacity in railway lines and multiple types of containers, this paper treats the railway empty container allocation as a multi-origin, multi-destination, and multi-commodity problem. An optimal model of this problem is formulated. In view of the interact ability in solving the model, a heuristic method is developed. A numeric example indicates that the model is correct and the method is effective.

In order to analyze the effects of railway productivity distribution adjustment on the timeliness of freight forwarding, on the basis of investigation and present achievement, the railway freight forwarding process and the change of each factor which affects freight forwarding time under the condition of railway productivity distribution adjustment are analyzed. The average speed of each kind of freight train including non through train, ordinary through train and fast freight train, as well as daily average running time of heavy train is analyzed, and the statistical results are examined by adopting χ2 testing method, from which daily average running tariff kilometre is obtained. Finally , daily average running tariff kilometre before and after railway productivity distribution adjustment is compared. The analytical results show that through the adjustment of railway productivity distribution, the operating taches of railway freight forwarding gradually reduce, and the primary parameters influencing thetas all have changes to different degree, and the timeliness of railway freight forwarding improves constantly.

With the development of ATIS(Advanced traveler information system), it is valuable to provide travel plans for the trip makers. For the bus trip makers, transfer times are always considered firstly. However, in most of cases, only the information about the bus routes, serving time, bus stops on these bus routes, etc, are offered ATIS. Considering the stochastic bus traveling time and transfer waiting time, the non-linear 0-1 programming model and the hyper- grahp model for optimal riding routes with transfer times limited under the random circumstance are formulated in this paper. And the improved priority-based encoding genetic algorithm is also designed. Finally, we give a numerical example.

In railway passenger transportation systems, it is a practical significance to find riding route. It is very difficult for passengers to choose the best transfer routes when a transportation network is complex. Considering the traits of railway transportation networks, we formulate a 0-1 programming model and a hypergraph model to the optimal path problem with transfer times limited. An improved priority-based encoding genetic algorithm is presented to solve the models. By using the method proposed in the paper, the transfer paths in the railway transportation network are analyzed and calculated. Finally, we give a numerical example. The computer simulations show the effectiveness of the proposed methods.

Urban traffic congestion is one of the most severe problems of everyday life in many cities. In an effort to deal with this problem, traffic control system technologies have concentrated in recent years on dealing with urban congestion. In the paper, a compound mechanism particle swarm optimisation (CMPSO) algorithm has been proposed, and the algorithm has been effectively used in dealing with the optimisation of city latent ringlike road traffic control system. In the algorithm, the tabu search algorithm and the particle swarm algorithm are combined effectively, and the tabu can offset the local optimums and avoid searching one solution repeatedly. Simulations show the CMPSO algorithm cannot only avoid premature convergence effectively, but also has powerful optimisation ability and higher precision. It makes the rate of delay and numbers of stopping reduce obviously.

After a sudden public event, how to optimise emergency evacuation routings and enhance the response capability of emergency system is an important issue for emergency management agency. In this paper, we choose the optimal evacuation routing under fuzzy environment to make sure the injured reach the destination safely and quickly. Chance-constrained programming model of emergency evacuation routings and chance-dependent constraint programming models are setup respectively and a hybrid intelligence algorithm is designed to solve the model. The algorithm, which combines simulation technique and the genetic algorithm, produces chromosomes according to priority-based coding mode, uses simulation technique to calculate target values of chromosomes, produces a new group by using improved roulette wheel method to select chromosomes, crosses by adopting the partially matched crossover method, and uses reciprocal exchange mutation method to mutate. Finally, an example is presented to validate the feasibility of this model and the algorithm. The research result can help emergency decision-makers to find an optimal route after a sudden public event and reduce the casualties caused by transportation delays.