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Excessively chronic inflammation and cardiac fibrosis post myocardial infarction(MI) cause progressive ventricular remodeling, which finally leads to heart failure. We previously found a high level of IL-27 in the heart and serum till day 14 post ischemia-reperfusion heart. However, whether IL-27 is involved in chronic inflammation-mediated ventricular remodeling remains unclear. In the present study, we found IL-27 expression levels in serum and damaged heart tissue were elevated and continued to day14 in mice after myocardial infarction. We also found MI triggered high IL-27 expression in cardiac macrophages. The increased expression of IL-27 in the serum is correlated with cardiac dysfunction and fibrosis after MI in mice. Furthermore, addition of IL-27 significantly activated the JAK/STAT signaling pathway in cardiac fibroblasts (CFs). Meanwhile, treatment with IL-27 promoted the proliferation and migration, as well as the differentiation of CFs to myofibroblasts and ECM production induced by angiotensin II (Ang II). Collectively, high level of IL-27 mainly produced by cardiac macrophages post MI promotes the collagen production of CFs and aggravates cardiac fibrosis.

Background: Sepsis still remains a major challenge in intensive care medicine with unacceptably high mortality in patients with septic shock. Due to the current limitations of human CD20+CD24+CD38+ Breg (Breg) cells studies among sepsis, here, we tried to evaluate the early alterations of Breg cells and its severity and prognostic value in patients with sepsis.Methods: Peripheral blood from 58 patients with sepsis and 22 healthy controls was analyzed using flow cytometry to evaluate the frequency and numbers of Breg cells. All cases were divided into the sepsis group and septic shock group according to sepsis 3.0 criteria and were grouped according to whether they non-survived (dead group) or survived (alive group) followed up until death or 28 days after enrollment and were compared the differences of Breg cells frequency and numbers, separately. Interleukin (IL)-10 serum levels were measured by ELISA. Spearman's correlation analysis was performed on the Breg cell frequency and clinical indices. Receiver-operating characteristic (ROC) curves and the area under the ROC curve (AUC) were used to assess the sensitivity and specificity of Breg cells for the prognosis of sepsis. Survival curve analysis and binary logistic regression were performed to estimate the value of Breg cells in prognosis among cases with sepsis. Results: Compared to controls, sepsis patients had decreased proportions and numbers of Breg cells in their peripheral blood and increased serum levels of IL-10. The sepsis patients with low frequency of Breg cells were associated with an increased risk of septic shock. Breg cells frequency is inversely associated with lactate, SOFA and APACHE II and positively correlated with Treg frequency. Low levels of Breg cells were associated with an increased risk of death in patients with sepsis. The patients with lower Breg cells frequency and numbers showed a shorter overall survival. Numbers of Breg cells were prediction factors for poor prognosis.Conclusions: For sepsis patients, the frequency and numbers of Breg cells were decreased, and low frequency and numbers of Breg cells revealed poor prognosis. Breg cells may be used as a reliable resource in the determination of sepsis severity and prognosis.

Sepsis still remains a major challenge in intensive care medicine with unacceptably high mortality among patients with septic shock. Due to current limitations of human CD19Peripheral blood from 58 patients with sepsis and 22 healthy controls was analyzed using flow cytometry to evaluate the frequency and number of Bregs. All cases were divided into non-survived or survived group after 28 days followed up. Spearman's correlation analysis was performed on Bregs frequency and clinical indices. The area under the curve was acquired using the receiver operating characteristic analysis to assess the sensitivity and specificity of Bregs for outcome of sepsis. Survival curve analysis and binary logistic regression were applied to estimate the value of Bregs in prognosis among cases with sepsis.Sepsis patients had decreased proportions and number of Bregs. Sepsis patients with low frequency of Bregs were associated with an increased risk of septic shock. Bregs frequency is inversely associated with lactate, SOFA, and APACHE II and positively correlated with Tregs frequency. Low levels of Bregs closely correlated with septic outcomes. Numbers of Bregs were prediction factors for poor prognosis.Frequency and number of Bregs decreased, and Bregs deficiency revealed poor prognosis in patients with sepsis.

Artificial bee colony (ABC) algorithm is widely known for its distinguished exploration ability. However, its exploitation ability is relatively poor. To solve the problem, we propose a novel combinatorial search strategy, whose guided vector can be freely switched between a random vector and the global best vector. It can help improve the exploitation ability of ABC. At the same time, a random vector is beneficial to regulate the enhanced exploitation ability. In addition, both of them can pass information on to a current vector instead of only perturbing a current vector itself. The two guided vectors are chosen with a probability depending on the ratio of fitness information of a current vector to that of the global-best vector. Thus, one of the two guided vectors can be adaptively selected to direct the search. In addition, a mechanism of frequency of perturbation is employed to enhance the scale of information sharing between a current vector and a guided vector for each onlooker bee. Moreover, a modified greedy selection mechanism is designed to choose a child vector inspired by simulated annealing. Furthermore, the search strategy of multiple scouts is also employed in the last stage. Based on all these modifications, an improved ABC (IABC) is proposed. Finally, a few experiments are carried on 58 benchmark problems, including CEC2014 benchmark problems. The computational results exhibit the merit of IABC.

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.

The paper proposes a novel permanent magnet synchronous machine (NPMSM) with one radial permanent magnet synchronous machine (RPMSM) in the center and two axial permanent magnet synchronous machines (APMSMs) on both sides. The two APMSMs take advantage of the stator end winding spaces of the RPMSM and provide additional output power without increasing the whole machine volume. The emphasis of this study is on the unique rotor structure design and its merits for producing additional output power. The 3D finite element method (FEM), coupling with the equivalent circuit method is used in the study for designing the NPMSM. And the NPMSM prototype test results match well with the calculated results.

Artificial bee colony (ABC) algorithm is one of famous swarm intelligence approaches for continuous optimization. With the help of a solution transformation technique, it can evolve in continuous space but consequences belong to binary space. On the basis of binary ABC, a novel artificial bee colony algorithm (nABC for short) is first proposed for better solving the uncapacitated facility location problem (UFLP). In nABC, a pure crossover operation is proposed to improve information sharing quality and remove random perturbation of original search strategy in employed bees phase. Next, a new frequency of perturbation is presented for enhancing the scale of information sharing between different individuals. Then, a new search strategy without probability mechanism of basic ABC is introduced in the onlooker bees phase. To further balance the enhanced exploitation ability, the original strategy of randomly producing an individual is substituted with an opposition-based learning technique with multiple scout bees and the frequency of perturbation mechanism. To testify the effectiveness and the convergence performance of nABC, it is compared with basic ABC and other famous methods for solving fifteen UFLPs from OR-library. Experimental results demonstrate that the proposed nABC is superior to other state-of-the-art approaches in terms of solution accuracy, convergence speed and robustness.

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.

In this paper, an improved global-best harmony search algorithm, named IGHS, is proposed. In the IGHS algorithm, initialization based on opposition-based learning for improving the solution quality of the initial harmony memory, a new improvisation scheme based on differential evolution for enhancing the local search ability, a modified random consideration based on artificial bee colony algorithm for reducing randomness of the global-best harmony search (GHS) algorithm, as well as two perturbation schemes for avoiding premature convergence, are integrated. In addition, two parameters of IGHS, harmony memory consideration rate and pitch adjusting rate, are dynamically updated based on a composite function composed of a linear time-varying function, a periodic function and a sign function in view of approximate periodicity of evolution in nature. Experimental results tested on twenty-eight benchmark functions indicate that IGHS is far better than basic harmony search (HS) algorithm and GHS. In further study, IGHS has also been compared with other eight well known metaheuristics. The results show that IGHS is better than or at least similar to those approaches on most of test functions.

We study coordination mechanisms through penalty schemes to cooperate the behavior of two firms as successive segment carriers to make transport plan separate in freight intermodal market. Based on the different cost structure and service level constraint to two firms, we compare the decision making in two possible decision systems, that is, centralized system and decentralized system. In a centralized system—the first best case as a benchmark is contrasted with decentralized system. In the decentralized system, a Stackelberg game model is formulated between two firms. Some discordant decisions would be made by firmI's overestimate motivation and firmII's undersupply motivation. Our primary objective is to design penalty schemes to coordinate the interactions for two firms. The study shows in a decentralized system, setting suitable penalty schemes can coordinate the two firms' decision. We also study the feasible range of penalty parameters, and some important managerial insights are then deduced. In the end, a numerical example is provided to verify the validity of results, some concluding remarks are presented subsequently.

This paper investigates an oriented spanning tree (OST) based genetic algorithm (GA) for the multi-criteria shortest path problem (MSPP) as well as the multi-criteria constrained shortest path problem (MCSPP). By encoding a path as an OST, in contrast with the existing evolutionary algorithms (EA) for shortest path problem (SPP), the designed GA provides a ''search from a paths set to another paths set'' mutation mechanism such that both of its local search and global search capabilities are greatly improved. Because the possibility to find a feasible path in a paths set is usually larger than that of only one path is feasible, the designed GA has more predominance for solving MCSPPs. Some computational tests are presented and the test results are compared with those obtained by a recent EA of which the encoding approach and the ideas of evolution operators such as mutation and crossover are adopted in most of the existing EAs for shortest path problems. The test results indicate that the new algorithm is available for both of MSPP and MCSPP.

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.

Networks provide a useful way for modeling real world problems and are extensively used in many different types of systems: communications, hydraulic, mechanical, electronic, and logistics. The transportation problem (TP) is known as one of the important network problems. When the TP problem is associated with additional fixed cost for establishing the facilities or fulfilling the demand of customers, then it is called fixed charge transportation problem (fcTP). This problem is one of the NP-hard (IE/OR) problems that are difficult to solve using traditional methods. This paper aims to show the application of spanning tree-based Genetic Algorithm (GA) approach for solving nonlinear fixed charge transportation problem. Our new idea lies on the GA representation that includes the feasibility criteria and repairing procedure for the chromosome. Several numerical experimental results are presented to show the effectiveness of the proposed method.

Differential evolution algorithm is a simple yet efficient metaheuristic for global optimization over continuous spaces. However, there is a shortcoming of premature convergence in standard DE, especially in DE/best/1/bin. In order to take advantage of direction guidance information of the best individual of DE/best/1/bin and avoid getting into local trap, based on multiple mutation strategies, an enhanced differential evolution algorithm, named EDE, is proposed in this paper. In the EDE algorithm, an initialization technique, opposition-based learning initialization for improving the initial solution quality, and a new combined mutation strategy composed of DE/current/1/bin together with DE/pbest/bin/1 for the sake of accelerating standard DE and preventing DE from clustering around the global best individual, as well as a perturbation scheme for further avoiding premature convergence, are integrated. In addition, we also introduce two linear time-varying functions, which are used to decide which solution search equation is chosen at the phases of mutation and perturbation, respectively. Experimental results tested on twenty-five benchmark functions show that EDE is far better than the standard DE. In further comparisons, EDE is compared with other five state-of-the-art approaches and related results show that EDE is still superior to or at least equal to these methods on most of benchmark functions.

Let G = (V, E) be a graph. For a function f : V → {−1, 1}, the weight of f is w(f) = Σv ∈ V f(v). For a vertex v in V, we define f[v] = Σu∈N[v] f(u). A signed dominating function of G is a function f : V → {−1, 1} such that f[v] ⩾ 1 for all v ∈ V. The signed domination number γs(G) of G is the minimum weight of a signed dominating function on G. A signed dominating function of a weight γs(G) we call a γs-function of G. In this paper, we study the signed domination problem of general graph, and obtain some lower bounds of the signed domination number of a graph, and show that these lower bounds are sharp, and extend a result in Dunbar et al. (1995).

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 a hybrid genetic algorithm to solve the bicriteria transportation problem. we absorb the concept on spanning tree and adopt the Prufer number as it is capable of equally and uniquely representing all possible basic solutions. We designed the criterion which chromosomes can be always feasibly converted to a transportation tree. In order to improve the efficiency of evolutionary algorithm, the reduced cost for optimality of a solution was hybridized to genetic algorithm. Numerical experiments show the effectiveness and efficiency of the proposed algorithm.

In order to better solve discrete 0-1 knapsack problems, a novel global-best harmony search algorithm with binary coding, called DGHS, is proposed. First, an initialization based on a greedy mechanism is employed to improve the initial solution quality in DGHS. Next, we present a novel improvisation process based on intuitive cognition of improvising a new harmony, in which the best harmony of harmony memory (HM) is used to guide the searching direction of evolution during the process of memory consideration, or else a harmony is randomly chosen from HM and then a discrete genetic mutation is done with some probability during the phase of pitch adjustment. Third, a two-phase repair operator is employed to repair an infeasible harmony vector and to further improve a feasible solution. Last, a new selection scheme is applied to decide whether or not a new randomly generated harmony is included into the HM. The proposed DGHS is evaluated on twenty knapsack problems with different scales and compared with other three metaheuristics from the literature. The experimental results indicate that DGHS is efficient, effective, and robust for solving difficult 0-1 knapsack problems.

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.

In this paper, we propose neural network approach for multicriteria solid transportation problems(STP). First we suggest a neural network architecture to solve single-objective STP according to augmented Lagrange multiplier method. Due to the massive computing unit-neurons and parallel mechanism of neural network approach can solve the large scale problem efficiently and optimal solution can be got. Then we transform the original multicriteria problem into a single objective problem by global criteria method and adopt the neural network approach to solve it. By this way we can get the satisfactory solution of the original problem. The procedure and efficiency of this approach are shown with numerical simulations.

In this paper, we present neural networks for solving multicriteria solid transportation problems. The original problem is transformed into an equivalent continuous problem from the continuous-time dynamic system and its optimal solution can be got. The procedure and efficiency of this approach are shown with numerical simulations.

In this paper, we introduce the neural networks for solving fuzzy shortest path problems. The penalization of the neural networks is realized after transforming into crisp shortest path model. The procedure and efficiency of this approach are shown with numerical simulations.

The proposed Genetic Algorithms (GAs) are incorporated with problem-specific knowledge and the decision maker's degree of optimism, and the criteria space approach for bicriteria linear program conductive to find out the nondominated extreme points in the criteria space.

In order to overcome the drawback of basic particle swarm optimisation algorithm, such as being subject to fall into local optimisation and being poor in performance of precision, a chaos multi-population particle swarm optimisation algorithm has been proposed, and the algorithm has been effectively used in dealing with the optimisation of transportation continuous network design problem. In this algorithm, multi-population parallel tactics are introduced to improve the global optimising ability and the chaotic search which behaves well in local searching is introduced to improve the solution. The transportation continuous network design problem is solved based on chaos multi-population particle swarm optimisation algorithm. Two examples are presented to compare the proposed method with some existing algorithms. The simulation results show the new particle swarm optimisation algorithm can effectively alleviate the problem of premature convergence and strengthen the global searching ability. The algorithm presented in the paper can apply to solve the large scale traffic design problems.