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The association between breastfeeding and Kawasaki disease is not fully understood. We performed a case-control study to examine the association between breastfeeding and Kawasaki disease. In this study, 389 children diagnosed with Kawasaki disease and 426 gender- and age-matched controls were identified at Renmin Hospital of Wuhan University between November 2013 and March 2019. Demographic and clinical data were collected from a structured telephone interview and medical record database. Odds ratio and 95% confidence interval for risk of Kawasaki disease were estimated. Children who were breastfed exclusively had a decrease in developing Kawasaki disease (adjusted odds ratios and 95% confidence intervals 0.53 (0.38-0.74). Although the risk reduction was not statistically different, partial breastfeeding also provided a protective effect (adjusted odds ratios and 95% confidence intervals 0.70 (0.48-1.01). In the stratified analysis, we still observed that exclusive breastfeeding was inversely associated with the development of complete Kawasaki disease (adjusted odds ratios and 95% confidence intervals 0.52 (0.31-0.88) and incomplete Kawasaki disease (adjusted odds ratios and 95% confidence intervals 0.54 (0.38-0.77). However, there was no significant association between exclusive breastfeeding and the intravenous immunoglobulin treatment response (adjusted odds ratios and 95% confidence intervals 0.69 (0.27-1.69) and the risk of coronary artery lesions (adjusted odds ratios and 95% confidence intervals 0.79 (0.49-1.31) in Kawasaki disease.Conclusion: Our analysis suggests that exclusive breastfeeding was inversely associated with the development of Kawasaki disease and that breastfeeding might be a potential protective factor against Kawasaki diseaseWhat is known• Previous studies have demonstrated that breastfeeding has been shown to potentially confer protection against several autoimmune disorders of childhood.• The association between breastfeeding and Kawasaki disease is not fully understood.What is newThe first study to evaluate the association between breastfeeding and the development of Kawasaki disease in China with a large sample size.• Exclusive breastfeeding was inversely associated with the development of Kawasaki disease and breastfeeding might be a potential protective factor against Kawasaki disease.

The purpose of this study is to address the allocation of transit lines problem in operation-sharing. An allocation method for urban transit lines is proposed to guide public authorities to pursue an optimal plan considering the User Equilibrium for operators (UE-O). The method utilizes the concepts from mathematical programming and game theory to present the UE-O and proposes a set partitioning formulation considering the benefits of both passengers and operators. A branch-and-price algorithm employing both column generation and branch-and-bound is used to tackle the problem. The proposed method is validated through a case study using data from the Development District of Dalian. Results show that the proposed line allocation method considering the UE-O can reduce the potential competitions among operators. This method and findings can provide a guidance to the problems in operation-sharing regarding allocation of transit lines.

This paper describes a bi-objective optimization model for the traffic count location problem in stochastic origin–destination (OD) traffic demand estimation. Two measures are defined to capture the maximum possible absolute error of the mean and the covariance of the estimated OD demand. The bounds of these two measures are mathematically deduced, and then the bi-objective optimization model is formulated to minimize the two upper bounds simultaneously. A surrogate-assisted genetic algorithm is proposed to solve this model, and a series of numerical examples are presented to demonstrate the applicability of the proposed model and the efficiency of the proposed algorithm.

Objective Preeclampsia (PE) is a potentially fatal pregnancy-specific complication. Nevertheless, the pathogenesis of PE remains indistinct. Recently, increasing studies emphasized that long noncoding RNAs (lncRNAs) functions as imperative regulators in PE. The aim of this study was to compare the lncRNAs transcript profile of placentae in early onset severe preeclampsia (EOSP) with lncRNAs in normal pregnancy (NP) and to evaluate the role of lncRNA MIR210HG (microRNA 210 host gene) in the PE pathogenesis. Methods Using RNA sequencing, we compared transcriptome profiles of placentae in EOSP (n = 3) and NP (n = 3). Bioinformatic tools were used to predict the function of differentially expressed genes while qRT-PCR was used to verify RNA sequencing data. The role of MIR210HG in HTR8/SVneo migration and invasion were analyzed by in vitro MIR210HG gene overexpression. Results Our results showed that 527 lncRNAs and 600 mRNAs were differentially expressed in placental samples of EOSP, and the analysis identified 63 key EOSP related genes. As indicated by bioinformatics analyses, lncRNA MIR210HG was a potential pathogenic marker of PE. LncRNA-MIR210HG expression was upregulated in placental samples of PE and enriched in the canonical Wnt signalling pathway. MiR210HG overexpression inhibited HTR8/SVneo cell migration and invasion in vitro. Additionally, miR210HG upregulated dickkopf-1 expression via the sponging of microRNA-520a-3p (miR-520a-3p), thus repressing trophoblast migration and invasion. Conclusion Our study showed that MiR210HG is a novel upregulated lncRNA in the placentas of PE and MiR210HG regulates the migration and invasive potential of HTR-8/SVneo cell by targeting the miR-520a-3p/Dickkopf-1 axis.

We present a method for identifying bus rapid transit routes optimized for serving the greatest number of passengers. Because formulating a relevant model is a complex problem, a particle swarm opt...

In this paper, a bi-level model is proposed, which considers the benefits of taxi driver and passenger. The upper-level model is a bi-objective program. The first objective is to minimize the waiting time of passenger in rush hour. And the second objective is to maximize the income of the driver for one day. The lower-level model is a demand function model, calculating the demand of taxi based on given fleet size and fare. The lower-level model can show the influence of fleet size and fare on the potential demand. At last, the taxi current condition of Dalian city in China is chosen to test the proposed method. The results show that the increase of fleet size can attract potential demand. But the degree of attraction mostly depends on the waiting time of passengers and taxi fare. Furthermore, both fleet size and fare of tax in Dalian are on a low level. It is necessary to increase the fleet size and the fare in the meantime.

Driver fatigue is the major reason for severe traffic accidents. At present, the driver’s driving state evaluation, based on multi-source information fusion, has become a hotspot in the research field of vehicle safety assistant driving. The purpose of this paper is to build a Bayesian network model for driver fatigue causation analysis considering several visual cues, such as Percentage of Eyelid Closure over the Pupil over Time, Average Eye Closure Speed, etc. The proposed method was divided into three stages, that is, variables analysis, model structure design, and model parameter determination. Finally, the presented model and algorithm were illustrated with a simulation experiment and conclusions were inferred from the experiment data analysis.

Short-term traffic speed prediction is one of the most critical components of an intelligent transportation system ITS. The accurate and real-time prediction of traffic speeds can support travellers' route choices and traffic guidance/control. In this article, a support vector machine model single-step prediction model composed of spatial and temporal parameters is proposed. Furthermore, a short-term traffic speed prediction model is developed based on the single-step prediction model. To test the accuracy of the proposed short-term traffic speed prediction model, its application is illustrated using GPS data from taxis in Foshan city, China. The results indicate that the error of the short-term traffic speed prediction varies from 3.31% to 15.35%. The support vector machine model with spatial-temporal parameters exhibits good performance compared with an artificial neural network, a k-nearest neighbor model, a historical data-based model, and a moving average data-based model.

Effective prediction of real-time traffic flow is important for traffic management and intelligent traffic systems. This paper proposes a hybrid model, consisting of the k-nearest neighbours (k-NN) method and the Kalman filter (KF) technique, to dynamically predict real-time traffic flow. In the model, the k-NN method predicts a baseline speed of traffic flow on the basis of historical travel data of the target road link. To reflect the dynamic evolution of traffic flow in the prediction, a KF-based algorithm that uses the latest travel data, is developed to adjust the baseline travel speed. The hybrid model is tested with global positioning system data of Foshan City, China. In the numerical test, the proposed hybrid model is compared with a single k-NN model based on the same database. The results show that the hybrid model can provide more accurate prediction and thus holds potential for use in practice.

Congestion pricing is considered as an effective measure to ease urban traffic congestion. This paper develops a bi-level model that designs more equitable congestion pricing with bus subsidies. In the upper-level model, the constraint of alternative path is proposed to ensure that there is at least one non-toll path for any origin–destination (OD) pair. If one link is the only way for an OD pair just like Lidingo, Stockholm, this link should not be tolled. Meanwhile, toll revenue is used to improve the service level of transit system in which transfer fare is subsidized and the frequency of the most crowded bus is improved. The lower-level model is a multimodal transportation network equilibrium model. A shuffled pattern search algorithm and method of successive averages are used to optimize congestion pricing, transfer fare and bus frequency in the bi-level model. The results of the numerical test show that the model can meet the travel demand of travelers after tolling. Moreover, traffic congestion is alleviated while service level of transit is improved.

The school bus routing problem (SBRP) seeks to the optimal routes for a fleet of school buses to transport students to schools. To share resources between schools, school bus routing problem with mixed load plan (SBRP-MLP) is proposed in this paper, in which school buses are allowed to pick up students of different schools and deliver them to their schools. This paper presents two modes, SBRP-MLP with virtual stops and SBRP-MLP with interscholastic transportation. To solve the two SBRP-MLPs efficiently, a two-stage heuristic algorithm is developed. The results of several tests suggest that the proposed SBRP-MLP modes take less time than the SBRP with single load plan. Moreover, it was found that the SBRP-MLP with virtual stops is suitable for small-scale cases, while the SBRP-MLP with interscholastic transportation is appropriate for large-scale cases. In addition, the results also indicate that the algorithm is feasible for solving the SBRP-MLP.

One-way traffic is a kind of traffic management measure with less investment, simple operation, and obvious effects. Considering some irrational one-way lane configurations currently in practice, a bi-level optimization programming model is proposed in this paper. The decision-making variable of the upper level model is the scheme of one-way traffic reconfiguration, and the optimization target is to minimize the total travel time. The lower level programming is an equilibrium traffic assignment model. The Shuffled Complex Evolution Algorithm (SCE-UA) and VisSim simulation are applied for the upper and lower models, respectively. A numerical test based on Dalian Harbour Plaza is employed to validate the proposed model. In the test, the effects of the current scheme, the scheme solved by the bi-level model using the SCE-UA and Frank-Wolfe algorithm, and the scheme proposed by this paper (simulation-optimization) are compared. As the results show, the bi-level programming model in this paper can perform well in reducing the total travel time of travelers and in improving traffic efficiency.

A change in bus fare influences passenger choice behavior, resulting in a change of passenger flow and passengers’ total travel time. In this paper, a bi-level model was built to optimize the bus fare. The upper-level used the passenger flow and the travel time of passengers to optimize the bus fare. The lower-level is an agent-based simulation model that can simulate the passenger flow based on the passenger choice behavior. Then the tabu search algorithm was used to solve the bi-level model. Finally, the bi-level model is verified using two bus routes of Dalian, China. The result proves the bi-level model is reliable.

This paper compares the operation of waterbuses and overland buses from an environmental perspective. A two-stage model considering carbon dioxide emissions is established. The first stage covers the transit modal split and the objective of the second stage is to maximise the benefit of waterbus operations. A heuristic algorithm is proposed to solve the model. Dalian city in China is chosen as an example to prove the feasibility of the model and the algorithm. The data show that the proposed model for waterbus operational optimisation has practical significance.

This paper proposes a bi-level programming model to solve the design problem for bus lane distribution in multi-modal transport networks. The upper level model aims at minimizing the average travel time of travelers, as well as minimizing the difference of passengers’ comfort among all the bus lines by optimizing bus frequencies. The lower level model is a multi-modal transport network equilibrium model for the joint modal split/traffic assignment problem. The column generation algorithm, the branch-and-bound algorithm and the method of successive averages are comprehensively applied in this paper for the solution of the bi-level model. A simple numerical test and an empirical test based on Dalian economic zone are employed to validate the proposed model. The results show that the bi-level model performs well with regard to the objective of reducing travel time costs for all travelers and balancing transit service level among all bus lines.

In this paper, a carton heterogeneous vehicle routing problem with a collection depot is presented, which can collaboratively pick the cartons from several carton factories to a collection depot and then from the depot to serve their corresponding customers by using of heterogeneous fleet. Since the carton heterogeneous vehicle routing problem with a collection depot is a very complex problem, particle swarm optimization (PSO) is used to solve the problem in this paper. To improve the performance of the PSO, a self-adaptive inertia weight and a local search strategy are used. At last, the model and the algorithm are illustrated with two test examples. The results show that the proposed PSO is an effective method to solve the multi-depot vehicle routing problem, and the carton heterogeneous vehicle routing problem with a collection depot. Moreover, the proposed model is feasible with a saving of about 28 % in total delivery cost and could obviously reduce the required number of vehicles when comparing to the actual instance.

Marine transportation can provide more comfortable transit service compared to road-based public transportation. This paper proposes an optimization method for planning the lines and the operational strategies of waterbuses. In this method, the candidate hub ports are selected first, and then a two-stage optimization model is constructed. The model comprehensively considers the interests of both the passengers and the operators by optimizing the lines and the operational strategies of the waterbuses. To solve the model, a shuffled genetic algorithm is proposed. Furthermore, Zhoushan city in China has been chosen as the case study to test the proposed method. The results show that the optimized waterbus lines and operational strategies are better than those that are currently used by the water transportation system in Zhoushan city.

Cardiovascular dysfunction is a major complication associated with sepsis induced mortality. Cardiac fibrosis plays a critical role in sepsis induced cardiac dysfunction. The mechanisms of the activation of cardiac fibrosis is unclarified. In this study, we found that microRNA-23b (miR-23b) was up-regulated in heart tissue during cecal ligation and puncture (CLP)-induced sepsis and transfection of miR-23b inhibitor improved survival in late sepsis. Inhibition of miR-23b in the myocardium protected against cardiac output and enhanced left ventricular systolic function. miR-23b inhibitor also alleviated cardiac fibrosis in late sepsis. MiR-23b mediates the activation of TGF-β1/Smad2/3 signaling to promote the differentiation of cardiac fibroblasts through suppression of 5'TG3'-interacting factor 1 (TGIF1). MiR-23b also induces AKT/N-Cadherin signaling to contribute to the deposition of extracellular matrix by inhibiting phosphatase and tensin homologue (PTEN). TGIF1 and PTEN were confirmed as the targets of miR-23b in vitro by Dual-Glo Luciferase assay. miR-23b inhibitor blocked the activation of adhesive molecules and restored the imbalance of pro-fibrotic and anti-fibrotic factors. These data provide direct evidence that miR-23b is a critical contributor to the activation of cardiac fibrosis to mediate the development of myocardial dysfunction in late sepsis. Blockade of miR-23b expression may be an effective approach for prevention sepsis-induced cardiac dysfunction.

The study is dedicated to an intelligent fault diagnosis approach for vehicle maintenance which integrates both the cloud model and case-based reasoning (CBR). The cloud model is used to transform the uncertainty of the subjective quantitative information into qualitative values to calculate the case similarity, which greatly simplifies the input conditions in case retrieval and improves the operability of fault diagnosis. The improved Euclidean distance formula is taken as a measure of the similarity between the fault cases. Compared with the traditional method, it eliminates the similarity deviation and improves the accuracy of case retrieval. The case study of vehicle electrical and electronic equipment is reported, which can prove the approach proposed in this paper is correct and efficient.

Thousands of manufacturing firms clustering in China's coastal regions show signs of relocation in inland provinces because of the disadvantages of location condition and laborer's quality of life (QOL) since the opening policy. Also the signs will induce redistribution of export freight flow of seaports. This paper intends to explore the interaction relationship between firm's relocation and laborer's workplace choices from a micro-decision level. First, based on the discrete choice theory, the choice behavior models of manufacturing firms and laborers are built. Second, taken the wages as bond, the interactive equilibrium model of firm's relocation and laborer's workplace choice is built. Finally, the “communication equipment, computers, and related electronic equipment manufacturing” in pan-Pearl River Delta (PRD) region of China is taken as an example. Under equilibrium situation, in parallel with the backflow of outside laborers, the manufacturing firms will relocate in inland provinces either with f...

This paper presents a transit network optimization method, in which travel time reliability on road is considered. A robust optimization model, taking into account the stochastic travel time, is formulated to satisfy the demand of passengers and provide reliable transit service. The optimization model aims to maximize the efficiency of passenger trips in the optimized transit network. Tabu search algorithm is defined and implemented to solve the problem. Then, transit network optimization method proposed in this paper is tested with two numerical examples: a simple route and a medium-size network. The results show the proposed method can effectively improve the reliability of a transit network and reduce the travel time of passengers in general.

Automated Incident Detection (AID) is an important part of Advanced Traffic Management and Information Systems (ATMISs). An automated incident detection system can effectively provide information on an incident, which can help initiate the required measure to reduce the influence of the incident. To accurately detect incidents in expressways, a Support Vector Machine (SVM) is used in this paper. Since the selection of optimal parameters for the SVM can improve prediction accuracy, the tabu search algorithm is employed to optimize the SVM parameters. The proposed model is evaluated with data for two freeways in China. The results show that the tabu search algorithm can effectively provide better parameter values for the SVM, and SVM models outperform Artificial Neural Networks (ANNs) in freeway incident detection.

This paper proposes an approach to deal with the hub location problem in urban public transit network design, which includes two phases: to determine the candidate nodes and to optimize the distribution of transit hubs. Firstly, an indicator defined as passenger attraction is introduced to describe the attraction level of a transit node to passengers. A selection model based on passenger attraction is built to choose several candidate nodes from all the transit nodes. Secondly, a mathematical model, aiming at maximizing served populations per construction cost, is proposed to optimize multi-hub location based on the selected candidate nodes. At last, the data of Dalian city, China is used for testing the validity of the proposed approach. The results indicate that the candidate node selection model according to passenger attraction can provide an appropriate searching space for hub location optimization and the selected candidate nodes can almost cover all the passenger flow collection and distribution centers in the urban area of Dalian. Furthermore, the optimized hub location in the case study is consistent with the practice situation in Dalian. This indicates that the proposed two-phase optimization approach is a competitive method for transit hub location.

The periodic vehicle routing problem (PVRP) is an extension of the vehicle routing problem (VRP). Because it extends the single delivery period to a T-day period ( T > 1), PVRP has strong theoretical and practical significance. Since PVRP is an embedded VRP, it is more complex and difficult compared with the general VRP. In this paper, the bee colony algorithm is used to solve the PVRP. To improve the performance of this algorithm, multidimensional heuristic information and a local optimization based on a scanning strategy are used. At the end of this paper, the algorithm is tested by some well-known examples. The results show that the proposed improved bee colony algorithm is a powerful tool for solving the PVRP. It also shows that these two kinds of strategies can significantly improve the performance of the algorithm.

Dynamic vehicle routing problem (DVRP) with single depot has received increasing interest from engineers and scientists. Dynamic multi-depot vehicle routing problem (DMDVRP), an extension of DVRP, however, has not received much attention. In our paper, a distance-based clustering approach is introduced to simplify the DMDVRP by allocating each customer to its nearest depot. Thus, DMDVRP is decomposed to a sequence of DVRPs. An improved ant colony optimisation (IACO) with ant-weight strategy and mutation operation is presented to optimise vehicle routing problem (VRP) in this paper. Moreover, to satisfy the real-time feature of DMDVRP, the nearest addition approach is used to handle the new orders occurring during a time slice on the basis of VRP solution. Finally, the computational results for 17 benchmark problems are reported to validate that IACO with the distance-based clustering approach is more suitable for solving DMDVRP.

The building density of Central Business District (CBD) is usually high. Land for a bus terminal is insufficient. In this situation, passengers in CBD have to walk far to take a bus, or take a long time to wait for a taxi. To solve this problem, this paper proposes an indirect approach: the design of a circle line of shuttle bus as a dynamic bus terminal in CBD. The shuttle bus can deliver people to the bus station through a circle line. This approach not only reduces the traffic pressure in CBD, but also saves travel time of the passenger. A bi-objective model is proposed to design a circle line of a shuttle bus for CBD. The problem is solved by non-dominated sorting genetic algorithm (NSGA-II). Furthermore, the Dalian city in China has been chosen as the case study to test the proposed method. The results indicate that the method is effective for circle line optimization of shuttle bus in central business district without a bus terminal.

The present study aimed to investigate the roles of the hydrolytic process of N-cadherin by A disintegrin and metalloproteases 10 (ADAM10) in sustaining myocardial structure and integrity, and discuss the mechanisms of ventricular remodeling in dilated cardiomyopathy (DCM). Single chain variable fragment antibody (ScFv) with the ability to specifically block the ADAM10 hydrolysis site of N-cadherin was designed and constructed. Western blot analysis and flow cytometry were used to detect the expression of N-cadherin and its C-terminal fragment 1 (CTF1) on cardiomyocytes, and cells were also subjected to a cell adhesion assay. Furthermore, in a rat model of dilated cardiomyopathy (DCM), the effects of intracardiac injection of the recombinant adenovirus on cardiac structure and contractile function were observed by hematoxylin and eosin staining and color Doppler echocardiography. The recombinant ScFv-expressing adenoviral plasmid with the ability to block the ADAM10 hydrolysis site on N-cadherin was successfully constructed and efficiently transfected into H9C2 cells. After transfection, N-cadherin protein expression was significantly increased, CTF1 protein was significantly decreased and the adhesion capability of myocardial cells was significantly improved. In the in vivo experiment, N-cadherin expression was significantly increased in the treatment group compared with that in the model group, and the structure and function of the heart were significantly improved. In conclusion, blocking of the ADAM10 hydrolysis site on N-cadherin by ScFv increased N-cadherin expression and improved ventricular remodeling. The present study provided experimental evidence for a novel approach for the treatment and prevention of DCM.

The mode of automotive maintenance chain is gaining more and more attention in China because of its advantages such as the lower cost, higher speed, higher availability, and strong adaptability. Since the automobile chain maintenance parts delivery problem is a very complex multi-depot vehicle routing problem with time windows, a virtual center depot is assumed and adopted to transfer multi-depot vehicle routing problem with time windows to multi-depot vehicle routing problem with the virtual central depot, which is similar to a vehicle routing problem with time windows. Then an improved ant colony optimization with saving algorithms, mutation operation, and adaptive ant-weight strategy is presented for the multi-depot vehicle routing problem with the virtual central depot. At last, the computational results for benchmark problems indicate that the proposed algorithm is an effective method to solve the multi-depot vehicle routing problem with time windows. Furthermore, the results of an automobile chain maintenance parts delivery problem also indicate that the improved ant colony algorithm is feasible for solving this kind of delivery problem.

One of the most critical functions of an intelligent transportation system (ITS) is to provide accurate and real-time prediction of traffic condition. This paper develops a short-term traffic condition prediction model based on the k-nearest neighbor algorithm. In the prediction model, the time-varying and continuous characteristic of traffic flow is considered, and the multi-time-step prediction model is proposed based on the single-time-step model. To test the accuracy of the proposed multi-time-step prediction model, GPS data of taxis in Foshan city, China, are used. The results show that the multi-time-step prediction model with spatial-temporal parameters provides a good performance compared with the support vector machine (SVM) model, artificial neural network (ANN) model, real-time-data model, and history-data model. The results also appear to indicate that the proposed k-nearest neighbor model is an effective approach in predicting the short-term traffic condition.

In this study, a double closed-loop control structure with a disturbance observer is reported. This combining structure could globally realize postures-tracking stabilization and have disturbance adaptive capability. On the outer loop, a new function having sinusoidal and inverse tangent characteristics, which are continuously differentiable, is applied to construct the velocity controller by using backstepping technology. Meanwhile, a disturbance-observer-based controller is applied to the inner loop to estimate the dynamic disturbances acting on the wheels; these disturbances are unavoidable and uncertain in an unstructured environment. To enhance the robustness of the system, sliding-mode control technology is applied to construct the system's dynamic controller. Finally, simulations are carried out to verify the effectiveness of the proposed control methods.

Transit network design is an important part of urban transportation planning. The purpose of this paper is to build on direct traveler density model and extend it to design transit network considering demand density relating to direct demands and transfers, and lengths of routes. The proposed method aiming to maximize demand density of route under some resource constraints divides transit network design problem into three stages, i.e., skeleton route design, main route design and branch route design, based on the objective functions with different transfer coefficients. An ant colony optimization (ACO) is used to solve the model. The model and algorithm are illustrated with data from Dalian city, China and results show that the approach can improve the solution quality if the transfer coefficient is reasonably set.

Dynamic vehicle dispatching at the transfer station can improve the transit service quality by optimizing the transfer coordination of routes. In this paper, a dynamic vehicle dispatching model is ...

This paper presents a hybrid method to predict tunnel surrounding rock displacement, which is one of the most important factors for quality control and safety during tunnel construction. The hybrid method comprises two phases, one is support vector machine (SVM)-based model for predicting the tunnel surrounding rock displacement, and the other is GA-based model for optimizing the parameters in the SVM. The proposed model is evaluated with the data of tunnel surrounding rock displacement on the tunnel of Wuhan-Guangzhou railway in China. The results show that genetic algorithm (GA) has a good convergence and relative stable performance. The comparison results also show that the hybrid method can generally provide a better performance than artificial neural network (ANN) and finite element method (FEM) for tunnel surrounding rock displacement prediction.

In this paper, a model for optimizing bus route headway is presented in a given network configuration and demand matrix, which aims to find an acceptable balance between passenger costs and operator costs, namely the maximization of service quality and the minimization of operational costs. An integrated approach is also proposed in the paper to determine the relative weights between passenger costs and operator costs. A parallel genetic algorithm (PGA), in which a coarse-grained strategy and a local search algorithm based on Tabu search are applied to improve the performance of genetic algorithm, is developed to solve the headway optimization model. Data collected in Dalian City, China, is used to verify the feasibility of the model and the algorithm. Results show that the reasonable resource assessment can increase the benefits of transit system.

Multi-step-ahead prediction of tunnel surrounding rock displacement is an effective way to ensure the safe and economical construction of tunnels. This paper presents a multi-step-ahead prediction model, which is based on support vector machine (SVM), for tunnel surrounding rock displacement prediction. To improve the training efficiency of SVM, shuffled complex evolution algorithm (SCE-UA) is also performed through some exponential transformation. The data from the Chijiangchong tunnel are used to examine the performance of the prediction model. Results show that SVM is generally better than artificial neural network (ANN). This indicates that SVM is a feasible and effective multi-step method for tunnel surrounding rock displacement prediction.

The vehicle routing problem (VRP), a well-known combinatorial optimization problem, holds a central place in logistics management. This paper proposes an improved ant colony optimization (IACO), which possesses a new strategy to update the increased pheromone, called ant-weight strategy, and a mutation operation, to solve VRP. The computational results for fourteen benchmark problems are reported and compared to those of other metaheuristic approaches.

To find a more sensitive and earlier diagnostic marker for hepatocellular carcinoma, methylation-profils of CpG islands in the promoter of eleven genes in hepatocellular carcinoma(HCC) carcinoma and pericarcinoma tissues from ten patients were examined by using methylation-specific PCR (MSP) method. MSP was used to detect the methylation of p16, p53, Secreted frizzled-related protein 1 (SFRP1) and SFRP2 promoters. Meanwhile, plasma alpha-fetoprotein (AFP) levels in 53 patients were also determined. The results showed that HCC was closely correlated to methylation in promoter of tumor-suppressing gene p16, p53, SFRP1 and SFRP2. The results suggest that methylation of SFRP2 promoter is possible a better marker for diagnosis of HCC than plasma Alpha-fetoprotein (AFP) levels. The false negative of SFRP1 and SFRP2 are perfectly complementary. If SFRP1 and SFRP2 were both considered as a complementary positive marker at the same time, the accurate rate for diagnosis of HCC is 100%.

This paper presents a dynamic extra buses scheduling strategy to improve the transit service of transit routes. In this strategy, in order to decide when to dispatch an extra bus, the service reliability of transit route is assessed firstly. A model aimed at maximizing the benefit of the extra buses scheduling strategy is constructed to determine how many stops extra buses need to skip from the terminal to accommodate passengers at the following stops. A heuristic algorithm is defined and implemented to estimate the service reliability of transit route and to optimize the initial stop of extra buses scheduling strategy. Finally, the strategy is tested on two examples: a simple and a real-life transit route in the Dalian city in China. The results show that the extra buses scheduling strategy based on terminal stops with a reasonable threshold can save 8.01% waiting time of passengers.

This paper investigates a well-known complex combinatorial problem known as the vehicle routing problem with time windows (VRPTW). Unlike the standard vehicle routing problem, each customer in the VRPTW is served within a given time constraint. This paper solves the VRPTW using an improved artificial bee colony (IABC) algorithm. The performance of this algorithm is improved by a local optimization based on a crossover operation and a scanning strategy. Finally, the effectiveness of the IABC is evaluated on some well-known benchmarks. The results demonstrate the power of IABC algorithm in solving the VRPTW.

1 School of Automotive Engineering, Dalian University of Technology, Dalian 116024, China 2 Transportation Management College, Dalian Maritime University, Dalian 116024, China 3Department of Mechanical and Manufacturing Engineering, Aalborg University, Denmark 4 Faculty of Technology, Policy and Management, Delft University of Technology, Delft, The Netherlands 5 College of Transportation, Jilin University, Changchun 30022, China

Energy management control strategy of hybrid electric vehicle has a great influence on the vehicle fuel consumption with electric motors adding to the traditional vehicle power system. As vehicle real driving cycles seem to be uncertain, the dynamic driving cycles will have an impact on control strategy’s energy-saving effect. In order to better adapt the dynamic driving cycles, control strategy should have the ability to recognize the real-time driving cycle and adaptively adjust to the corresponding off-line optimal control parameters. In this paper, four types of representative driving cycles are constructed based on the actual vehicle operating data, and a fuzzy driving cycle recognition algorithm is proposed for online recognizing the type of actual driving cycle. Then, based on the equivalent fuel consumption minimization strategy, an ant colony optimization algorithm is utilized to search the optimal control parameters “charge and discharge equivalent factors” for each type of representative driving cycle. At last, the simulation experiments are conducted to verify the accuracy of the proposed fuzzy recognition algorithm and the validity of the designed control strategy optimization method.

This paper deals with a real-life vehicle delivery routing problem, which is a seafood product delivery routing problem. Considering the features of the seafood product delivery routing problem, this paper formulated this problem as a multi-depot open vehicle routing problem. Since the multi-depot open vehicle routing problem is a very complex problem, a method is used to reduce the complexity of the problem by changing the multi-depot open vehicle routing problem into an open vehicle routing problem with a dummy central depot in this paper. Then, ant colony optimization is used to solve the problem. To improve the performance of the algorithm, crossover operation and some adaptive strategies are used. Finally, the computational results for the benchmark problems of the multi-depot vehicle routing problem indicate that the proposed ant colony optimization is an effective method to solve the multi-depot vehicle routing problem. Furthermore, the computation results of the seafood product delivery problem from Dalian, China also suggest that the proposed ant colony optimization is feasible to solve the seafood product delivery routing problem.

This paper presents a hybrid model for early onset prediction of driver fatigue, which is the major reason of severe traffic accidents. The proposed method divides the prediction problem into three stages, that is, SVM-based model for predicting the early onset driver fatigue state, GA-based model for optimizing the parameters in the SVM, and PCA-based model for reducing the dimensionality of the complex features datasets. The model and algorithm are illustrated with driving experiment data and comparison results also show that the hybrid method can generally provide a better performance for driver fatigue state prediction.