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This paper constructed a stacked-autoencoder neural network model (SAE model) to estimate sea state bias (SSB) based on radar altimeter data. Six cycles of the geophysical data record (GDR) from Jason-1/2 radar altimeters were used as a training dataset, and the other 2 cycles of the GDR from Jason-1/2 were used for testing. The inputs to this SAE model include the significant wave height (SWH), wind speed (U), sea surface height (SSH), backscatter coefficient (σ0) and automatic gain control (AGC), and the model outputs the SSB. The model includes one input layer, three hidden layers and one output layer. The SSBs in the GDR of Jason-1/2 were obtained from a nonparametric model based on the SWH and U as input variables; thus, the model has high accuracy but low efficiency. The SSBs in the GDR of HY-2A were computed using a four-parameter parametric model that uses the SWH and U as input variables; therefore, this model’s computational speed is high but its accuracy is low. Thus, we used the HY-2A radar altimeter as an unseen validation dataset to evaluate the performance of the SAE model. Then, we analyzed the contrasting results of these methods, including the differences in the SSB, explained variance, residual error and operational efficiency. The results demonstrate not only that the accuracy of the SAE model is superior to that of the conventional parametric model but also that its operational efficiency is better than that of the nonparametric model. [ABSTRACT FROM AUTHOR]

Big data classification is very slow when using traditional machine learning classifiers, particularly when using a lazy and slow-by-nature classifier such as the k-nearest neighbors algorithm (KNN). This paper proposes a new approach which is based on sorting the feature vectors of training data in a binary search tree to accelerate big data classification using the KNN approach. This is done using two methods, both of which utilize two local points to sort the examples based on their similarity to these local points. The first method chooses the local points based on their similarity to the global extreme points, while the second method chooses the local points randomly. The results of various experiments conducted on different big datasets show reasonable accuracy rates compared to state-of-the-art methods and the KNN classifier itself. More importantly, they show the high classification speed of both methods. This strong trait can be used to further improve the accuracy of the proposed methods. [ABSTRACT FROM AUTHOR]

Due to the complexity of large production systems, maintenance events are diverse, simultaneous and dynamic. Appropriate maintenance management of complex large production systems can guarantee high availability and save maintenance costs. However, current maintenance decision-making methods mainly focus on the maintenance events of single-components and series connection multi-components; little research pays attention to the combination maintenance of different maintenance events. Therefore, this paper proposes a multi-event combination maintenance model based on event correlation. First, the maintenance downtime and cost of three types of maintenance events under different maintenance beginning times and degrees are analysed. Then, shared maintenance downtime and cost models are established by maintenance event correlations. In addition, a multi-event combination maintenance model is constructed to achieve the goal of the highest availability and the lowest cost rate in both the decision-making cycle and the remaining life. Moreover, a particle swarm optimization algorithm based on interval segmentation for model solving is designed. Finally, a numerical example is presented to illustrate the model. [ABSTRACT FROM AUTHOR]

Coordination is essential for improving supply chain performance, and one of the most critical factors in achieving the coordination of a supply chain is the integrated research of production and distribution. In this paper, a novel two-stage hybrid solution methodology is proposed. In the first stage, products are processed on the serial machines of multiple manufacturers located in two industrial parks. A fuzzy multi-objective scheduling optimization is performed using a modified non-dominated sorting genetic algorithm II (NSGA-II). The result obtained in the first stage is used in the second stage to optimize the distribution scheduling problem using a modified genetic annealing algorithm (GAA). Finally, simulation results verify both the feasibility and efficiency of the proposed solution methodology. [ABSTRACT FROM AUTHOR]

In this paper, the accelerated failure time (AFT) model is modified to analyze post-work grocery shopping activity duration. Much previous shopping duration analysis was conducted using the proportional hazard (PH) modeling approach. Once the proportionality assumption was violated, the traditional accelerated failure time (TAFT) model was usually selected as an alternative modeling approach. However, a TAFT model only has covariates with non-proportional and time-dependent effects on the hazard overtime while a PH model only accommodates covariates with proportional and time-independent effects. Neither of them considers the possibility that some of covariates may have proportional and time-independent effects and some may have non-proportional and time-dependent effects on the hazard value in one model. To address this issue, the paper generalizes the TAFT model and develops a modified accelerated failure time (MAFT) model to accommodate both time-dependent and time-independent covariates for activity duration analysis. Checking on the proportionality assumption indicates that the assumption is not valid in the post-work grocery shopping activity data extracted from the 2017 National Household Travel Survey (NHTS) conducted by the U.S. Department of Transportation (USDOT). Both TAFT and MAFT models are developed for comparisons and analysis. The empirical and statistical results show that there do exist two different types of covariates affecting shopping activity duration, including covariates only with proportional and time-independent effects (i.e. working duration, commute travel time) and those with non-proportional and time-dependent effects. The MAFT model can capture the subtleties in various types of covariate effects and help better understand how those covariates affect activity duration overtime. This paper also shows the importance to develop a flexible duration model with both time-dependent and time-independent covariates for accurately evaluating travel demand management (TDM) policies, like flexible work hours. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR) is a disease which is widely diagnosed using (colour fundus) images. Efficiency and accuracy are critical in diagnosing DR as lack of timely intervention can lead to irreversible visual impairment. In this paper, we examine strategies for scrutinizing images which affect diagnostic performance of medical practitioners via an eye-tracking study. A total of 56 subjects with 0 to 18 years of experience participated in the study. Every subject was asked to detect DR from 40 images. The findings indicate that practitioners use mainly two types of strategies characterized by either higher dwell duration or longer track length. The main findings of the study are that higher dwell-based strategy led to higher average accuracy (> 85%) in diagnosis, irrespective of the expertise of practitioner; whereas, the average obtained accuracy with a long-track length-based strategy was dependent on the expertise of the practitioner. In the second part of the paper, we use the experimental findings to recommend a scanning strategy for fast and accurate diagnosis of DR that can be potentially used by image readers. This is derived by combining the eye-tracking gaze maps of medical experts in a novel manner based on a set of rules. This strategy requires scrutiny of images in a manner which is consistent with spatial preferences found in human perception in general and in the domain of fundus images in particular. The Levenshtein distance-based assessment of gaze patterns also establish the effectiveness of the derived scanning pattern and is thus recommended for image readers. [ABSTRACT FROM AUTHOR]

This paper develops a new machine vision framework for efficient detection and classification of manufacturing defects in metal boxes. Previous techniques, which are based on either visual inspection or on hand-crafted features, are both inaccurate and time consuming. In this paper, we show that by using autoencoder deep neural network (DNN) architecture, we are able to not only classify manufacturing defects, but also localize them with high accuracy. Compared to traditional techniques, DNNs are able to learn, in a supervised manner, the visual features that achieve the best performance. Our experiments on a database of real images demonstrate that our approach overcomes the state-of-the-art while remaining computationally competitive. [ABSTRACT FROM AUTHOR]

Particle Swarm Optimization (PSO) is widely used in maximum power point tracking (MPPT) of photovoltaic (PV) energy systems. Nevertheless, this technique suffers from two main problems in the case of partial shading conditions (PSCs). The first problem is that PSO is a time invariant optimization technique that cannot follow the dynamic global peak (GP) under time variant shading patterns (SPs) and sticks to the first GP that occurs at the beginning. This problem can be solved by dispersing the PSO particles using two new techniques introduced in this paper. The two new proposed PSO re-initialization techniques are to disperse the particles upon the SP changes and the other one is upon a predefined time (PDT). The second problem is regarding the high oscillations around steady state, which can be solved by using fuzzy logic controller (FLC) to fine-tune the output power and voltage from the PV system. The new contribution of this paper is the hybrid PSO-FLC with two PSO particles dispersing techniques that is able to solve the two previous mentioned problems effectively and improve the performance of the PV system in both normal and PSCs. A detailed list of comparisons between hybrid PSO-FLC and original PSO using the two proposed methodologies are achieved. The results prove the superior performance of hybrid PSO-FLC compared to PSO in terms of efficiency, accuracy, oscillations reduction around steady state and soft tuning of the GP tracked. [ABSTRACT FROM AUTHOR]

In the existing centralized location services system structure, the server is easily attracted and be the communication bottleneck. It caused the disclosure of users’ location. For this, we presented a new distributed collaborative recommendation strategy that is based on the distributed system. In this strategy, each node establishes profiles of their own location information. When requests for location services appear, the user can obtain the corresponding location services according to the recommendation of the neighboring users’ location information profiles. If no suitable recommended location service results are obtained, then the user can send a service request to the server according to the construction of a k-anonymous data set with a centroid position of the neighbors. In this strategy, we designed a new model of distributed collaborative recommendation location service based on the users’ location information profiles and used generalization and encryption to ensure the safety of the user’s location information privacy. Finally, we used the real location data set to make theoretical and experimental analysis. And the results show that the strategy proposed in this paper is capable of reducing the frequency of access to the location server, providing better location services and protecting better the user’s location privacy. [ABSTRACT FROM AUTHOR]

Product aspect recognition is a key task in fine-grained opinion mining. Current methods primarily focus on the extraction of aspects from the product reviews. However, it is also important to cluster synonymous extracted aspects into the same category. In this paper, we focus on the problem of product aspect clustering. The primary challenge is to properly cluster and generalize aspects that have similar meanings but different representations. To address this problem, we learn two types of background knowledge for each extracted aspect based on two types of effective aspect relations: relevant aspect relations and irrelevant aspect relations, which describe two different types of relationships between two aspects. Based on these two types of relationships, we can assign many relevant and irrelevant aspects into two different sets as the background knowledge to describe each product aspect. To obtain abundant background knowledge for each product aspect, we can enrich the available information with background knowledge from the Web. Then, we design a hierarchical clustering algorithm to cluster these aspects into different groups, in which aspect similarity is computed using the relevant and irrelevant aspect sets for each product aspect. Experimental results obtained in both camera and mobile phone domains demonstrate that the proposed product aspect clustering method based on two types of background knowledge performs better than the baseline approach without the use of background knowledge. Moreover, the experimental results also indicate that expanding the available background knowledge using the Web is feasible. [ABSTRACT FROM AUTHOR]

Motor control is a challenging task for the central nervous system, since it involves redundant degrees of freedom, nonlinear dynamics of actuators and limbs, as well as noise. When an action is carried out, which factors does your nervous system consider to determine the appropriate set of muscle forces between redundant degrees-of-freedom? Important factors determining motor output likely encompass effort and the resulting motor noise. However, the tasks used in many previous motor control studies could not identify these two factors uniquely, as signal-dependent noise monotonically increases as a function of the effort. To address this, a recent paper introduced a force control paradigm involving one finger in each hand that can disambiguate these two factors. It showed that the central nervous system considers both force noise and amplitude, with a larger weight on the absolute force and lower weights on both noise and normalized force. While these results are valid for the relatively low force range considered in that paper, the magnitude of the force shared between the fingers for large forces is not known. This paper investigates this question experimentally, and develops an appropriate Markov chain Monte Carlo method in order to estimate the weightings given to these factors. Our results demonstrate that the force sharing strongly depends on the force level required, so that for higher force levels the normalized force is considered as much as the absolute force, whereas the role of noise minimization becomes negligible. [ABSTRACT FROM AUTHOR]

This paper provides a description of how market competitiveness evaluations concerning mechanical equipment can be made in the context of multi-criteria decision environments. It is assumed that, when we are evaluating the market competitiveness, there are limited number of candidates with some required qualifications, and the alternatives will be pairwise compared on a ratio scale. The qualifications are depicted as criteria in hierarchical structure. A hierarchical decision model called PCbHDM was used in this study based on an analysis of its desirable traits. Illustration and comparison shows that the PCbHDM provides a convenient and effective tool for evaluating the market competitiveness of mechanical equipment. The researchers and practitioners might use findings of this paper in application of PCbHDM. [ABSTRACT FROM AUTHOR]

Classical definitions of observability classify a system as either being observable or not. Observability has been recognized as an important feature to study complex networks, and as for dynamical systems the focus has been on determining conditions for a network to be observable. About twenty years ago continuous measures of observability for nonlinear dynamical systems started to be used. In this paper various aspects of observability that are established for dynamical systems will be investigated in the context of networks. In particular it will be discussed in which ways simple networks can be ranked in terms of observability using continuous measures of such a property. Also it is pointed out that the analysis of the network topology is typically not sufficient for observability purposes, since both the dynamics and the coupling of such nodes play a vital role. Some of the main ideas are illustrated by means of numerical simulations. [ABSTRACT FROM AUTHOR]

In this paper, a new method is proposed for prediction of ship roll motion based on extreme learning machine (ELM). To improve the prediction accuracy and avoid over or under fitting, two techniques are adopted to select the appropriate structure of ELM. First, the inputs of the ELM are selected from the roll motion time series using Lipschitz quotient method. Second, the number of hidden layer nodes is determined via ℓ1 regularized technique. Finally, the ℓ1 regularized ELM is solved by least angle regression (LAR) algorithm. The effectiveness of the proposed method is demonstrated by ship roll motion prediction experiments based on the real measured ship roll motion time series. [ABSTRACT FROM AUTHOR]

The problem that passengers are hard to take taxis while empty driving rate is high widely exists under the traditional taxi operation mode. The implementation of taxi carpooling mode can alleviate the problem in a certain extent. The objective of this study is to optimize the taxi carpooling path. Firstly, the taxi carpooling path optimization model with single objective and its extended model with multiple objectives are built respectively. Then, the single objective path optimization model of taxi carpooling is solved based on the improved single objective genetic algorithm, and the multiple-objective path optimization model of taxi carpooling is solved based on the improved multiple-objective genetic algorithm. Finally, a case study is carried out based on a road network with 24 nodes. The case study results show the path optimization models and algorithms of taxi carpooling proposed in the paper can quickly get the taxi carpooling path, and can increase the income of taxi driver while reduce the cost for passengers. [ABSTRACT FROM AUTHOR]

Feature selection and classification are the main topics in microarray data analysis. Although many feature selection methods have been proposed and developed in this field, SVM-RFE (Support Vector Machine based on Recursive Feature Elimination) is proved as one of the best feature selection methods, which ranks the features (genes) by training support vector machine classification model and selects key genes combining with recursive feature elimination strategy. The principal drawback of SVM-RFE is the huge time consumption. To overcome this limitation, we introduce a more efficient implementation of linear support vector machines and improve the recursive feature elimination strategy and then combine them together to select informative genes. Besides, we propose a simple resampling method to preprocess the datasets, which makes the information distribution of different kinds of samples balanced and the classification results more credible. Moreover, the applicability of four common classifiers is also studied in this paper. Extensive experiments are conducted on six most frequently used microarray datasets in this field, and the results show that the proposed methods have not only reduced the time consumption greatly but also obtained comparable classification performance. [ABSTRACT FROM AUTHOR]

This paper addresses the overlearning problem in the independent component analysis (ICA) used for the removal of muscular artifacts from electroencephalographic (EEG) records. We note that for short EEG records with high number of channels the ICA fails to separate artifact-free EEG and muscular artifacts, which has been previously attributed to the phenomenon called overlearning. We address this problem by projecting an EEG record into several subspaces with a lower dimension, and perform the ICA on each subspace separately. Due to a reduced dimension of the subspaces, the overlearning is suppressed, and muscular artifacts are better separated. Once the muscular artifacts are removed, the signals in the individual subspaces are combined to provide an artifact free EEG record. We show that for short signals and high number of EEG channels our approach outperforms the currently available ICA based algorithms for muscular artifact removal. The proposed technique can efficiently suppress ICA overlearning for short signal segments of high density EEG signals. [ABSTRACT FROM AUTHOR]

In this paper we compare systematically the most promising neuroevolutionary methods and two new original methods on the double-pole balancing problem with respect to: the ability to discover solutions that are robust to variations of the environment, the speed with which such solutions are found, and the ability to scale-up to more complex versions of the problem. The results indicate that the two original methods introduced in this paper and the Exponential Natural Evolutionary Strategy method largely outperform the other methods with respect to all considered criteria. The results collected in different experimental conditions also reveal the importance of regulating the selective pressure and the importance of exposing evolving agents to variable environmental conditions. The data collected and the results of the comparisons are used to identify the most effective methods and the most promising research directions. [ABSTRACT FROM AUTHOR]

This paper addresses a chaos kernel function for the relevance vector machine (RVM) in EEG signal classification, which is an important component of Brain-Computer Interface (BCI). The novel kernel function has evolved from a chaotic system, which is inspired by the fact that human brain signals depict some chaotic characteristics and behaviors. By introducing the chaotic dynamics to the kernel function, the RVM will be enabled for higher classification capacity. The proposed method is validated within the framework of one versus one common spatial pattern (OVO-CSP) classifier to classify motor imagination (MI) of four movements in a public accessible dataset. To illustrate the performance of the proposed kernel function, Gaussian and Polynomial kernel functions are considered for comparison. Experimental results show that the proposed kernel function achieved higher accuracy than Gaussian and Polynomial kernel functions, which shows that the chaotic behavior consideration is helpful in the EEG signal classification. [ABSTRACT FROM AUTHOR]

With the booming development of evacuation simulation software, developing an extensive database in indoor scenarios for evacuation models is imperative. In this paper, we conduct a qualitative and quantitative analysis of the collected videotapes and aim to provide a complete and unitary database of pedestrians’ earthquake emergency response behaviors in indoor scenarios, including human-environment interactions. Using the qualitative analysis method, we extract keyword groups and keywords that code the response modes of pedestrians and construct a general decision flowchart using chronological organization. Using the quantitative analysis method, we analyze data on the delay time, evacuation speed, evacuation route and emergency exit choices. Furthermore, we study the effect of classroom layout on emergency evacuation. The database for indoor scenarios provides reliable input parameters and allows the construction of real and effective constraints for use in software and mathematical models. The database can also be used to validate the accuracy of evacuation models. [ABSTRACT FROM AUTHOR]

Aiming to solve the safety problems of high-speed railway operation and management, one new method is urgently needed to construct on the basis of the rough set theory and the uncertainty measurement theory. The method should carefully consider every factor of high-speed railway operation that realizes the measurement indexes of its safety operation. After analyzing the factors that influence high-speed railway safety operation in detail, a rough measurement model is finally constructed to describe the operation process. Based on the above considerations, this paper redistricts the safety influence factors of high-speed railway operation as 16 measurement indexes which include staff index, vehicle index, equipment index and environment. And the paper also provides another reasonable and effective theoretical method to solve the safety problems of multiple attribute measurement in high-speed railway operation. As while as analyzing the operation data of 10 pivotal railway lines in China, this paper respectively uses the rough set-based measurement model and value function model (one model for calculating the safety value) for calculating the operation safety value. The calculation result shows that the curve of safety value with the proposed method has smaller error and greater stability than the value function method’s, which verifies the feasibility and effectiveness. [ABSTRACT FROM AUTHOR]

In business replenishment, discount offers of multi-item may either provide different discount schedules with a single discount type, or provide schedules with multiple discount types. The paper investigates the joint effects of multiple discount schemes on the decisions of multi-item joint replenishment. In this paper, a joint replenishment problem (JRP) model, considering three discount (all-unit discount, incremental discount, total volume discount) offers simultaneously, is constructed to determine the basic cycle time and joint replenishment frequencies of multi-item. To solve the proposed problem, a heuristic algorithm is proposed to find the optimal solutions and the corresponding total cost of the JRP model. Numerical experiment is performed to test the algorithm and the computational results of JRPs under different discount combinations show different significance in the replenishment cost reduction. [ABSTRACT FROM AUTHOR]

This paper presents an integrated hybrid optimization algorithm for training the radial basis function neural network (RBF NN). Training of neural networks is still a challenging exercise in machine learning domain. Traditional training algorithms in general suffer and trap in local optima and lead to premature convergence, which makes them ineffective when applied for datasets with diverse features. Training algorithms based on evolutionary computations are becoming popular due to their robust nature in overcoming the drawbacks of the traditional algorithms. Accordingly, this paper proposes a hybrid training procedure with differential search (DS) algorithm functionally integrated with the particle swarm optimization (PSO). To surmount the local trapping of the search procedure, a new population initialization scheme is proposed using Logistic chaotic sequence, which enhances the population diversity and aid the search capability. To demonstrate the effectiveness of the proposed RBF hybrid training algorithm, experimental analysis on publicly available 7 benchmark datasets are performed. Subsequently, experiments were conducted on a practical application case for wind speed prediction to expound the superiority of the proposed RBF training algorithm in terms of prediction accuracy. [ABSTRACT FROM AUTHOR]

Data gathering is a fundamental task in Wireless Visual Sensor Networks (WVSNs). Features of directional antennas and the visual data make WVSNs more complex than the conventional Wireless Sensor Network (WSN). The virtual backbone is a technique, which is capable of constructing clusters. The version associating with the aggregation operation is also referred to as the virtual backbone tree. In most of the existing literature, the main focus is on the efficiency brought by the construction of clusters that the existing methods neglect local-balance problems in general. To fill up this gap, Directional Virtual Backbone based Data Aggregation Scheme (DVBDAS) for the WVSNs is proposed in this paper. In addition, a measurement called the energy consumption density is proposed for evaluating the adequacy of results in the cluster-based construction problems. Moreover, the directional virtual backbone construction scheme is proposed by considering the local-balanced factor. Furthermore, the associated network coding mechanism is utilized to construct DVBDAS. Finally, both the theoretical analysis of the proposed DVBDAS and the simulations are given for evaluating the performance. The experimental results prove that the proposed DVBDAS achieves higher performance in terms of both the energy preservation and the network lifetime extension than the existing methods. [ABSTRACT FROM AUTHOR]

Public transport (PT) is a key element in most major cities around the world. With the development of smartphones, available journey planning information is becoming an integral part of the PT system. Each traveler has specific preferences when undertaking a trip, and these preferences can also be reflected on the smartphone. This paper considers transit assignment in urban public-transport networks in which the passengers receive smartphone-based information containing elements that might influence the travel decisions in relation to line loads, as well as passenger benefits, and the paper discusses the transition from the current widespread choosing approach to a personalized decision-making approach based on smartphone information. The approach associated with smartphone guidance that considers passengers’ preference on travel time, waiting time and transfer is proposed in the process of obtaining his/her preferred route from the potential travel routes generated by the Deep First Search (DFS) method. Two other approaches, based on the scenarios reflecting reality, include passengers with access to no real time information, and passengers that only have access to the arrival time at the platform are used as comparisons. For illustration, the same network proposed by Spiess and Florian is utilized on the experiments in an agent-based model. Two experiments are conducted respectively according to whether each passenger’s choosing method is consistent. As expected, the results in the first experiment showed that the travel for consistent passengers with smartphone guidance was clearly shorter and that it can reduce travel time exceeding 15% and weighted cost exceeding 20%, and the average saved time approximated 3.88 minutes per passenger. The second experiment presented that travel cost, as well as cost savings, gradually decreased by employing smartphone guidance, and the maximum cost savings accounted for 14.2% of the total weighted cost. [ABSTRACT FROM AUTHOR]

This work presents optimal linear quadratic regulator (LQR) based on genetic algorithm (GA) to solve the two degrees of freedom (2 DoF) motion control problem in head seas for wave piercing catamarans (WPC). The proposed LQR based GA control strategy is to select optimal weighting matrices (Q and R). The seakeeping performance of WPC based on proposed algorithm is challenged because of multi-input multi-output (MIMO) system of uncertain coefficient problems. Besides the kinematical constraint problems of WPC, the external conditions must be considered, like the sea disturbance and the actuators (a T-foil and two flaps) control. Moreover, this paper describes the MATLAB and LabVIEW software plats to simulate the reduction effects of WPC. Finally, the real-time (RT) NI CompactRIO embedded controller is selected to test the effectiveness of the actuators based on proposed techniques. In conclusion, simulation and experimental results prove the correctness of the proposed algorithm. The percentage of heave and pitch reductions are more than 18% in different high speeds and bad sea conditions. And the results also verify the feasibility of NI CompactRIO embedded controller. [ABSTRACT FROM AUTHOR]

A significant approach for the discovery of biological regulatory rules of genes, protein and their inheritance relationships is the extraction of meaningful patterns from biological sequence data. The existing algorithms of sequence pattern discovery, like MSPM and FBSB, suffice their low efficiency and accuracy. In order to deal with this issue, this paper presents a new algorithm for biological sequence pattern mining abbreviated MpBsmi based on the data index structure. The MpBsmi algorithm employs a sequence position table abbreviated ST and a sequence database index structure named DB-Index for data storing, mining and pattern expansion. The ST and DB-Index of single items are firstly obtained through scanning sequence database once. Then a new algorithm for fast support counting is developed to mine the table ST to identify the frequent single items. Based on a connection strategy, the frequent patterns are expanded and the expanded table ST is updated by scanning the DB-Index. The fast support counting algorithm is used for obtaining the frequent expansion patterns. Finally, a new pruning technique is developed for extended pattern to avoid the generation of unnecessarily large number of candidate patterns. The experiments results on multiple classical protein sequences from the Pfam database validate the performance of the proposed algorithm including the accuracy, stability and scalability. It is showed that the proposed algorithm has achieved the better space efficiency, stability and scalability comparing with MSPM, FBSB which are the two main algorithms for biological sequence mining. [ABSTRACT FROM AUTHOR]

This paper studies the Retailer Stackelberg game in a supply chain consisting of two manufacturers and one retailer where they compete simultaneously under three factors including price, service and simple price discount contract. It is assumed that the second manufacturer provides service directly to his customers, and the retailer provides service for the first product’s customers, while the retailer buys the first product under price discount from the first manufacturer. The analysis of the optimal equilibrium solutions and the results of the numerical examples show that if a manufacturer chooses the appropriate range of discount rate, he will gain more profit than when there is no discount given to the retailer. This situation can be considered as an effective tool for the coordination of the first manufacturer and the retailer to offer discount by manufacturer and to provide the service by the retailer. We obtain equilibrium solution of Retailer Stackelberg game and analyze the numerical examples under two cases: a) the manufacturers sell their products to the retailer without price discount contract. b) The first manufacturer sells his products to the retailer with the simple price discount contract. The preliminary results show that the service and the price discount contract can improve the performance of supply chain. [ABSTRACT FROM AUTHOR]

Pruning is applied in order to combat over-fitting problem where the tree is pruned back with the goal of identifying decision tree with the lowest error rate on previously unobserved instances, breaking ties in favour of smaller trees with high accuracy. In this paper, pruning with Bayes minimum risk is introduced for estimating the risk-rate. This method proceeds in a bottom-up fashion converting a parent node of a subtree to a leaf node if the estimated risk-rate of the parent node for that subtree is less than the risk-rates of its leaf. This paper proposes a post-pruning method that considers various evaluation standards such as attribute selection, accuracy, tree complexity, and time taken to prune the tree, precision/recall scores, TP/FN rates and area under ROC. The experimental results show that the proposed method produces better classification accuracy and its complexity is not much different than the complexities of reduced-error pruning and minimum-error pruning approaches. The experiments also demonstrate that the proposed method shows satisfactory performance in terms of precision score, recall score, TP rate, FP rate and area under ROC. [ABSTRACT FROM AUTHOR]

Human-computer interface systems whose input is based on eye movements can serve as a means of communication for patients with locked-in syndrome. Eye-writing is one such system; users can input characters by moving their eyes to follow the lines of the strokes corresponding to characters. Although this input method makes it easy for patients to get started because of their familiarity with handwriting, existing eye-writing systems suffer from slow input rates because they require a pause between input characters to simplify the automatic recognition process. In this paper, we propose a continuous eye-writing recognition system that achieves a rapid input rate because it accepts characters eye-written continuously, with no pauses. For recognition purposes, the proposed system first detects eye movements using electrooculography (EOG), and then a hidden Markov model (HMM) is applied to model the EOG signals and recognize the eye-written characters. Additionally, this paper investigates an EOG adaptation that uses a deep neural network (DNN)-based HMM. Experiments with six participants showed an average input speed of 27.9 character/min using Japanese Katakana as the input target characters. A Katakana character-recognition error rate of only 5.0% was achieved using 13.8 minutes of adaptation data. [ABSTRACT FROM AUTHOR]

The selection of seismic attributes is a key process in reservoir prediction because the prediction accuracy relies on the reliability and credibility of the seismic attributes. However, effective selection method for useful seismic attributes is still a challenge. This paper presents a novel selection method of seismic attributes for reservoir prediction based on the gray relational degree (GRD) and support vector machine (SVM). The proposed method has a two-hierarchical structure. In the first hierarchy, the primary selection of seismic attributes is achieved by calculating the GRD between seismic attributes and reservoir parameters, and the GRD between the seismic attributes. The principle of the primary selection is that these seismic attributes with higher GRD to the reservoir parameters will have smaller GRD between themselves as compared to those with lower GRD to the reservoir parameters. Then the SVM is employed in the second hierarchy to perform an interactive error verification using training samples for the purpose of determining the final seismic attributes. A real-world case study was conducted to evaluate the proposed GRD-SVM method. Reliable seismic attributes were selected to predict the coalbed methane (CBM) content in southern Qinshui basin, China. In the analysis, the instantaneous amplitude, instantaneous bandwidth, instantaneous frequency, and minimum negative curvature were selected, and the predicted CBM content was fundamentally consistent with the measured CBM content. This real-world case study demonstrates that the proposed method is able to effectively select seismic attributes, and improve the prediction accuracy. Thus, the proposed GRD-SVM method can be used for the selection of seismic attributes in practice. [ABSTRACT FROM AUTHOR]

In many applications involving epistemic uncertainties usually modeled by belief functions, it is often necessary to approximate general (non-Bayesian) basic belief assignments (BBAs) to subjective probabilities (called Bayesian BBAs). This necessity occurs if one needs to embed the fusion result in a system based on the probabilistic framework and Bayesian inference (e.g. tracking systems), or if one needs to make a decision in the decision making problems. In this paper, we present a new fast combination method, called modified rigid coarsening (MRC), to obtain the final Bayesian BBAs based on hierarchical decomposition (coarsening) of the frame of discernment. Regarding this method, focal elements with probabilities are coarsened efficiently to reduce computational complexity in the process of combination by using disagreement vector and a simple dichotomous approach. In order to prove the practicality of our approach, this new approach is applied to combine users’ soft preferences in recommender systems (RSs). Additionally, in order to make a comprehensive performance comparison, the proportional conflict redistribution rule #6 (PCR6) is regarded as a baseline in a range of experiments. According to the results of experiments, MRC is more effective in accuracy of recommendations compared to original Rigid Coarsening (RC) method and comparable in computational time. [ABSTRACT FROM AUTHOR]

Security is considered a major challenge for self-driving and semi self-driving vehicles. These vehicles depend heavily on communications to predict and sense their external environment used in their motion. They use a type of ad hoc network termed Vehicular ad hoc networks (VANETs). Unfortunately, VANETs are potentially exposed to many attacks on network and application level. This paper, proposes a new intrusion detection system to protect the communication system of self-driving cars; utilising a combination of hierarchical models based on clusters and log parameters. This security system is designed to detect Sybil and Wormhole attacks in highway usage scenarios. It is based on clusters, utilising Time Division Multiple Access (TDMA) to overcome some of the obstacles of VANETs such as high density, high mobility and bandwidth limitations in exchanging messages. This makes the security system more efficient, accurate and capable of real time detection and quick in identification of malicious behaviour in VANETs. In this scheme, each vehicle log calculates and stores different parameter values after receiving the cooperative awareness messages from nearby vehicles. The vehicles exchange their log data and determine the difference between the parameters, which is utilised to detect Sybil attacks and Wormhole attacks. In order to realize efficient and effective intrusion detection system, we use the well-known network simulator (ns-2) to verify the performance of the security system. Simulation results indicate that the security system can achieve high detection rates and effectively detect anomalies with low rate of false alarms. [ABSTRACT FROM AUTHOR]

Traffic surveillance systems are interesting to many researchers to improve the traffic control and reduce the risk caused by accidents. In this area, many published works are only concerned about vehicle detection in normal conditions. The camera may vibrate due to wind or bridge movement. Detection and tracking of vehicles is a very difficult task when we have bad weather conditions in winter (snowy, rainy, windy, etc.), dusty weather in arid and semi-arid regions, at night, etc. Also, it is very important to consider speed of vehicles in the complicated weather condition. In this paper, we improved our method to track and count vehicles in dusty weather with vibrating camera. For this purpose, we used a background subtraction based strategy mixed with an extra processing to segment vehicles. In this paper, the extra processing included the analysis of the headlight size, location, and area. In our work, tracking was done between consecutive frames via a generalized particle filter to detect the vehicle and pair the headlights using the connected component analysis. So, vehicle counting was performed based on the pairing result, with Centroid of each blob we calculated distance between two frames by simple formula and hence dividing it by the time between two frames obtained from the video. Our proposed method was tested on several video surveillance records in different conditions such as dusty or foggy weather, vibrating camera, and in roads with medium-level traffic volumes. The results showed that the new proposed method performed better than our previously published method and other methods, including the Kalman filter or Gaussian model, in different traffic conditions. [ABSTRACT FROM AUTHOR]

In smart parking environments, how to choose suitable parking facilities with various attributes to satisfy certain criteria is an important decision issue. Based on the multiple attributes decision making (MADM) theory, this study proposed a smart parking guidance algorithm by considering three representative decision factors (i.e., walk duration, parking fee, and the number of vacant parking spaces) and various preferences of drivers. In this paper, the expected number of vacant parking spaces is regarded as an important attribute to reflect the difficulty degree of finding available parking spaces, and a queueing theory-based theoretical method was proposed to estimate this expected number for candidate parking facilities with different capacities, arrival rates, and service rates. The effectiveness of the MADM-based parking guidance algorithm was investigated and compared with a blind search-based approach in comprehensive scenarios with various distributions of parking facilities, traffic intensities, and user preferences. Experimental results show that the proposed MADM-based algorithm is effective to choose suitable parking resources to satisfy users’ preferences. Furthermore, it has also been observed that this newly proposed Markov Chain-based availability attribute is more effective to represent the availability of parking spaces than the arrival rate-based availability attribute proposed in existing research. [ABSTRACT FROM AUTHOR]

Authorship attribution is to identify the most likely author of a given sample among a set of candidate known authors. It can be not only applied to discover the original author of plain text, such as novels, blogs, emails, posts etc., but also used to identify source code programmers. Authorship attribution of source code is required in diverse applications, ranging from malicious code tracking to solving authorship dispute or software plagiarism detection. This paper aims to propose a new method to identify the programmer of Java source code samples with a higher accuracy. To this end, it first introduces back propagation (BP) neural network based on particle swarm optimization (PSO) into authorship attribution of source code. It begins by computing a set of defined feature metrics, including lexical and layout metrics, structure and syntax metrics, totally 19 dimensions. Then these metrics are input to neural network for supervised learning, the weights of which are output by PSO and BP hybrid algorithm. The effectiveness of the proposed method is evaluated on a collected dataset with 3,022 Java files belong to 40 authors. Experiment results show that the proposed method achieves 91.060% accuracy. And a comparison with previous work on authorship attribution of source code for Java language illustrates that this proposed method outperforms others overall, also with an acceptable overhead. [ABSTRACT FROM AUTHOR]

This paper studies the problem of the restoration of images corrupted by mixed Gaussian-impulse noise. In recent years, low-rank matrix reconstruction has become a research hotspot in many scientific and engineering domains such as machine learning, image processing, computer vision and bioinformatics, which mainly involves the problem of matrix completion and robust principal component analysis, namely recovering a low-rank matrix from an incomplete but accurate sampling subset of its entries and from an observed data matrix with an unknown fraction of its entries being arbitrarily corrupted, respectively. Inspired by these ideas, we consider the problem of recovering a low-rank matrix from an incomplete sampling subset of its entries with an unknown fraction of the samplings contaminated by arbitrary errors, which is defined as the problem of matrix completion from corrupted samplings and modeled as a convex optimization problem that minimizes a combination of the nuclear norm and the -norm in this paper. Meanwhile, we put forward a novel and effective algorithm called augmented Lagrange multipliers to exactly solve the problem. For mixed Gaussian-impulse noise removal, we regard it as the problem of matrix completion from corrupted samplings, and restore the noisy image following an impulse-detecting procedure. Compared with some existing methods for mixed noise removal, the recovery quality performance of our method is dominant if images possess low-rank features such as geometrically regular textures and similar structured contents; especially when the density of impulse noise is relatively high and the variance of Gaussian noise is small, our method can outperform the traditional methods significantly not only in the simultaneous removal of Gaussian noise and impulse noise, and the restoration ability for a low-rank image matrix, but also in the preservation of textures and details in the image. [ABSTRACT FROM AUTHOR]

This paper presents a stable and fast algorithm for independent component analysis with reference (ICA-R). This is a technique for incorporating available reference signals into the ICA contrast function so as to form an augmented Lagrangian function under the framework of constrained ICA (cICA). The previous ICA-R algorithm was constructed by solving the optimization problem via a Newton-like learning style. Unfortunately, the slow convergence and potential misconvergence limit the capability of ICA-R. This paper first investigates and probes the flaws of the previous algorithm and then introduces a new stable algorithm with a faster convergence speed. There are two other highlights in this paper: first, new approaches, including the reference deflation technique and a direct way of obtaining references, are introduced to facilitate the application of ICA-R; second, a new method is proposed that the new ICA-R is used to recover the complete underlying sources with new advantages compared with other classical ICA methods. Finally, the experiments on both synthetic and real-world data verify the better performance of the new algorithm over both previous ICA-R and other well-known methods. [ABSTRACT FROM AUTHOR]

Vagus nerve stimulation (VNS) is an established adjunctive therapy for pharmacologically refractory epilepsy and depression and is currently in active clinical research for other applications. In current clinical studies, VNS is delivered in an open-loop approach, where VNS parameters are defined during a manual titration phase. However, the physiological response to a given VNS configuration shows significant inter and intra-patient variability and may significantly evolve through time. VNS closed-loop approaches, allowing for the optimization of the therapy in an adaptive manner, may be necessary to improve efficacy while reducing side effects. This paper proposes a generic, closed-loop control VNS system that is able to optimize a number of VNS parameters in an adaptive fashion, in order to keep a control variable within a specified range. Although the proposed control method is completely generic, an example application using the cardiac beat to beat interval (RR) as control variable will be developed in this paper. The proposed controller is based on a state transition model (STM) that can be configured using a partially or a fully-connected architecture, different model orders and different state-transition algorithms. The controller is applied to the adaptive regulation of heart rate and evaluated on 6 sheep, for 13 different targets, using partially-connected STM with 10 states. Also, partially and fully-connected STM defined by 30 states were applied to 7 other sheep for the same 10 targets. Results illustrate the interest of the proposed fully-connected STM and the feasibility of integrating this control system into an implantable neuromodulator. [ABSTRACT FROM AUTHOR]

Approximate string matching with k-differences has a number of practical applications, ranging from pattern recognition to computational biology. This paper proposes an efficient memory-access algorithm for parallel approximate string matching with k-differences on Graphics Processing Units (GPUs). In the proposed algorithm, all threads in the same GPUs warp share data using warp-shuffle operation instead of accessing the shared memory. Moreover, we implement the proposed algorithm by exploiting the memory structure of GPUs to optimize its performance. Experiment results for real DNA packages revealed that the performance of the proposed algorithm and its implementation archived up to 122.64 and 1.53 times compared to that of sequential algorithm on CPU and previous parallel approximate string matching algorithm on GPUs, respectively. [ABSTRACT FROM AUTHOR]

Many countries have some kind of energy-system transformation either planned or ongoing for various reasons, such as to curb carbon emissions or to compensate for the phasing out of nuclear energy. One important component of these transformations is the overall reduction in energy demand. It is generally acknowledged that the domestic sector represents a large share of total energy consumption in many countries. Increased energy efficiency is one factor that reduces energy demand, but behavioral approaches (known as “sufficiency”) and their respective interventions also play important roles. In this paper, we address citizens’ heterogeneity regarding both their current behaviors and their willingness to realize their sufficiency potentials—that is, to reduce their energy consumption through behavioral change. We collaborated with three Swiss cities for this study. A survey conducted in the three cities yielded thematic sets of energy-consumption behavior that various groups of participants rated differently. Using this data, we identified four groups of participants with different patterns of both current behaviors and sufficiency potentials. The paper discusses intervention types and addresses citizens’ heterogeneity and behaviors from a city-based perspective. [ABSTRACT FROM AUTHOR]

With the rapid development of the Metro network in China, the greatly increased route alternatives make passengers’ route choice behavior and passenger flow assignment more complicated, which presents challenges to the operation management. In this paper, a path sized logit model is adopted to analyze passengers’ route choice preferences considering such parameters as in-vehicle time, number of transfers, and transfer time. Moreover, the “perceived transfer threshold” is defined and included in the utility function to reflect the penalty difference caused by transfer time on passengers’ perceived utility under various numbers of transfers. Next, based on the revealed preference data collected in the Guangzhou Metro, the proposed model is calibrated. The appropriate perceived transfer threshold value and the route choice preferences are analyzed. Finally, the model is applied to a personalized route planning case to demonstrate the engineering practicability of route choice behavior analysis. The results show that the introduction of the perceived transfer threshold is helpful to improve the model’s explanatory abilities. In addition, personalized route planning based on route choice preferences can meet passengers’ diversified travel demands. [ABSTRACT FROM AUTHOR]

Urban spaces have a great impact on how people’s emotion and behaviour. There are number of factors that impact our brain responses to a space. This paper presents a novel urban place recommendation approach, that is based on modelling in-situ EEG data. The research investigations leverages on newly affordable Electroencephalogram (EEG) headsets, which has the capability to sense mental states such as meditation and attention levels. These emerging devices have been utilized in understanding how human brains are affected by the surrounding built environments and natural spaces. In this paper, mobile EEG headsets have been used to detect mental states at different types of urban places. By analysing and modelling brain activity data, we were able to classify three different places according to the mental state signature of the users, and create an association map to guide and recommend people to therapeutic places that lessen brain fatigue and increase mental rejuvenation. Our mental states classifier has achieved accuracy of (%90.8). NeuroPlace breaks new ground not only as a mobile ubiquitous brain monitoring system for urban computing, but also as a system that can advise urban planners on the impact of specific urban planning policies and structures. We present and discuss the challenges in making our initial prototype more practical, robust, and reliable as part of our on-going research. In addition, we present some enabling applications using the proposed architecture. [ABSTRACT FROM AUTHOR]

Introducing electric vehicles (EVs) into urban transportation network brings higher requirement on travel time reliability and charging reliability. Specifically, it is believed that travel time reliability is a key factor influencing travelers’ route choice. Meanwhile, due to the limited cruising range, EV drivers need to better learn about the required energy for the whole trip to make decisions about whether charging or not and where to charge (i.e., charging reliability). Since EV energy consumption is highly related to travel speed, network uncertainty affects travel time and charging demand estimation significantly. Considering the network uncertainty resulted from link degradation, which influences the distribution of travel demand on transportation network and the energy demand on power network, this paper aims to develop a reliability-based network equilibrium framework for accommodating degradable road conditions with the addition of EVs. First, based on the link travel time distribution, the mean and variance of route travel time and monetary expenses related to energy consumption are deduced, respectively. And the charging time distribution of EVs with charging demand is also estimated. Then, a nested structure is considered to deal with the difference of route choice behavior derived by the different uncertainty degrees between the routes with and without degradable links. Given the expected generalized travel cost and a psychological safety margin, a traffic assignment model with the addition of EVs is formulated. Subsequently, a heuristic solution algorithm is developed to solve the proposed model. Finally, the effects of travelers’ risk attitude, network degradation degree, and EV penetration rate on network performance are illustrated through an example network. The numerical results show that the difference of travelers’ risk attitudes does have impact on the route choice, and the widespread adoption of EVs can cut down the total system travel cost effectively when the transportation network is more reliable. [ABSTRACT FROM AUTHOR]

This paper presents the use of the Aimsun microsimulation program to simulate vehicle violating behaviors and observe their impact on road traffic crash risk. Plugins for violations of speeding, slow driving, and abrupt stopping were developed using Aimsun’s API and SDK module. A safety analysis plugin for investigating probability of rear-end collisions was developed, and a method for analyzing collision risk is proposed. A Fuzzy C-mean Clustering algorithm was developed to identify high risk states in different road segments over time. Results of a simulation experiment based on the G15 Expressway in Shanghai showed that abrupt stopping had the greatest impact on increasing collision risk, and the impact of violations increased with traffic volume. The methodology allows for the evaluation and monitoring of risks, alerting of road hazards, and identification of hotspots, and could be applied to the operations of existing facilities or planning of future ones. [ABSTRACT FROM AUTHOR]

Background: Daodi-herb is a part of Chinese culture, which has been naturally selected by traditional Chinese medicine clinical practice for many years. Sweet wormwood herb is a kind of Daodi-herb, and comes from Artemisia annua L. Artemisinin is a kind of effective antimalarial drug being extracted from A. annua. Because of artemisinin, Sweet wormwood herb earns a reputation. Based on the Pharmacopoeia of the People's Republic of China (PPRC), Sweet wormwood herb can be used to resolve summerheat-heat, and prevent malaria. Besides, it also has other medical efficacies. A. annua, a medicinal plant that is widely distributed in the world contains many kinds of chemical composition. Research has shown that compatibility of artemisinin, scopoletin, arteannuin B and arteannuic acid has antimalarial effect. Compatibility of scopoletin, arteannuin B and arteannuic acid is conducive to resolving summerheat-heat. Chemical constituents in A. annua vary significantly according to geographical locations. So, distribution of A. annua may play a key role in the characteristics of efficacy and chemical constituents of Sweet wormwood herb. It is of great significance to study this relationship. Objectives: We mainly analyzed the relationship between the chemical constituents (arteannuin B, artemisinin, artemisinic acid, and scopoletin) with special efficacy in A. annua that come from different provinces in china, and analyzed the relationship between chemical constituents and spatial distribution, in order to find out the relationship between efficacy, chemical constituents and distribution. Methods: A field survey was carried out to collect A. annua plant samples. A global positioning system (GPS) was used for obtaining geographical coordinates of sampling sites. Chemical constituents in A. annua were determined by liquid chromatography tandem an atmospheric pressure ionization-electrospray mass spectrometry. Relationship between chemical constituents including proportions, correlation analysis (CoA), principal component analysis (PCA) and cluster analysis (ClA) was displayed through Excel and R software version2.3.2(R), while the one between efficacy, chemical constituents and spatial distribution was presented through ArcGIS10.0, Excel and R software. Results: According to the results of CoA, arteannuin B content presented a strong positive correlation with artemisinic acid content (p = 0), and a strong negative correlation with artemisinin content (p = 0). Scopoletin content presented a strong positive correlation with artemisinin content (p = 0), and a strong negative correlation with artemisinic acid content (p = 0). According to the results of PCA, the first two principal components accounted for 81.57% of the total accumulation contribution rate. The contribution of the first principal component is about 45.12%, manly including arteannuin B and artemisinic acid. The contribution of the second principal component is 36.45% of the total, manly including artemisinin and scopoletin. According to the ClA by using the principal component scores, 19 provinces could be divided into two groups. In terms of provinces in group one, the proportions of artemisinin are all higher than 80%. Based on the results of PCA, ClA, percentages and scatter plot analysis, chemical types are defined as "QHYS type", "INT type" and "QHS type." Conclusion: As a conclusion, this paper shows the relationship between efficacy, chemical constituents and distribution. Sweet wormwood herb with high arteannuin B and artemisinic acid content, mainly distributes in northern China. Sweet wormwood herb with high artemisinin and scopoletin content has the medical function of preventing malaria, which mainly distributes in southern China. In this paper, it is proved that Sweet wormwood Daodi herb growing in particular geographic regions, has more significant therapeutical effect and higher chemical constituents compared with other same kind of CMM. And also, it has proved the old saying in China that Sweet wormwood Daodi herb which has been used to resolve summerheat-heat and prevent malaria, which distributed in central China. But in modern time, Daodi Sweet wormwood herb mainly has been used to extract artemisinin and prevent malaria, so the Daod-region has transferred to the southern China. [ABSTRACT FROM AUTHOR]

Recently, echo state network (ESN) has attracted a great deal of attention due to its high accuracy and efficient learning performance. Compared with the traditional random structure and classical sigmoid units, simple circle topology and leaky integrator neurons have more advantages on reservoir computing of ESN. In this paper, we propose a new model of ESN with both circle reservoir structure and leaky integrator units. By comparing the prediction capability on Mackey-Glass chaotic time series of four ESN models: classical ESN, circle ESN, traditional leaky integrator ESN, circle leaky integrator ESN, we find that our circle leaky integrator ESN shows significantly better performance than other ESNs with roughly 2 orders of magnitude reduction of the predictive error. Moreover, this model has stronger ability to approximate nonlinear dynamics and resist noise than conventional ESN and ESN with only simple circle structure or leaky integrator neurons. Our results show that the combination of circle topology and leaky integrator neurons can remarkably increase dynamical diversity and meanwhile decrease the correlation of reservoir states, which contribute to the significant improvement of computational performance of Echo state network on time series prediction. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) are becoming ubiquitous in everyday life due to their applications in weather forecasting, surveillance, implantable sensors for health monitoring and other plethora of applications. WSN is equipped with hundreds and thousands of small sensor nodes. As the size of a sensor node decreases, critical issues such as limited energy, computation time and limited memory become even more highlighted. In such a case, network lifetime mainly depends on efficient use of available resources. Organizing nearby nodes into clusters make it convenient to efficiently manage each cluster as well as the overall network. In this paper, we extend our previous work of grid-based hybrid network deployment approach, in which merge and split technique has been proposed to construct network topology. Constructing topology through our proposed technique, in this paper we have used analytical network process (ANP) model for cluster head selection in WSN. Five distinct parameters: distance from nodes (DistNode), residual energy level (REL), distance from centroid (DistCent), number of times the node has been selected as cluster head (TCH) and merged node (MN) are considered for CH selection. The problem of CH selection based on these parameters is tackled as a multi criteria decision system, for which ANP method is used for optimum cluster head selection. Main contribution of this work is to check the applicability of ANP model for cluster head selection in WSN. In addition, sensitivity analysis is carried out to check the stability of alternatives (available candidate nodes) and their ranking for different scenarios. The simulation results show that the proposed method outperforms existing energy efficient clustering protocols in terms of optimum CH selection and minimizing CH reselection process that results in extending overall network lifetime. This paper analyzes that ANP method used for CH selection with better understanding of the dependencies of different components involved in the evaluation process. [ABSTRACT FROM AUTHOR]

The effectiveness of rapid rail transit system is analyzed using tools of complex network for the first time. We evaluated the effectiveness of the system in Beijing quantitatively from different perspectives, including descriptive statistics analysis, bridging property, centrality property, ability of connecting different part of the system and ability of disease spreading. The results showed that the public transport of Beijing does benefit from the rapid rail transit lines, and the benefit of different regions from RRTS is gradually decreased from the north to the south. The paper concluded with some policy suggestions regarding how to promote the system. This study offered significant insight that can help understand the public transportation better. The methodology can be easily applied to analyze other urban public systems, such as electricity grid, water system, to develop more livable cities. [ABSTRACT FROM AUTHOR]

Double cycling is an efficient tool to increase the efficiency of quay crane (QC) in container terminals. In this paper, an optimization model for double cycling is developed to optimize the operation sequence of multiple QCs. The objective is to minimize the makespan of the ship handling operation considering the ship balance constraint. To solve the model, an algorithm based on Lagrangian relaxation is designed. Finally, we compare the efficiency of the Lagrangian relaxation based heuristic with the branch-and-bound method and a genetic algorithm using instances of different sizes. The results of numerical experiments indicate that the proposed model can effectively reduce the unloading and loading times of QCs. The effects of the ship balance constraint are more notable when the number of QCs is high. [ABSTRACT FROM AUTHOR]