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Objectives. To establish referential values ranges of hyposalivation and normosalivation for the salivary flow rate (SFR) of upper labial (LS) and palatal (PS) mucosa using Schirmer's test strips paper and as a second goal to determine the values ranges of the SFR of palatal (PS) and upper labial (LS) mucosa in subjects with and without xerostomia.Methods. A cross-sectional study was conducted among subjects distributed in three groups according to their unstimulated and stimulated whole saliva.Results. 144 subjects were enrolled in groups as follows: severe hyposalivation (n=24), mild hyposalivation (n=78), and normosalivation (n=42). The mean and the 95% confidence interval for the LS flow rate (μL/cm2/min) were 3.2 (2.46 to 3.94), 5.86 (4.96 to 6.75), and 9.08 (7.63 to 10.53) (P<0.001) for each group, respectively. The PS results were 1.01 (0.68 to 1.34), 1.72 (1.31 to 2.13), and 2.44 (1.66 to 3.22) (P=0.014). Xerostomia complainers presented lower rates of LS (5.17 (4.06 to 6.23)) than non-complainers (7.33 (6.4 to 8.27)) (P=0.003).Conclusions. The test was reliable to provide referential values ranges for LS flow rate measurement and was shown to be valid to distinguish normosalivation from severe and mild hyposalivation and also to predict xerostomia.

Life cycle assessment (LCA) has been widely used to analyze various pathways of biofuel preparation from “cradle to grave.” Effects of nitrogen supply for algae cultivation and technology of algal oil extraction on life cycle fossil energy ratio of biodiesel are assessed in this study. Life cycle fossil energy ratio ofChlorella vulgarisbased biodiesel is improved by growing algae under nitrogen-limited conditions, while the life cycle fossil energy ratio of biodiesel production fromPhaeodactylum tricornutumgrown with nitrogen deprivation decreases. Compared to extraction of oil from dried algae, extraction of lipid from wet algae with subcritical cosolvents achieves a 43.83% improvement in fossil energy ratio of algal biodiesel when oilcake drying is not considered. The outcome for sensitivity analysis indicates that the algal oil conversion rate and energy content of algae are found to have the greatest effects on the LCA results of algal biodiesel production, followed by utilization ratio of algal residue, energy demand for algae drying, capacity of water mixing, and productivity of algae.

It is the intention of this paper to demonstrate that environmental technology must be supplemented by other tools to be able to solve environmental problems properly. Five cases are used to illustrate the possibilities of ecological engineering, a new engineering field based on ecology, as chemical engineering is based on chemistry. It encompasses restoration of ecosystems, utilization of ecosystems to the benefit of both mankind and nature, construction of ecosystems, and ecologically sound planning of ecosystems from a holistic point of view. Ecological engineering requires a good knowledge of the system properties of ecosystems to be able to fully utilize the possibilities that ecosystem management offers. Models reflecting the ecosystem properties are furthermore needed to be able to quantify the effects of the ecological engineering solutions to the environmental problems. This is clearly demonstrated in two of the five case studies presented in the paper.

Unobtrusive Authentication Using ACTIvity-Related and Soft BIOmetrics (ACTIBIO) is an EU Specific Targeted Research Project (STREP) where new types of biometrics are combined with state-of-the-art unobtrusive technologies in order to enhance security in a wide spectrum of applications. The project aims to develop a modular, robust, multimodal biometrics security authentication and monitoring system, which uses a biodynamic physiological profile, unique for each individual, and advancements of the state of the art in unobtrusive behavioral and other biometrics, such as face, gait recognition, and seat-based anthropometrics. Several shortcomings of existing biometric recognition systems are addressed within this project, which have helped in improving existing sensors, in developing new algorithms, and in designing applications, towards creating new, unobtrusive, biometric authentication procedures in security-sensitive, Ambient Intelligence environments. This paper presents the concept of the ACTIBIO project and describes its unobtrusive authentication demonstrator in a real scenario by focusing on the vision-based biometric recognition modalities.

Clinoptilolite was investigated for the removal of Cu(II) ions from industrial leachate. Adaptive neural fuzzy interface system (ANFIS) was used for modeling the batch experimental system and predicting the optimal input values, that is, initial pH, adsorbent dosage, and contact time. Experiments were studied under laboratory batch and fixed bed conditions. The outcomes of suggested ANFIS modeling were then compared to a full factorial experimental design (23), which was utilized to assess the effect of three factors on the adsorption of Cu(II) ions in aqueous leachate of industrial waste. It was observed that the optimized parameters are almost close to each other. The highest removal efficiency was found as about 93.65% at pH 6, adsorbent dosage 11.4 g/L, and contact time 33 min for batch conditions of 23experimental design and about 90.43% at pH 5, adsorbent dosage 15 g/L and contact time 35 min for batch conditions of ANFIS. The results show that clinoptilolite is an efficient sorbent and ANFIS, which is easy to implement and is able to model the batch experimental system.

Sago industries effluent containing large amounts of organic content produced excess sludge which is a serious problem in wastewater treatment. In this study ozonation has been employed for the reduction of excess sludge production in activated sludge process. Central composite design is used to study the effect of ozone treatment for the reduction of excess sludge production in sago effluent and to optimise the variables such as pH, ozonation time, and retention time. ANOVA showed that the coefficient determination value (R2) of VSS and COD reduction were 0.9689 and 0.8838, respectively. VSS reduction (81%) was achieved at acidic pH 6.9, 12 minutes ozonation, and retention time of 10 days. COD reduction (87%) was achieved at acidic pH 6.7, 8 minutes of ozonation time, and retention time of 6 days. Low ozonation time and high retention time influence maximum sludge reduction, whereas low ozonation time with low retention time was effective for COD reduction.

Dental caries detection, in the past, has been a challenging task given the amount of information got from various radiographic images. Several methods have been introduced to improve the quality of images for faster caries detection. Deep learning has become the methodology of choice when it comes to analysis of medical images. This survey gives an in-depth look into the use of deep learning for object detection, segmentation, and classification. It further looks into literature on segmentation and detection methods of dental images through deep learning. From the literature studied, we found out that methods were grouped according to the type of dental caries (proximal, enamel), type of X-ray images used (extraoral, intraoral), and segmentation method (threshold-based, cluster-based, boundary-based, and region-based). From the works reviewed, the main focus has been found to be on threshold-based segmentation methods. Most of the reviewed papers have preferred the use of intraoral X-ray images over extraoral X-ray images to perform segmentation on dental images of already isolated parts of the teeth. This paper presents an in-depth analysis of recent research in deep learning for dental caries segmentation and detection. It involves discussing the methods and algorithms used in segmenting and detecting dental caries. It also discusses various existing models used and how they compare with each other in terms of system performance and evaluation. We also discuss the limitations of these methods, as well as future perspectives on how to improve their performance.

The design of bone scaffolds for tissue regeneration is a topic of great interest, which involves different issues related to geometry of architectures, mechanical behavior, and biological requirements, whose optimal combination determines the success of an implant. Additive manufacturing (AM) has widened the capability to produce structures with complex geometries, which should potentially satisfy the different requirements. These architectures can be obtained by means of refined methods and have to be assessed in terms of geometrical and mechanical properties. In this paper a triply periodic minimal surface (TPMS), the Schwarz’s Primitive surface (P-surface), has been considered as scaffold unit cell and conveniently parameterized in order to investigate the effect of modulation of analytical parameters on the P-cell geometry and on its properties. Several are the cell properties, which can affect the scaffold performance. Due to the important biofunctional role that the surface curvature plays in mechanisms of cellular proliferation and differentiation, in this paper, in addition to properties considering the cell geometry in its whole (such as volume fraction or pore size), new properties were proposed. These properties involve, particularly, the evaluation of local geometrical-differential properties of the P-surface. The results of this P-cell comprehensive characterization are very useful for the design of customized bone scaffolds able to satisfy both biological and mechanical requirements. A numerical structural evaluation, by means of finite element method (FEM), was performed in order to assess the stiffness of solid P-cells as a function of the changes of the analytical parameters of outer surface and the thickness of cell. Finally, the relationship between stiffness and porosity has been analyzed, given the relevance that this property has for bone scaffolds design.

Harmony issues are widespread in human society and nature. To analyze these issues, harmony theory has been proposed as the main theoretical approach for the study of interpersonal relationships and relationships between humans and nature. Therefore, it is of great importance to study harmony theory. After briefly introducing the basic concepts of harmony theory, this paper expounds the five elements that are essential for the quantitative description of harmony issues in water resources management: harmony participant, harmony objective, harmony regulation, harmony factor, and harmony action. A basic mathematical equation for the harmony degree, that is, a quantitative expression of harmony issues, is introduced in the paper:HD=ai − bj, whereais the uniform degree,bis the difference degree,iis the harmony coefficient, andjis the disharmony coefficient. This paper also discusses harmony assessment and harmony regulation and introduces some application examples.

The segmentation of organs in CT volumes is a prerequisite for diagnosis and treatment planning. In this paper, we focus on liver segmentation from contrast-enhanced abdominal CT volumes, a challenging task due to intensity overlapping, blurred edges, large variability in liver shape, and complex background with cluttered features. The algorithm integrates multidiscriminative cues (i.e., prior domain information, intensity model, and regional characteristics of liver in a graph-cut image segmentation framework). The paper proposes a swarm intelligence inspired edge-adaptive weight function for regulating the energy minimization of the traditional graph-cut model. The model is validated both qualitatively (by clinicians and radiologists) and quantitatively on publically available computed tomography (CT) datasets (MICCAI 2007 liver segmentation challenge, 3D-IRCAD). Quantitative evaluation of segmentation results is performed using liver volume calculations and a mean score of 80.8% and 82.5% on MICCAI and IRCAD dataset, respectively, is obtained. The experimental result illustrates the efficiency and effectiveness of the proposed method.

For music emotion detection, this paper presents a music emotion verification system based on hierarchical sparse kernel machines. With the proposed system, we intend to verify if a music clip possesses happiness emotion or not. There are two levels in the hierarchical sparse kernel machines. In the first level, a set of acoustical features are extracted, and principle component analysis (PCA) is implemented to reduce the dimension. The acoustical features are utilized to generate the first-level decision vector, which is a vector with each element being a significant value of an emotion. The significant values of eight main emotional classes are utilized in this paper. To calculate the significant value of an emotion, we construct its 2-class SVM with calm emotion as the global (non-target) side of the SVM. The probability distributions of the adopted acoustical features are calculated and the probability product kernel is applied in the first-level SVMs to obtain first-level decision vector feature. In the second level of the hierarchical system, we merely construct a 2-class relevance vector machine (RVM) with happiness as the target side and other emotions as the background side of the RVM. The first-level decision vector is used as the feature with conventional radial basis function kernel. The happiness verification threshold is built on the probability value. In the experimental results, the detection error tradeoff (DET) curve shows that the proposed system has a good performance on verifying if a music clip reveals happiness emotion.

This paper addresses the problems and threats associated with verification of integrity, proof of authenticity, tamper detection, and copyright protection for digital-text content. Such issues were largely addressed in the literature for images, audio, and video, with only a few papers addressing the challenge of sensitive plain-text media under known constraints. Specifically, with text as the predominant online communication medium, it becomes crucial that techniques are deployed to protect such information. A number of digital-signature, hashing, and watermarking schemes have been proposed that essentially bind source data or embed invisible data in a cover media to achieve its goal. While many such complex schemes with resource redundancies are sufficient in offline and less-sensitive texts, this paper proposes a hybrid approach based on zero-watermarking and digital-signature-like manipulations for sensitive text documents in order to achieve content originality and integrity verification without physically modifying the cover text in anyway. The proposed algorithm was implemented and shown to be robust against undetected content modifications and is capable of confirming proof of originality whilst detecting and locating deliberate/nondeliberate tampering. Additionally, enhancements in resource utilisation and reduced redundancies were achieved in comparison to traditional encryption-based approaches. Finally, analysis and remarks are made about the current state of the art, and future research issues are discussed under the given constraints.

In order to utilize the energy from the renewable energy sources, power conversion system is necessary, in which the voltage source inverter (VSI) is usually the last stage for injecting power to the grid. It is an economical solution to add the function of power quality conditioning to the grid-connected VSI in the low-voltage distribution system. Two multifunctional VSIs are studied in this paper, that is, inductive-coupling VSI and capacitive-coupling VSI, which are named after the fundamental frequency impedance of their coupling branch. The operation voltages of the two VSIs are compared when they are used for renewable energy integration and power quality conditioning simultaneously. The operation voltage of the capacitive-coupling VSI can be set much lower than that of the inductive-coupling VSI when reactive power is for compensating inductive loads. Since a large portion of the loads in the distribution system are inductive, the capacitive-coupling VSI is further studied. The design and control method of the multifunctional capacitive-coupling VSI are proposed in this paper. Simulation and experimental results are provided to show its validity.

Cognitive radio (CR) enables unlicensed users to exploit the underutilized spectrum in licensed spectrum whilst minimizing interference to licensed users. Reinforcement learning (RL), which is an artificial intelligence approach, has been applied to enable each unlicensed user to observe and carry out optimal actions for performance enhancement in a wide range of schemes in CR, such as dynamic channel selection and channel sensing. This paper presents new discussions of RL in the context of CR networks. It provides an extensive review on how most schemes have been approached using the traditional and enhanced RL algorithms through state, action, and reward representations. Examples of the enhancements on RL, which do not appear in the traditional RL approach, are rules and cooperative learning. This paper also reviews performance enhancements brought about by the RL algorithms and open issues. This paper aims to establish a foundation in order to spark new research interests in this area. Our discussion has been presented in a tutorial manner so that it is comprehensive to readers outside the specialty of RL and CR.

In resource-constrained project scheduling (RCPS) problems, ongoing tasks are restricted to utilizing a fixed number of resources. This paper investigates a dynamic version of the RCPS problem where the number of tasks varies in time. Our previous work investigated a technique called mapping of task IDs for centroid-based approach with random immigrants (McBAR) that was used to solve the dynamic problem. However, the solution-searching ability of McBAR was investigated over only a few instances of the dynamic problem. As a consequence, only a small number of characteristics of McBAR, under the dynamics of the RCPS problem, were found. Further, only a few techniques were compared to McBAR with respect to its solution-searching ability for solving the dynamic problem. In this paper, (a) the significance of the subalgorithms of McBAR is investigated by comparing McBAR to several other techniques; and (b) the scope of investigation in the previous work is extended. In particular, McBAR is compared to a technique called, Estimation Distribution Algorithm (EDA). As with McBAR, EDA is applied to solve the dynamic problem, an application that is unique in the literature.

This paper presents a grammar and semantic corpus based similarity algorithm for natural language sentences. Natural language, in opposition to “artificial language”, such as computer programming languages, is the language used by the general public for daily communication. Traditional information retrieval approaches, such as vector models, LSA, HAL, or even the ontology-based approaches that extend to include concept similarity comparison instead of cooccurrence terms/words, may not always determine the perfect matching while there is no obvious relation or concept overlap between two natural language sentences. This paper proposes a sentence similarity algorithm that takes advantage of corpus-based ontology and grammatical rules to overcome the addressed problems. Experiments on two famous benchmarks demonstrate that the proposed algorithm has a significant performance improvement in sentences/short-texts with arbitrary syntax and structure.

Most large-scale peer-to-peer (P2P) live streaming systems use mesh to organize peers and leverage pull scheduling to transmit packets for providing robustness in dynamic environment. The pull scheduling brings large packet delay. Network coding makes the push scheduling feasible in mesh P2P live streaming and improves the efficiency. However, it may also introduce some extra delays and coding computational overhead. To improve the packet delay, streaming quality, and coding overhead, in this paper are as follows. we propose a QoS driven push scheduling approach. The main contributions of this paper are: (i) We introduce a new network coding method to increase the content diversity and reduce the complexity of scheduling; (ii) we formulate the push scheduling as an optimization problem and transform it to a min-cost flow problem for solving it in polynomial time; (iii) we propose a push scheduling algorithm to reduce the coding overhead and do extensive experiments to validate the effectiveness of our approach. Compared with previous approaches, the simulation results demonstrate thatpacket delay,continuity index,andcoding ratioof our system can be significantly improved, especially in dynamic environments.

Several key techniques in 3D geological modeling including planar mesh generation, spatial interpolation, and surface intersection are summarized in this paper. Note that these techniques are generic and widely used in various applications but play a key role in 3D geological modeling. There are two essential procedures in 3D geological modeling: the first is the simulation of geological interfaces using geometric surfaces and the second is the building of geological objects by means of various geometric computations such as the intersection of surfaces. Discrete geometric surfaces that represent geological interfaces can be generated by creating planar meshes first and then spatially interpolating; those surfaces intersect and then form volumes that represent three-dimensional geological objects such as rock bodies. In this paper, the most commonly used algorithms of the key techniques in 3D geological modeling are summarized.

This paper describes the acquisition setup and development of a new gait database, MMUGait. This database consists of 82 subjects walking under normal condition and 19 subjects walking with 11 covariate factors, which were captured under two views. This paper also proposes a multiview model-based gait recognition system with joint detection approach that performs well under different walking trajectories and covariate factors, which include self-occluded or external occluded silhouettes. In the proposed system, the process begins by enhancing the human silhouette to remove the artifacts. Next, the width and height of the body are obtained. Subsequently, the joint angular trajectories are determined once the body joints are automatically detected. Lastly, crotch height and step-size of the walking subject are determined. The extracted features are smoothened by Gaussian filter to eliminate the effect of outliers. The extracted features are normalized with linear scaling, which is followed by feature selection prior to the classification process. The classification experiments carried out on MMUGait database were benchmarked against the SOTON Small DB from University of Southampton. Results showed correct classification rate above 90% for all the databases. The proposed approach is found to outperform other approaches on SOTON Small DB in most cases.

This paper tackles location privacy protection in current location-based services (LBS) where mobile users have to report their exact location information to an LBS provider in order to obtain their desired services. Location cloaking has been proposed and well studied to protect user privacy. It blurs the user’s accurate coordinate and replaces it with a well-shaped cloaked region. However, to obtain such an anonymous spatial region (ASR), nearly all existent cloaking algorithms require knowing the accurate locations of all users. Therefore, location cloaking without exposing the user’s accurate location to any party is urgently needed. In this paper, we present such two nonexposure accurate location cloaking algorithms. They are designed forK-anonymity, and cloaking is performed based on the identifications (IDs) of the grid areas which were reported by all the users, instead of directly on their accurate coordinates. Experimental results show that our algorithms are more secure than the existent cloaking algorithms, need not have all the users reporting their locations all the time, and can generate smaller ASR.

Computer-based sensors and actuators such as global positioning systems, machine vision, and laser-based sensors have progressively been incorporated into mobile robots with the aim of configuring autonomous systems capable of shifting operator activities in agricultural tasks. However, the incorporation of many electronic systems into a robot impairs its reliability and increases its cost. Hardware minimization, as well as software minimization and ease of integration, is essential to obtain feasible robotic systems. A step forward in the application of automatic equipment in agriculture is the use of fleets of robots, in which a number of specialized robots collaborate to accomplish one or several agricultural tasks. This paper strives to develop a system architecture for both individual robots and robots working in fleets to improve reliability, decrease complexity and costs, and permit the integration of software from different developers. Several solutions are studied, from a fully distributed to a whole integrated architecture in which a central computer runs all processes. This work also studies diverse topologies for controlling fleets of robots and advances other prospective topologies. The architecture presented in this paper is being successfully applied in the RHEA fleet, which comprises three ground mobile units based on a commercial tractor chassis. © 2014 Luis Emmi et al., The research leading to these results has received funding from the European Union’s Seventh Framework Programme [FP7/2007-2013] under Grant Agreement no. 245986.

This paper deals with evaluating congestion in free disposal hull (FDH) models. There are several approaches in data envelopment analysis (DEA) literatures which discuss the theory and application of congestion. However, almost all of these approaches considered convex DEA technologies. So, in the case of nonconvex technologies, including FDH technology, this field is almost nil. This paper makes an attempt to fill in this void. To do so, this study provides a pairwise comparisons-based algorithm to evaluate congestion in FDH model. This algorithm identifies the sources of congestion and estimates its amounts. It is also capable of detecting the losses amounts of output due to congestion. The validity of the proposed model is demonstrated using some numerical and empirical examples.

Cloud computing helps users and companies to share computing resources instead of having local servers or personal devices to handle the applications. Smart devices are becoming one of the main information processing devices. Their computing features are reaching levels that let them create a mobile cloud computing network. But sometimes they are not able to create it and collaborate actively in the cloud because it is difficult for them to build easily a spontaneous network and configure its parameters. For this reason, in this paper, we are going to present the design and deployment of a spontaneous ad hoc mobile cloud computing network. In order to perform it, we have developed a trusted algorithm that is able to manage the activity of the nodes when they join and leave the network. The paper shows the network procedures and classes that have been designed. Our simulation results using Castalia show that our proposal presents a good efficiency and network performance even by using high number of nodes.

Multiobjective (MO) optimization is an emerging field which is increasingly being encountered in many fields globally. Various metaheuristic techniques such as differential evolution (DE), genetic algorithm (GA), gravitational search algorithm (GSA), and particle swarm optimization (PSO) have been used in conjunction with scalarization techniques such as weighted sum approach and the normal-boundary intersection (NBI) method to solve MO problems. Nevertheless, many challenges still arise especially when dealing with problems with multiple objectives (especially in cases more than two). In addition, problems with extensive computational overhead emerge when dealing with hybrid algorithms. This paper discusses these issues by proposing an alternative framework that utilizes algorithmic concepts related to the problem structure for generating efficient and effective algorithms. This paper proposes a framework to generate new high-performance algorithms with minimal computational overhead for MO optimization.

In association rule mining, evaluating an association rule needs to repeatedly scan database to compare the whole database with the antecedent, consequent of a rule and the whole rule. In order to decrease the number of comparisons and time consuming, we present an attribute index strategy. It only needs to scan database once to create the attribute index of each attribute. Then all metrics values to evaluate an association rule do not need to scan database any further, but acquire data only by means of the attribute indices. The paper visualizes association rule mining as a multiobjective problem rather than a single objective one. In order to make the acquired solutions scatter uniformly toward the Pareto frontier in the objective space, elitism policy and uniform design are introduced. The paper presents the algorithm of attribute index and uniform design based multiobjective association rule mining with evolutionary algorithm, abbreviated as IUARMMEA. It does not require the user-specified minimum support and minimum confidence anymore, but uses a simple attribute index. It uses a well-designed real encoding so as to extend its application scope. Experiments performed on several databases demonstrate that the proposed algorithm has excellent performance, and it can significantly reduce the number of comparisons and time consumption.

An efficient approach to the sharpening of color images is proposed in this paper. For this, the image to be sharpened is first transformed to theHSVcolor model, and then only the channel ofValuewill be used for the process of sharpening while the other channels are left unchanged. We then apply a proposed edge detector and low-pass filter to the channel ofValueto pick out pixels around boundaries. After that, those pixels detected as around edges or boundaries are adjusted so that the boundary can be sharpened, and those nonedge pixels are kept unaltered. The increment or decrement magnitude that is to be added to those edge pixels is determined in an adaptive manner based on global statistics of the image and local statistics of the pixel to be sharpened. With the proposed approach, the discontinuities can be highlighted while most of the original information contained in the image can be retained. Finally, the adjusted channel ofValueand that ofHueandSaturationwill be integrated to get the sharpened color image. Extensive experiments on natural images will be given in this paper to highlight the effectiveness and efficiency of the proposed approach.

Computer-aided process planning (CAPP) is an important interface between computer-aided design (CAD) and computer-aided manufacturing (CAM) in computer-integrated manufacturing environments (CIMs). In this paper, process planning problem is described based on a weighted graph, and an ant colony optimization (ACO) approach is improved to deal with it effectively. The weighted graph consists of nodes, directed arcs, and undirected arcs, which denote operations, precedence constraints among operation, and the possible visited path among operations, respectively. Ant colony goes through the necessary nodes on the graph to achieve the optimal solution with the objective of minimizing total production costs (TPCs). A pheromone updating strategy proposed in this paper is incorporated in the standard ACO, which includes Global Update Rule and Local Update Rule. A simple method by controlling the repeated number of the same process plans is designed to avoid the local convergence. A case has been carried out to study the influence of various parameters of ACO on the system performance. Extensive comparative experiments have been carried out to validate the feasibility and efficiency of the proposed approach.

In the recent years, China’s auto industry develops rapidly, thus bringing a series of burdens to society and environment. This paper uses Logistic model to simulate the future trend of China’s vehicle population and finds that China’s auto industry would come into high speed development time during 2020–2050. Moreover, this paper predicts vehicles’ fuel consumption and exhaust emissions (CO, HC, NOx, and PM) and quantificationally evaluates related industry policies. It can be concluded that (1) by 2020, China should develop at least 47 million medium/heavy hybrid cars to prevent the growth of vehicle fuel consumption; (2) China should take the more stringent vehicle emission standard V over 2017–2021 to hold back the growth of exhaust emissions; (3) developing new energy vehicles is the most effective measure to ease the pressure brought by auto industry.

Bioinspired computation (BIC) has become an emerging and popular part of modern computer science, artificial intelligence, computational intelligence, and evolutionary intelligence. BIC is usually based on biological systems that can be often self-organizing, adaptive, and tolerant of random defects. Most BIC algorithms are mostly swarm intelligence (SI) based, and they mimic the successful characteristics of biological and swarm systems. However, bioinspired computation can be very broad, including swarm intelligence [1–7], membrane [8] and memetic computing, DNA and molecular computing, neural computing, and others. Their applications are also very diverse, including computational intelligence, computational neuroscience, bioinformatics, natural language processing, machine learning, software engineering, scheduling and timetabling, data mining, algorithm theory, and many other areas [9–11]. Recent advances in this area are relatively extensive, and a special issue can serve as a good summary to provide a snapshot of the recent advances. The responses to our call for papers are overwhelming, and we received nearly two hundred submissions, and after rigorous peer review, only a small fraction of the submissions were accepted for this special issue. This special issue starts with the heterogeneous differential evolution by J. Wang et al., followed by the correction of faulty sensors in phased array radars by J. U. Khan et al. A new variant of harmony search has been proposed to solve binary knapsack problems; then a theoretical model for location of terror response facilities was studied by L. P. Meng et al. In addition, a novel variant of bat algorithm based on naive Bayes methods has been presented to carry out feature selection, followed by a hybrid model based on genetic algorithms and support vector machine for fruit juice classifications by C. Fernandez-Lozano et al., while a modified dynamic neural-fuzzy approach to model customer satisfaction was carried out by C. K. Kwong et al. On the other hand, an improved firefly algorithm for nonlinear and convex optimization with applications to CAD/CAM has been proposed by A. Galvez and A. Iglesias, and an effective hybrid by combining the firefly algorithm and harmony search was found to be efficient for global numerical optimization by L. H. Guo et al. Furthermore, noise-assisted multipath traffic distribution networks were studied by N. Asvarujanon et al. Other studies have focussed on the various improvements and diverse applications with detailed case studies. Interested readers are encouraged to read more about this special issue and the open access mode means that you can download papers and study them in great detail when appropriate. Analyzing the current developments, we can expect to look forward to the future. From the bioinspired computation point of view, we think the following eight areas need more research. Complex, real-world applications: more applications should focus on complex, real-world applications in various engineering and industrial designs. Such applications can be highly nonlinear and multimodal, under stringent constraints. Computationally expensive methods: most applications nowadays are computationally expensive because the evaluations of designs, objectives, and computational tasks are becoming computationally extensive. Many design options have to be evaluated by finite element methods, large-scale finite volume methods, and boundary and extended element methods. For example, applications in aerospace and electromagnetic engineering can take hours up to weeks to evaluate computationally. Therefore, further developments in this area to speed up the computational methods are very important for practical applications. Data-intensive applications: as the data volumes are expanding dramatically, due to drive in information technology and social media, data-intensive applications become more important. Data mining techniques become more relevant, and bioinspired methods such as PSO, cuckoo search, and firefly algorithm become increasingly popular in such applications [1, 9, 12]. Network and systems: current research activities have also focused on the modeling and simulation of complex networks and systems such as computer networks, electricity grids, energy networks, and biological systems. Other networks such as social networks have also received some attention. It can be expected that more research will be done in this area. Biological applications: bioinspired computation has been applied to biological applications such as protein folding and biological systems [12]. Bioinspired computation has also found applications in bioinformatics and genetic engineering. Combinatorial problems: most combinatorial optimization problems such as the traveling salesman problem are NP-hard and thus are very challenging to solve. Bioinspired algorithms such as ant colony optimization and cuckoo search can be very powerful alternatives for dealing with such challenging problems. Large-scale problems: the current applications tend to have problem sizes with a few, up to a few dozen, design variables, while real-world applications tend to have thousands of design variables. Therefore, more applications are becoming increasingly large-scale, and it is not yet clear if the methods that work for small-scale problems can still work for large-scale problems. For example, protein folding problems can be very large-scale, and at the moment, nature-inspired methods such as simulated annealing and firefly algorithm have been found to be useful and efficient. Data mining and image processing: data mining tends to have data-intensive computation, while image processing can also be very computational extensive as well. Important applications such as feature selection and classifications need efficient methods to solve, especially those large-scale problems. As we have seen, bioinspired computation is a very active area, and many challenges still remain. It can be expected that more exciting research activities will be seen in the near future.

This paper presents an overview of the application of models on aquatic ecosystems. More than 17% of the models published in the focal journal in the field, Ecological Modelling, are aquatic ecosystem models. An increasing number of papers are dealing with the theoretical aspects of modeling – new modeling approaches and techniques, how models can be used to reveal ecosystem properties, and how models can better reflect the properties of ecosystems. This development implies that today we have more types of models. The characteristics, the advantages, and the disadvantages of these model types are presented briefly. The selection criteria for the presented model types are discussed, and the application of these types to models for aquatic ecosystems is reviewed. A recent improvement in model calibration of particular interest for aquatic ecosystems is presented, and the perspectives resulting from this new calibration procedure and from possible hybrids of the presented model types are discussed.

This paper presents a new method for solving higher order nonlinear evolution partial differential equations (NPDEs). The method combines quasilinearisation, the Chebyshev spectral collocation method, and bivariate Lagrange interpolation. In this paper, we use the method to solve several nonlinear evolution equations, such as the modified KdV-Burgers equation, highly nonlinear modified KdV equation, Fisher's equation, Burgers-Fisher equation, Burgers-Huxley equation, and the Fitzhugh-Nagumo equation. The results are compared with known exact analytical solutions from literature to confirm accuracy, convergence, and effectiveness of the method. There is congruence between the numerical results and the exact solutions to a high order of accuracy. Tables were generated to present the order of accuracy of the method; convergence graphs to verify convergence of the method and error graphs are presented to show the excellent agreement between the results from this study and the known results from literature.

The aim of this paper is to develop a method for ranking trapezoidal intuitionistic fuzzy numbers (TrIFNs) in the process of decision making in the intuitionistic fuzzy environment. Firstly, the concept of TrIFNs is introduced. Arithmetic operations and cut sets over TrIFNs are investigated. Then, the values and ambiguities of the membership degree and the nonmembership degree for TrIFNs are defined as well as the value-index and ambiguity-index. Finally, a value and ambiguity-based ranking method is developed and applied to solve multiattribute decision making problems in which the ratings of alternatives on attributes are expressed using TrIFNs. A numerical example is examined to demonstrate the implementation process and applicability of the method proposed in this paper. Furthermore, comparison analysis of the proposed method is conducted to show its advantages over other similar methods.

In the real world, the object does not exist in isolation, and it always appears in a certain scene. Usually the object is fixed in a particular scene and even in special spatial location. In this paper, we propose a method for judging scene consistency effectively. Scene semantics and geometry relation play a key role. In this paper, we use PatchNet to deal with these high-level scene structures. We construct a consistent scene database, using semantic information of PatchNet to determine whether the scene is consistent. The effectiveness of the proposed algorithm is verified by a lot of experiments.

Service oriented modeling and simulation are hot issues in the field of modeling and simulation, and there is need to call service resources when simulation task workflow is running. How to optimize the service resource allocation to ensure that the task is complete effectively is an important issue in this area. In military modeling and simulation field, it is important to improve the probability of success and timeliness in simulation task workflow. Therefore, this paper proposes an optimization algorithm for multipath service resource parallel allocation, in which multipath service resource parallel allocation model is built and multiple chains coding scheme quantum optimization algorithm is used for optimization and solution. The multiple chains coding scheme quantum optimization algorithm is to extend parallel search space to improve search efficiency. Through the simulation experiment, this paper investigates the effect for the probability of success in simulation task workflow from different optimization algorithm, service allocation strategy, and path number, and the simulation result shows that the optimization algorithm for multipath service resource parallel allocation is an effective method to improve the probability of success and timeliness in simulation task workflow.

This paper investigates a novel joint problem of routing, scheduling, and channel allocation for single-radio multichannel wireless mesh networks in which multiple channel widths can be adjusted dynamically through a new software technology so that more concurrent transmissions and suppressed overlapping channel interference can be achieved. Although the previous works have studied this joint problem, their linear programming models for the problem were not incorporated with some delicate constraints. As a result, this paper first constructs a linear programming model with more practical concerns and then proposes a simulated annealing approach with a novel encoding mechanism, in which the configurations of multiple time slots are devised to characterize the dynamic transmission process. Experimental results show that our approach can find the same or similar solutions as the optimal solutions for smaller-scale problems and can efficiently find good-quality solutions for a variety of larger-scale problems.

Finding ways to distribute workloads to each processor core and efficiently reduce power consumption is of vital importance, especially for real-time systems. In this paper, a novel scheduling algorithm is proposed for real-time multicore systems to balance the computation loads and save power. The developed algorithm simultaneously considers multiple criteria, a novel factor, and task deadline, and is called power and deadline-aware multicore scheduling (PDAMS). Experiment results show that the proposed algorithm can greatly reduce energy consumption by up to 54.2% and the deadline times missed, as compared to the other scheduling algorithms outlined in this paper.

Products such as cars, trucks, and heavy machinery are assembled by two-sided assembly line. Assembly line balancing has significant impacts on the performance and productivity of flow line manufacturing systems and is an active research area for several decades. This paper addresses the line balancing problem of a two-sided assembly line in which the tasks are to be assigned at L side or R side or any one side (addressed as E). Two objectives, minimum number of workstations and minimum unbalance time among workstations, have been considered for balancing the assembly line. There are two approaches to solve multiobjective optimization problem: first approach combines all the objectives into a single composite function or moves all but one objective to the constraint set; second approach determines the Pareto optimal solution set. This paper proposes two heuristics to evolve optimal Pareto front for the TALBP under consideration: Enumerative Heuristic Algorithm (EHA) to handle problems of small and medium size and Simulated Annealing Algorithm (SAA) for large-sized problems. The proposed approaches are illustrated with example problems and their performances are compared with a set of test problems.

Notions of core, support and inversion of a soft set have been defined and studied. Soft approximations are soft sets developed through core and support, and are used for granulating the soft space. Membership structure of a soft set has been probed in and many interesting properties presented. The mathematical apparatus developed so far in this paper yields a detailed analysis of two works viz. [N. Cagman, S. Enginoglu, Soft set theory and uni-int decision making, European Jr. of Operational Research (article in press, available online 12 May 2010)] and [N. Cagman, S. Enginoglu, Soft matrix theory and its decision making, Computers and Mathematics with Applications 59 (2010) 3308 - 3314.]. We prove (Theorem 8.1) that uni-int method of Cagman is equivalent to a core-support expression which is computationally far less expansive than uni-int. This also highlights some shortcomings in Cagman's uni-int method and thus motivates us to improve the method. We first suggest an improvement in uni-int method and then present a new conjecture to solve the optimum choice problem given by Cagman and Enginoglu. Our Example 8.6 presents a case where the optimum choice is intuitively clear yet both uni-int methods (Cagman's and our improved one) give wrong answer but the new conjecture solves the problem correctly., Comment: This paper has been withdrawn by the author due to further expansion of this work. Work is also submitted to a peer reviewed journal and is expected to be published very soon

In medical datasets classification, support vector machine (SVM) is considered to be one of the most successful methods. However, most of the real-world medical datasets usually contain some outliers/noise and data often have class imbalance problems. In this paper, a fuzzy support machine (FSVM) for the class imbalance problem (called FSVM-CIP) is presented, which can be seen as a modified class of FSVM by extending manifold regularization and assigning two misclassification costs for two classes. The proposed FSVM-CIP can be used to handle the class imbalance problem in the presence of outliers/noise, and enhance the locality maximum margin. Five real-world medical datasets, breast, heart, hepatitis, BUPA liver, and pima diabetes, from the UCI medical database are employed to illustrate the method presented in this paper. Experimental results on these datasets show the outperformed or comparable effectiveness of FSVM-CIP.

Improving the energy efficiency in wireless sensor networks (WSN) has attracted considerable attention nowadays. The multiple-input multiple-output (MIMO) technique has been proved as a good candidate for improving the energy efficiency, but it may not be feasible in WSN which is due to the size limitation of the sensor node. As a solution, the cooperative multiple-input multiple-output (CMIMO) technique overcomes this constraint and shows a dramatically good performance. In this paper, a new CMIMO scheme based on the spatial modulation (SM) technique named CMIMO-SM is proposed for energy-efficiency improvement. We first establish the system model of CMIMO-SM. Based on this model, the transmission approach is introduced graphically. In order to evaluate the performance of the proposed scheme, a detailed analysis in terms of energy consumption per bit of the proposed scheme compared with the conventional CMIMO is presented. Later, under the guide of this new scheme we extend our proposed CMIMO-SM to a multihop clustered WSN for further achieving energy efficiency by finding an optimal hop-length. Equidistant hop as the traditional scheme will be compared in this paper. Results from the simulations and numerical experiments indicate that by the use of the proposed scheme, significant savings in terms of total energy consumption can be achieved. Combining the proposed scheme with monitoring sensor node will provide a good performance in arbitrary deployed WSN such as forest fire detection system.

In order to offer mobile customers better service, we should classify the mobile user firstly. Aimed at the limitations of previous classification methods, this paper puts forward a modified decision tree algorithm for mobile user classification, which introduced genetic algorithm to optimize the results of the decision tree algorithm. We also take the context information as a classification attributes for the mobile user and we classify the context into public context and private context classes. Then we analyze the processes and operators of the algorithm. At last, we make an experiment on the mobile user with the algorithm, we can classify the mobile user into Basic service user, E-service user, Plus service user, and Total service user classes and we can also get some rules about the mobile user. Compared to C4.5 decision tree algorithm and SVM algorithm, the algorithm we proposed in this paper has higher accuracy and more simplicity.

This paper is the further investigation of work of Yan and Liu, 2011, and considers the global practical tracking problem by output feedback for a class of uncertain nonlinear systems with not only unmeasured states dependent growth but also time-varying time delay. Compared with the closely related works, the remarkableness of the paper is that the time-varying time delay and unmeasurable states are permitted in the system nonlinear growth. Motivated by the related tracking results and flexibly using the ideas and techniques of universal control and dead zone, an adaptive output-feedback tracking controller is explicitly designed with the help of a new Lyapunov-Krasovskii functional, to make the tracking error prescribed arbitrarily small after a finite time while keeping all the closed-loop signals bounded. A numerical example demonstrates the effectiveness of the results.

With the rapid development of wireless sensor networks, reducing energy consumption is becoming one of the important factors to extend node lifetime, and it is necessary to adjust the launching power of each node because of the limited energy available to the sensor nodes in the networks. This paper proposes a power and rate control model based on the network utility maximization (NUM) framework, where a weighting factor is used to reflect the influence degree of the sending power and transmission rate to the utility function. In real networks, nodes interfere with each other in the procedure of transmitting signal, which may lead to signal transmission failure and may negatively have impacts on networks throughput. Using dual decomposition techniques, the NUM problem is decomposed into two distributed subproblems, and then the conjugate gradient method is applied to solve the optimization problem with the calculation of the Hessian matrix and its inverse in order to guarantee fast convergence of the algorithm. The convergence proof is also provided in this paper. Numerical examples show that the proposed solution achieves significant throughput compared with exiting approaches.

This paper aims to present a method of volumetric verification in machine tools with linear and rotary axes using a laser tracker. Beyond a method for a particular machine, it presents a methodology that can be used in any machine type. Along this paper, the schema and kinematic model of a machine with three axes of movement, two linear and one rotational axes, including the measurement system and the nominal rotation matrix of the rotational axis are presented. Using this, the machine tool volumetric error is obtained and nonlinear optimization techniques are employed to improve the accuracy of the machine tool. The verification provides a mathematical, not physical, compensation, in less time than other methods of verification by means of the indirect measurement of geometric errors of the machine from the linear and rotary axes. This paper presents an extensive study about the appropriateness and drawbacks of the regression function employed depending on the types of movement of the axes of any machine. In the same way, strengths and weaknesses of measurement methods and optimization techniques depending on the space available to place the measurement system are presented. These studies provide the most appropriate strategies to verify each machine tool taking into consideration its configuration and its available work space.

We developed mixed integer programming (MIP) models and hybrid genetic-local search algorithms for the scheduling problem of unrelated parallel machines with job sequence and machine-dependent setup times and with job splitting property. The first contribution of this paper is to introduce novel algorithms which make splitting and scheduling simultaneously with variable number of subjobs. We proposed simple chromosome structure which is constituted by random key numbers in hybrid genetic-local search algorithm (GAspLA). Random key numbers are used frequently in genetic algorithms, but it creates additional difficulty when hybrid factors in local search are implemented. We developed algorithms that satisfy the adaptation of results of local search into the genetic algorithms with minimum relocation operation of genes’ random key numbers. This is the second contribution of the paper. The third contribution of this paper is three developed new MIP models which are making splitting and scheduling simultaneously. The fourth contribution of this paper is implementation of the GAspLAMIP. This implementation let us verify the optimality of GAspLA for the studied combinations. The proposed methods are tested on a set of problems taken from the literature and the results validate the effectiveness of the proposed algorithms.

In this paper, we propose three discriminative feature selection strategies and weighted subregion matching method to improve the performance of iris recognition system. Firstly, we introduce the process of feature extraction and representation based on scale invariant feature transformation (SIFT) in detail. Secondly, three strategies are described, which are orientation probability distribution function (OPDF) based strategy to delete some redundant feature keypoints, magnitude probability distribution function (MPDF) based strategy to reduce dimensionality of feature element, and compounded strategy combined OPDF and MPDF to further select optimal subfeature. Thirdly, to make matching more effective, this paper proposes a novel matching method based on weighted sub-region matching fusion. Particle swarm optimization is utilized to accelerate achieve different sub-region’s weights and then weighted different subregions’ matching scores to generate the final decision. The experimental results, on three public and renowned iris databases (CASIA-V3 Interval, Lamp, andMMU-V1), demonstrate that our proposed methods outperform some of the existing methods in terms of correct recognition rate, equal error rate, and computation complexity.

Biometric template protection is indispensable to protect personal privacy in large-scale deployment of biometric systems. Accuracy, changeability, and security are three critical requirements for template protection algorithms. However, existing template protection algorithms cannot satisfy all these requirements well. In this paper, we propose a hybrid approach that combines random projection and fuzzy vault to improve the performances at these three points. Heterogeneous space is designed for combining random projection and fuzzy vault properly in the hybrid scheme. New chaff point generation method is also proposed to enhance the security of the heterogeneous vault. Theoretical analyses of proposed hybrid approach in terms of accuracy, changeability, and security are given in this paper. Palmprint database based experimental results well support the theoretical analyses and demonstrate the effectiveness of proposed hybrid approach.

Due to the defects of all kinds of modularity, this paper defines a weighted modularity based on the density and cohesion as the new evaluation measurement. Since the proportion of the overlapping nodes in network is very low, the number of the nodes’ repeat visits can be reduced by signing the vertices with the overlapping attributes. In this paper, we propose three test conditions for overlapping nodes and present a fast overlapping community detection algorithm with self-correcting ability, which is decomposed into two processes. Under the control of overlapping properties, the complexity of the algorithm tends to be approximate linear. And we also give a new understanding on membership vector. Moreover, we improve the bridgeness function which evaluates the extent of overlapping nodes. Finally, we conduct the experiments on three networks with well known community structures and the results verify the feasibility and effectiveness of our algorithm.

Time diversity is achieved in direct sequence spread spectrum by receiving different faded delayed copies of the transmitted symbols from different uncorrelated channel paths when the transmission signal bandwidth is greater than the coherence bandwidth of the channel. In this paper, a new time diversity scheme is proposed for spread spectrum systems. It is called code-time diversity. In this new scheme,Nspreading codes are used to transmit one data symbol overNsuccessive symbols interval. The diversity order in the proposed scheme equals to the number of the used spreading codesNmultiplied by the number of the uncorrelated paths of the channelL. The paper represents the transmitted signal model. Two demodulators structures will be proposed based on the received signal models from Rayleigh flat and frequency selective fading channels. Probability of error in the proposed diversity scheme is also calculated for the same two fading channels. Finally, simulation results are represented and compared with that of maximal ration combiner (MRC) and multiple-input and multiple-output (MIMO) systems.

This paper describes a master-slave visual surveillance system that uses stationary-dynamic camera assemblies to achieve wide field of view and selective focus of interest. In this system, the fish-eye panoramic camera is capable of monitoring a large area, and the PTZ dome camera has high mobility and zoom ability. In order to achieve the precise interaction, preprocessing spatial calibration between these two cameras is required. This paper introduces a novel calibration approach to automatically calculate a transformation matrix model between two coordinate systems by matching feature points. In addition, a distortion correction method based on Midpoint Circle Algorithm is proposed to handle obvious horizontal distortion in the captured panoramic image. Experimental results using realistic scenes have demonstrated the efficiency and applicability of the system with real-time surveillance.