Enhanced graphitic domains of unreduced graphene oxide and the interplay of hydration behaviour and catalytic activity

Previous studies indicate that the properties of graphene oxide (GO) can be significantly improved by enhancing its graphitic domain size through thermal diffusion and clustering of functional groups. Remarkably, this transition takes place below the decomposition temperature of the functional groups and thus allows fine tuning of graphitic domains without compromising with the functionality of GO. By studying the transformation of GO under mild thermal treatment, we directly observe this size enhancement of graphitic domains from originally ≤40 nm² to >200 nm² through an extensive transmission electron microscopy (TEM) study. Additionally, we confirm the integrity of the functional groups during this process by a comprehensive chemical analysis. A closer look into the process confirms the theoretical predicted relevance for the room temperature stability of GO and the development of the composition of functional groups is explained with reaction pathways from theoretical calculations. We further investigate the influence of enlarged graphitic domains on the hydration behaviour of GO and the catalytic performance of single atom catalysts supported by GO. Additionally, we show that the sheet resistance of GO is reduced by several orders of magnitude during the mild thermal annealing process.     

A design of a robust discrete-time controller

In this paper, we proposed a robust discrete-time controller. This control system, which is derived from the idea of the normalized plant, does not include plant parameters. Thus, we obtain a control system independent of plant parameters and that has the same structure as a conventional optimal servo control system. Simulation and experimental results show that the proposed method is fairly robust to plant parameter variations and external disturbances.     

Learning search keywords for construction procurement

Seeking information from websites has become an essential part of a contractor's procurement undertaking, as more and more procurement websites become available on the Internet. Websites host extremely large amounts of information; a keyword search, therefore, is often more efficient than browsing via an index. However, in order to find the desired information, it may be necessary to enter keywords using a trial-and-error process. This research recognizes that professional procurement experience can help users search website information more effectively, by using fewer keywords, and so proposes a learning model and suggestion model that can capture such experience, thus guiding inexperienced users in their search. Experiments, evaluating the performance of the system, were also conducted.     

Paradigm and performance analysis of distributed frequent itemset mining algorithms based on Mapreduce

FrequentItemsetMining (FIM) is one of the most important data mining tasks and is the foundation of many data mining tasks. In Big Data era, centralized FIM algorithms cannot meet the needs of FIM for big data in terms of time and space, so Distributed Frequent Itemset Mining (DFIM) algorithms have been designed to meet the above challenges. In this paper, LocalGlobal and RedistributionMining which are two main paradigms of DFIM algorithm are discussed; Two algorithms of these paradigms on MapReduce named LG and RM are proposed while MapReduce is a popular distributed computing model, and also the related work is discussed. The experimental results show that the RM algorithm has better performance in terms of computation and scalability of sites, and can be used as the basis for designing the DFIM algorithm based on MapReduce. This paper also discusses the main ideas of improving the DFIM algorithms based on MapReduce.     

Multi-agent systems for protecting critical infrastructures: A survey

Multi-agent systems have emerged as a very significant platform in provisioning distributed and collaborative services to critical applications. Such applications require ubiquitous agent presence in the environment for monitoring, collecting data, communication, and subsequent data analysis, where the sensitivity of the application's nature cannot be understated. Recent advances in the field of autonomous, ubiquitous, intelligent and distributed computing have led to corresponding developments in the use of collaborating multi-agents to protect critical infrastructures. Such systems have witnessed crucial demand for deployment in diverse application scenarios such as E-commerce, E-health, Network Intrusion Detection, Telematics and Transport Systems, Environmental Monitoring, as well as for distributed information processing in general. Critical infrastructures have longed for a distributed system in place for their uninterrupted and accurate operations. Multi-agents have provided one such approach towards addressing the issue of protecting such infrastructures through collaborative and distributed information processing. In this paper, a state-of-the-art on the use of multi-agent based systems for protecting five most common critical infrastructures, is presented.     

Alcohol consumption detection through behavioural analysis using intelligent systems

We describe in this paper a new methodology for blood alcohol content (BAC) estimation of a subject. Rather than using external devices to determine the BAC value of a subject, we perform a behaviour analysis of this subject using intelligent systems. We monitor the user’s actions in an ordinary task and label those data to various measured BAC values. The obtained data-set is then used to train learning systems to detect alcoholic consumption and perform BAC estimation. We obtain good results on a mono-user base, and lower results with multiple users. We improve the results by combining multiple classifiers and regression algorithms.     

A hybrid algorithm for Bayesian network structure learning with application to multi-label learning

We present a novel hybrid algorithm for Bayesian network structure learning, called H2PC. It first reconstructs the skeleton of a Bayesian network and then performs a Bayesian-scoring greedy hill-climbing search to orient the edges. The algorithm is based on divide-and-conquer constraint-based subroutines to learn the local structure around a target variable. We conduct two series of experimental comparisons of H2PC against Max–Min Hill-Climbing (MMHC), which is currently the most powerful state-of-the-art algorithm for Bayesian network structure learning. First, we use eight well-known Bayesian network benchmarks with various data sizes to assess the quality of the learned structure returned by the algorithms. Our extensive experiments show that H2PC outperforms MMHC in terms of goodness of fit to new data and quality of the network structure with respect to the true dependence structure of the data. Second, we investigate H2PC’s ability to solve the multi-label learning problem. We provide theoretical results to characterize and identify graphically the so-called minimal label powersets that appear as irreducible factors in the joint distribution under the faithfulness condition. The multi-label learning problem is then decomposed into a series of multi-class classification problems, where each multi-class variable encodes a label powerset. H2PC is shown to compare favorably to MMHC in terms of global classification accuracy over ten multi-label data sets covering different application domains. Overall, our experiments support the conclusions that local structural learning with H2PC in the form of local neighborhood induction is a theoretically well-motivated and empirically effective learning framework that is well suited to multi-label learning. The source code (in R) of H2PC as well as all data sets used for the empirical tests are publicly available.     

A new iterative algorithm for solving H∞ control problem of continuous‐time Markovian jumping linear systems based on online implementation

A new online iterative algorithm for solving the H_∞ control problem of continuous‐time Markovian jumping linear systems is developed. For comparison, an available offline iterative algorithm for converging to the solution of the H_∞ control problem is firstly proposed. Based on the offline iterative algorithm and a new online decoupling technique named subsystems transformation method, a set of linear subsystems, which implementation in parallel, are obtained. By means of the adaptive dynamic programming technique, the two‐player zero‐sum game with the coupled game algebraic Riccati equation is solved online thereafter. The convergence of the novel policy iteration algorithm is also established. At last, simulation results have illustrated the effectiveness and applicability of these two methods. Copyright © 2016 John Wiley & Sons, Ltd.     

Dispersion Constraint Based Non-negative Sparse Coding Model

Based on advantages of basic non-negative sparse coding (NNSC) model, and considered the prior class constraint of image features, a novel NNSC model is discussed here. In this NNSC model, the sparseness criteria is selected as a two-parameter density estimation model and the dispersion ratio of within-class and between-class is used as the class constraint. Utilizing this NNSC model, image features can be extracted successfully. Further, the feature recognition task by using different classifiers can be implemented well. Simulation results prove that our NNSC model proposed is indeed effective in extracting image features and recognition task in application.     

Mobile service oriented architectures for NN-queries

The ever-increasing number of mobile device users has also raised the users’ expectation of mobile services accordingly. This phenomenon has given pressures to the mobile service providers to improve their services in order to stay competitive in the market. The service oriented approach is seen to be a promising scheme for mobile services. This paper presents mobile service oriented architectures for Nearest-Neighbor (NN) queries that are classified into five categories, namely (i) intermittent query mobile services, (ii) continuous query mobile services, (iii) context-aware mobile services, (iv) continuous moving object query mobile services, and (v) data broadcast mobile services. These services incorporate query, location and context-aware services, ontological context model, and broadcast. The proposed architectures are concerned with mobile services for clients on the move requesting services based on their current location, which is arguably the most important feature in a wireless environment. Furthermore, we also discuss the Quality-of-Service (QoS) requirement for mobile services in which request latency time is one of the most important parameters to consider. Some analytical models for query latency measurement are presented and the results are compared with the simulation experiments.