Modelling regulatory change V's volume of trading effects in HSIF and HSI volatility

In an earlier paper, we adopted a bi-variate BEKK–GARCH framework and employed a systematic approach to examine structural breaks in the Hang Seng Index and Index Futures market volatility. Switching dummy variables were included and tested in the variance equations to check for any structural changes in the autoregressive volatility structure due to the events that have taken place in the Hong Kong market surrounding the Asian markets crisis. In this paper, we include measures of daily trading volume from both markets in the estimation. Likelihood ratio tests indicate the switching dummy variables become insignificant and the GARCH effects diminish but remain significant. There is some evidence that the Sequential Arrival of Information Model (SIM) provides a platform to explain these market induced effects when volume of trade is accounted for.

BIM adoption : expectations across disciplines

This chapter presents a comprehensive analysis of the current state of Building Information Modelling (BIM) in the Architecture, Engineering, Construction and Facility Management (AEC/FM) industry and a  re-assessment of its role and potential contribution in the near future, given the apparent slow rate of adoption by the industry. The chapter analyses the readiness of the industry with respect to the (1) tools, (2) processes and (3) people to position BIM adoption in terms of current status and expectations
across disciplines. The findings are drawn from an ongoing research project funded by the Australian Cooperative Research Centre for Construction Innovation (CRC-CI) that aims at developing a technological, operational and strategic analysis of adopting BIM in the AEC/FM industry as a
collaboration platform.

Architecture of supramolecular soft functional materials: from understanding to micro-/nanoscale engineering

This article gives an overview of the current progress of a class of supramolecular soft materials consisting of fiber networks and the trapped liquid. After discussing the up-to-date knowledge on the types of fiber networks and the correlation to the rheological properties, the gelation mechanism turns out to be one of the key subjects for this review. In this concern, the following two aspects will be focused upon: the single fiber network formation and the multi-domain fiber network formation of this type of material. Concerning the fiber network formation, taking place via nucleation, and the nucleation-mediated growth and branching mechanism, the theoretical basis of crystallographic mismatch nucleation that governs fiber branching and formation of three-dimensional fiber networks is presented. In connection to the multi-domain fiber network formation, which is governed by the primary nucleation and the subsequent formation of single fiber networks from nucleation centers, the control of the primary nucleation rate will be considered. Based on the understanding on the the gelation mechanism, the engineering strategies of soft functional materials of this type will be systematically discussed. These include the control of the nucleation and branching-controlled fiber network formation in terms of tuning the thermodynamic driving force of the gelling system and introducing suitable additives, as well as introducing ultrasound. Finally, a summary and the outlook of future research on the basis of the nucleation-growth-controlled fiber network formation are given.

Enhancing business intelligence traceability through an integrated metadata framework

The Business Intelligence (BI) system provides users with multi-dimensional information (so-called BI product) to support their decision-making. However, very often business users still could not fully understand the BI product, nor have a clear picture of the entire information manufacturing chain of the BI product. In response to this situation, this paper presents an integrated metadata framework (“BIP-Map”) to facilitate the traceability and accountability of a BI product following the design science research approach. Specifically, the salient modelling and management techniques from the business process modelling notation (BPMN), the information product map (IP-Map), and the metadata management are adapted to construct a three-layered integrated metadata framework enabling the business users to make timely and informed decisions. A BIP-Map informed prototype system has been developed in collaboration with online job recruitment firms. The authors conducted in-depth interviews with seven key BI stakeholders of the recruitment firms to evaluate the usefulness of the BIP-Map. It is envisaged that the metadata framework allows the technical personnel to understand the business processes that relate to certain information provided in the BI reports. Business users will also be able to gain insights into the logic behind any BI report.

Building fuzzy inference systems with similarity reasoning : NSGAII-based fuzzy rule selection and evidential functions

In our previous investigations, two Similarity Reasoning (SR)-based frameworks for tackling real-world problems have been proposed. In both frameworks, SR is used to deduce unknown fuzzy rules based on similarity of the given and unknown fuzzy rules for building a Fuzzy Inference System (FIS). In this paper, we further extend our previous findings by developing (1) a multi-objective evolutionary model for fuzzy rule selection; and (2) an evidential function to facilitate the use of both frameworks. The Non-Dominated Sorting Genetic Algorithms-p (NSGA-p) is adopted for fuzzy rule selection, in accordance with the Pareto optimal criterion. Besides that, two new evidential functions are developed, whereby given fuzzy rules are considered as evidence. Simulated and benchmark examples are included to demonstrate the applicability of these suggestions. Positive results were obtained.

Expression and purification of soluble recombinant full length HIV-1 Pr55(Gag) protein in Escherichia coli

The HIV-1 Gag precursor protein, Pr55(Gag), is a multi-domain polyprotein that drives HIV-1 assembly. The morphological features of HIV-1 suggested Pr55(Gag) assumes a variety of different conformations during virion assembly and maturation, yet structural determination of HIV-1 Pr55(Gag) has not been possible due to an inability to express and to isolate large amounts of full-length recombinant Pr55(Gag) for biophysical and biochemical analyses. This challenge is further complicated by HIV-1 Gag's natural propensity to multimerize for the formation of viral particle (with ∼2500 Gag molecules per virion), and this has led Pr55(Gag) to aggregate and be expressed as inclusion bodies in a number of in vitro protein expression systems. This study reported the production of a recombinant form of HIV-1 Pr55(Gag) using a bacterial heterologous expression system. Recombinant HIV-1 Pr55(Gag) was expressed with a C-terminal His×6 tag, and purified using a combination of immobilized metal affinity chromatography and size exclusion chromatography. This procedure resulted in the production of milligram quantities of high purity HIV-1 Pr55(Gag) that has a mobility that resembles a trimer in solution using size exclusion chromatography analysis. The high quantity and purity of the full length HIV Gag will be suitable for structural and functional studies to further understand the process of viral assembly, maturation and the development of inhibitors to interfere with the process.

Silk fibroin as a water-soluble bio-resist and its thermal properties

Thermal diffusivity of silk fibroin films, α = (1.6 ± 0.24) × 10-7 m2 s-1, was measured by a direct contact method. It was shown to be reduced down to ∼1 × 10-7 m2 s-1 in the crystallized phase, consistent with the multi-domain composition of β-sheet assemblies. Crystalline silk with β-sheets was made by dipping into alcohol and was used as a positive electron beam lithography (EBL) resist. It is shown by direct IR imaging of the 1619 cm-1 amide-I CO spectral signature and 3290 cm-1 amide-A N-H stretching band that an e-beam is responsible for unzipping β-sheets, which subsequently results in exposed areas returning to a water soluble state. This makes it possible to develop a water-based biocompatible silk resist and use it in lithography applications. The general principles of protein crystallization, traceable to spectral changes in IR amide bands of silk, can be used as a guide for the creation of new protein EBL resists and to quantify the electron dose required for solubility. Foam formation and laser treatments of silk can provide new approaches in surface functionalization and fabrication of 3D bio-scaffolds.

Information sharing between heterogeneous uncertain reasoning models in a multi-agent environment : a case study

The issue of information sharing and exchanging is one of the most important issues in the areas of artificial intelligence and knowledge-based systems (KBSs), or even in the broader areas of computer and information technology. This paper deals with a special case of this issue by carrying out a case study of information sharing between two well-known heterogeneous uncertain reasoning models: the certainty factor model and the subjective Bayesian method. More precisely, this paper discovers a family of exactly isomorphic transformations between these two uncertain reasoning models. More interestingly, among isomorphic transformation functions in this family, different ones can handle different degrees to which a domain expert is positive or negative when performing such a transformation task. The direct motivation of the investigation lies in a realistic consideration. In the past, expert systems exploited mainly these two models to deal with uncertainties. In other words, a lot of stand-alone expert systems which use the two uncertain reasoning models are available. If there is a reasonable transformation mechanism between these two uncertain reasoning models, we can use the Internet to couple these pre-existing expert systems together so that the integrated systems are able to exchange and share useful information with each other, thereby improving their performance through cooperation. Also, the issue of transformation between heterogeneous uncertain reasoning models is significant in the research area of multi-agent systems because different agents in a multi-agent system could employ different expert systems with heterogeneous uncertain reasonings for their action selections and the information sharing and exchanging is unavoidable between different agents. In addition, we make clear the relationship between the certainty factor model and probability theory.

A computation trust model with trust network in multi-agent systems

Trust is a fundamental issue in multi-agent systems, especially when they are applied in e-commence. The computational models of trust play an important role in determining who and how to interact in open and dynamic environments. To this end, a computation trust model is proposed in which the confidence information based on direct prior interactions with the target agent and the reputation information from trust network are used. In this way, agents can autonomously deal with deception and identify trustworthy parties in multi-agent systems. The ontological property of trust is also considered in the model. A case study is provided to show the effectiveness of the proposed model.

Clusters driven implementation of a brain inspired model for multi-view pattern identifications

The human brain processes information in both unimodal and multimodal fashion where information is progressively captured, accumulated, abstracted and seamlessly fused. Subsequently, the fusion of multimodal inputs allows a holistic understanding of a problem. The proliferation of technology has produced various sources of electronic data and continues to do so exponentially. Finding patterns from such multi-source and multimodal data could be compared to the multimodal and multidimensional information processing in the human brain. Therefore, such brain functionality could be taken as an inspiration to develop a methodology for exploring multimodal and multi-source electronic data and further identifying multi-view patterns. In this paper, we first propose a brain inspired conceptual model that allows exploration and identification of patterns at different levels of granularity, different types of hierarchies and different types of modalities. Secondly, we present a cluster driven approach for the implementation of the proposed brain inspired model. Particularly, the Growing Self Organising Maps (GSOM) based cross-clustering approach is discussed. Furthermore, the acquisition of multi-view patterns with clusters driven implementation is demonstrated with experimental results.

Identifying multi-view patterns with hierarchy and granularity based multimodal (HGM) cognitive model

Humans perceive entities such as objects, patterns, events, etc. as concepts, which are the basic units in human intelligence and communications. In addition, perceptions of these entities could be abstracted and generalised at multiple levels of granularity. In particular, such granulation allows the formation and usage of concepts in human intelligence. Such natural granularity in human intelligence could inspire and motivate the design and development of pattern identification approach in Data Mining. In our opinion, a pattern could be perceived at multiple levels of granularity and thus we advocate for the co-existence of hierarchy and granularity. In addition, granular patterns exist across different sources of data (multimodality). In this paper, we present a cognitive model that incorporates the characteristics of Hierarchy, Granularity and Multimodality for multi-view patterns identification in crime domain. Such framework is implemented with Growing Self Organising Maps (GSOM) and some experimental results are presented and discussed.

FEM calibrated ARMAX model updating method for time domain damage identification

Due to environmental loads, mechanical damages, structural aging and human factors, civil infrastructure inevitably deteriorate during their service lives. Since their damage may claim human lives and cause significant economic losses, how to identify damages and assess structural conditions timely and accurately has drawn increasingly more attentions from structural engineering community worldwide. In this study, a fast and sensitive time domain damage identification method will be developed. To do this, a finite element model of a steel pipe laid on the soil is built and the structural responses are simulated under different damage scenarios. Based on the simulated data, an Auto Regressive Moving Average Exogenous (ARMAX) model is then built and calibrated. The calibrated ARMAX model is used to identify different damage scenarios through model updating process using clonal selection algorithm (CSA). The results demonstrate the application potential of the proposed method in identifying the pipeline conditions. To further verify its performance, laboratory tests of a steel pipe laid on the soil with and without soil support (free span damage) are carried out. The identification results of pipe-soil system show that the proposed method is capable of identifying damagein a complex structural system. Therefore, it can be applied to identifying pipeline conditions.

Structural damage identification based on self-fitting ARMAX model and multi-sensor data fusion

Statistical time series methods have proven to be a promising technique in structural health monitoring, since it provides a direct form of data analysis and eliminates the requirement for domain transformation. Latest research in structural health monitoring presents a number of statistical models that have been successfully used to construct quantified models of vibration response signals. Although a majority of these studies present viable results, the aspects of practical implementation, statistical model construction and decision-making procedures are often vaguely defined or omitted from presented work. In this article, a comprehensive methodology is developed, which essentially utilizes an auto-regressive moving average with exogenous input model to create quantified model estimates of experimentally acquired response signals. An iterative self-fitting algorithm is proposed to construct and fit the auto-regressive moving average with exogenous input model, which is capable of integrally finding an optimum set of auto-regressive moving average with exogenous input model parameters. After creating a dataset of quantified response signals, an unlabelled response signal can be identified according to a 'closest-fit' available in the dataset. A unique averaging method is proposed and implemented for multi-sensor data fusion to decrease the margin of error with sensors, thus increasing the reliability of global damage identification. To demonstrate the effectiveness of the developed methodology, a steel frame structure subjected to various bolt-connection damage scenarios is tested. Damage identification results from the experimental study suggest that the proposed methodology can be employed as an efficient and functional damage identification tool. © The Author(s) 2014.