Traffic incident clearance time and arrival time prediction based on hazard models

Accurate prediction of incident duration is not only important information of Traffic Incident Management System, but also an ffective input for travel time prediction. In this paper, the hazard based prediction odels are developed for both incident clearance time and arrival time. The data are obtained from the Queensland Department of Transport and Main Roads’ STREAMS Incident Management System (SIMS) for one year ending in November 2010. The best fitting distributions are drawn for both clearance and arrival time for 3 types of incident: crash, stationary vehicle, and hazard. The results show that Gamma, Log-logistic, and Weibull are the best fit for crash, stationary vehicle, and hazard incident, respectively. The obvious impact factors are given for crash clearance time and arrival time. The quantitative influences for crash and hazard incident are presented for both clearance and arrival. The model accuracy is analyzed at the end.

Mitigation of distorted and unbalanced stator voltage of stand-alone doubly fed induction generators using repetitive control technique

Unbalanced or non-linear loads result in distorted stator currents and electromagnetic torque pulsations in stand-alone doubly fed induction generators (DFIGs). This study proposes the use of a proportional-integral repetitive control (PIRC) scheme so as to mitigate the levels of harmonic and unbalance at the stator terminals of the DFIG. The PIRC is structurally simpler and requires much less computation than existing methods. Analysis of the PIRC operation and the methodology to determine the control parameters is included. Simulation study as well as laboratory test measurements demonstrate clearly the effectiveness of the proposed PIRC control scheme.

Structural identification of hindered amine light stabilisers in coil coatings using electrospray ionisation tandem mass spectrometry

Hindered amine light stabilisers (HALS) are the most effective antioxidants currently available for polymer systems in post-production, in-service applications, yet the mechanism of their action is still not fully understood. Structural characterisation of HALS in polymer matrices, particularly the identification of structural modifications brought about by oxidative conditions, is critical to aid mechanistic understanding of the prophylactic effects of these molecules. In this work, electrospray ionisation tandem mass spectrometry (ESI-MS/MS) was applied to the analysis of a suite of commercially available 2,2,6,6-tetramethylpiperidine-based HALS. Fragmentation mechanisms for the \[M + H](+) ions are proposed, which provide a rationale for the product ions observed in the MS/MS and MS(3) mass spectra of N-H, N-CH(3), N-C(O)CH(3) and N-OR containing HALS (where R is an alkyl substituent). A common product ion at m/z 123 was identified for the group of antioxidants containing N-H, N-CH3 or N-C(0)CH3 functionality, and this product ion was employed in precursor ion scans on a triple quadrupole mass spectrometer to identify the HALS species present in a crude extract from of a polyester-based coil coating. Using MS/MS, two degradation products were unambiguously identified. This technique provides a simple and selective approach to monitoring HALS structures within complex matrices. Copyright (C) 2010 John Wiley & Sons, Ltd.

Crisis on impact : responding to cyber attacks on critical information infrastructures

In the developing digital economy, the notion of traditional attack on enterprises of national significance or interest has transcended into different modes of electronic attack, surpassing accepted traditional forms of physical attack upon a target. The terrorist attacks that took place in the United States on September 11, 2001 demonstrated the physical devastation that could occur if any nation were the target of a large-scale terrorist attack. Therefore, there is a need to protect criticalnational infrastructure and critical information infrastructure. In particular,this protection is crucial for the proper functioning of a modern society and for a government to fulfill one of its most important prerogatives – namely, the protection of its people. Computer networks have many benefits that governments, corporations, and individuals alike take advantage of in order to promote and perform their duties and roles. Today, there is almost complete dependence on private sector telecommunication infrastructures and the associated computer hardware and software systems.1 These infrastructures and systems even support government and defense activity.2 This Article discusses possible attacks on critical information infrastructures and the government reactions to these attacks.

A criteria based approach for evaluating innovation commercialisation

This study attempts to provide a criteria-based approach that can be used to evaluate the potential for technology transfer and commercialisation of a new technology from university research. More specifically, this study offers the critical factors for assessing marketability and feasibility of an innovation for the commercialisation and technology transfer process. The Delphi technique has been used to refine and categorise assessment criteria identified from various models and frameworks that emerged from literature. Proposed categories of criteria that are found to be important in the evaluation and assessment of a new technology for the commercialisation purpose include: Technological Readiness; Legal and Regulatory; Social Benefits and Impact; Economic and Market Factors.

Reconstruction of critical-sized ovine mandibular defects - a pilot study

Establishing the sheep model for translational research of mandible (jaw) segmental defect regeneration. Providing a framework from which additional experimentation and evaluation of novel tissue engineered constructs may be undertaken, compared and collated. For current and future novel approaches to mandible segmental defect reconstruction that may be transferable to the human condition and, ultimately, the operative table.

Critical review of time-dependent shortest path algorithms: A multimodal trip planner perspective

A multimodal trip planner that produces optimal journeys involving both public transport and private vehicle legs has to solve a number of shortest path problems, both on the road network and the public transport network. The algorithms that are used to solve these shortest path problems have been researched since the late 1950s. However, in order to provide accurate journey plans that can be trusted by the user, the variability of travel times caused by traffic congestion must be taken into consideration. This requires the use of more sophisticated time-dependent shortest path algorithms, which have only been researched in depth over the last two decades, from the mid-1990s. This paper will review and compare nine algorithms that have been proposed in the literature, discussing the advantages and disadvantages of each algorithm on the basis of five important criteria that must be considered when choosing one or more of them to implement in a multimodal trip planner.

STOF Model : critical design issues and critical success factors

In this chapter we will make the transition towards the design of business models and the related critical issues. We develop a model that helps us understand the causalities that play a role in understanding the viability and feasibility of the business models, i.e. long-term profitability and market adoption. We argue that designing viable business models requires balancing the requirements and interests of the actors involved, within and between the various business model domains. Requirements in the service domain guide the design choices in the technology domain, which in turn affect network formation and the financial arrangements. It is important to understand the Critical Design Issues (CDIs) involved in business models and their interdependencies. In this chapter, we present the Critical Design Issues involved in designing mobile service business models, and demonstrate how they are linked to the Critical Success Factors (CSFs) with regard to business model viability. This results in a causal model for understanding business model viability, as well as providing grounding for the business model design approach outlined in Chapter 5.

Challenges to ES Success : A Critical Need for Knowledge Integration

Recent literature on Enterprise System (ES) implementation projects highlights the importance of Knowledge Integration (KI) for implementation success. The fundamental characteristics of ES - integration of modules, business process view, and aspects of information transparency - necessitate that all frequent end-users share a reasonable amount of common knowledge and integrate their knowledge to yield new knowledge. Unfortunately, the importance of KI is often overlooked and little about the role of KI in ES success is known. In this chapter, the authors study the KI impact on ES success that is relevant to the ES post-implementation in support of organizations' returns on their ES investments. They adopt the ES post-implementation segment of ES utilization to explore whether the KI approach is causally linked to ES success. The research model was tested in a multi-industry sample in Malaysia from which data was gathered from managerial and operational employees spread across six large organizations. Consistent with the explanation by knowledge-based theory, the results show that KI was valid and significantly related to the outcome of ES that relates to an organization's performance, which the authors refer to as ES success. The KI positive impact on the success of ES drives one to highlight the importance of ontological KI in the complexity of the ES environment. The authors believe that focusing on an ontology through the KI perspective can make significant contributions to current ES problems.

A particle based model to simulate microscale morphological changes of plant tissues during drying

Fundamental understanding on microscopic physical changes of plant materials is vital to optimize product quality and processing techniques, particularly in food engineering. Although grid-based numerical modelling can assist in this regard, it becomes quite challenging to overcome the inherited complexities of these biological materials especially when such materials undergo critical processing conditions such as drying, where the cellular structure undergoes extreme deformations. In this context, a meshfree particle based model was developed which is fundamentally capable of handling extreme deformations of plant tissues during drying. The model is built by coupling a particle based meshfree technique: Smoothed Particle Hydrodynamics (SPH) and a Discrete Element Method (DEM). Plant cells were initiated as hexagons and aggregated to form a tissue which also accounts for the characteristics of the middle lamella. In each cell, SPH was used to model cell protoplasm and DEM was used to model the cell wall. Drying was incorporated by varying the moisture content, the turgor pressure, and cell wall contraction effects. Compared to the state of the art grid-based microscale plant tissue drying models, the proposed model can be used to simulate tissues under excessive moisture content reductions incorporating cell wall wrinkling. Also, compared to the state of the art SPH-DEM tissue models, the proposed model better replicates real tissues and the cell-cell interactions used ensure efficient computations. Model predictions showed good agreement both qualitatively and quantitatively with experimental findings on dried plant tissues. The proposed modelling approach is fundamentally flexible to study different cellular structures for their microscale morphological changes at dehydration.

Condition monitoring and fault diagnosis of diesel engines using instantaneous angular speed analysis

Continuous monitoring of diesel engine performance is critical for early detection of fault developments in an engine before they materialize into a functional failure. Instantaneous crank angular speed (IAS) analysis is one of a few nonintrusive condition monitoring techniques that can be utilized for such a task. Furthermore, the technique is more suitable for mass industry deployments than other non-intrusive methods such as vibration and acoustic emission techniques due to the low instrumentation cost, smaller data size and robust signal clarity since IAS is not affected by the engine operation noise and noise from the surrounding environment. A combination of IAS and order analysis was employed in this experimental study and the major order component of the IAS spectrum was used for engine loading estimation and fault diagnosis of a four-stroke four-cylinder diesel engine. It was shown that IAS analysis can provide useful information about engine speed variation caused by changing piston momentum and crankshaft acceleration during the engine combustion process. It was also found that the major order component of the IAS spectra directly associated with the engine firing frequency (at twice the mean shaft rotating speed) can be utilized to estimate engine loading condition regardless of whether the engine is operating at healthy condition or with faults. The amplitude of this order component follows a distinctive exponential curve as the loading condition changes. A mathematical relationship was then established in the paper to estimate the engine power output based on the amplitude of this order component of the IAS spectrum. It was further illustrated that IAS technique can be employed for the detection of a simulated exhaust valve fault in this study.

Critical dependence of blood-borne biomarker concentrations on the half-lives of their carrier proteins

Until recently, the low-abundance (LA) range of the serum proteome was an unexplored reservoir of diagnostic information. Today it is increasingly appreciated that a diagnostic goldmine of LA biomarkers resides in the blood stream in complexed association with more abundant higher molecular weight carrier proteins such as albumin and immunoglobulins. As we now look to the possibility of harvesting these LA biomarkers more efficiently through engineered nano-scale particles, mathematical approaches are needed in order to reveal the mechanisms by which blood carrier proteins act as molecular 'mops' for LA diagnostic cargo, and the functional relationships between bound LA biomarker concentrations and other variables of interest such as biomarker intravasation and clearance rates and protein half-lives in the bloodstream. Here we show, by simple mathematical modeling, how the relative abundance of large carrier proteins and their longer half-lives in the bloodstream work together to amplify the total blood concentration of these tiny biomarkers. The analysis further suggests that alterations in the production of biomarkers lead to gradual rather than immediate changes in biomarker levels in the blood circulation. The model analysis also points to the characteristics of artificial nano-particles that would render them more efficient harvesters of tumor biomarkers in the circulation, opening up possibilities for the early detection of curable disease, rather than simply better detection of advanced disease.