A Multi-Objective Approach for VNE: Problems using multiple ILP formulations

Network Virtualization is a key technology for the Future Internet, allowing the deployment of multiple independent virtual networks that use resources of the same basic infrastructure. An important challenge in the dynamic provision of virtual networks resides in the optimal allocation of physical resources (nodes and links) to requirements of virtual networks. This problem is known as Virtual Network Embedding (VNE). For the resolution of this problem, previous research has focused on designing algorithms based on the optimization of a single objective. On the contrary, in this work we present a multi-objective algorithm, called VNE-MO-ILP, for solving dynamic VNE problem, which calculates an approximation of the Pareto Front considering simultaneously resource utilization and load balancing. Experimental results show evidences that the proposed algorithm is better or at least comparable to a state-of-the-art algorithm. Two performance metrics were simultaneously evaluated: (i) Virtual Network Request Acceptance Ratio and (ii) Revenue/Cost Relation. The size of test networks used in the experiments shows that the proposed algorithm scales well in execution times, for networks of 84 nodes

Portuguese children exposure to multiple mycotoxins through food consumption: toward a holistic approach for multiple mycotoxins risk assessment

Humans can be exposed to multiple chemicals at once from a variety of sources, and human risk assessment of multiple chemicals poses several challenges to scientists, risk assessors and risk managers. Ingestion of food is considered a major route of exposure to many contaminants, namely mycotoxins, especially for vulnerable population groups, as children. A lack of sufficient data regarding mycotoxins children risk assessment, could contribute to an inaccuracy of the estimated risk. Efforts must be undertaken to develop initiatives that promote a broad overview of multiple mycotoxins risk assessment. The present work, developed within the MYCOMIX project, aims to assess the risk associated to the exposure of Portuguese children (< 3 years old) to multiple mycotoxins through consumption of foods primarily marketed for this age group. A holistic approach was developed applying deterministic and probabilistic tools to the calculation of mycotoxin daily intake values, integrating children food consumption (3-days food diary), mycotoxins occurrence (HPLC-UV, HPLC-FD, LC-MS/MS and GC-MS), bioaccessibility (standardized in vitro digestion model) and toxicological data (in vitro evaluation of cytotoxicity, genotoxicity and intestinal impact). A case study concerning Portuguese children exposure to patulin (PAT) and ochratoxin A (OTA), two mycotoxins co-occurring in processed cereal-based foods (PCBF) marketed in Portugal, was developed. Main results showed that there is low concern from a public health point of view relatively to PAT and OTA Portuguese children exposure through consumption of PCBF, considering the estimated daily intakes of these two mycotoxins (worst case scenarios, 22.930 ng/kg bw/day and 0.402 ng/kg bw/day, for PAT and OTA, respectively), their bioaccessibility and toxicology results. However, the present case study only concerns the risk associated with the consumption of PCBF and child diet include several other foods. The present work underlines the need to adopt a holistic approach for multiple mycotoxins risk assessment integrating data from exposure, bioacessibility and toxicity domains in order to contribute to a more accurate risk assessment.

Teachers’ perspectives and practices on social and emotional learning: multiple case study approach

 This thesis has explored how Preparatory teachers in two Victorian primary schools understand and implement social and emotional learning in their classrooms. The findings of this research indicated that the participating teachers paid more attention to children's social skills rather than to their emotional skills. Another important finding was that social and emotional learning has a very limited place within the policies and the curricula of the participating schools.

Damage assessment in multiple-girder composite bridge using vibration characteristics

This paper uses dynamic computer simulation techniques to apply a procedure using vibration-based methods for damage assessment in multiple-girder composite bridge. In addition to changes in natural frequencies, this multi-criteria procedure incorporates two methods, namely the modal flexibility and the modal strain energy method. Using the numerically simulated modal data obtained through finite element analysis software, algorithms based on modal flexibility and modal strain energy change before and after damage are obtained and used as the indices for the assessment of structural health state. The feasibility and capability of the approach is demonstrated through numerical studies of proposed structure with six damage scenarios. It is concluded that the modal strain energy method is competent for application on multiple-girder composite bridge, as evidenced through the example treated in this paper.

Relative entropy rate based multiple hidden Markov Model Approximation

This paper proposes a novel relative entropy rate (RER) based approach for multiple HMM (MHMM) approximation of a class of discrete-time uncertain processes. Under different uncertainty assumptions, the model design problem is posed either as a min-max optimisation problem or stochastic minimisation problem on the RER between joint laws describing the state and output processes (rather than the more usual RER between output processes). A suitable filter is proposed for which performance results are established which bound conditional mean estimation performance and show that estimation performance improves as the RER is reduced. These filter consistency and convergence bounds are the first results characterising multiple HMM approximation performance and suggest that joint RER concepts provide a useful model selection criteria. The proposed model design process and MHMM filter are demonstrated on an important image processing dim-target detection problem.

Crowd Counting Using Multiple Local Features

In public venues, crowd size is a key indicator of crowd safety and stability. Crowding levels can be detected using holistic image features, however this requires a large amount of training data to capture the wide variations in crowd distribution. If a crowd counting algorithm is to be deployed across a large number of cameras, such a large and burdensome training requirement is far from ideal. In this paper we propose an approach that uses local features to count the number of people in each foreground blob segment, so that the total crowd estimate is the sum of the group sizes. This results in an approach that is scalable to crowd volumes not seen in the training data, and can be trained on a very small data set. As a local approach is used, the proposed algorithm can easily be used to estimate crowd density throughout different regions of the scene and be used in a multi-camera environment. A unique localised approach to ground truth annotation reduces the required training data is also presented, as a localised approach to crowd counting has different training requirements to a holistic one. Testing on a large pedestrian database compares the proposed technique to existing holistic techniques and demonstrates improved accuracy, and superior performance when test conditions are unseen in the training set, or a minimal training set is used.

A practical approach for reliability prediction of pipeline systems

Pipelines play an important role in the modern society. Failures of pipelines can have great impacts on economy, environment and community. Preventive maintenance (PM) is often conducted to improve the reliability of pipelines. Modern asset management practice requires accurate predictability of the reliability of pipelines with multiple PM actions, especially when these PM actions involve imperfect repairs. To address this issue, a split system approach (SSA) based model is developed in this paper through an industrial case study. This new model enables maintenance personnel to predict the reliability of pipelines with different PM strategies and hence effectively assists them in making optimal PM decisions.

Sustainable housing implementation through mutual benefits to stakeholders : a decision making approach

Improved public awareness and strong sentiments towards environmental issues will continue to create increasing demand for sustainable housing (SH) in the coming years. Despite this potential, the up-take rate of sustainable housing in new build and through home renovation is not as high as expected within the housing industry. This is in contrast to the influx of emerging building technologies, new materials and innovative designs seen in exemplar homes built worldwide. How we should use the increasing awareness of SH and emerging technologies as an impetus to change the un-sustainable designs and practices of the building industry is high on the agenda of the government and majority of the stakeholders involved. This warrants the study of multifaceted strategies that meet the needs of multiple stakeholders and integrated seamlessly into housing development processes. Specifically, the different perceptions, roles and incentives of stakeholders, who inevitably need to ensure their benefits and commercial returns, should be highlighted and acted upon. ----- This paper discusses the preliminary findings of a research project that aims to promote SH implementation by identifying and materializing the mutual benefits among key stakeholders. The aim is to be achieved through questionnaire surveys, structural equation modelling, interviews and case studies with seven major stakeholders within the Australian housing industry. This research identifies the influence and relationship of relevant factors, investigates preferences, similarities and differences between stakeholders on perceived benefits and in turn explores the mutual-benefit strategy package that facilitates decision making towards sustainable housing development.

The population approach to falls injury prevention in older people : findings of a two community trial

Background: There is a sound rationale for the population-based approach to falls injury prevention but there is currently insufficient evidence to advise governments and communities on how they can use population-based strategies to achieve desired reductions in the burden of falls-related injury.---------- Aim: To quantify the effectiveness of a streamlined (and thus potentially sustainable and cost-effective), population-based, multi-factorial falls injury prevention program for people over 60 years of age.---------- Methods: Population-based falls-prevention interventions were conducted at two geographically-defined and separate Australian sites: Wide Bay, Queensland, and Northern Rivers, NSW. Changes in the prevalence of key risk factors and changes in rates of injury outcomes within each community were compared before and after program implementation and changes in rates of injury outcomes in each community were also compared with the rates in their respective States.---------- Results: The interventions in neither community substantially decreased the rate of falls-related injury among people aged 60 years or older, although there was some evidence of reductions in occurrence of multiple falls reported by women. In addition, there was some indication of improvements in fall-related risk factors, but the magnitudes were generally modest.---------- Conclusion: The evidence suggests that low intensity population-based falls prevention programs may not be as effective as those are intensively implemented.

Infrastructure Sustainability : Damage Assessment in Multiple-Girder Composite Bridges Using Vibration Characteristics

Assessing the structural health state of urban infrastructure is crucial in terms of infrastructure sustainability. This chapter uses dynamic computer simulation techniques to apply a procedure using vibration-based methods for damage assessment in multiple-girder composite bridges. In addition to changes in natural frequencies, this multi-criteria procedure incorporates two methods, namely, the modal flexibility and the modal strain energy method. Using the numerically simulated modal data obtained through finite element analysis software, algorithms based on modal flexibility and modal strain energy change, before and after damage, are obtained and used as the indices for the assessment of structural health state. The feasibility and capability of the approach is demonstrated through numerical studies of a proposed structure with six damage scenarios. It is concluded that the modal strain energy method is capable of application to multiple-girder composite bridges, as evidenced through the example treated in this chapter.

A hidden Markov model and relative entropy rate approach to vision-based dim target detection for UAV sense-and-avoid

Uninhabited aerial vehicles (UAVs) are a cutting-edge technology that is at the forefront of aviation/aerospace research and development worldwide. Many consider their current military and defence applications as just a token of their enormous potential. Unlocking and fully exploiting this potential will see UAVs in a multitude of civilian applications and routinely operating alongside piloted aircraft. The key to realising the full potential of UAVs lies in addressing a host of regulatory, public relation, and technological challenges never encountered be- fore. Aircraft collision avoidance is considered to be one of the most important issues to be addressed, given its safety critical nature. The collision avoidance problem can be roughly organised into three areas: 1) Sense; 2) Detect; and 3) Avoid. Sensing is concerned with obtaining accurate and reliable information about other aircraft in the air; detection involves identifying potential collision threats based on available information; avoidance deals with the formulation and execution of appropriate manoeuvres to maintain safe separation. This thesis tackles the detection aspect of collision avoidance, via the development of a target detection algorithm that is capable of real-time operation onboard a UAV platform. One of the key challenges of the detection problem is the need to provide early warning. This translates to detecting potential threats whilst they are still far away, when their presence is likely to be obscured and hidden by noise. Another important consideration is the choice of sensors to capture target information, which has implications for the design and practical implementation of the detection algorithm. The main contributions of the thesis are: 1) the proposal of a dim target detection algorithm combining image morphology and hidden Markov model (HMM) filtering approaches; 2) the novel use of relative entropy rate (RER) concepts for HMM filter design; 3) the characterisation of algorithm detection performance based on simulated data as well as real in-flight target image data; and 4) the demonstration of the proposed algorithm's capacity for real-time target detection. We also consider the extension of HMM filtering techniques and the application of RER concepts for target heading angle estimation. In this thesis we propose a computer-vision based detection solution, due to the commercial-off-the-shelf (COTS) availability of camera hardware and the hardware's relatively low cost, power, and size requirements. The proposed target detection algorithm adopts a two-stage processing paradigm that begins with an image enhancement pre-processing stage followed by a track-before-detect (TBD) temporal processing stage that has been shown to be effective in dim target detection. We compare the performance of two candidate morphological filters for the image pre-processing stage, and propose a multiple hidden Markov model (MHMM) filter for the TBD temporal processing stage. The role of the morphological pre-processing stage is to exploit the spatial features of potential collision threats, while the MHMM filter serves to exploit the temporal characteristics or dynamics. The problem of optimising our proposed MHMM filter has been examined in detail. Our investigation has produced a novel design process for the MHMM filter that exploits information theory and entropy related concepts. The filter design process is posed as a mini-max optimisation problem based on a joint RER cost criterion. We provide proof that this joint RER cost criterion provides a bound on the conditional mean estimate (CME) performance of our MHMM filter, and this in turn establishes a strong theoretical basis connecting our filter design process to filter performance. Through this connection we can intelligently compare and optimise candidate filter models at the design stage, rather than having to resort to time consuming Monte Carlo simulations to gauge the relative performance of candidate designs. Moreover, the underlying entropy concepts are not constrained to any particular model type. This suggests that the RER concepts established here may be generalised to provide a useful design criterion for multiple model filtering approaches outside the class of HMM filters. In this thesis we also evaluate the performance of our proposed target detection algorithm under realistic operation conditions, and give consideration to the practical deployment of the detection algorithm onboard a UAV platform. Two fixed-wing UAVs were engaged to recreate various collision-course scenarios to capture highly realistic vision (from an onboard camera perspective) of the moments leading up to a collision. Based on this collected data, our proposed detection approach was able to detect targets out to distances ranging from about 400m to 900m. These distances, (with some assumptions about closing speeds and aircraft trajectories) translate to an advanced warning ahead of impact that approaches the 12.5 second response time recommended for human pilots. Furthermore, readily available graphic processing unit (GPU) based hardware is exploited for its parallel computing capabilities to demonstrate the practical feasibility of the proposed target detection algorithm. A prototype hardware-in- the-loop system has been found to be capable of achieving data processing rates sufficient for real-time operation. There is also scope for further improvement in performance through code optimisations. Overall, our proposed image-based target detection algorithm offers UAVs a cost-effective real-time target detection capability that is a step forward in ad- dressing the collision avoidance issue that is currently one of the most significant obstacles preventing widespread civilian applications of uninhabited aircraft. We also highlight that the algorithm development process has led to the discovery of a powerful multiple HMM filtering approach and a novel RER-based multiple filter design process. The utility of our multiple HMM filtering approach and RER concepts, however, extend beyond the target detection problem. This is demonstrated by our application of HMM filters and RER concepts to a heading angle estimation problem.

Virtual product placement as a new approach to measure effectiveness of placements

Product placement is a fast growing multi-billion dollar industry yet measures of its effectiveness, which influence the critical area of pricing, have been problematic. Past attempts to measure the effect of a placement, and therefore provide a basis for pricing of placements, have been confounded by the effect on consumers of multiple prior exposures of a brand name in all marketing communications. Virtual product placement offers certain advantages: as a tool to measure the effectiveness of product placements; assistance with the problem of lack of audience selectivity in traditional product placement; testing different audiences for brands and addressing a gap in the existing academic literature by focusing on the impact of product placement on recall and recognition of new brands.

A collaborative approach to home care delivery for older clients: Perspectives of home care providers

A collaborative approach to home care (HC) delivery for older clients has taken centre stage (Nies, 2006). In Finland, public home help and home health care services have been combined to form the home care unit, whose goal is to provide a collaborative approach to care delivery through cooperation and sharing of responsibilities. In this model, the general practitioner (GP), home care nurses (HCN) and home help workers (HHW) care for shared clients. GPs and HCNs provide health care, such as monitoring of clients’ health status, and HHWs assist with personal care tasks such as dressing, washing and meal preparation. As the needs of older clients are multiple, collaboration is needed as one professional group cannot take sole responsibility (Nies, 2006). This paper reports on a study undertaken to examine home care unit care providers’ perspectives of the collaborative approach to HC delivery for older clients.

Conflict resolution through negotiation in a railway open access market : a multi-agent system approach

Open access reforms to railway regulations allow multiple train operators to provide rail services on a common infrastructure. As railway operations are now independently managed by different stakeholders, conflicts in operations may arise, and there have been attempts to derive an effective access charge regime so that these conflicts may be resolved. One approach is by direct negotiation between the infrastructure manager and the train service providers. Despite the substantial literature on the topic, few consider the benefits of employing computer simulation as an evaluation tool for railway operational activities such as access pricing. This article proposes a multi-agent system (MAS) framework for the railway open market and demonstrates its feasibility by modelling the negotiation between an infrastructure provider and a train service operator. Empirical results show that the model is capable of resolving operational conflicts according to market demand.

Business Process Model Merging : An Approach to Business Process Consolidation

This paper addresses the problem of constructing consolidated business process models out of collections of process models that share common fragments. The paper considers the construction of unions of multiple models (called merged models) as well as intersections (called digests). Merged models are intended for analysts who wish to create a model that subsumes a collection of process models - typically representing variants of the same underlying process - with the aim of replacing the variants with the merged model. Digests, on the other hand, are intended for analysts who wish to identify the most recurring fragments across a collection of process models, so that they can focus their efforts on optimizing these fragments. The paper presents an algorithm for computing merged models and an algorithm for extracting digests from a merged model. The merging and digest extraction algorithms have been implemented and tested against collections of process models taken from multiple application domains. The tests show that the merging algorithm produces compact models and scales up to process models containing hundreds of nodes. Furthermore, a case study conducted in a large insurance company has demonstrated the usefulness of the merging and digest extraction operators in a practical setting.