Comparative analysis of traffic state estimation ─ Cumulative counts-based and trajectory-based methods

The Macroscopic Fundamental Diagram (MFD) relates space-mean density and flow. Since the MFD represents the area-wide network traffic performance, studies on perimeter control strategies and network-wide traffic state estimation utilising the MFD concept have been reported. Most previous works have utilised data from fixed sensors, such as inductive loops, to estimate the MFD, which can cause biased estimation in urban networks due to queue spillovers at intersections. To overcome the limitation, recent literature reports the use of trajectory data obtained from probe vehicles. However, these studies have been conducted using simulated datasets; limited works have discussed the limitations of real datasets and their impact on the variable estimation. This study compares two methods for estimating traffic state variables of signalised arterial sections: a method based on cumulative vehicle counts (CUPRITE), and one based on vehicles’ trajectory from taxi Global Positioning System (GPS) log. The comparisons reveal some characteristics of taxi trajectory data available in Brisbane, Australia. The current trajectory data have limitations in quantity (i.e., the penetration rate), due to which the traffic state variables tend to be underestimated. Nevertheless, the trajectory-based method successfully captures the features of traffic states, which suggests that the trajectories from taxis can be a good estimator for the network-wide traffic states.

Building capacity for cancer nursing research and evidence based practice: The cancer nursing professorial precinct initiative

The demand for cancer care is growing due to the increasing incidence of cancer and the improved effectiveness of cancer treatments. It is important that cancer nurses continue to improve patient outcomes through research and the use of evidence in practice development, education and policy. This paper describes a case report of a collaborative academic healthcare model that creates capacity for cancer nursing research and evidence-based practice. The Cancer Nursing Professorial Precinct is a strategic collaboration between the Royal Brisbane and Women’s Hospital (RBWH) and Queensland University of Technology (QUT), in Brisbane Australia. The outcomes of this initiative has been remarkable. The principles and strategies used in this initiative may be useful for cancer services in other countries.

VisArchive: a time and relevance based visual interface for searching, browsing, and exploring project archives

Background Project archives are becoming increasingly large and complex. On construction projects in particular, the increasing amount of information and the increasing complexity of its structure make searching and exploring information in the project archive challenging and time-consuming. Methods This research investigates a query-driven approach that represents new forms of contextual information to help users understand the set of documents resulting from queries of construction project archives. Specifically, this research extends query-driven interface research by representing three types of contextual information: (1) the temporal context is represented in the form of a timeline to show when each document was created; (2) the search-relevance context shows exactly which of the entered keywords matched each document; and (3) the usage context shows which project participants have accessed or modified a file. Results We implemented and tested these ideas within a prototype query-driven interface we call VisArchive. VisArchive employs a combination of multi-scale and multi-dimensional timelines, color-coded stacked bar charts, additional supporting visual cues and filters to support searching and exploring historical project archives. The timeline-based interface integrates three interactive timelines as focus + context visualizations. Conclusions The feasibility of using these visual design principles is tested in two types of project archives: searching construction project archives of an educational building project and tracking of software defects in the Mozilla Thunderbird project. These case studies demonstrate the applicability, usefulness and generality of the design principles implemented.

Early post-operative mortality after major lower limb amputation: A systematic review of population and regional based studies

OBJECTIVE: Lower limb amputation is often associated with a high risk of early post-operative mortality. Mortality rates are also increasingly being put forward as a possible benchmark for surgical performance. The primary aim of this systematic review is to investigate early post-operative mortality following a major lower limb amputation in population/regional based studies, and reported factors that might influence these mortality outcomes. METHODS: Embase, PubMed, Cinahl and Psycinfo were searched for publications in any language on 30 day or in hospital mortality after major lower limb amputation in population/regional based studies. PRISMA guidelines were followed. A self developed checklist was used to assess quality and susceptibility to bias. Summary data were extracted for the percentage of the population who died; pooling of quantitative results was not possible because of methodological differences between studies. RESULTS: Of the 9,082 publications identified, results were included from 21. The percentage of the population undergoing amputation who died within 30 days ranged from 7% to 22%, the in hospital equivalent was 4-20%. Transfemoral amputation and older age were found to have a higher proportion of early post-operative mortality, compared with transtibial and younger age, respectively. Other patient factors or surgical treatment choices related to increased early post-operative mortality varied between studies. CONCLUSIONS: Early post-operative mortality rates vary from 4% to 22%. There are very limited data presented for patient related factors (age, comorbidities) that influence mortality. Even less is known about factors related to surgical treatment choices, being limited to amputation level. More information is needed to allow comparison across studies or for any benchmarking of acceptable mortality rates. Agreement is needed on key factors to be reported.

Creative writing: Literature review and evidence-based research

There is growing evidence, especially in the USA and UK, that creative writing can form an important part of the recovery experience of people affected by severe mental illness. In this chapter, I consider theoretical models that explain how creative writing might contribute to recovery, and discuss the potential for creative writing in psychosocial rehabilitation. It is argued that the rehabilitation benefits of creative writing might be optimized through focus on process and technique in writing, rather than expression or content alone, and that consequently, the involvement of professional writers might be important. I will explore the recent history of theoretical frameworks and explanatory models that link creative writing and recovery, and examine such empirical evidence as is available on the contribution of creative writing to recovery from severe mental illness.

Cilengitide combined with cetuximab and platinum-based chemotherapy as first-line treatment in advanced non-small-cell lung cancer (NSCLC) patients: Results of an open-label, randomized, controlled phase II study (CERTO)

Background: This multicentre, open-label, randomized, controlled phase II study evaluated cilengitide in combination with cetuximab and platinum-based chemotherapy, compared with cetuximab and chemotherapy alone, as first-line treatment of patients with advanced non-small-cell lung cancer (NSCLC). Patients and methods: Patients were randomized 1:1:1 to receive cetuximab plus platinum-based chemotherapy alone (control), or combined with cilengitide 2000 mg 1×/week i.v. (CIL-once) or 2×/week i.v. (CIL-twice). A protocol amendment limited enrolment to patients with epidermal growth factor receptor (EGFR) histoscore ≥200 and closed the CIL-twice arm for practical feasibility issues. Primary end point was progression-free survival (PFS; independent read); secondary end points included overall survival (OS), safety, and biomarker analyses. A comparison between the CIL-once and control arms is reported, both for the total cohorts, as well as for patients with EGFR histoscore ≥200. Results: There were 85 patients in the CIL-once group and 84 in the control group. The PFS (independent read) was 6.2 versus 5.0 months for CIL-once versus control [hazard ratio (HR) 0.72; P = 0.085]; for patients with EGFR histoscore ≥200, PFS was 6.8 versus 5.6 months, respectively (HR 0.57; P = 0.0446). Median OS was 13.6 for CIL-once versus 9.7 months for control (HR 0.81; P = 0.265). In patients with EGFR ≥200, OS was 13.2 versus 11.8 months, respectively (HR 0.95; P = 0.855). No major differences in adverse events between CIL-once and control were reported; nausea (59% versus 56%, respectively) and neutropenia (54% versus 46%, respectively) were the most frequent. There was no increased incidence of thromboembolic events or haemorrhage in cilengitide-treated patients. αvβ3 and αvβ5 expression was neither a predictive nor a prognostic indicator. Conclusions: The addition of cilengitide to cetuximab/chemotherapy indicated potential clinical activity, with a trend for PFS difference in the independent-read analysis. However, the observed inconsistencies across end points suggest additional investigations are required to substantiate a potential role of other integrin inhibitors in NSCLC treatment.

The role of knowledge integration in innovation and capability development

Purpose This paper investigates the interrelationships between knowledge integration (KI), product innovation and capability development to enhance our understanding of how firms can develop capability at the firm level, which in turn enhances their performance. One of the critical underlying mechanisms for capability building identified in the literature is the role of knowledge integration, which operates within product innovation projects and contributes to dynamic capability development. Therefore, the main research question is “how does the integration of knowledge across product innovation projects lead to the development of capability?” Design/methodology/approach We adopted a case-based approach and investigated the case of a successful firm that was able to sustain its performance through a series of product innovation projects. In particular this research focused on the role of KI and firm-level capability development over the course of four projects, during which the firm successfully managed the transformation of its product base and renewal of its competitive advantage. For this purpose an in-depth case study of capability development was undertaken at the Iran Khodro Company (IKCO), the key player in the Iranian auto industry transformation. Originality/value This research revealed that along with changes at each level of product architecture “design knowledge” and “design capability” have been developed at the same level of product architecture, leading to capability development at that level. It can be argued that along the step by step maturation of radical innovation across the four case projects, architectural knowledge and capability have been developed at the case company, resulting in the gradual emergence of a modular product and capability architecture across different levels of product architecture. Such findings basically add to extensive emphasis in the literature on the interrelationship of the concept of modularity with knowledge management and capability development. Practical implications Findings of this study indicate that firms manage their knowledge in accordance with the level of specialization in knowledge and capability. Furthermore, firms design appropriate knowledge integration mechanisms within and among functions in order dynamically align knowledge processes at different levels of the product architecture. Accordingly, the outcomes of this study may guide practitioners in managing their knowledge processes, through dynamically employing knowledge integration modes step-by-step and from the part level to the architectural level of product architecture across a sequence of product innovation projects to encourage learning and radical innovation.

A comparison of methods for classifying clinical samples based on proteomics data : a case study for statistical and machine learning approaches

The discovery of protein variation is an important strategy in disease diagnosis within the biological sciences. The current benchmark for elucidating information from multiple biological variables is the so called “omics” disciplines of the biological sciences. Such variability is uncovered by implementation of multivariable data mining techniques which come under two primary categories, machine learning strategies and statistical based approaches. Typically proteomic studies can produce hundreds or thousands of variables, p, per observation, n, depending on the analytical platform or method employed to generate the data. Many classification methods are limited by an n≪p constraint, and as such, require pre-treatment to reduce the dimensionality prior to classification. Recently machine learning techniques have gained popularity in the field for their ability to successfully classify unknown samples. One limitation of such methods is the lack of a functional model allowing meaningful interpretation of results in terms of the features used for classification. This is a problem that might be solved using a statistical model-based approach where not only is the importance of the individual protein explicit, they are combined into a readily interpretable classification rule without relying on a black box approach. Here we incorporate statistical dimension reduction techniques Partial Least Squares (PLS) and Principal Components Analysis (PCA) followed by both statistical and machine learning classification methods, and compared them to a popular machine learning technique, Support Vector Machines (SVM). Both PLS and SVM demonstrate strong utility for proteomic classification problems.

Damage assessment in reinforced concrete flexural members using modal strain energy based method

Damage assessment (damage detection, localization and quantification) in structures and appropriate retrofitting will enable the safe and efficient function of the structures. In this context, many Vibration Based Damage Identification Techniques (VBDIT) have emerged with potential for accurate damage assessment. VBDITs have achieved significant research interest in recent years, mainly due to their non-destructive nature and ability to assess inaccessible and invisible damage locations. Damage Index (DI) methods are also vibration based, but they are not based on the structural model. DI methods are fast and inexpensive compared to the model-based methods and have the ability to automate the damage detection process. DI method analyses the change in vibration response of the structure between two states so that the damage can be identified. Extensive research has been carried out to apply the DI method to assess damage in steel structures. Comparatively, there has been very little research interest in the use of DI methods to assess damage in Reinforced Concrete (RC) structures due to the complexity of simulating the predominant damage type, the flexural crack. Flexural cracks in RC beams distribute non- linearly and propagate along all directions. Secondary cracks extend more rapidly along the longitudinal and transverse directions of a RC structure than propagation of existing cracks in the depth direction due to stress distribution caused by the tensile reinforcement. Simplified damage simulation techniques (such as reductions in the modulus or section depth or use of rotational spring elements) that have been extensively used with research on steel structures, cannot be applied to simulate flexural cracks in RC elements. This highlights a big gap in knowledge and as a consequence VBDITs have not been successfully applied to damage assessment in RC structures. This research will address the above gap in knowledge and will develop and apply a modal strain energy based DI method to assess damage in RC flexural members. Firstly, this research evaluated different damage simulation techniques and recommended an appropriate technique to simulate the post cracking behaviour of RC structures. The ABAQUS finite element package was used throughout the study with properly validated material models. The damaged plasticity model was recommended as the method which can correctly simulate the post cracking behaviour of RC structures and was used in the rest of this study. Four different forms of Modal Strain Energy based Damage Indices (MSEDIs) were proposed to improve the damage assessment capability by minimising the numbers and intensities of false alarms. The developed MSEDIs were then used to automate the damage detection process by incorporating programmable algorithms. The developed algorithms have the ability to identify common issues associated with the vibration properties such as mode shifting and phase change. To minimise the effect of noise on the DI calculation process, this research proposed a sequential order of curve fitting technique. Finally, a statistical based damage assessment scheme was proposed to enhance the reliability of the damage assessment results. The proposed techniques were applied to locate damage in RC beams and slabs on girder bridge model to demonstrate their accuracy and efficiency. The outcomes of this research will make a significant contribution to the technical knowledge of VBDIT and will enhance the accuracy of damage assessment in RC structures. The application of the research findings to RC flexural members will enable their safe and efficient performance.

Ultimate load behaviour of box-columns under combined loading of axial compression and torsion

This paper describes a study of the theoretical and experimental behaviour of box-columns of varying b/t ratios under loadings of axial compression and torsion and their combinations. Details of the testing rigs and the testing methods, the results obtained such as the load-deflection curves and the interaction diagrams, and experimental observations regarding the behaviour of box-models and the types of local plastic mechanisms associated with each type of loading are presented. A simplified rigid-plastic analysis is carried out to study the collapse behaviour of box-columns under these loadings, based on the observed plastic mechanisms, and the results are compared with those of experiments.

Vision-based simultaneous localization and mapping in changing outdoor environments

For robots operating in outdoor environments, a number of factors, including weather, time of day, rough terrain, high speeds, and hardware limitations, make performing vision-based simultaneous localization and mapping with current techniques infeasible due to factors such as image blur and/or underexposure, especially on smaller platforms and low-cost hardware. In this paper, we present novel visual place-recognition and odometry techniques that address the challenges posed by low lighting, perceptual change, and low-cost cameras. Our primary contribution is a novel two-step algorithm that combines fast low-resolution whole image matching with a higher-resolution patch-verification step, as well as image saliency methods that simultaneously improve performance and decrease computing time. The algorithms are demonstrated using consumer cameras mounted on a small vehicle in a mixed urban and vegetated environment and a car traversing highway and suburban streets, at different times of day and night and in various weather conditions. The algorithms achieve reliable mapping over the course of a day, both when incrementally incorporating new visual scenes from different times of day into an existing map, and when using a static map comprising visual scenes captured at only one point in time. Using the two-step place-recognition process, we demonstrate for the first time single-image, error-free place recognition at recall rates above 50% across a day-night dataset without prior training or utilization of image sequences. This place-recognition performance enables topologically correct mapping across day-night cycles.

Examining the critical interplay of knowledge acquisition and integration capabilities in service innovation-based competitive advantage in project oriented service firms

While past knowledge-based approaches to service innovation have emphasized the role of knowledge integration in the delivery of customer-focused solutions, these approaches do not adequately address the complexities inherent in knowledge acquisition and integration in project-oriented firms. Adopting a dynamic capability framework and building on knowledge-based approaches to innovation, the current study examines how the interplay of learning capabilities and knowledge integration capability impacts service innovation and sustained competitive advantage. This two-stage multi-sample study finds that entrepreneurial project-oriented service firms in their quest for competitive advantage through greater innovation invest in knowledge acquisition and integration capabilities. Implications for theory and practice are discussed and directions for future research provided.

The learning capability of construction organisations engaged in collaborative contracting - Literature Review [industry report]

Background Value for money (VfM) on collaborative construction projects is dependent on the learning capabilities of the organisations and people involved. Within the context of infrastructure delivery, there is little research about the impact of organisational learning capability on project value. The literature contains a multiplicity of often un-testable definitions about organisational learning abilities. This paper defines learning capability as a dynamic capability that participant organisations purposely develop to add value to collaborative projects. The paper reports on a literature review that proposes a framework that conceptualises learning capability to explore the topic. This work is the first phase of a large-scale national survey funded by the Alliancing Association of Australasia and the Australian Research Council. Methodology Desk-top review of leading journals in the areas of strategic management, strategic alliances and construction management, as well as recent government documents and industry guidelines, was undertaken to synthesise, conceptualise and operationalise the concept of learning capability. The study primarily draws on the theoretical perspectives of the resource-based view of the firm (e.g. Barney 1991; Wernerfelt 1984), absorptive capacity (e.g. Cohen and Levinthal 1990; Zahra and George 2002); and dynamic capabilities (e.g. Helfat et al. 2007; Teece et al. 1997; Winter 2003). Content analysis of the literature was undertaken to identify key learning routines. Content analysis is a commonly used methodology in the social sciences area. It provides rich data through the systematic and objective review of literature (Krippendorff 2004). NVivo 9, a qualitative data analysis software package, was used to assist in this process. Findings and Future Research The review process resulted in a framework for the conceptualisation of learning capability that shows three phases of learning: (1) exploratory learning, (2) transformative learning and (3) exploitative learning. These phases combine both internal and external learning routines to influence project performance outcomes and thus VfM delivered under collaborative contracts. Sitting within these phases are eight categories of learning capability comprising knowledge articulation, identification, acquisition, dissemination, codification, internationalisation, transformation and application. The learning routines sitting within each category will be disaggregated in future research as the basis for measureable items in a large-scale survey study. The survey will examine the extent to which various learning routines influence project outcomes, as well as the relationships between them. This will involve identifying the routines that exist within organisations in the construction industry, their resourcing and rate of renewal, together with the extent of use and perceived value within the organisation. The target population is currently estimated to be around 1,000 professionals with experience in relational contracting in Australia. This future research will build on the learning capability framework to provide data that will assist construction organisations seeking to maximise VfM on construction projects.

Structural damage alarming and localization of cable-supported bridges using multi-novelty indices: A feasibility study

This paper presents a feasibility study on structural damage alarming and localization of long-span cable-supported bridges using multi-novelty indices formulated by monitoring-derived modal parameters. The proposed method which requires neither structural model nor damage model is applicable to structures of arbitrary complexity. With the intention to enhance the tolerance to measurement noise/uncertainty and the sensitivity to structural damage, an improved novelty index is formulated in terms of auto-associative neural networks (ANNs) where the output vector is designated to differ from the input vector while the training of the ANNs needs only the measured modal properties of the intact structure under in-service conditions. After validating the enhanced capability of the improved novelty index for structural damage alarming over the commonly configured novelty index, the performance of the improved novelty index for damage occurrence detection of large-scale bridges is examined through numerical simulation studies of the suspension Tsing Ma Bridge (TMB) and the cable-stayed Ting Kau Bridge (TKB) incurred with different types of structural damage. Then the improved novelty index is extended to formulate multi-novelty indices in terms of the measured modal frequencies and incomplete modeshape components for damage region identification. The capability of the formulated multi-novelty indices for damage region identification is also examined through numerical simulations of the TMB and TKB.

Diffusion indices on magnetic resonance imaging and neuropsychological performance in amnestic mild cognitive impairment

Background: Magnetic resonance diffusion tensor imaging (DTI) shows promise in the early detection of microstructural pathophysiological changes in the brain. Objectives: To measure microstructural differences in the brains of participants with amnestic mild cognitive impairment (MCI) compared with an age-matched control group using an optimised DTI technique with fully automated image analysis tools and to investigate the correlation between diffusivity measurements and neuropsychological performance scores across groups. Methods: 34 participants (17 participants with MCI, 17 healthy elderly adults) underwent magnetic resonance imaging (MRI)-based DTI. To control for the effects of anatomical variation, diffusion images of all participants were registered to standard anatomical space. Significant statistical differences in diffusivity measurements between the two groups were determined on a pixel-by-pixel basis using gaussian random field theory. Results: Significantly raised mean diffusivity measurements (p<0.001) were observed in the left and right entorhinal cortices (BA28), posterior occipital-parietal cortex (BA18 and BA19), right parietal supramarginal gyrus (BA40) and right frontal precentral gyri (BA4 and BA6) in participants with MCI. With respect to fractional anisotropy, participants with MCI had significantly reduced measurements (p<0.001) in the limbic parahippocampal subgyral white matter, right thalamus and left posterior cingulate. Pearson's correlation coefficients calculated across all participants showed significant correlations between neuropsychological assessment scores and regional measurements of mean diffusivity and fractional anisotropy. Conclusions: DTI-based diffusivity measures may offer a sensitive method of detecting subtle microstructural brain changes associated with preclinical Alzheimer's disease.