HFL micro inverter with front-end diode clamped multi-level inverter and half-wave cycloconverter

A high-frequency-link (HFL) micro inverter with a front-end diode clamped multi-level inverter and a grid-connected half-wave cycloconverter is proposed. The diode clamped multi-level inverter with an auxiliary capacitor is used to generate high-frequency (HF) three level quasi square-wave output and it is fed into a series resonant tank to obtain high frequency continuous sinusoidal current. The obtained continuous sinusoidal current is modulated by using the grid-connected half-wave cycloconverter to obtain grid synchronized output current in phase with the grid voltage. The phase shift power modulation is used with auxiliary capacitor at the front-end multi-level inverter to have soft-switching. The phase shift between the HFL resonant current and half-wave cycloconverter input voltage is modulated to obtain grid synchronized output current.

Space vector modulated cascade multi-level inverter for PMSG wind generation systems

Modulation and control of a cascade multilevel inverter, which has a high potential in future wind generation applications, are presented. The inverter is a combination of a high power, three level “bulk inverter” and a low power “conditioning inverter”. To minimize switching losses, the bulk inverter operates at a low frequency producing square wave outputs while high frequency conditioning inverter is used to suppress harmonic content produced by the bulk inverter output. This paper proposes an improved Space Vector Modulation (SVM) algorithm and a neutral point potential balancing technique for the inverter. Furthermore, a maximum power tracking controller for the Permanent Magnet Synchronous Generator (PMSG) is described in detail. The proposed SVM technique eliminates most of the computational burdens on the digital controller and renders a greater controllability under varying DC-link voltage conditions. The DC-link capacitor voltage balancing of both bulk and conditioning inverters is carried out using Redundant State Selection (RSS) method and is explained in detail. Experimental results are presented to verify the proposed modulation and control techniques.

Transmedia Storytelling: From Interviewing to Multi-Platform

There is nothing new under the sun – so the saying goes, and in a digital age of recording oral histories, this holds true. Despite advances and innovations across the board in information and communication technology in the field of oral history it is essentially only the devices we record on that have changed. However, what has emerged is a plethora of ways that oral history interviews can be used to produce multimedia, or transmedia storytelling outputs- for exhibitions in public institutions, schools and by communities to engage interested groups, and in families and by individuals wanting to play with new ways of telling their family stories and histories. In 2010, QUT’s Creative Industries introduced a postgraduate unit called Transmedia Storytelling: From Interviewing to Multi-Platform, which was the first postgraduate course of its kind in Australia. Based in a Creative Writing discipline, but open to all coursework Masters, PhD, Research Masters and Doctorate of Creative Industries students, this unit introduces students to the theory and practice of semi-structured interviewing techniques, oral history conventions and applications, and the art of storytelling across various platforms.

STAC living. Promoting efficient multi-residential buildings in subtropical climates.

STAC is a mobile application (app) designed to promote the benefits of climate-aware urban development in Subtropical environments. Although, STAC is primarily tool for understanding climate efficient buildings in Brisbane, Australia, it also demonstrates how other exemplary buildings operate in other subtropical cities of the world. The STAC research and development team applied research undertaken by the Centre for Subtropical Design (Brisbane) to profile buildings past and present that have contributed to the creation of a vibrant society, a viable economy, a healthy environment, and an authentic sense of place. In collaboration with researchers from the field of Interaction Design, this knowledge and data was collated, processed and curated for presentation via a custom mobile application designed to distribute this important research for review and consideration on-location in local settings and for comparison across all other global subtropical regions and projects identified by this research. This collaboration adopted a Design-based Research (DBR) Methodology guided by the main tenets of research and design iteration and cross-discipline collaboration in real-world settings, resulting in the formulation of contextually-sensitive design principles, theories, and tools for design intervention. Combined with significant context review of available technology and data and subsequent case study analysis of exemplar design applications.

Value management in design planning : a systems-based framework for multi-disciplinary team involvement

This thesis investigates the Value Management processes used by construction project clients that effects project team involvement in VM workshops during the design stage of the projects. It is based on five case studies of the Malaysian international airport construction project packages. The focus of the research is on how issues related to infrastructure design that can improve construction processes on-site are being identified, analysed and resolved through multi-disciplinary team participation. The degrees of interaction, diversity of visualisation aids, certain cultural dimensions and the system thinking approach are found to have significant influence in maximizing participation among project team members during the entire VM workshop process.

Integrated chronic disease management in primary-secondary care : a systematic literature review of the evidence

Introduction: Diabetes has traditionally been managed as a single chronic disease state, but it exists with co-morbidities such as depression and metabolic syndrome. Treatment is multifaceted, requiring both primary and secondary care, however, the delivery of diabetes care is often fragmented. Integrated chronic disease management is a growing model of interest, and is underpinned by the chronic care model (CCM), devised as a guide for primary care management of patients with chronic conditions. The model identifies six key elements for effective care, and has shown promise in improving the management of diabetes. Aim: To find empirical evidence of integrated care interventions targeted at co-morbidities including diabetes, across primary/secondary care. Method: A systematic review of peer reviewed literature from PubMed, CINAHL, Embase, Cochrane Library and Joanna Briggs was performed. Studies were reviewed according to inclusion criteria- studies published in English, between 2004-2014, empirical studies, studies with evidence of primary/secondary implementation, and those dealing with chronic co-morbid disease states. Results: 51 studies met the inclusion criteria. Included studies were mostly from the US (38), with five from Australia, UK (2), Canada (2), Netherlands (1), Norway (1), Ireland (1), and one multi-country study. It was found that all interventions adopted at least one (average 3-4) of the chronic care model, with the majority implementing delivery system redesign activities within the primary care practice/s. We found evidence of interventions which significantly reduced emergency department and hospital admissions, improved processes of care, patient health outcomes such as HbA1c, improved patient satisfaction, and reduced costs. Conclusion/Implications for practice: Diabetes exists as a co-morbid disease, requiring both primary and secondary care. We found that integrated care interventions adopting elements of the chronic care model positively impacted on patient outcomes, service utilisation, as well as costs. This review has highlighted that it may not be necessary to adopt all CCM elements to improve clinical outcomes, patient satisfaction and costs.

Multiple criteria-based real-time decision making by autonomous city vehicles

This paper addresses the topic of real-time decision making for autonomous city vehicles, i.e. the autonomous vehicles’ ability to make appropriate driving decisions in city road traffic situations. After decomposing the problem into two consecutive decision making stages, and giving a short overview about previous work, the paper explains how Multiple Criteria Decision Making (MCDM) can be used in the process of selecting the most appropriate driving maneuver.

Utilizing voting systems for ranking user tweets

Twitter is a very popular social network website that allows users to publish short posts called tweets. Users in Twitter can follow other users, called followees. A user can see the posts of his followees on his Twitter profile home page. An information overload problem arose, with the increase of the number of followees, related to the number of tweets available in the user page. Twitter, similar to other social network websites, attempts to elevate the tweets the user is expected to be interested in to increase overall user engagement. However, Twitter still uses the chronological order to rank the tweets. The tweets ranking problem was addressed in many current researches. A sub-problem of this problem is to rank the tweets for a single followee. In this paper we represent the tweets using several features and then we propose to use a weighted version of the famous voting system Borda-Count (BC) to combine several ranked lists into one. A gradient descent method and collaborative filtering method are employed to learn the optimal weights. We also employ the Baldwin voting system for blending features (or predictors). Finally we use the greedy feature selection algorithm to select the best combination of features to ensure the best results.

Building resilient communities : creating effective multi-channel communication during disaster response and recovery

Aim: To examine evidence-based strategies that motivate appropriate action and increase informed decision-making during the response and recovery phases of disasters.

Expanded review criteria : the case of nonpharmacological interventions in dementia

This paper challenges the assumptions underlying many reviews and offers alternative criteria for examining evidence for nonpharmacological interventions. We evaluated 27 reviews examining interventions for persons with dementia as they relate to the issues of selection based on randomized controlled trial (RCT) design. Reviews were described by type of intervention, level of cognitive function, and criteria for inclusion. Of the 27 reviews, 46% required RCTs for inclusion and most had stringent inclusion criteria. This resulted in poor utilization of the literature and low ecological validity. Eliminating most of the available data poses a critical problem to clinical and research development. Studies meeting strict methodological criteria may not generalize to the greater population or may exclude sub-populations and interventions. Limitations of double-blind RCTs and potential design solutions are set forth based on appropriate populations, problems, interventions, and settings characteristics.

Brief interventions to reduce ecstasy use : a multi-site randomized controlled trial

Studies examining the ability of motivational enhancement therapy (MET) to augment education provision among ecstasy users have produced mixed results and none have examined whether treatment fidelity was related to ecstasy use outcomes. The primary objectives of this multi-site, parallel, two-group randomized controlled trial were to determine if a single-session of MET could instill greater commitment to change and reduce ecstasy use and related problems more so than an education-only intervention and whether MET sessions delivered with higher treatment fidelity are associated with better outcomes. The secondary objective was to assess participants’ satisfaction with their assigned interventions. Participants (N = 174; Mage = 23.62) at two Australian universities were allocated randomly to receive a 15-minute educational session on ecstasy use (n = 85) or a 50-minute session of MET that included an educational component (n = 89). Primary outcomes were assessed at baseline, and then at 4-, 16-, and 24-weeks post-baseline, while the secondary outcome measure was assessed 4-weeks post-baseline by researchers blind to treatment allocation. Overall, the treatment fidelity was acceptable to good in the MET condition. There were no statistical differences at follow-up between the groups on the primary outcomes of ecstasy use, ecstasy-related problems, and commitment to change. Both interventions groups reported a 50% reduction in their ecstasy use and a 20% reduction in the severity of their ecstasy-related problems at the 24-week follow up. Commitment to change slightly improved for both groups (9% - 17%). Despite the lack of between-group statistical differences on primary outcomes, participants who received a single session of MET were slightly more satisfied with their intervention than those who received education only. MI fidelity was not associated with ecstasy use outcomes. Given these findings, future research should focus on examining mechanisms of change. Such work may suggest new methods for enhancing outcomes.

An efficient and robust system for multi-person event detection in real world indoor surveillance scenes

Due to the popularity of security cameras in public places, it is of interest to design an intelligent system that can efficiently detect events automatically. This paper proposes a novel algorithm for multi-person event detection. To ensure greater than real-time performance, features are extracted directly from compressed MPEG video. A novel histogram-based feature descriptor that captures the angles between extracted particle trajectories is proposed, which allows us to capture motion patterns of multi-person events in the video. To alleviate the need for fine-grained annotation, we propose the use of Labelled Latent Dirichlet Allocation, a “weakly supervised” method that allows the use of coarse temporal annotations which are much simpler to obtain. This novel system is able to run at approximately ten times real-time, while preserving state-of-theart detection performance for multi-person events on a 100-hour real-world surveillance dataset (TRECVid SED).

Understanding causal relationships between the built and social environment, health and associated behaviours and risk factors

Introduction: Built environment interventions designed to reduce non-communicable diseases and health inequity, complement urban planning agendas focused on creating more ‘liveable’, compact, pedestrian-friendly, less automobile dependent and more socially inclusive cities.However, what constitutes a ‘liveable’ community is not well defined. Moreover, there appears to be a gap between the concept and delivery of ‘liveable’ communities. The recently funded NHMRC Centre of Research Excellence (CRE) in Healthy Liveable Communities established in early 2014, has defined ‘liveability’ from a social determinants of health perspective. Using purpose-designed multilevel longitudinal data sets, it addresses five themes that address key evidence-base gaps for building healthy and liveable communities. The CRE in Healthy Liveable Communities seeks to generate and exchange new knowledge about: 1) measurement of policy-relevant built environment features associated with leading non-communicable disease risk factors (physical activity, obesity) and health outcomes (cardiovascular disease, diabetes) and mental health; 2) causal relationships and thresholds for built environment interventions using data from longitudinal studies and natural experiments; 3) thresholds for built environment interventions; 4) economic benefits of built environment interventions designed to influence health and wellbeing outcomes; and 5) factors, tools, and interventions that facilitate the translation of research into policy and practice. This evidence is critical to inform future policy and practice in health, land use, and transport planning. Moreover, to ensure policy-relevance and facilitate research translation, the CRE in Healthy Liveable Communities builds upon ongoing, and has established new, multi-sector collaborations with national and state policy-makers and practitioners. The symposium will commence with a brief introduction to embed the research within an Australian health and urban planning context, as well as providing an overall outline of the CRE in Healthy Liveable Communities, its structure and team. Next, an overview of the five research themes will be presented. Following these presentations, the Discussant will consider the implications of the research and opportunities for translation and knowledge exchange. Theme 2 will establish whether and to what extent the neighbourhood environment (built and social) is causally related to physical and mental health and associated behaviours and risk factors. In particular, research conducted as part of this theme will use data from large-scale, longitudinal-multilevel studies (HABITAT, RESIDE, AusDiab) to examine relationships that meet causality criteria via statistical methods such as longitudinal mixed-effect and fixed-effect models, multilevel and structural equation models; analyse data on residential preferences to investigate confounding due to neighbourhood self-selection and to use measurement and analysis tools such as propensity score matching and ‘within-person’ change modelling to address confounding; analyse data about individual-level factors that might confound, mediate or modify relationships between the neighbourhood environment and health and well-being (e.g., psychosocial factors, knowledge, perceptions, attitudes, functional status), and; analyse data on both objective neighbourhood characteristics and residents’ perceptions of these objective features to more accurately assess the relative contribution of objective and perceptual factors to outcomes such as health and well-being, physical activity, active transport, obesity, and sedentary behaviour. At the completion of the Theme 2, we will have demonstrated and applied statistical methods appropriate for determining causality and generated evidence about causal relationships between the neighbourhood environment, health, and related outcomes. This will provide planners and policy makers with a more robust (valid and reliable) basis on which to design healthy communities.

A tag-based personalized item recommendation system using tensor modeling and topic model approaches

This research falls in the area of enhancing the quality of tag-based item recommendation systems. It aims to achieve this by employing a multi-dimensional user profile approach and by analyzing the semantic aspects of tags. Tag-based recommender systems have two characteristics that need to be carefully studied in order to build a reliable system. Firstly, the multi-dimensional correlation, called as tag assignment , should be appropriately modelled in order to create the user profiles [1]. Secondly, the semantics behind the tags should be considered properly as the flexibility with their design can cause semantic problems such as synonymy and polysemy [2]. This research proposes to address these two challenges for building a tag-based item recommendation system by employing tensor modeling as the multi-dimensional user profile approach, and the topic model as the semantic analysis approach. The first objective is to optimize the tensor model reconstruction and to improve the model performance in generating quality rec-ommendation. A novel Tensor-based Recommendation using Probabilistic Ranking (TRPR) method [3] has been developed. Results show this method to be scalable for large datasets and outperforming the benchmarking methods in terms of accuracy. The memory efficient loop implements the n-mode block-striped (matrix) product for tensor reconstruction as an approximation of the initial tensor. The probabilistic ranking calculates the probabil-ity of users to select candidate items using their tag preference list based on the entries generated from the reconstructed tensor. The second objective is to analyse the tag semantics and utilize the outcome in building the tensor model. This research proposes to investigate the problem using topic model approach to keep the tags nature as the “social vocabulary” [4]. For the tag assignment data, topics can be generated from the occurrences of tags given for an item. However there is only limited amount of tags availa-ble to represent items as collection of topics, since an item might have only been tagged by using several tags. Consequently, the generated topics might not able to represent the items appropriately. Furthermore, given that each tag can belong to any topics with various probability scores, the occurrence of tags cannot simply be mapped by the topics to build the tensor model. A standard weighting technique will not appropriately calculate the value of tagging activity since it will define the context of an item using a tag instead of a topic.

Supervisor conflict management, justice and strain : multi-level relationships

Purpose The purpose of this paper is to test a multilevel model of the main and mediating effects of supervisor conflict management style (SCMS) climate and procedural justice (PJ) climate on employee strain. It is hypothesized that workgroup-level climate induced by SCMS can fall into four types: collaborative climate, yielding climate, forcing climate, or avoiding climate; that these group-level perceptions will have differential effects on employee strain, and will be mediated by PJ climate. Design/methodology/approach Multilevel SEM was used to analyze data from 420 employees nested in 61 workgroups. Findings Workgroups that perceived high supervisor collaborating climate reported lower sleep disturbance, job dissatisfaction, and action-taking cognitions. Workgroups that perceived high supervisor yielding climate and high supervisor forcing climate reported higher anxiety/depression, sleep disturbance, job dissatisfaction, and action-taking cognitions. Results supported a PJ climate mediation model when supervisors’ behavior was reported to be collaborative and yielding. Research limitations/implications The cross-sectional research design places limitations on conclusions about causality; thus, longitudinal studies are recommended. Practical implications Supervisor behavior in response to conflict may have far-reaching effects beyond those who are a party to the conflict. The more visible use of supervisor collaborative CMS may be beneficial. Social implications The economic costs associated with workplace conflict may be reduced through the application of these findings. Originality/value By applying multilevel theory and analysis, we extend workplace conflict theory.