Evaluation of green public road procurement in Australia : current practices and gaps to fill

Public road authorities have a key responsibility in driving initiatives for reducing greenhouse gas (GHG) emissions in the road construction project lifecycle. A coherent and efficient chain of procurement processes and methods is needed to convert green policies into tangible actions that capture the potential for GHG reduction. Yet, many infrastructure clients lack developed methodologies regarding green procurement practices. Designing more efficient solutions for green procurement requires an evaluation of the current initiatives and stages of development. A mapping of the current GHG reduction initiatives in Australian public road procurement is presented in this paper. The study includes the five largest Australian state road authorities, which cover 94% of the total 817,089 km of Australian main roads (not local) and account for 96% of the total A$13 billion annual major road construction and maintenance expenditure. The state road authorities’ green procurement processes and tools are evaluated based on interviews and a review of documents. Altogether 12 people, comprising 1-3 people of each organisation, participated in the interviews and provided documents. An evaluation matrix was developed for mapping the findings across the lifecycle of road construction project delivery. The results show how Australian state road authorities drive decisions with an impact on GHG emissions on the strategic planning phase, project development phase, and project implementation phase. The road authorities demonstrate varying levels of advancement in their green procurement methodologies. Six major gaps in the current green procurement processes are identified and, respectively, six recommendations for future research and development are suggested. The greatest gaps remain in the project development phase, which has a critical role in fixing the project (GHG reduction) goals, identifying risks and opportunities, and selecting the contractor to deliver the project. Specifically, the role of mass-haul optimisation as a part of GHG minimisation was reviewed, and mass-haul management was found to be an underutilised element with GHG reduction potential.

Empirical evaluation of alternative approaches in identifying crash hot spots

This study proposes a framework of a model-based hot spot identification method by applying full Bayes (FB) technique. In comparison with the state-of-the-art approach [i.e., empirical Bayes method (EB)], the advantage of the FB method is the capability to seamlessly integrate prior information and all available data into posterior distributions on which various ranking criteria could be based. With intersection crash data collected in Singapore, an empirical analysis was conducted to evaluate the following six approaches for hot spot identification: (a) naive ranking using raw crash data, (b) standard EB ranking, (c) FB ranking using a Poisson-gamma model, (d) FB ranking using a Poisson-lognormal model, (e) FB ranking using a hierarchical Poisson model, and (f) FB ranking using a hierarchical Poisson (AR-1) model. The results show that (a) when using the expected crash rate-related decision parameters, all model-based approaches perform significantly better in safety ranking than does the naive ranking method, and (b) the FB approach using hierarchical models significantly outperforms the standard EB approach in correctly identifying hazardous sites.

Evaluation of an intervention to promote protective infant feeding practices to prevent childhood obesity : outcomes of the NOURISH RCT at 14 months of age and 6 months post the first of two intervention modules

OBJECTIVE: To evaluate a universal obesity prevention intervention, which commenced at infant age 4-6 months, using outcome data assessed 6-months after completion of the first of two intervention modules and 9 months from baseline. DESIGN: Randomised controlled trial of a community-based early feeding intervention. SUBJECTS AND METHODS: 698 first-time mothers (mean age 30±5 years) with healthy term infants (51% male) aged 4.3±1.0 months at baseline. Mothers and infants were randomly allocated to self-directed access to usual care or to attend two group education modules, each delivered over three months, that provided anticipatory guidance on early feeding practices. Outcome data reported here were assessed at infant age 13.7±1.3 months. Anthropometrics were expressed as z-scores (WHO reference). Rapid weight gain was defined as change in weight-for-age z-score (WAZ) > +0.67. Maternal feeding practices were assessed via self-administered questionnaire. RESULTS: There were no differences according to group allocation on key maternal and infant characteristics. At follow up (n=598 [86%]) the intervention group infants had lower BMIZ (0.42±0.85 vs 0.23±0.93, p=0.009) and infants in the control group were more likely to show rapid weight gain from baseline to follow up (OR=1.5 CI95%1.1-2.1, p=0.014). Mothers in the control group were more likely to report using non- responsive feeding practices that fail to respond to infant satiety cues such as encouraging eating by using food as a reward (15% vs 4%, p=0.001) or using games ( 67% vs 29%, p<0.001). CONCLUSIONS: These results provide early evidence that anticipatory guidance targeting the ‘when, what and how’ of solid feeding can be effective in changing maternal feeding practices and, at least in the short term, reducing anthropometric indicators of childhood obesity risk. Analyses of outcomes at later ages are required to determine if these promising effects can be sustained.

Digital Storytelling within a Service-Learning Partnership: Technology as Product and Process for University Students and Culturally and Linguistically Diverse High School Youth

This chapter describes a university/high school partnership focused on digital storytelling. It also explains the multi-stage process used to establish this successful partnership and project. The authors discuss the central role that technology played in developing this university/high school partnership, a collaboration that extended the impact of a digital storytelling project to reach high school students, university students, educators, high school administrators, and the local community. Valuing a reflective process that can lead to the creation of a powerful final product, the authors describe the impact of digital storytelling on multiple stakeholders, including the 13 university students and 33 culturally and linguistically diverse high school youth who participated during the fall of 2009. In addition, the chapter includes reflections from university and high school student participants expressed during focus groups conducted throughout the project. While most participants had a positive experience with the project, complications with the technology component often caused frustrations and additional challenges. Goals for sharing this project are to critically evaluate digital storytelling, describe lessons learned, and recommend good practices for others working within a similar context or with parallel goals.

Termination And Discipline Procedures, Disputes Procedures & Work Evaluation

This series of paper examines industrial issues from a nonprofit management perspective.

Empirical IEEE 802.11p performance evaluation on test tracks

IEEE 802.11p is the new standard for inter-vehicular communications (IVC) using the 5.9 GHz frequency band; it is planned to be widely deployed to enable cooperative systems. 802.11p uses and performance have been studied theoretically and in simulations over the past years. Unfortunately, many of these results have not been confirmed by on-tracks experimentation. In this paper, we describe field trials of 802.11p technology with our test vehicles. Metrics such as maximum range, latency and frame loss are examined.

Anticipating user experience with a desired product : the AUX Framework

Positive user experience (UX) has become a key factor in designing interactive products. It acts as a differentiator which can determine a product’s success on the mature market. However, current UX frameworks and methods do not fully support the early stages of product design and development. During these phases, assessment of UX is challenging as no actual user-product interaction can be tested. This qualitative study investigated anticipated user experience (AUX) to address this problem. Using the co-discovery method, participants were asked to imagine a desired product, anticipate experiences with it, and discuss their views with another participant. Fourteen sub-categories emerged from the data, and relationships among them were defined through co-occurrence analysis. These data formed the basis of the AUX framework which consists of two networks which elucidate 1) how users imagine a desired product and 2) how they anticipate positive experiences with that product. Through this AUX framework, important factors in the process of imagining future products and experiences were learnt, including the way in which these factors interrelate. Focusing on and exploring each component of the two networks in the framework will allow designers to obtain a deeper understanding of the required pragmatic and hedonic qualities of product, intended uses of product, user characteristics, potential contexts of experience, and anticipated emotions embedded within the experience. This understanding, in turn, will help designers to better foresee users’ underlying needs and to focus on the most important aspects of their positive experience. Therefore, the use of the AUX framework in the early stages of product development will contribute to the design for pleasurable UX.

Non-destructive evaluation of articular cartilage defects using near-infrared (NIR) spectroscopy in osteoarthritic rat models and its direct relation to Mankin Score

Objective The aim of this study was to demonstrate the potential of near-infrared (NIR) spectroscopy for categorizing cartilage degeneration induced in animal models. Method Three models of osteoarthritic degeneration were induced in laboratory rats via one of the following methods: (i) menisectomy (MSX); (ii) anterior cruciate ligament transaction (ACLT); and (iii) intra-articular injection of mono-ido-acetete (1 mg) (MIA), in the right knee joint, with 12 rats per model group. After 8 weeks, the animals were sacrificed and tibial knee joints were collected. A custom-made nearinfrared (NIR) probe of diameter 5 mm was placed on the cartilage surface and spectral data were acquired from each specimen in the wavenumber range 4 000 – 12 500 cm−1. Following spectral data acquisition, the specimens were fixed and Safranin–O staining was performed to assess disease severity based on the Mankin scoring system. Using multivariate statistical analysis based on principal component analysis and partial least squares regression, the spectral data were then related to the Mankinscores of the samples tested. Results Mild to severe degenerative cartilage changes were observed in the subject animals. The ACLT models showed mild cartilage degeneration, MSX models moderate, and MIA severe cartilage degenerative changes both morphologically and histologically. Our result demonstrate that NIR spectroscopic information is capable of separating the cartilage samples into different groups relative to the severity of degeneration, with NIR correlating significantly with their Mankinscore (R2 = 88.85%). Conclusion We conclude that NIR is a viable tool for evaluating articularcartilage health and physical properties such as change in thickness with degeneration.

Learning domain-specific sentiment lexicons for predicting product sales

Generic sentiment lexicons have been widely used for sentiment analysis these days. However, manually constructing sentiment lexicons is very time-consuming and it may not be feasible for certain application domains where annotation expertise is not available. One contribution of this paper is the development of a statistical learning based computational method for the automatic construction of domain-specific sentiment lexicons to enhance cross-domain sentiment analysis. Our initial experiments show that the proposed methodology can automatically generate domain-specific sentiment lexicons which contribute to improve the effectiveness of opinion retrieval at the document level. Another contribution of our work is that we show the feasibility of applying the sentiment metric derived based on the automatically constructed sentiment lexicons to predict product sales of certain product categories. Our research contributes to the development of more effective sentiment analysis system to extract business intelligence from numerous opinionated expressions posted to the Web