New criteria for evaluating methods of identifying hot spots

Identification of hot spots, also known as the sites with promise, black spots, accident-prone locations, or priority investigation locations, is an important and routine activity for improving the overall safety of roadway networks. Extensive literature focuses on methods for hot spot identification (HSID). A subset of this considerable literature is dedicated to conducting performance assessments of various HSID methods. A central issue in comparing HSID methods is the development and selection of quantitative and qualitative performance measures or criteria. The authors contend that currently employed HSID assessment criteria—namely false positives and false negatives—are necessary but not sufficient, and additional criteria are needed to exploit the ordinal nature of site ranking data. With the intent to equip road safety professionals and researchers with more useful tools to compare the performances of various HSID methods and to improve the level of HSID assessments, this paper proposes four quantitative HSID evaluation tests that are, to the authors’ knowledge, new and unique. These tests evaluate different aspects of HSID method performance, including reliability of results, ranking consistency, and false identification consistency and reliability. It is intended that road safety professionals apply these different evaluation tests in addition to existing tests to compare the performances of various HSID methods, and then select the most appropriate HSID method to screen road networks to identify sites that require further analysis. This work demonstrates four new criteria using 3 years of Arizona road section accident data and four commonly applied HSID methods [accident frequency ranking, accident rate ranking, accident reduction potential, and empirical Bayes (EB)]. The EB HSID method reveals itself as the superior method in most of the evaluation tests. In contrast, identifying hot spots using accident rate rankings performs the least well among the tests. The accident frequency and accident reduction potential methods perform similarly, with slight differences explained. The authors believe that the four new evaluation tests offer insight into HSID performance heretofore unavailable to analysts and researchers.

Multi-criteria ranking and receptor modelling of airborne fine particles at three sites in the Pearl River Delta region of China

The multi-criteria decision making methods, Preference METHods for Enrichment Evaluation (PROMETHEE) and Graphical Analysis for Interactive Assistance (GAIA), and the two-way Positive Matrix Factorization (PMF) receptor model were applied to airborne fine particle compositional data collected at three sites in Hong Kong during two monitoring campaigns held from November 2000 to October 2001 and November 2004 to October 2005. PROMETHEE/GAIA indicated that the three sites were worse during the later monitoring campaign, and that the order of the air quality at the sites during each campaign was: rural site > urban site > roadside site. The PMF analysis on the other hand, identified 6 common sources at all of the sites (diesel vehicle, fresh sea salt, secondary sulphate, soil, aged sea salt and oil combustion) which accounted for approximately 68.8 ± 8.7% of the fine particle mass at the sites. In addition, road dust, gasoline vehicle, biomass burning, secondary nitrate, and metal processing were identified at some of the sites. Secondary sulphate was found to be the highest contributor to the fine particle mass at the rural and urban sites with vehicle emission as a high contributor to the roadside site. The PMF results are broadly similar to those obtained in a previous analysis by PCA/APCS. However, the PMF analysis resolved more factors at each site than the PCA/APCS. In addition, the study demonstrated that combined results from multi-criteria decision making analysis and receptor modelling can provide more detailed information that can be used to formulate the scientific basis for mitigating air pollution in the region.

Skills for creative industries graduate success

Purpose: Although there is increasing evidence that the creative industries are essential to national economic growth as well as social and cultural well-being, creative graduates often find it difficult to become established professionally. This study investigates the value of career management competence and intrinsic career motivations (as elements of ‘protean career orientation’) in predicting positive graduate outcomes. ----- ----- Design/methodology: Self-report surveys were administered to 208 creative industries graduates from two Australian universities at two points in time: at course completion, and one year later. ----- ----- Findings: Individual career management competence and intrinsic work motivations, measured at course completion, were significant predictors of early career success, using both subjective and objective measures, measured one year later. ----- ----- Practical implications: This study suggests that an emphasis on student development beyond the traditional ‘key’ employability skills may well be worthwhile. The article also suggests a broad learning and teaching approach by which universities can encourage the development of student career identity, and thus engender student intrinsic career motivations and career self management skills and behaviours. ----- ----- Originality/value: This is one of the first studies to demonstrate empirically a link between a particular set of skills and graduate outcomes. In addition, it provides insights into the role of student career motivations in positive transitions to the world of work in the creative industries.

Multi-criteria assessment of innovative technology in the beef industry

In Australia rural research and development corporations and companies expended over $AUS500 million on agricultural research and development. A substantial proportion of this is invested in R&D in the beef industry. The Australian beef industry exports almost $AUS5billionof product annually and invest heavily in new product development to improve the beef quality and improve production efficiency. Review points are critical for effective new product development, yet many research and development bodies, particularly publicly funded ones, appear to ignore the importance of assessing products prior to their release. Significant sums of money are invested in developing technological innovations that have low levels and rates of adoption. The adoption rates could be improved if the developers were more focused on technology uptake and less focused on proving their technologies can be applied in practice. Several approaches have been put forward in an effort to improve rates of adoption into operational settings. This paper presents a study of key technological innovations in the Australian beef industry to assess the use of multiple criteria in evaluating the potential uptake of new technologies. Findings indicate that using multiple criteria to evaluate innovations before commercializing a technology enables researchers to better understand the issues that may inhibit adoption.

Critical success factors of project management for Brunei construction projects : improving project performance

The problem of delays in the construction industry is a global phenomenon and the construction industry in Brunei Darussalam is no exception. The goal of all parties involved in construction projects – owners, contractors, engineers and consultants in either the public or private sector is to successfully complete the project on schedule, within planned budget, with the highest quality and in the safest manner. Construction projects are frequently influenced by either success factors that help project parties reach their goal as planned, or delay factors that stifle or postpone project completion. The purpose of this research is to identify success and delay factors which can help project parties reach their intended goals with greater efficiency. This research extracted seven of the most important success factors according to the literature and seven of the most important delay factors identified by project parties, and then examined correlations between them to determine which were the most influential in preventing project delays. This research uses a comprehensive literature review to design and conduct a survey to investigate success and delay factors and then obtain a consensus of expert opinion using the Delphi methodology to rank the most needed critical success factors for Brunei construction projects. A specific survey was distributed to owners, contractors and engineers to examine the most critical delay factors. A general survey was distributed to examine the correlation between the identified delay factors and the seven most important critical success factors selected. A consensus of expert opinion using the Delphi methodology was used to rank the most needed critical success factors for Brunei building construction. Data was collected and evaluated by statistical methods to identify the most significant causes of delay and to measure the strength and direction of the relationship between critical success factors and delay factors in order to examine project parties’ evaluation of projects’ critical success and delay factors, and to evaluate the influence of critical success factors on critical delay factors. A relative importance index has been used to determine the relative importance of the various causes of delays. A one and two-way analysis of variance (ANOVA) has been used to examine how the group or groups evaluated the influence of the critical success factors in avoiding or preventing each of the delay factors, and which success factors were perceived as most influential in avoiding or preventing critical delay factors. Finally the Delphi method, using consensus from an expert panel, was employed to identify the seven most critical success factors used to avoid the delay factors, and thereby improve project performance.

Primary students' success on the structured number line

Number lines are part of our everyday life (e.g., thermometers, kitchen scales) and are frequently used in primary mathematics as instructional aids, in texts and for assessment purposes on mathematics tests. There are two major types of number lines; structured number lines, which are the focus of this paper, and empty number lines. Structured number lines represent mathematical information by the placement of marks on a horizontal or vertical line which has been marked into proportional segments (Figure 1). Empty number lines are blank lines which students can use for calculations (Figure 2) and are not discussed further here (see van den Heuvel-Panhuizen, 2008, on the role of empty number lines). In this article, we will focus on how students’ knowledge of the structured number line develops and how they become successful users of this mathematical tool.

Knowledge management competence for Enterprise System success

This study conceptualizes, operationalises and validates the concept of Knowledge Management Competence as a four-phase multidimensional formative index. Employing survey data from 310 respondents representing 27 organizations using the SAP Enterprise System Financial module, the study results demonstrate a large, significant, positive relationship between Knowledge Management Competence and Enterprise Systems Success (ES-success, as conceived by Gable Sedera and Chan (2008)); suggesting important implications for practice. Strong evidence of the validity of Knowledge Management Competence as conceived and operationalised, too suggests potential from future research evaluating its relationships with possible antecedents and consequences.

Visual field size criteria for mobility rehabilitation referral

Purpose. To investigate evidence-based visual field size criteria for referral of low-vision (LV) patients for mobility rehabilitation. Methods. One hundred and nine participants with LV and 41 age-matched participants with normal sight (NS) were recruited. The LV group was heterogeneous with diverse causes of visual impairment. We measured binocular kinetic visual fields with the Humphrey Field Analyzer and mobility performance on an obstacle-rich, indoor course. Mobility was assessed as percent preferred walking speed (PPWS) and number of obstacle-contact errors. The weighted kappa coefficient of association (κr) was used to discriminate LV participants with both unsafe and inefficient mobility from those with adequate mobility on the basis of their visual field size for the full sample and for subgroups according to type of visual field loss and whether or not the participants had previously received orientation and mobility training. Results. LV participants with both PPWS <38% and errors >6 on our course were classified as having inadequate (inefficient and unsafe) mobility compared with NS participants. Mobility appeared to be first compromised when the visual field was less than about 1.2 steradians (sr; solid angle of a circular visual field of about 70° diameter). Visual fields <0.23 and 0.63 sr (31 to 52° diameter) discriminated patients with at-risk mobility for the full sample and across the two subgroups. A visual field of 0.05 sr (15° diameter) discriminated those with critical mobility. Conclusions. Our study suggests that: practitioners should be alert to potential mobility difficulties when the visual field is less than about 1.2 sr (70° diameter); assessment for mobility rehabilitation may be warranted when the visual field is constricted to about 0.23 to 0.63 sr (31 to 52° diameter) depending on the nature of their visual field loss and previous history (at risk); and mobility rehabilitation should be conducted before the visual field is constricted to 0.05 sr (15° diameter; critical).

Improving the performance of model-order selection criteria by partial-model selection search

The traditional searching method for model-order selection in linear regression is a nested full-parameters-set searching procedure over the desired orders, which we call full-model order selection. On the other hand, a method for model-selection searches for the best sub-model within each order. In this paper, we propose using the model-selection searching method for model-order selection, which we call partial-model order selection. We show by simulations that the proposed searching method gives better accuracies than the traditional one, especially for low signal-to-noise ratios over a wide range of model-order selection criteria (both information theoretic based and bootstrap-based). Also, we show that for some models the performance of the bootstrap-based criterion improves significantly by using the proposed partial-model selection searching method. Index Terms— Model order estimation, model selection, information theoretic criteria, bootstrap 1. INTRODUCTION Several model-order selection criteria can be applied to find the optimal order. Some of the more commonly used information theoretic-based procedures include Akaike’s information criterion (AIC) [1], corrected Akaike (AICc) [2], minimum description length (MDL) [3], normalized maximum likelihood (NML) [4], Hannan-Quinn criterion (HQC) [5], conditional model-order estimation (CME) [6], and the efficient detection criterion (EDC) [7]. From a practical point of view, it is difficult to decide which model order selection criterion to use. Many of them perform reasonably well when the signal-to-noise ratio (SNR) is high. The discrepancies in their performance, however, become more evident when the SNR is low. In those situations, the performance of the given technique is not only determined by the model structure (say a polynomial trend versus a Fourier series) but, more importantly, by the relative values of the parameters within the model. This makes the comparison between the model-order selection algorithms difficult as within the same model with a given order one could find an example for which one of the methods performs favourably well or fails [6, 8]. Our aim is to improve the performance of the model order selection criteria in cases where the SNR is low by considering a model-selection searching procedure that takes into account not only the full-model order search but also a partial model order search within the given model order. Understandably, the improvement in the performance of the model order estimation is at the expense of additional computational complexity.

Good practice for enhancing the engagement and success of commencing students

There is widespread recognition that higher education institutions (HEIs) must actively support commencing students to ensure equity in access to the opportunities afforded by higher education. This role is particularly critical for students who because of educational, cultural or financial disadvantage or because they are members of social groups currently under-represented in higher education, may require additional transitional support to “level the playing field.” The challenge faced by HEIs is to provide this “support” in a way that is integrated into regular teaching and learning practices and reaches all commencing students. The Student Success Program (SSP) is an intervention in operation at the Queensland University of Technology (QUT) designed to identify and support those students deemed to be at risk of disengaging from their learning and their institution. Two sets of evidence of the impact of the SSP are presented: First, its expansion (a) from a one-faculty pilot project (Nelson, Duncan & Clarke, 2009) to all faculties and (b) into a variety of applications mirroring the student life cycle; and second, an evaluation of the impact of the SSP on students exposed to it. The outcomes suggest that: the SSP is an example of good practice that can be successfully applied to a variety of learning contexts and student enrolment situations; and the impact of the intervention on student persistence is sustained for at least 12 months and positively influences student retention. It is claimed that the good practice evidenced by the SSP is dependent on its integration into the broader First Year Experience Program at QUT as an example of transition pedagogy in action.

Alcohol expectancy changes over a 12-week cognitive–behavioral therapy program are predictive of treatment success

This study examines if outcome expectancies (perceived consequences of engaging in certain behavior) and self- efficacy expectancies (confidence in personal capacity to regulate behavior) contribute to treatment outcome for alcohol dependence. Few clinical studies have examined these constructs. The Drinking Expectancy Profile (DEP), a psychometric measure of alcohol expectancy and drinking refusal selfefficacy, was administered to 298 alcohol-dependent patients (207 males) at assessment and on completion of a 12-week cognitive–behavioral therapy alcohol abstinence program. Baseline measures of expectancy and self-efficacy were not strong predictors of outcome. However, for the 164 patients who completed treatment, all alcohol expectancy and self-efficacy factors of the DEP showed change over time. The DEP scores approximated community norms at the end of treatment. Discriminant analysis indicated that change in social pressure drinking refusal self-efficacy, sexual enhancement expectancies, and assertion expectancies successfully discriminated those who successfully completed treatment from those who did not. Future research should examine the basis of expectancies related to social functioning as a possible mechanism of treatment response and a means to enhance treatment outcome.

Theoretical model of user acceptance : in the view of measuring success in web personalization

This paper attempts to develop a theoretical acceptance model for measuring Web personalization success. Key factors impacting Web personalization acceptance are identified from a detailed literature review. The final model is then cast in a structural equation modeling (SEM) framework comprising nineteen manifest variables, which are grouped into three focal behaviors of Web users. These variables could provide a framework for better understanding of numerous factors that contribute to the success measures of Web personalization technology. Especially, those concerning the quality of personalized features and how personalized information through personalized Website can be delivered to the user. The interrelationship between success constructs is also explained. Empirical validations of this theoretical model are expected on future research.

Computed success rates of various carrier phase integer estimation solutions and their comparison with statistical success rates

The success rate of carrier phase ambiguity resolution (AR) is the probability that the ambiguities are successfully fixed to their correct integer values. In existing works, an exact success rate formula for integer bootstrapping estimator has been used as a sharp lower bound for the integer least squares (ILS) success rate. Rigorous computation of success rate for the more general ILS solutions has been considered difficult, because of complexity of the ILS ambiguity pull-in region and computational load of the integration of the multivariate probability density function. Contributions of this work are twofold. First, the pull-in region mathematically expressed as the vertices of a polyhedron is represented by a multi-dimensional grid, at which the cumulative probability can be integrated with the multivariate normal cumulative density function (mvncdf) available in Matlab. The bivariate case is studied where the pull-region is usually defined as a hexagon and the probability is easily obtained using mvncdf at all the grid points within the convex polygon. Second, the paper compares the computed integer rounding and integer bootstrapping success rates, lower and upper bounds of the ILS success rates to the actual ILS AR success rates obtained from a 24 h GPS data set for a 21 km baseline. The results demonstrate that the upper bound probability of the ILS AR probability given in the existing literatures agrees with the actual ILS success rate well, although the success rate computed with integer bootstrapping method is a quite sharp approximation to the actual ILS success rate. The results also show that variations or uncertainty of the unit–weight variance estimates from epoch to epoch will affect the computed success rates from different methods significantly, thus deserving more attentions in order to obtain useful success probability predictions.

Profiles of organisational culture in Indonesian construction companies

Although relatively few studies have been undertaken analyzing the drivers of performance for construction companies in producing and delivering satisfactory quality of project works, findings from previous research reveal that there is a significant correlation between the company’s organisational culture and the quality performance of contractors. It has also been noted that the nature of organisational culture is a major determinant factor for quality improvement. This paper presents a summary of the results of a pilot study investigating the organisational culture profiles of five Indonesian construction companies. The survey utilizes the Organisational Culture Assessment Instrument (OCAI), which is based on the Competing Values Framework (CVF). This instrument assesses six important and significant traits of organisational culture: dominant characteristics, organisational leadership, management of employees, organisational ‘glue’, strategic emphasis, and criteria of success. These assessed cultural dimensions identify the most close-fitting perspective of a company’s inherent culture drawn from four possible types: clan, adhocracy, market, or hierarchy culture. Further discussion is presented, which describes the companies’ dominant cultural profiles in terms of strength and congruence and how an effective quality management system operates within the dominant culture type. This analysis contributes to the finding that a suitably ‘strong’ organisational culture impacts positively on construction organisation success within its own specific sector.

Nascent entrepreneurship and the developing individual : early entrepreneurial competence in adolescence and venture creation success during the career

What predicts a person's venture creation success over the course of the career, such as making progress in the venture creation process and multiple successful venture creations? Applying a life span approach of human development, this study examined the effect of early entrepreneurial competence in adolescence, which was gathered retrospectively by means of the Life History Calendar method. Human and social capitals during the founding process were investigated as mediators between adolescent competence and performance. Findings were derived from regression analyses on the basis of prospective and retrospective data from two independent samples (N = 88 nascent founders; N = 148 founders). We found that early entrepreneurial competence in adolescence had a positive effect on making progress in the venture creation process. Nascent founders' current human and social capital also had a direct effect, but it did not mediate the effect of early competences. Finally, the data revealed that early entrepreneurial competence in adolescence positively predicted habitual entrepreneurship (multiple successful venture creations) exhibited over a longer period of the individual career (specifically, 18 years). In line with the results from prospective longitudinal studies on early precursors of entrepreneurship, our findings underscore the long neglected importance of adolescent development in the explanation of entrepreneurial performance during the subsequent working life.