A Bayesian latent class safety performance function for identifying motor vehicle crash blackspots

The current state of the practice in Blackspot Identification (BSI) utilizes safety performance functions based on total crash counts to identify transport system sites with potentially high crash risk. This paper postulates that total crash count variation over a transport network is a result of multiple distinct crash generating processes including geometric characteristics of the road, spatial features of the surrounding environment, and driver behaviour factors. However, these multiple sources are ignored in current modelling methodologies in both trying to explain or predict crash frequencies across sites. Instead, current practice employs models that imply that a single underlying crash generating process exists. The model mis-specification may lead to correlating crashes with the incorrect sources of contributing factors (e.g. concluding a crash is predominately caused by a geometric feature when it is a behavioural issue), which may ultimately lead to inefficient use of public funds and misidentification of true blackspots. This study aims to propose a latent class model consistent with a multiple crash process theory, and to investigate the influence this model has on correctly identifying crash blackspots. We first present the theoretical and corresponding methodological approach in which a Bayesian Latent Class (BLC) model is estimated assuming that crashes arise from two distinct risk generating processes including engineering and unobserved spatial factors. The Bayesian model is used to incorporate prior information about the contribution of each underlying process to the total crash count. The methodology is applied to the state-controlled roads in Queensland, Australia and the results are compared to an Empirical Bayesian Negative Binomial (EB-NB) model. A comparison of goodness of fit measures illustrates significantly improved performance of the proposed model compared to the NB model. The detection of blackspots was also improved when compared to the EB-NB model. In addition, modelling crashes as the result of two fundamentally separate underlying processes reveals more detailed information about unobserved crash causes.

Free energy calculations of the interactions of c-Jun-based synthetic peptides with the c-Fos protein

The c-Fos–c-Jun complex forms the activator protein 1 transcription factor, a therapeutic target in the treatment of cancer. Various synthetic peptides have been designed to try to selectively disrupt the interaction between c-Fos and c-Jun at its leucine zipper domain. To evaluate the binding affinity between these synthetic peptides and c-Fos, polarizable and nonpolarizable molecular dynamics (MD) simulations were conducted, and the resulting conformations were analyzed using the molecular mechanics generalized Born surface area (MM/GBSA) method to compute free energies of binding. In contrast to empirical and semiempirical approaches, the estimation of free energies of binding using a combination of MD simulations and the MM/GBSA approach takes into account dynamical properties such as conformational changes, as well as solvation effects and hydrophobic and hydrophilic interactions. The predicted binding affinities of the series of c-Jun-based peptides targeting the c-Fos peptide show good correlation with experimental melting temperatures. This provides the basis for the rational design of peptides based on internal, van der Waals, and electrostatic interactions.

Calculations of the free energy of interaction of the c-Fos−c-Jun coiled coil: Effects of the solvation model and the inclusion of polarization effects

The leucine zipper region of activator protein-1 (AP-1) comprises the c-Jun and c-Fos proteins and constitutes a well-known coiled coil protein−protein interaction motif. We have used molecular dynamics (MD) simulations in conjunction with the molecular mechanics/Poisson−Boltzmann generalized-Born surface area [MM/PB(GB)SA] methods to predict the free energy of interaction of these proteins. In particular, the influence of the choice of solvation model, protein force field, and water potential on the stability and dynamic properties of the c-Fos−c-Jun complex were investigated. Use of the AMBER polarizable force field ff02 in combination with the polarizable POL3 water potential was found to result in increased stability of the c-Fos−c-Jun complex. MM/PB(GB)SA calculations revealed that MD simulations using the POL3 water potential give the lowest predicted free energies of interaction compared to other nonpolarizable water potentials. In addition, the calculated absolute free energy of binding was predicted to be closest to the experimental value using the MM/GBSA method with independent MD simulation trajectories using the POL3 water potential and the polarizable ff02 force field, while all other binding affinities were overestimated.

The application of learning analytics to improve learning: Questions from the academy

Since 2007, close collaboration between the Learning and Teaching Unit’s Academic Quality and Standards team and the Department of Reporting and Analysis’ Business Objects team resulted in a generational approach to reporting where QUT established a place of trust. This place of trust is where data owners are confident in date storage, data integrity, reported and shared. While the role of the Department of Reporting and Analysis focused on the data warehouse, data security and publication of reports, the Academic Quality and Standards team focused on the application of learning analytics to solve academic research questions and improve student learning. Addressing questions such as: • Are all students who leave course ABC academically challenged? • Do the students who leave course XYZ stay within the faculty, university or leave? • When students withdraw from a unit do they stay enrolled on full or part load or leave? • If students enter through a particular pathway, what is their experience in comparison to other pathways? • With five years historic reporting, can a two-year predictive forecast provide any insight? In answering these questions, the Academic Quality and Standards team then developed prototype data visualisation through curriculum conversations with academic staff. Where these enquiries were applicable more broadly this information would be brought into the standardised reporting for the benefit of the whole institution. At QUT an annual report to the executive committees allows all stakeholders to record the performance and outcomes of all courses in a snapshot in time or use this live report at any point during the year. This approach to learning analytics was awarded the Awarded 2014 ATEM/Campus Review Best Practice Awards in Tertiary Education Management for The Unipromo Award for Excellence in Information Technology Management.

Leading Digital Transformation: Building Technology and Information Governance Capability in Boards of Directors and the C-Suite

Information and technology and its use in organisation transformation presents unprecedented opportunities and risks. Increasingly, the Governance of Enterprise Information and Technology (GEIT) competency in the board room and executive is needed. Whether your organization is small or large, public, private or not for profit or whether your industry is not considered high-tech, IT is impacting your sector – no exceptions. But there is a skill shortage in boards: GEIT capability is concerningly low. This capability is urgently needed across the board, including those directors who come from finance, legal, marketing, operations and HR backgrounds. Digital disruption also affects all occupations. Putting in place a vision will help ensure emergency responses will meet technology-related duty of care responsibilities. When GEIT-related forward thinking and planning is carried out at the same time that you put your business strategy and plan in place, your organization has a significantly increased chance of not only surviving, but thriving into the future. Those organizations that don’t build GEIT capability risk joining the growing list of once-leading firms left behind in the digital ‘cloud of smoke’. Those organizations that do will be better placed to reap the benefits and hedge against the risks of a digital world. This chapter provides actionable, research-based considerations and processes for boards to use, to build awareness, knowledge and skills in governing technology-related organization strategy, risk and value creation.