A recommendation system for predicting risks across multiple business process instances

This paper proposes a recommendation system that supports process participants in taking risk-informed decisions, with the goal of reducing risks that may arise during process execution. Risk reduction involves decreasing the likelihood and severity of a process fault from occurring. Given a business process exposed to risks, e.g. a financial process exposed to a risk of reputation loss, we enact this process and whenever a process participant needs to provide input to the process, e.g. by selecting the next task to execute or by filling out a form, we suggest to the participant the action to perform which minimizes the predicted process risk. Risks are predicted by traversing decision trees generated from the logs of past process executions, which consider process data, involved resources, task durations and other information elements like task frequencies. When applied in the context of multiple process instances running concurrently, a second technique is employed that uses integer linear programming to compute the optimal assignment of resources to tasks to be performed, in order to deal with the interplay between risks relative to different instances. The recommendation system has been implemented as a set of components on top of the YAWL BPM system and its effectiveness has been evaluated using a real-life scenario, in collaboration with risk analysts of a large insurance company. The results, based on a simulation of the real-life scenario and its comparison with the event data provided by the company, show that the process instances executed concurrently complete with significantly fewer faults and with lower fault severities, when the recommendations provided by our recommendation system are taken into account.

Tensor reduction for user profiling in personalized recommender systems

User profiling is the process of constructing user models which represent personal characteristics and preferences of customers. User profiles play a central role in many recommender systems. Recommender systems recommend items to users based on user profiles, in which the items can be any objects which the users are interested in, such as documents, web pages, books, movies, etc. In recent years, multidimensional data are getting more and more attention for creating better recommender systems from both academia and industry. Additional metadata provides algorithms with more details for better understanding the interactions between users and items. However, most of the existing user/item profiling techniques for multidimensional data analyze data through splitting the multidimensional relations, which causes information loss of the multidimensionality. In this paper, we propose a user profiling approach using a tensor reduction algorithm, which we will show is based on a Tucker2 model. The proposed profiling approach incorporates latent interactions between all dimensions into user profiles, which significantly benefits the quality of neighborhood formation. We further propose to integrate the profiling approach into neighborhoodbased collaborative filtering recommender algorithms. Experimental results show significant improvements in terms of recommendation accuracy.

Virtual business role-play : leveraging familiar environments to prime stakeholder memory during process elicitation

Business process models have traditionally been an effective way of examining business practices to identify areas for improvement. While common information gathering approaches are generally efficacious, they can be quite time consuming and have the risk of developing inaccuracies when information is forgotten or incorrectly interpreted by analysts. In this study, the potential of a role-playing approach for process elicitation and specification has been examined. This method allows stakeholders to enter a virtual world and role-play actions as they would in reality. As actions are completed, a model is automatically developed, removing the need for stakeholders to learn and understand a modelling grammar. Empirical data obtained in this study suggests that this approach may not only improve both the number of individual process task steps remembered and the correctness of task ordering, but also provide a reduction in the time required for stakeholders to model a process view.

Application of a Bayesian Network complex system model to a successful community electricity demand reduction program

Utilities worldwide are focused on supplying peak electricity demand reliably and cost effectively, requiring a thorough understanding of all the factors influencing residential electricity use at peak times. An electricity demand reduction project based on comprehensive residential consumer engagement was established within an Australian community in 2008, and by 2011, peak demand had decreased to below pre-intervention levels. This paper applied field data discovered through qualitative in-depth interviews of 22 residential households at the community to a Bayesian Network complex system model to examine whether the system model could explain successful peak demand reduction in the case study location. The knowledge and understanding acquired through insights into the major influential factors and the potential impact of changes to these factors on peak demand would underpin demand reduction intervention strategies for a wider target group.

Performance evaluation of non-thermal plasma on particulate matter, ozone and CO2 correlation for diesel exhaust emission reduction

This study is seeking to investigate the effect of non-thermal plasma technology in the abatement of particulate matter (PM) from the actual diesel exhaust. Ozone (O3) strongly promotes PM oxidation, the main product of which is carbon dioxide (CO2). PM oxidation into the less harmful product (CO2) is the main objective whiles the correlation between PM, O3 and CO2 is considered. A dielectric barrier discharge reactor has been designed with pulsed power technology to produce plasma inside the diesel exhaust. To characterise the system under varied conditions, a range of applied voltages from 11 kVPP to 21kVPP at repetition rates of 2.5, 5, 7.5 and 10 kHz, have been experimentally investigated. The results show that by increasing the applied voltage and repetition rate, higher discharge power and CO2 dissociation can be achieved. The PM removal efficiency of more than 50% has been achieved during the experiments and high concentrations of ozone on the order of a few hundreds of ppm have been observed at high discharge powers. Furthermore, O3, CO2 and PM concentrations at different plasma states have been analysed for time dependence. Based on this analysis, an inverse relationship between ozone concentration and PM removal has been found and the role of ozone in PM removal in plasma treatment of diesel exhaust has been highlighted.

Electrochemical restructuring of copper surfaces using organic additives and its effect on the electrocatalytic reduction of nitrate ions

This work describes the fabrication of nanostructured copper electrodes using a simple potential cycling protocol that involves oxidation and reduction of the surface in an alkaline solution. It was found that the inclusion of additives, such as benzyl alcohol and phenylacetic acid, has a profound effect on the surface oxidation process and the subsequent reduction of these oxides. This results in not only a morphology change, but also affects the electrocatalytic performance of the electrode for the reduction of nitrate ions. In all cases, the electrocatalytic performance of the restructured electrodes was significantly enhanced compared with the unmodified electrode. The most promising material was formed when phenylacetic acid was used as the additive. In addition, the reduction of residual oxides on the surface after the modification procedure to expose freshly active reaction sites on the surface before nitrate reduction was found to be a significant factor in dictating the overall electrocatalytic activity. It is envisaged that this approach offers an interesting way to fabricate other nanostructured electrode surfaces.

Solution chemistry impacts on the seawater neutralisation process: Benefits of nanofiltered seawater and reverse osmosis brine

It is well known that the neutralisation of Bayer liquor with seawater causes the precipitation of stable alkaline products and a reduction in pH and dissolved metal concentrations in the effluent. However, there is limited information available on solution chemistry effects on the stability and reaction kinetics of these precipitates. This investigation shows the influence of reactive species (magnesium and calcium) in seawater on precipitate stabilities and volumetric efficiencies during the neutralisation of bauxite refinery residues. Correlations between synthetic seawater solutions and real samples of seawater (filtered seawater, nanofiltered seawater and reverse osmosis brine) have been made. These investigations have been used to confirm that alternative seawater sources can be used to increase the productivity potential of the neutralisation process with minimal implications on the composition and stability of precipitates formed. The volume efficiency of the neutralisation process using synthetic analogues has been shown to be almost directly proportional with the concentration of magnesium. This was further confirmed in the nanofiltered seawater and reverse osmosis brine that showed increases in the efficiency of neutralisation by factors of 3 and 2 compared to seawater, which corresponds with relatively the same increase in the concentration of magnesium in these alternative seawater sources. An assessment of the chemical stability of the precipitates, volumetric efficiency, and discharge water quality have been determined using numerous techniques that include pH, conductivity, inductively coupled plasma optical emission spectroscopy, infrared spectroscopy, thermogravimetric analysis coupled to mass spectrometry and X-ray diffraction. Correlations between synthetic solution compositions and alternative seawater sources have been used to determine if alternative seawater sources are potential substitutes for seawater based on improvements in productivity, implementation costs, savings to operations and environmental benefits.

Older adults and driving reduction: Is the gender gap narrowing?

This study investigated driving reduction in a diverse sample of 229 male and female older drivers aged 70 years and above in Queensland, Australia. The study sought to determine whether differences existed between male and female older drivers in regard to driving patterns, and to identify factors that were predictive of driving reduction in female versus male older drivers. Participants provided information on their health, self-reported driving patterns, driving perceptions, alternative transport options, and feedback. Overall, females were more likely to avoid challenging situations but less likely to reduce their driving when compared to males. Self-rated health and driving confidence were significant predictors for driving reduction among females. For males, driving importance was the only significant predictor for driving reduction in this sample. This study indicates the need for longitudinal research on the process of driving reduction and whether the planning process for driving cessation differ between females and males.

Alloying gold with copper makes for a highly selective visible-light photocatalyst for the reduction of nitroaromatics to anilines

Finely control of product selectivity is an essential issue in organic chemical production. In the synthesis of functionalized anilines via reduction of the corresponding nitroarenes, the challenge is to selectively reduce only the nitro group in the presence of other reducible functional groups in nitroarene molecules at a high reaction rate. Normally, the nitroarene is reduced stepwise through a series of intermediates that remain as byproducts, increasing the aniline synthesis cost. Here we report that alloying small amounts of copper into gold nanoparticles can alter the reaction pathway of the catalytic reduction under visible-light irradiation at ambient temperature, allowing nitroaromatics to be transformed directly to anilines in a highly selective manner. The reasons for the high efficiency of the photocatalytic reduction under these comparatively benign conditions as well as the light-excited reaction mechanisms are discussed. This photocatalytic process avoids byproducts, exhibits a high reaction rate and excellent substituent tolerance, and can be used for the synthesis of many useful functionalized anilines under environmentally benign conditions. Switching of the reaction pathway simply by tailoring the bimetallic alloy NPs of the photocatalysts is effective for engineering of product chemoselectivity.

Augmenting process elicitation with visual priming: An empirical exploration of user behaviour and modelling outcomes

Business process models have become an effective way of examining business practices to identify areas for improvement. While common information gathering approaches are generally efficacious, they can be quite time consuming and have the risk of developing inaccuracies when information is forgotten or incorrectly interpreted by analysts. In this study, the potential of a role-playing approach to process elicitation and specification has been examined. This method allows stakeholders to enter a virtual world and role-play actions similarly to how they would in reality. As actions are completed, a model is automatically developed, removing the need for stakeholders to learn and understand a modelling grammar. An empirical investigation comparing both the modelling outputs and participant behaviour of this virtual world role-play elicitor with an S-BPM process modelling tool found that while the modelling approaches of the two groups varied greatly, the virtual world elicitor may not only improve both the number of individual process task steps remembered and the correctness of task ordering, but also provide a reduction in the time required for stakeholders to model a process view.