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.

The effect of using different regions of interest on local and mean skin temperature

The dynamic nature of tissue temperature and the subcutaneous properties, such as blood flow, fatness, and metabolic rate, leads to variation in local skin temperature. Therefore, we investigated the effects of using multiple regions of interest when calculating weighted mean skin temperature from four local sites. Twenty-six healthy males completed a single trial in a thermonetural laboratory (mean ± SD): 24.0 (1.2) °C; 56 (8%) relative humidity; < 0.1 m/s air speed). Mean skin temperature was calculated from four local sites (neck, scapula, hand and shin) in accordance with International Standards using digital infrared thermography. A 50 x 50 mm square, defined by strips of aluminium tape, created six unique regions of interest, top left quadrant, top right quadrant, bottom left quadrant, bottom right quadrant, centre quadrant and the entire region of interest, at each of the local sites. The largest potential error in weighted mean skin temperature was calculated using a combination of a) the coolest and b) the warmest regions of interest at each of the local sites. Significant differences between the six regions interest were observed at the neck (P < 0.01), scapula (P < 0.001) and shin (P < 0.05); but not at the hand (P = 0.482). The largest difference (± SEM) at each site was as follows: neck 0.2 (0.1) °C; scapula 0.2 (0.0) °C; shin 0.1 (0.0) °C and hand 0.1 (0.1) °C. The largest potential error (mean ± SD) in weighted mean skin temperature was 0.4 (0.1) °C (P < 0.001) and the associated 95% limits of agreement for these differences was 0.2 to 0.5 °C. Although we observed differences in local and mean skin temperature based on the region of interest employed, these differences were minimal and are not considered physiologically meaningful.

Course evidence matrix : a collaborative evaluation using multiple lines of evidence

Welcome to the Evaluation of course matrix. This matrix is designed for highly qualified discipline experts to evaluate their course, major or unit in a systemic manner. The primary purpose of the Evaluation of course matrix is to provide a tool that a group of academic staff at universities can collaboratively review the assessment within a course, major or unit annually. The annual review will result in you being ready for an external curricula review at any point in time. This tool is designed for use in a workshop format with one, two or more academic staff, and will lead to an action plan for implementation. I hope you find this tool useful in your assessment review.

Development of a climate assessment tool for hybrid air conditioner

Australian climate is highly suitable for using outdoor air for free building cooling. In order to evaluate the suitability of hybrid cooler for specific applications, a pre-design climate assessment tool is developed and presented in this paper. In addition to the consideration of the local climate, comfort zone proposed by ASHRAE handbook and specific design of building and operation of hybrid cooler, possible influence from environmental factors (e.g. air humidity and air velocity), as well as personal factors (e.g. activity level and clothing insulation) on occupant’s thermal comfort are also considered in this tool. It is demonstrated that with the input of climatic data for a particular location and the associated design data for a specific application, the developed climate assessment tool is able to not only sort outdoor air conditions into the different process regions but also project them onto the psychrometric chart. It can also be used to estimate the hours for an individual operational mode under various climate conditions and summarize them in a table “Results”.

The influence of internal load density on the energy and thermal performance of air-conditioned office buildings in the face of global warming

Internal heat sources may not only consume energy directly through their operation (e.g. lighting), but also contribute to building cooling or heating loads, which indirectly change building cooling and heating energy. Through the use of building simulation technique, this paper investigates the influence of building internal load densities on the energy and thermal performance of air conditioned office buildings in Australia. Case studies for air conditioned office buildings in major Australian capital cities are presented. It is found that with a decrease of internal load density in lighting and/or plug load, both the building cooling load and total energy use can be significantly reduced. Their effect on overheating hour reduction would be dependent on the local climate. In particular, it is found that if the building total internal load density is reduced from the base case of “medium” to “extra–low, the building total energy use under the future 2070 high scenario can be reduced by up to 89 to 120 kWh/m² per annum and the overheating problem could be completely avoided. It is suggested that the reduction in building internal load densities could be adopted as one of adaptation strategies for buildings in face of the future global warming.

Challenges in field monitoring of energy performance of air conditioners

Experimental work could be conducted in either laboratory or at field site. Generally, the laboratory experiments are carried out in an artificial setting and with a highly controlled environment. By contrast, the field experiments often take place in a natural setting, subject to the influences of many uncontrolled factors. Therefore, it is necessary to carefully assess the possible limitations and appropriateness of an experiment before embarking on it. In this paper, a case study of field monitoring of the energy performance of air conditioners is presented. Significant challenges facing the experimental work are described. Lessons learnt from this case study are also discussed. In particular, it was found that on-going analysis of the monitoring data and the correction of abnormal issues are two of the keys for a successful field test program. It was also shown that the installation of monitoring systems could have a significant impact on the accuracy of the data being collected. Before monitoring system was set up to collect monitoring data, it is recommended that an initial analysis of sample monitored data should be conducted to make sure that the monitoring data can achieve the expected precision. In the case where inevitable inherent errors were induced from the installation of field monitoring systems, appropriate remediation may need to be developed and implemented for the improved accuracy of the estimation of results. On-going analysis of monitoring data and correction of any abnormal issues would be the key to a successful field test program.

Development and integration of a solar powered unmanned aerial vehicle and a wireless sensor network to monitor greenhouse gases

Measuring gases for environmental monitoring is a demanding task that requires long periods of observation and large numbers of sensors. Wireless Sensor Networks (WSNs) and Unmanned Aerial Vehicles (UAVs) currently represent the best alternative to monitor large, remote, and difficult access areas, as these technologies have the possibility of carrying specialized gas sensing systems. This paper presents the development and integration of a WSN and an UAV powered by solar energy in order to enhance their functionality and broader their applications. A gas sensing system implementing nanostructured metal oxide (MOX) and non-dispersive infrared sensors was developed to measure concentrations of CH4 and CO2. Laboratory, bench and field testing results demonstrate the capability of UAV to capture, analyze and geo-locate a gas sample during flight operations. The field testing integrated ground sensor nodes and the UAV to measure CO2 concentration at ground and low aerial altitudes, simultaneously. Data collected during the mission was transmitted in real time to a central node for analysis and 3D mapping of the target gas. The results highlights the accomplishment of the first flight mission of a solar powered UAV equipped with a CO2 sensing system integrated with a WSN. The system provides an effective 3D monitoring and can be used in a wide range of environmental applications such as agriculture, bushfires, mining studies, zoology and botanical studies using a ubiquitous low cost technology.

IMU aided 3D visual odometry for car-like vehicles

We present a method for calculating odome- try in three-dimensions for car-like ground ve- hicles with an Ackerman-like steering model. In our approach we use the information from a single camera to derive the odometry in the plane and fuse it with roll and pitch informa- tion derived from an on-board IMU to extend to three-dimensions, thus providing odometric altitude as well as traditional x and y transla- tion. We have mounted the odometry module on a standard Toyota Prado SUV and present results from a car-park environment as well as from an off-road track.

Inferring phylogenies of evolving sequences without multiple sequence alignment

Alignment-free methods, in which shared properties of sub-sequences (e.g. identity or match length) are extracted and used to compute a distance matrix, have recently been explored for phylogenetic inference. However, the scalability and robustness of these methods to key evolutionary processes remain to be investigated. Here, using simulated sequence sets of various sizes in both nucleotides and amino acids, we systematically assess the accuracy of phylogenetic inference using an alignment-free approach, based on D2 statistics, under different evolutionary scenarios. We find that compared to a multiple sequence alignment approach, D2 methods are more robust against among-site rate heterogeneity, compositional biases, genetic rearrangements and insertions/deletions, but are more sensitive to recent sequence divergence and sequence truncation. Across diverse empirical datasets, the alignment-free methods perform well for sequences sharing low divergence, at greater computation speed. Our findings provide strong evidence for the scalability and the potential use of alignment-free methods in large-scale phylogenomics.

The effect of autonomy transparency in human-robot interactions : a preliminary study on operator cognitive workload and situation awareness in multiple heterogeneous UAV management

The autonomous capabilities in collaborative unmanned aircraft systems are growing rapidly. Without appropriate transparency, the effectiveness of the future multiple Unmanned Aerial Vehicle (UAV) management paradigm will be significantly limited by the human agent’s cognitive abilities; where the operator’s CognitiveWorkload (CW) and Situation Awareness (SA) will present as disproportionate. This proposes a challenge in evaluating the impact of robot autonomous capability feedback, allowing the human agent greater transparency into the robot’s autonomous status - in a supervisory role. This paper presents; the motivation, aim, related works, experiment theory, methodology, results and discussions, and the future work succeeding this preliminary study. The results in this paper illustrates that, with a greater transparency of a UAV’s autonomous capability, an overall improvement in the subjects’ cognitive abilities was evident, that is, with a confidence of 95%, the test subjects’ mean CW was demonstrated to have a statistically significant reduction, while their mean SA was demonstrated to have a significant increase.