Hybrid model for motorway travel time estimation considering increased detector spacing

Travel time estimation and prediction on motorways has long been a topic of research. Prediction modeling generally assumes that the estimation is perfect. No matter how good is the prediction modeling- the errors in estimation can significantly deteriorate the accuracy and reliability of the prediction. Models have been proposed to estimate travel time from loop detector data. Generally, detectors are closely spaced (say 500m) and travel time can be estimated accurately. However, detectors are not always perfect, and even during normal running conditions few detectors malfunction, resulting in increase in the spacing between the functional detectors. Under such conditions, error in the travel time estimation is significantly large and generally unacceptable. This research evaluates the in-practice travel time estimation model during different traffic conditions. It is observed that the existing models fail to accurately estimate travel time during large detector spacing and congestion shoulder periods. Addressing this issue, an innovative Hybrid model that only considers loop data for travel time estimation is proposed. The model is tested using simulation and is validated with real Bluetooth data from Pacific Motorway Brisbane. Results indicate that during non free flow conditions and larger detector spacing Hybrid model provides significant improvement in the accuracy of travel time estimation.

Exposure to solar ultraviolet radiation is associated with a decreased folate status in women of childbearing age

In vitro studies indicate that folate in collected human blood is vulnerable to degradation after exposure to ultraviolet (UV) radiation. This has raised concerns about folate depletion in individuals with high sun exposure. Here, we investigate the association between personal solar UV radiation exposure and serum folate concentration, using a three-week prospective study that was undertaken in females aged 18–47 years in Brisbane, Australia (153 E, 27 S). Following two weeks of supplementation with 500 μg of folic acid daily, the change in serum folate status was assessed over a 7-day period of measured personal sun exposure. Compared to participants with personal UV exposures of <200 Joules per day, participants with personal UV exposures of 200–599 and >600 Joules per day had significantly higher depletion of serum folate (p = 0.015). Multivariable analysis revealed personal UV exposure as the strongest predictor accounting for 20% of the overall change in serum folate (Standardised B = −0.49; t = −3.75; p = <0.01). These data show that increasing solar UV radiation exposures reduces the effectiveness of folic acid supplementation. The consequences of this association may be most pronounced for vulnerable individuals, such as women who are pregnant or of childbearing age with high sun exposures.

A 3D virtual medical imaging suite : bridging the academic-clinical training boundary

Aims The Medical Imaging Training Immersive Environment (MITIE) system is a recently developed virtual reality (VR) platform that allows students to practice a range of medical imaging techniques. The aim of this pilot study was to harvest user feedback about the educational value of the application and inform future pedagogical development. This presentation explores the use of this technology for skills training and blurring the boundaries between academic learning and clinical skills training. Background MITIE is a 3D VR environment that allows students to manipulate a patient and radiographic equipment in order to produce a VR-generated image for comparison with a gold standard. As with VR initiatives in other health disciplines (1-6) the software mimics clinical practice as much as possible and uses 3D technology to enhance immersion and realism. The software was developed by the Medical Imaging Course Team at a provider University with funding from a Health Workforce Australia “Simulated Learning Environments” grant. Methods Over 80 students undertaking the Bachelor of Medical Imaging Course were randomised to receive practical experience with either MITIE or radiographic equipment in the medical radiation laboratory. Student feedback about the educational value of the software was collected and performance with an assessed setup was measured for both groups for comparison. Ethical approval for the project was provided by the university ethics panel. Results This presentation provides qualitative analysis of student perceptions relating to satisfaction, usability and educational value as well as comparative quantitative performance data. Students reported high levels of satisfaction and both feedback and assessment results confirmed the application’s significance as a pre-clinical training tool. There was a clear emerging theme that MITIE could be a useful learning tool that students could access to consolidate their clinical learning, either during their academic timetables or their clinical placement. Conclusion Student feedback and performance data indicate that MITIE has a valuable role to play in the clinical skills training for medical imaging students both in the academic and the clinical environment. Future work will establish a framework for an appropriate supporting pedagogy that can cross the boundary between the two environments. This project was possible due to funding made available by Health Workforce Australia.

The radiation therapy patient as a student assessor

Background Radiation Therapy students at Queensland University of Technology undertake clinical placement across a wide range of sites Interpersonal skills with clinical staff and patients are an essential component: – Lectures – Role playing – Expert patient input

A hybrid genetic algorithm for the energy-efficient virtual machine placement problem in data centers

Server consolidation using virtualization technology has become an important technology to improve the energy efficiency of data centers. Virtual machine placement is the key in the server consolidation technology. In the past few years, many approaches to the virtual machine placement have been proposed. However, existing virtual machine placement approaches consider the energy consumption by physical machines only, but do not consider the energy consumption in communication network, in a data center. However, the energy consumption in the communication network in a data center is not trivial, and therefore should be considered in the virtual machine placement. In our preliminary research, we have proposed a genetic algorithm for a new virtual machine placement problem that considers the energy consumption in both physical machines and the communication network in a data center. Aiming at improving the performance and efficiency of the genetic algorithm, this paper presents a hybrid genetic algorithm for the energy-efficient virtual machine placement problem. Experimental results show that the hybrid genetic algorithm significantly outperforms the original genetic algorithm, and that the hybrid genetic algorithm is scalable.

Development of an integrated GIS and land use planning course : impacts of hybrid instructional methods

This study reports an action research undertaken at Queensland University of Technology. It evaluates the effectiveness of the integration of GIS within the substantive domains of an existing land use planning course in 2011. Using student performance, learning experience survey, and questionnaire survey data, it also evaluates the impacts of incorporating hybrid instructional methods (e.g., in-class and online instructional videos) in 2012 and 2013. Results show that: students (re)iterated the importance of GIS in the course justifying the integration; the hybrid methods significantly increased student performance; and unlike replacement, the videos are more suitable as a complement to in-class activity.

Measuring exposure to solar ultraviolet radiation using a dosimetric technique : understanding participant compliance issues

Personal ultraviolet dosimeters have been used in epidemiological studies to understand the risks and benefits of individuals' exposure to solar ultraviolet radiation (UVR). We investigated the types and determinants of non-compliance associated with a protocol for use of polysulphone UVR dosimeters. In the AusD Study, 1,002 Australian adults (aged 18-75 years) were asked to wear a new dosimeter on their wrist each day for 10 consecutive days to quantify their daily exposure to solar UVR. Of the 10,020 dosimeters distributed, 296 (3%) were not returned or used (Type I non-compliance) and other usage errors were reported for 763 (8%) returned dosimeters (Type II non-compliance). Type I errors were more common in participants with predominantly outdoor occupations. Type II errors were reported more frequently on the first day of measurement; weekend days or rainy days; and among females; younger people; more educated participants or those with outdoor occupations. Half (50%) the participants reported a non-compliance error on at least one day during the 10-day period. However, 92% of participants had at least 7 days of usable data without any apparent non-compliance issues. The factors identified should be considered when designing future UVR dosimetry studies.

The contributions of solar ultraviolet radiation exposure and other determinants to Serum 25-Hydroxyvitamin D concentrations in Australian adults : the AusD study

The Quantitative Assessment of Solar UV [ultraviolet] Exposure for Vitamin D Synthesis in Australian Adults (AusD) Study aimed to better define the relationship between sun exposure and serum 25-hydroxyvitamin D (25(OH)D) concentration. Cross-sectional data were collected between May 2009 and December 2010 from 1,002 participants aged 18-75 years in 4 Australian sites spanning 24° of latitude. Participants completed the following: 1) questionnaires on sun exposure, dietary vitamin D intake, and vitamin D supplementation; 2) 10 days of personal ultraviolet radiation dosimetry; 3) a sun exposure and physical activity diary; and 4) clinical measurements and blood collection for 25(OH)D determination. Our multiple regression model described 40% of the variance in 25(OH)D concentration; modifiable behavioral factors contributed 52% of the explained variance, and environmental and demographic or constitutional variables contributed 38% and 10%, respectively. The amount of skin exposed was the single strongest contributor to the explained variance (27%), followed by location (20%), season (17%), personal ultraviolet radiation exposure (8%), vitamin D supplementation (7%), body mass index (weight (kg)/height (m)2) (4%), and physical activity (4%). Modifiable behavioral factors strongly influence serum 25(OH)D concentrations in Australian adults. In addition, latitude was a strong determinant of the relative contribution of different behavioral factors.

Prediction of CO concentrations based on a hybrid Partial Least Square and Support Vector Machine model

Due to the health impacts caused by exposures to air pollutants in urban areas, monitoring and forecasting of air quality parameters have become popular as an important topic in atmospheric and environmental research today. The knowledge on the dynamics and complexity of air pollutants behavior has made artificial intelligence models as a useful tool for a more accurate pollutant concentration prediction. This paper focuses on an innovative method of daily air pollution prediction using combination of Support Vector Machine (SVM) as predictor and Partial Least Square (PLS) as a data selection tool based on the measured values of CO concentrations. The CO concentrations of Rey monitoring station in the south of Tehran, from Jan. 2007 to Feb. 2011, have been used to test the effectiveness of this method. The hourly CO concentrations have been predicted using the SVM and the hybrid PLS–SVM models. Similarly, daily CO concentrations have been predicted based on the aforementioned four years measured data. Results demonstrated that both models have good prediction ability; however the hybrid PLS–SVM has better accuracy. In the analysis presented in this paper, statistic estimators including relative mean errors, root mean squared errors and the mean absolute relative error have been employed to compare performances of the models. It has been concluded that the errors decrease after size reduction and coefficients of determination increase from 56 to 81% for SVM model to 65–85% for hybrid PLS–SVM model respectively. Also it was found that the hybrid PLS–SVM model required lower computational time than SVM model as expected, hence supporting the more accurate and faster prediction ability of hybrid PLS–SVM model.

Power sharing control with frequency droop in a hybrid microgrid

A microgrid may contain a large number of distributed generators (DGs). These DGs can be either inertial or non-inertial, either dispatchable or non-dispatchable. Moreover, the DGs may operate in plug and play fashion. The combination of these various types of operation makes the microgrid control a challenging task, especially when the microgrid operates in an autonomous mode. In this paper, a new control algorithm for converter interfaced (dispatchable) DG is proposed which facilitates smooth operation in a hybrid microgrid containing inertial and non-inertial DGs. The control algorithm works satisfactorily even when some of the DGs operate in plug and play mode. The proposed strategy is validated through PSCAD simulation studies.

Residential voltage dip and swell mitigation using Plug-in Hybrid Electric Vehicle in smart grid

Solutions to remedy the voltage disturbances have been mostly suggested only for industrial customers. However, not much research has been done on the impact of the voltage problems on residential facilities. This paper proposes a new method to reduce the effect of voltage dip and swell in smart grids equipped by communication systems. To reach this purpose, a voltage source inverter and the corresponding control system are employed. The behavior of a power system during voltage dip and swell are analyzed. The results demonstrate reasonable improvement in terms of voltage dip and swell mitigation. All simulations are implemented in MATLAB/Simulink environment.

A phase-locked-loop design for the smooth operation of a hybrid microgrid

A microgrid contains both distributed generators (DGs) and loads and can be viewed by a controllable load by utilities. The DGs can be either inertial synchronous generators or non-inertial converter interfaced. Moreover, some of them can come online or go offline in plug and play fashion. The combination of these various types of operation makes the microgrid control a challenging task, especially when the microgrid operates in an autonomous mode. In this paper, a new phase locked loop (PLL) algorithm is proposed for smooth synchronization of plug and play DGs. A frequency droop for power sharing is used and a pseudo inertia has been introduced to non-inertial DGs in order to match their response with inertial DGs. The proposed strategy is validated through PSCAD simulation studies.

Hybrid multi-channel cooperative spectrum sensing to satisfy channel target

Spectrum sensing of multiple primary user channels is a crucial function in cognitive radio networks. In this paper we propose an optimal, sensing resource allocation algorithm for multi-channel cooperative spectrum sensing. The channel target is implemented as an objective and constraint to ensure a pre-determined number of empty channels are detected for secondary user network operations. Based on primary user traffic parameters, we calculate the minimum number of primary user channels that must be sensed to satisfy the channel target. We implement a hybrid sensing structure by grouping secondary user nodes into clusters and assign each cluster to sense a different primary user channels. We then solve the resource allocation problem to find the optimal sensing configuration and node allocation to minimise sensing duration. Simulation results show that the proposed algorithm requires the shortest sensing duration to achieve the channel target compared to existing studies that require long sensing and cannot guarantee the target.