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This paper describes an experimental investigation into the performance of a Hybrid Model Predictive Control (HMPC) system implemented to control a novel solar-assisted HVAC system servicing the Team UOW Solar Decathlon house, the overall winner of the Solar Decathlon China 2013 competition. This HVAC system consists of an air-based photovoltaic thermal (PVT) collector and a phase change material (PCM) thermal store integrated with a conventional ducted reverse-cycle heat pump system. The system was designed for operation during both winter and summer, using daytime solar radiation and night sky radiative cooling to increase the energy efficiency of the air-conditioning system. The PVT collector can exchange heat with the PCM thermal storage unit, and the stored heat can be used to condition the space or precondition the air entering the air handling unit (AHU). The HMPC controller includes two levels of control, where the high-level controller has a 24-hour prediction horizon and a 1-hour control step is used to select the operating mode of the HVAC system. Low-level controllers for each HVAC operational mode have a 1–hour prediction horizon and a 5–minute control step, and are used to track the trajectory defined by the high-level controller and to optimize the operating mode selected. The results from this preliminary experimental work have demonstrated the value of the HMPC approach in optimally controlling the solar-assisted HVAC system in the Solar Decathlon house. Results show that the HMPC controller successfully selected the appropriate operating mode to achieve multiple objectives, including: maintenance of indoor comfort conditions within a defined, and potentially variable, thermal comfort band; and optimization of the overall energy efficiency of the system using all available on-site energy resources. [ABSTRACT FROM AUTHOR]

Energy efficiency is a complex topic to integrate into higher education curricula, with limited success internationally or in Australia. This paper discusses one of the successful initiatives within the Energy Efficiency Training Program , which was jointly managed and implemented by the New South Wales Office of Environment and Heritage and Department of Education and Communities. The state government initiative aimed to increase the knowledge and skills of the New South Wales workforce, help business to identify and implement energy efficiency projects, and provide professional development for the training providers. Key sectors targeted included property, construction, manufacturing and services. The Program was externally evaluated over the three years 2011–2013 and a range of insights were gained through these facilitated reflective opportunities, confirming and building upon literature on the topic to date. This paper presents lessons learned from the engineering part of the program (‘the project’), spanning government agencies, academic institutions, and academia. The paper begins with a contextual summary, followed by a synthesis of key learnings and implications for future training initiatives. It is intended that sharing these lessons will contribute to literature in the field, and assist other organisations in Australia and overseas planning similar initiatives. [ABSTRACT FROM AUTHOR]

Study Objective: We compared the performance of a handheld quantitative electroencephalogram (QEEG) acquisition device to New Orleans Criteria (NOC), Canadian CT Head Rule (CCHR), and National Emergency X-Radiography Utilization Study II (NEXUS II) Rule in predicting intracranial lesions on head computed tomography (CT) in acute mild traumatic brain injury in the emergency department (ED). Methods: Patients between 18 and 80 years of age who presented to the ED with acute blunt head trauma were enrolled in this prospective observational study at 2 urban academic EDs in Detroit, MI. Data were collected for 10 minutes from frontal leads to determine a QEEG discriminant score that could maximally classify intracranial lesions on head CT. Results: One hundred fifty-two patients were enrolled from July 2012 to February 2013. A total 17.1% had acute traumatic intracranial lesions on head CT. Quantitative electroencephalogram discriminant score of greater than or equal to 31 was found to be a good cutoff (area under receiver operating characteristic curve = 0.84; 95% confidence interval [CI], 0.76-0.93) to classify patients with positive head CT. The sensitivity of QEEG discriminant score was 92.3 (95% CI, 73.4-98.6), whereas the specificity was 57.1 (95% CI, 48.0-65.8). The sensitivity and specificity of the decision rules were as follows: NOC 96.1 (95% CI, 78.4-99.7) and 15.8 (95% CI, 10.1-23.6); CCHR 46.1 (95% CI, 27.1-66.2) and 86.5 (95% CI, 78.9-91.7); NEXUS II 96.1 (95% CI, 78.4-99.7) and 31.7 (95% CI, 23.9-40.7). Conclusion: At a sensitivity of greater than 90%, QEEG discriminant score had better specificity than NOC and NEXUS II. Only CCHR had better specificity than QEEG discriminant score but at the cost of low (<50%) sensitivity. [ABSTRACT FROM AUTHOR]

Abstract: Bacterial infections are a costly sequela in any wound. The corrosion properties of 0.15, 0.30, 0.45 and 0.60g of Mg metal were determined in Mueller–Hinton broth by serially measuring the Mg2+ concentrations and pH over 72h. In addition, the effect of Mg metal, increased Mg2+ concentration and alkaline pH on the in vitro growth of Escherichia coli, Pseudomonas aeruginosa and Staphylococcus aureus were evaluated in three separate experiments. The primary outcome measure for culture studies was colony-forming units/ml compared to appropriate positive and/or negative controls. Regardless of the mass of Mg added, there was a predictable increase in pH and Mg2+ concentration. The addition of Mg and an increase of pH resulted in antibacterial effects similar to the fluoroquinolone antibiotic; however, a simple increase in Mg2+ concentration alone had no effect. The results demonstrate an antibacterial effect of Mg on three common aerobic bacterial organisms, the mechanism of which appears to be an alkaline pH. [Copyright &y& Elsevier]

This study presents an agglomerative hierarchical clustering-based strategy using Shared Nearest Neighbours and multiple dissimilarity measures to identify typical daily electricity usage profiles of university library buildings. The proposed strategy takes the advantages of three dissimilarity measures (i.e. Euclidean distance, Pearson distance and Chebyshev distance) to calculate the difference between daily electricity usage profiles. Two-year hourly electricity usage data collected from two different university library buildings were employed to evaluate the performance of this strategy. It was shown that this strategy, which considered both magnitude dissimilarity and variation dissimilarity simultaneously, can identify more informative typical daily electricity usage profiles, in comparison with other twelve clustering-based strategies which used a single dissimilarity measure. Some interesting information related to building energy usage behaviours was also discovered with the help of visualisation techniques. Additional or hidden information discovered using this strategy can potentially be useful for fault detection and diagnosis and performance enhancement of library buildings. • A clustering-based strategy for multi-function educational buildings was presented. • It was developed based on three dissimilarity measures and Shared Nearest Neighbours. • An agglomerative hierarchical clustering method was used to group the load profiles. • It outperformed twelve other strategies that used a single dissimilarity measure. [ABSTRACT FROM AUTHOR]

Highlights • A strategy was developed to identify building typical electricity usage profiles. • This strategy consists of intra-building clustering and inter-building clustering. • This strategy can discover electricity usage behaviors of multiple buildings. • This strategy outperformed two single-step clustering strategies. Abstract This paper presents a clustering-based strategy to identify typical daily electricity usage (TDEU) profiles of multiple buildings. Different from the majority of existing clustering strategies, the proposed strategy consists of two levels of clustering, i.e. intra-building clustering and inter-building clustering. The intra-building clustering used a Gaussian mixture model-based clustering to identify the TDEU profiles of each individual building. The inter-building clustering used an agglomerative hierarchical clustering to identify the TDEU profiles of multiple buildings based on the TDEU profiles identified for each individual building through intra-building clustering. The performance of this strategy was evaluated using two-year hourly electricity consumption data collected from 40 university buildings. The results showed that this strategy can discover useful information related to building electricity usage, including typical patterns of daily electricity usage (DEU) and periodical variation of DEU. It was also shown that this proposed strategy can identify additional electricity usage patterns with a less computational cost, in comparison to two single-step clustering strategies including a Partitioning Around Medoids-based clustering strategy and a hierarchical clustering strategy. The results obtained from this study could be potentially used to assist in improving energy performance of university buildings and other types of buildings. [ABSTRACT FROM AUTHOR]

Background: An intrahospital casualty throughput system modeled after air traffic control (ATC) tracking procedures was tested in mass casualty exercises. ATC uses a simple tactile process involving informational progress strips representing each aircraft, which are held in bays representing each stage of flight to prioritize and manage aircraft. These strips can be reordered within the bays to indicate a change in priority of aircraft sequence. In this study, a similar system was designed for patient tracking. Study Design: We compared the ATC model and traditional casualty tracking methods of paper and clipboard in 18 four-hour casualty scenarios, each with 5 to 30 mock casualties. The experimental and control groups were alternated to maximize exposure and minimize training effects. Results were analyzed with Mann-Whitney statistical analysis with p value < 0.05 (two-sided). Results: The ATC method had significantly (p = 0.017) fewer errors in critical patient data (eg, name, social security number, diagnosis). Specifically, the ATC method better tracked the mechanism of injury, working diagnosis, and disposition of patients. The ATC method also performed considerably better with patient accountability during mass casualty scenarios. Data strips were comparable with the control method in terms of ease of use. In addition, participants preferred the ATC method to the control (p = 0.003) and preferred using the ATC method (p = 0.003) to traditional methods in the future. Conclusions: The ATC model more effectively tracked patient data with fewer errors when compared with the clipboard method. Application of these principles can enhance trauma management and can have application in civilian and military trauma centers and emergency rooms. [Copyright &y& Elsevier]

Abstract: Cryoinsult-induced osteonecrosis (ON) in the emu femoral head provides a unique opportunity to systematically explore the pathogenesis of ON in an animal model that progresses to human-like femoral head collapse. Among the various characteristics of cryoinsult, the maximally cold temperature attained is one plausible determinant of tissue necrosis. To identify the critical isotherm required to induce development of ON in the cancellous bone of the emu femoral head, a thermal finite element (FE) model of intraoperative cryoinsults was developed. Thermal material property values of emu cancellous bone were estimated from FE simulations of cryoinsult to emu cadaver femora, by varying model properties until the FE-generated temperatures matched corresponding thermocouple measurements. The resulting FE model, with emu bone-specific thermal properties augmented to include blood flow effects, was then used to study intraoperatively performed in vivo cryoinsults. Comparisons of minimum temperatures attained at FE nodes corresponding to the three-dimensional histologically apparent boundary of the region of ON were made for six experimental cryoinsults. Series-wide, a critical isotherm of 3.5°C best corresponded to the boundary of the osteonecrotic lesions. [Copyright &y& Elsevier]

This study presents a new strategy using cluster analysis, Cubist regression models and Particle Swarm Optimization to forecast next-day total electricity usage and peak electricity demand of a building portfolio. Cluster analysis with a combined dissimilarity measure was first used to group daily electricity usage profiles of the building portfolio. The clustering result was then considered in the training of the Cubist-based forecasting models in order to improve the forecasting accuracy. A Particle Swarm Optimization algorithm was used to determine the optimal parameters in the cluster analysis to further improve the forecasting accuracy. The performance of this strategy was evaluated using the electricity usage data of 40 university buildings. The results showed that the difference between the measured and predicted daily total electricity usage was 4.7% in terms of the coefficient of variation of the root-mean-squared error (CV(RMSE)) and 3.3% in terms of mean absolute percentage error (MAPE) and the difference between the measured and predicted daily peak load was 6.0% in CV(RMSE) and 5.3% in MAPE. The proposed strategy can effectively improve the accuracy of the forecasting result by up to 18.1% and 12.2% when compared to the strategy which did not consider the clustering result of the daily electricity usage profiles in the forecasting models and the strategy which considered the clustering result obtained using a single dissimilarity measure. Compared to the mean level of the nine strategies that used different regression methods, the proposed strategy can improve the forecasting accuracy by up to 42.2%. The results of this study can be further used to assist in the development of building optimal control and operation strategies. • A strategy was developed for electricity usage forecasting of campus buildings. • Clustering with a combined distance was used to improve forecasting performance. • Parameters in the clustering were optimized using Particle Swarm Optimization. • Cubist regression models were used to increase forecasting accuracy. • This strategy outperformed other electricity usage forecasting strategies compared. [ABSTRACT FROM AUTHOR]

Abstract: The emu is a large, (bipedal) flightless bird that potentially can be used to study various orthopaedic disorders in which load protection of the experimental limb is a limitation of quadrupedal models. An anatomy-based analysis of normal emu walking gait was undertaken to determine hip contact forces for comparison with human data. Kinematic and kinetic data captured for two laboratory-habituated emus were used to drive the model. Muscle attachment data were obtained by dissection, and bony geometries were obtained by CT scan. Inverse dynamics calculations at all major lower-limb joints were used in conjunction with optimization of muscle forces to determine hip contact forces. Like human walking gait, emu ground reaction forces showed a bimodal distribution over the course of the stance phase. Two-bird averaged maximum hip contact force was approximately 5.5 times body weight, directed nominally axially along the femur. This value is only modestly larger than optimization-based hip contact forces reported in literature for humans. The interspecies similarity in hip contact forces makes the emu a biomechanically attractive animal in which to model loading-dependent human orthopaedic hip disorders. [Copyright &y& Elsevier]

• High frequency (HF) emissions are a new power quality concern. • Photovoltaic (PV) inverters are major HF emission sources. • No PV inverter models suitable for HF studies are found in current literature. • A generic method is proposed to develop black box models of PV inverters for HF studies. • The models developed have less than 6.5% errors with 95% confidence. There is a growth of high frequency (HF) emissions in the range of 2–150 kHz (also known as Supraharmonics) in electricity distribution networks, primarily due to the increasing number and capacity of AC grid connected equipment having power electronic interfaces. Although PV inverters are a major HF source in electricity distribution networks, PV inverter models that are suitable for HF emission studies are yet to be developed. To this end, a generic method that can be used to develop HF models of small-scale (<5 kW), grid-tied, single-phase PV inverters using a black box approach is presented in this paper. Accordingly, HF models of three PV inverters that are commonly used in domestic and commercial installations are developed assuming standard network conditions. It is shown that these HF models are capable of successfully capturing the HF performance of the selected PV inverters under a wide range of operating conditions. The outcomes of this work are expected to broaden the knowledge pertaining to the HF emissions in the frequency range considered. [ABSTRACT FROM AUTHOR]