Planning volunteer responses to low-volume mass gatherings: do event characteristics predict patient workload?

INTRODUCTION: Workforce planning for first aid and medical coverage of mass gatherings is hampered by limited research. In particular, the characteristics and likely presentation patterns of low-volume mass gatherings of between several hundred to several thousand people are poorly described in the existing literature. OBJECTIVES: This study was conducted to: 1. Describe key patient and event characteristics of medical presentations at a series of mass gatherings, including events smaller than those previously described in the literature; 2. Determine whether event type and event size affect the mean number of patients presenting for treatment per event, and specifically, whether the 1:2,000 deployment rule used by St John Ambulance Australia is appropriate; and 3. Identify factors that are predictive of injury at mass gatherings. METHODS: A retrospective, observational, case-series design was used to examine all cases treated by two Divisions of St John Ambulance (Queensland) in the greater metropolitan Brisbane region over a three-year period (01 January 2002-31 December 2004). Data were obtained from routinely collected patient treatment forms completed by St John officers at the time of treatment. Event-related data (e.g., weather, event size) were obtained from event forms designed for this study. Outcome measures include: total and average number of patient presentations for each event; event type; and event size category. Descriptive analyses were conducted using chi-square tests, and mean presentations per event and event type were investigated using Kruskal-Wallis tests. Logistic regression analyses were used to identify variables independently associated with injury presentation (compared with non-injury presentations). RESULTS: Over the three-year study period, St John Ambulance officers treated 705 patients over 156 separate events. The mean number of patients who presented with any medical condition at small events (less than or equal to 2,000 attendees) did not differ significantly from that of large (>2,000 attendees) events (4.44 vs. 4.67, F = 0.72, df = 1, 154, p = 0.79). Logistic regression analyses indicated that presentation with an injury compared with non-injury was independently associated with male gender, winter season, and sporting events, even after adjusting for relevant variables. CONCLUSIONS: In this study of low-volume mass gatherings, a similar number of patients sought medical treatment at small (<2,000 patrons) and large (>2,000 patrons) events. This demonstrates that for low-volume mass gatherings, planning based solely on anticipated event size may be flawed, and could lead to inappropriate levels of first-aid coverage. This study also highlights the importance of considering other factors, such as event type and patient characteristics, when determining appropriate first-aid resourcing for low-volume events. Additionally, identification of factors predictive of injury presentations at mass gatherings has the potential to significantly enhance the ability of event coordinators to plan effective prevention strategies and response capability for these events.

Quantification of particle emission characteristics and development of an emission model for use in transport microenvironments affected by traffic emissions

Vehicle emitted particles are of significant concern based on their potential to influence local air quality and human health. Transport microenvironments usually contain higher vehicle emission concentrations compared to other environments, and people spend a substantial amount of time in these microenvironments when commuting. Currently there is limited scientific knowledge on particle concentration, passenger exposure and the distribution of vehicle emissions in transport microenvironments, partially due to the fact that the instrumentation required to conduct such measurements is not available in many research centres. Information on passenger waiting time and location in such microenvironments has also not been investigated, which makes it difficult to evaluate a passenger’s spatial-temporal exposure to vehicle emissions. Furthermore, current emission models are incapable of rapidly predicting emission distribution, given the complexity of variations in emission rates that result from changes in driving conditions, as well as the time spent in driving condition within the transport microenvironment. In order to address these scientific gaps in knowledge, this work conducted, for the first time, a comprehensive statistical analysis of experimental data, along with multi-parameter assessment, exposure evaluation and comparison, and emission model development and application, in relation to traffic interrupted transport microenvironments. The work aimed to quantify and characterise particle emissions and human exposure in the transport microenvironments, with bus stations and a pedestrian crossing identified as suitable research locations representing a typical transport microenvironment. Firstly, two bus stations in Brisbane, Australia, with different designs, were selected to conduct measurements of particle number size distributions, particle number and PM2.5 concentrations during two different seasons. Simultaneous traffic and meteorological parameters were also monitored, aiming to quantify particle characteristics and investigate the impact of bus flow rate, station design and meteorological conditions on particle characteristics at stations. The results showed higher concentrations of PN20-30 at the station situated in an open area (open station), which is likely to be attributed to the lower average daily temperature compared to the station with a canyon structure (canyon station). During precipitation events, it was found that particle number concentration in the size range 25-250 nm decreased greatly, and that the average daily reduction in PM2.5 concentration on rainy days compared to fine days was 44.2 % and 22.6 % at the open and canyon station, respectively. The effect of ambient wind speeds on particle number concentrations was also examined, and no relationship was found between particle number concentration and wind speed for the entire measurement period. In addition, 33 pairs of average half-hourly PN7-3000 concentrations were calculated and identified at the two stations, during the same time of a day, and with the same ambient wind speeds and precipitation conditions. The results of a paired t-test showed that the average half-hourly PN7-3000 concentrations at the two stations were not significantly different at the 5% confidence level (t = 0.06, p = 0.96), which indicates that the different station designs were not a crucial factor for influencing PN7-3000 concentrations. A further assessment of passenger exposure to bus emissions on a platform was evaluated at another bus station in Brisbane, Australia. The sampling was conducted over seven weekdays to investigate spatial-temporal variations in size-fractionated particle number and PM2.5 concentrations, as well as human exposure on the platform. For the whole day, the average PN13-800 concentration was 1.3 x 104 and 1.0 x 104 particle/cm3 at the centre and end of the platform, respectively, of which PN50-100 accounted for the largest proportion to the total count. Furthermore, the contribution of exposure at the bus station to the overall daily exposure was assessed using two assumed scenarios of a school student and an office worker. It was found that, although the daily time fraction (the percentage of time spend at a location in a whole day) at the station was only 0.8 %, the daily exposure fractions (the percentage of exposures at a location accounting for the daily exposure) at the station were 2.7% and 2.8 % for exposure to PN13-800 and 2.7% and 3.5% for exposure to PM2.5 for the school student and the office worker, respectively. A new parameter, “exposure intensity” (the ratio of daily exposure fraction and the daily time fraction) was also defined and calculated at the station, with values of 3.3 and 3.4 for exposure to PN13-880, and 3.3 and 4.2 for exposure to PM2.5, for the school student and the office worker, respectively. In order to quantify the enhanced emissions at critical locations and define the emission distribution in further dispersion models for traffic interrupted transport microenvironments, a composite line source emission (CLSE) model was developed to specifically quantify exposure levels and describe the spatial variability of vehicle emissions in traffic interrupted microenvironments. This model took into account the complexity of vehicle movements in the queue, as well as different emission rates relevant to various driving conditions (cruise, decelerate, idle and accelerate), and it utilised multi-representative segments to capture the accurate emission distribution for real vehicle flow. This model does not only helped to quantify the enhanced emissions at critical locations, but it also helped to define the emission source distribution of the disrupted steady flow for further dispersion modelling. The model then was applied to estimate particle number emissions at a bidirectional bus station used by diesel and compressed natural gas fuelled buses. It was found that the acceleration distance was of critical importance when estimating particle number emission, since the highest emissions occurred in sections where most of the buses were accelerating and no significant increases were observed at locations where they idled. It was also shown that emissions at the front end of the platform were 43 times greater than at the rear of the platform. The CLSE model was also applied at a signalled pedestrian crossing, in order to assess increased particle number emissions from motor vehicles when forced to stop and accelerate from rest. The CLSE model was used to calculate the total emissions produced by a specific number and mix of light petrol cars and diesel passenger buses including 1 car travelling in 1 direction (/1 direction), 14 cars / 1 direction, 1 bus / 1 direction, 28 cars / 2 directions, 24 cars and 2 buses / 2 directions, and 20 cars and 4 buses / 2 directions. It was found that the total emissions produced during stopping on a red signal were significantly higher than when the traffic moved at a steady speed. Overall, total emissions due to the interruption of the traffic increased by a factor of 13, 11, 45, 11, 41, and 43 for the above 6 cases, respectively. In summary, this PhD thesis presents the results of a comprehensive study on particle number and mass concentration, together with particle size distribution, in a bus station transport microenvironment, influenced by bus flow rates, meteorological conditions and station design. Passenger spatial-temporal exposure to bus emitted particles was also assessed according to waiting time and location along the platform, as well as the contribution of exposure at the bus station to overall daily exposure. Due to the complexity of the interrupted traffic flow within the transport microenvironments, a unique CLSE model was also developed, which is capable of quantifying emission levels at critical locations within the transport microenvironment, for the purpose of evaluating passenger exposure and conducting simulations of vehicle emission dispersion. The application of the CLSE model at a pedestrian crossing also proved its applicability and simplicity for use in a real-world transport microenvironment.

A two-stage decision model for information filtering

Information mismatch and overload are two fundamental issues influencing the effectiveness of information filtering systems. Even though both term-based and pattern-based approaches have been proposed to address the issues, neither of these approaches alone can provide a satisfactory decision for determining the relevant information. This paper presents a novel two-stage decision model for solving the issues. The first stage is a novel rough analysis model to address the overload problem. The second stage is a pattern taxonomy mining model to address the mismatch problem. The experimental results on RCV1 and TREC filtering topics show that the proposed model significantly outperforms the state-of-the-art filtering systems.

Ureaplasma parvum : understanding the complexities of intra-amniotic infection in an ovine model

The human Ureaplasma species are the most frequently isolated bacteria from the upper genital tract of pregnant women and can cause clinically asymptomatic, intra-uterine infections, which are difficult to treat with antimicrobials. Ureaplasma infection of the upper genital tract during pregnancy has been associated with numerous adverse outcomes including preterm birth, chorioamnionitis and neonatal respiratory diseases. The mechanisms by which ureaplasmas are able to chronically colonise the amniotic fluid and avoid eradication by (i) the host immune response and (ii) maternally-administered antimicrobials, remain virtually unexplored. To address this gap within the literature, this study investigated potential mechanisms by which ureaplasmas are able to cause chronic, intra-amniotic infections in an established ovine model. In this PhD program of research the effectiveness of standard, maternal erythromycin for the treatment of chronic, intra-amniotic ureaplasma infections was evaluated. At 55 days of gestation pregnant ewes received an intra-amniotic injection of either: a clinical Ureaplasma parvum serovar 3 isolate that was sensitive to macrolide antibiotics (n = 16); or 10B medium (n = 16). At 100 days of gestation, ewes were then randomised to receive either maternal erythromycin treatment (30 mg/kg/day for four days) or no treatment. Ureaplasmas were isolated from amniotic fluid, chorioamnion, umbilical cord and fetal lung specimens, which were collected at the time of preterm delivery of the fetus (125 days of gestation). Surprisingly, the numbers of ureaplasmas colonising the amniotic fluid and fetal tissues were not different between experimentally-infected animals that received erythromycin treatment or infected animals that did not receive treatment (p > 0.05), nor were there any differences in fetal inflammation and histological chorioamnionitis between these groups (p > 0.05). These data demonstrate the inability of maternal erythromycin to eradicate intra-uterine ureaplasma infections. Erythromycin was detected in the amniotic fluid of animals that received antimicrobial treatment (but not in those that did not receive treatment) by liquid chromatography-mass spectrometry; however, the concentrations were below therapeutic levels (<10 – 76 ng/mL). These findings indicate that the ineffectiveness of standard, maternal erythromycin treatment of intra-amniotic ureaplasma infections may be due to the poor placental transfer of this drug. Subsequently, the phenotypic and genotypic characteristics of ureaplasmas isolated from the amniotic fluid and chorioamnion of pregnant sheep after chronic, intra-amniotic infection and low-level exposure to erythromycin were investigated. At 55 days of gestation twelve pregnant ewes received an intra-amniotic injection of a clinical U. parvum serovar 3 isolate, which was sensitive to macrolide antibiotics. At 100 days of gestation, ewes received standard maternal erythromycin treatment (30 mg/kg/day for four days, n = 6) or saline (n = 6). Preterm fetuses were surgically delivered at 125 days of gestation and ureaplasmas were cultured from the amniotic fluid and the chorioamnion. The minimum inhibitory concentrations (MICs) of erythromycin, azithromycin and roxithromycin were determined for cultured ureaplasma isolates, and antimicrobial susceptibilities were different between ureaplasmas isolated from the amniotic fluid (MIC range = 0.08 – 1.0 mg/L) and chorioamnion (MIC range = 0.06 – 5.33 mg/L). However, the increased resistance to macrolide antibiotics observed in chorioamnion ureaplasma isolates occurred independently of exposure to erythromycin in vivo. Remarkably, domain V of the 23S ribosomal RNA gene (which is the target site of macrolide antimicrobials) of chorioamnion ureaplasmas demonstrated significant variability (125 polymorphisms out of 422 sequenced nucleotides, 29.6%) when compared to the amniotic fluid ureaplasma isolates and the inoculum strain. This sequence variability did not occur as a consequence of exposure to erythromycin, as the nucleotide substitutions were identical between chorioamnion ureaplasmas isolated from different animals, including those that did not receive erythromycin treatment. We propose that these mosaic-like 23S ribosomal RNA gene sequences may represent gene fragments transferred via horizontal gene transfer. The significant differences observed in (i) susceptibility to macrolide antimicrobials and (ii) 23S ribosomal RNA sequences of ureaplasmas isolated from the amniotic fluid and chorioamnion suggests that the anatomical site from which they were isolated may exert selective pressures that alter the socio-microbiological structure of the bacterial population, by selecting for genetic changes and altered antimicrobial susceptibility profiles. The final experiment for this PhD examined antigenic size variation of the multiple banded antigen (MBA, a surface-exposed lipoprotein and predicted ureaplasmal virulence factor) in chronic, intra-amniotic ureaplasma infections. Previously defined ‘virulent-derived’ and ‘avirulent-derived’ clonal U. parvum serovar 6 isolates (each expressing a single MBA protein) were injected into the amniotic fluid of pregnant ewes (n = 20) at 55 days of gestation, and amniotic fluid was collected by amniocentesis every two weeks until the time of near-term delivery of the fetus (at 140 days of gestation). Both the avirulent and virulent clonal ureaplasma strains generated MBA size variants (ranging in size from 32 – 170 kDa) within the amniotic fluid of pregnant ewes. The mean number of MBA size variants produced within the amniotic fluid was not different between the virulent (mean = 4.2 MBA variants) and avirulent (mean = 4.6 MBA variants) ureaplasma strains (p = 0.87). Intra-amniotic infection with the virulent strain was significantly associated with the presence of meconium-stained amniotic fluid (p = 0.01), which is an indicator of fetal distress in utero. However, the severity of histological chorioamnionitis was not different between the avirulent and virulent groups. We demonstrated that ureaplasmas were able to persist within the amniotic fluid of pregnant sheep for 85 days, despite the host mounting an innate and adaptive immune response. Pro-inflammatory cytokines (interleukin (IL)-1â, IL-6 and IL-8) were elevated within the chorioamnion tissue of pregnant sheep from both the avirulent and virulent treatment groups, and this was significantly associated with the production of anti-ureaplasma IgG antibodies within maternal sera (p < 0.05). These findings suggested that the inability of the host immune response to eradicate ureaplasmas from the amniotic cavity may be due to continual size variation of MBA surface-exposed epitopes. Taken together, these data confirm that ureaplasmas are able to cause long-term in utero infections in a sheep model, despite standard antimicrobial treatment and the development of a host immune response. The overall findings of this PhD project suggest that ureaplasmas are able to cause chronic, intra-amniotic infections due to (i) the limited placental transfer of erythromycin, which prevents the accumulation of therapeutic concentrations within the amniotic fluid; (ii) the ability of ureaplasmas to undergo rapid selection and genetic variation in vivo, resulting in ureaplasma isolates with variable MICs to macrolide antimicrobials colonising the amniotic fluid and chorioamnion; and (iii) antigenic size variation of the MBA, which may prevent eradication of ureaplasmas by the host immune response and account for differences in neonatal outcomes. The outcomes of this program of study have improved our understanding of the biology and pathogenesis of this highly adapted microorganism.

Modeling heat and mass transfer process during convection drying of fruit

Fruit drying is a process of removing moisture to preserve fruits by preventing microbial spoilage. It increases shelf life, reduce weight and volume thus minimize packing, storage, and transportation cost and enable storage of food under ambient environment. But, it is a complex process which involves combination of heat and mass transfer and physical property change and shrinkage of the material. In this background, the aim of this paper to develop a mathematical model to simulate coupled heat and mass transfer during convective drying of fruit. This model can be used predict the temperature and moisture distribution inside the fruits during drying. Two models were developed considering shrinkage dependent and temperature dependent moisture diffusivity and the results were compared. The governing equations of heat and mass transfer are solved and a parametric study has been done with Comsol Multiphysics 4.3. The predicted results were validated with experimental data.

Anomalous event detection using a semi-two dimensional hidden Markov model

The rapid increase in the deployment of CCTV systems has led to a greater demand for algorithms that are able to process incoming video feeds. These algorithms are designed to extract information of interest for human operators. During the past several years, there has been a large effort to detect abnormal activities through computer vision techniques. Typically, the problem is formulated as a novelty detection task where the system is trained on normal data and is required to detect events which do not fit the learned `normal' model. Many researchers have tried various sets of features to train different learning models to detect abnormal behaviour in video footage. In this work we propose using a Semi-2D Hidden Markov Model (HMM) to model the normal activities of people. The outliers of the model with insufficient likelihood are identified as abnormal activities. Our Semi-2D HMM is designed to model both the temporal and spatial causalities of the crowd behaviour by assuming the current state of the Hidden Markov Model depends not only on the previous state in the temporal direction, but also on the previous states of the adjacent spatial locations. Two different HMMs are trained to model both the vertical and horizontal spatial causal information. Location features, flow features and optical flow textures are used as the features for the model. The proposed approach is evaluated using the publicly available UCSD datasets and we demonstrate improved performance compared to other state of the art methods.

Two novel numerical methods for solving the two-dimensional fractional Fitzhugh-Nagumo-monodomain model

Fractional mathematical models represent a new approach to modelling complex spatial problems in which there is heterogeneity at many spatial and temporal scales. In this paper, a two-dimensional fractional Fitzhugh-Nagumo-monodomain model with zero Dirichlet boundary conditions is considered. The model consists of a coupled space fractional diffusion equation (SFDE) and an ordinary differential equation. For the SFDE, we first consider the numerical solution of the Riesz fractional nonlinear reaction-diffusion model and compare it to the solution of a fractional in space nonlinear reaction-diffusion model. We present two novel numerical methods for the two-dimensional fractional Fitzhugh-Nagumo-monodomain model using the shifted Grunwald-Letnikov method and the matrix transform method, respectively. Finally, some numerical examples are given to exhibit the consistency of our computational solution methodologies. The numerical results demonstrate the effectiveness of the methods.

A comprehensive dual-scale wood torrefaction model : application to the analysis of thermal run-away in industrial heat treatment processes

A dual-scale model of the torrefaction of wood was developed and used to study industrial configurations. At the local scale, the computational code solves the coupled heat and mass transfer and the thermal degradation mechanisms of the wood components. At the global scale, the two-way coupling between the boards and the stack channels is treated as an integral component of the process. This model is used to investigate the effect of the stack configuration on the heat treatment of the boards. The simulations highlight that the exothermic reactions occurring in each single board can be accumulated along the stack. This phenomenon may result in a dramatic eterogeneity of the process and poses a serious risk of thermal runaway, which is often observed in industrial plants. The model is used to explain how thermal runaway can be lowered by increasing the airflow velocity, the sticker thickness or by gas flow reversal.

Generation of two isomers of C5H from the corresponding anions. A theoretically motivated mass spectrometric study

Molecular orbital calculations have predicted the stability of a range of connectivities for the radical C5H potential surface. The most energetically favorable of these include the linear C4CH geometry and two ring-chain structures HC2C3 and C2C3H The corresponding anions are also shown to be theoretically stable, and furthermore, a fourth isomer, C2CHC2, is predicted to be the most stable anion connectivity. These results have motivated experimental efforts. Methodologies for the generation of the non-ring-containing isomeric anions C4CH and C2CHC2 have been developed utilizing negative ion mass spectrometry. The absolute connectivities of the anions have been established using deuterium labeling, charge reversal, and neutralization reionization techniques. The success of the latter experiment confirms theoretical predictions of stability of the corresponding neutral species. This is the first reported observation of the neutral C2CHC2 species that calculations predict to be substantially less stable than the C4CH connectivity but still bound relative to isomerization processes.

Revolute joint approach to model joint mechanism in water-filled road safety barriers

Portable water-filled road barriers (PWFB) are roadside structures placed on temporary construction zones to separate work site from traffic. Recent changes in governing standards require PWFB to adhere to strict compliance in terms of lateral displacement and vehicle redirectionality. Actual PWFB test can be very costly, thus researchers resort to Finite Element Analysis (FEA) in the initial designs phase. There has been many research conducted on concrete barriers and flexible steel barriers using FEA, however not many was done pertaining to PWFB. This research probes a new technique to model joints in PWFB. Two methods to model the joining mechanism are presented and discussed in relation to its practicality and accuracy. Moreover, the study of the physical gap and mass of the barrier was investigated. Outcome from this research will benefit PWFB research and allow road barrier designers better knowledge in developing the next generation of road safety structures.

Generation of two isomers of C5H from the corresponding anions. A theoretically motivated mass spectrometric study

Molecular orbital calculations have predicted the stability of a range of connectivities for the radical C5H potential surface. The most energetically favorable of these include the linear C4CH geometry and two ring-chain structures HC2C3 and C2C3H The corresponding anions are also shown to be theoretically stable, and furthermore, a fourth isomer, C2CHC2, is predicted to be the most stable anion connectivity. These results have motivated experimental efforts. Methodologies for the generation of the non-ring-containing isomeric anions C4CH and C2CHC2 have been developed utilizing negative ion mass spectrometry. The absolute connectivities of the anions have been established using deuterium labeling, charge reversal, and neutralization reionization techniques. The success of the latter experiment confirms theoretical predictions of stability of the corresponding neutral species. This is the first reported observation of the neutral C2CHC2 species that calculations predict to be substantially less stable than the C4CH connectivity but still bound relative to isomerization processes.

A meshfree model for plant tissue deformations during drying

Plant tissue has a complex cellular structure which is an aggregate of individual cells bonded by middle lamella. During drying processes, plant tissue undergoes extreme deformations which are mainly driven by moisture removal and turgor loss. Numerical modelling of this problem becomes challenging when conventional grid-based modelling techniques such as Finite Element Methods (FEM) and Finite Difference Methods (FDM) have grid-based limitations. This work presents a meshfree approach to model and simulate the deformations of plant tissues during drying. This method demonstrates the fundamental capabilities of meshfree methods in handling extreme deformations of multiphase systems. A simplified 2D tissue model is developed by aggregating individual cells while accounting for the stiffness of the middle lamella. Each individual cell is simply treated as consisting of two main components: cell fluid and cell wall. The cell fluid is modelled using Smoothed Particle Hydrodynamics (SPH) and the cell wall is modelled using a Discrete Element Method (DEM). During drying, moisture removal is accounted for by reduction of cell fluid and wall mass, which causes local shrinkage of cells eventually leading to tissue scale shrinkage. The cellular deformations are quantified using several cellular geometrical parameters and a favourably good agreement is observed when compared to experiments on apple tissue. The model is also capable of visually replicating dry tissue structures. The proposed model can be used as a step in developing complex tissue models to simulate extreme deformations during drying.

A two-stage item recommendation method using probabilistic ranking with reconstructed tensor model

In a tag-based recommender system, the multi-dimensional correlation should be modeled effectively for finding quality recommendations. Recently, few researchers have used tensor models in recommendation to represent and analyze latent relationships inherent in multi-dimensions data. A common approach is to build the tensor model, decompose it and, then, directly use the reconstructed tensor to generate the recommendation based on the maximum values of tensor elements. In order to improve the accuracy and scalability, we propose an implementation of the -mode block-striped (matrix) product for scalable tensor reconstruction and probabilistically ranking the candidate items generated from the reconstructed tensor. With testing on real-world datasets, we demonstrate that the proposed method outperforms the benchmarking methods in terms of recommendation accuracy and scalability.

Multiphase porous media model for heat and mass transfer during drying of agricultural products

Modelling of food processing is complex because it involves sophisticated material and transport phenomena. Most of the agricultural products such fruits and vegetables are hygroscopic porous media containing free water, bound water, gas and solid matrix. Considering all phase in modelling is still not developed. In this article, a comprehensive porous media model for drying has been developed considering bound water, free water separately, as well as water vapour and air. Free water transport was considered as diffusion, pressure driven and evaporation. Bound water assumed to be converted to free water due to concentration difference and also can diffuse. Binary diffusion between water vapour and air was considered. Since, the model is fundamental physics based it can be applied to any drying applications and other food processing where heat and mass transfer takes place in porous media with significant evaporation and other phase change.