Acoustic identification of twelve species of echolocating bat by discriminant function analysis and artificial neural networks

We recorded echolocation calls from 14 sympatric species of bat in Britain. Once digitised, one temporal and four spectral features were measured from each call. The frequency-time course of each call was approximated by fitting eight mathematical functions, and the goodness of fit, represented by the mean-squared error, was calculated. Measurements were taken using an automated process that extracted a single call from background noise and measured all variables without intervention. Two species of Rhinolophus were easily identified from call duration and spectral measurements. For the remaining 12 species, discriminant function analysis and multilayer back-propagation perceptrons were used to classify calls to species level. Analyses were carried out with and without the inclusion of curve-fitting data to evaluate its usefulness in distinguishing among species. Discriminant function analysis achieved an overall correct classification rate of 79% with curve-fitting data included, while an artificial neural network achieved 87%. The removal of curve-fitting data improved the performance of the discriminant function analysis by 2 %, while the performance of a perceptron decreased by 2 %. However, an increase in correct identification rates when curve-fitting information was included was not found for all species. The use of a hierarchical classification system, whereby calls were first classified to genus level and then to species level, had little effect on correct classification rates by discriminant function analysis but did improve rates achieved by perceptrons. This is the first published study to use artificial neural networks to classify the echolocation calls of bats to species level. Our findings are discussed in terms of recent advances in recording and analysis technologies, and are related to factors causing convergence and divergence of echolocation call design in bats.

Model-based analysis and optimization of bioreactor for hematopoietic stem cell cultivation

One of the problems to be solved in attaining the full potentials of hematopoietic stem cell (HSC) applications is the limited availability of the cells. Growing HSCs in a bioreactor offers an alternative solution to this problem. Besides, it also offers the advantages of eliminating labour intensive process as well as the possible contamination involved in the periodic nutrient replenishments in the traditional T-flask stem cell cultivation. In spite of this, the optimization of HSC cultivation in a bioreactor has been barely explored. This manuscript discusses the development of a mathematical model to describe the dynamics in nutrient distribution and cell concentration of an ex vivo HSC cultivation in a microchannel perfusion bioreactor. The model was further used to optimize the cultivation by proposing three alternative feeding strategies in order to prevent the occurrence of nutrient limitation in the bioreactor. The evaluation of these strategies, the periodic step change increase in the inlet oxygen concentration, the periodic step change increase in the media inflow, and the feedback control of media inflow, shows that these strategies can successfully improve the cell yield of the bioreactor. In general, the developed model is useful for the design and optimization of bioreactor operation.

Technoeconomic analysis of an integrated microalgae photobioreactor, biodiesel and biogas production facility

As fossil fuel prices increase and environmental concerns gain prominence, the development of alternative fuels from biomass has become more important. Biodiesel produced from microalgae is becoming an attractive alternative to share the role of petroleum. Currently it appears that the production of microalgal biodiesel is not economically viable in current environment because it costs more than conventional fuels. Therefore, a new concept is introduced in this article as an option to reduce the total production cost of microalgal biodiesel. The integration of biodiesel production system with methane production via anaerobic digestion is proved in improving the economics and sustainability of overall biodiesel stages. Anaerobic digestion of microalgae produces methane and further be converted to generate electricity. The generated electricity can surrogate the consumption of energy that require in microalgal cultivation, dewatering, extraction and transesterification process. From theoretical calculations, the electricity generated from methane is able to power all of the biodiesel production stages and will substantially reduce the cost of biodiesel production (33% reduction). The carbon emissions of biodiesel production systems are also reduced by approximately 75% when utilizing biogas electricity compared to when the electricity is otherwise purchased from the Victorian grid. The overall findings from this study indicate that the approach of digesting microalgal waste to produce biogas will make the production of biodiesel from algae more viable by reducing the overall cost of production per unit of biodiesel and hence enable biodiesel to be more competitive with existing fuels.

Large-volume methacrylate monolith for plasmid purification : process engineering approach to synthesis and application

The extent of exothermicity associated with the construction of large-volume methacrylate monolithic columns has somewhat obstructed the realisation of large-scale rapid biomolecule purification especially for plasmid-based products which have proven to herald future trends in biotechnology. A novel synthesis technique via a heat expulsion mechanism was employed to prepare a 40 mL methacrylate monolith with a homogeneous radial pore structure along its thickness. Radial temperature gradient was recorded to be only 1.8 °C. Maximum radial temperature recorded at the centre of the monolith was 62.3 °C, which was only 2.3 °C higher than the actual polymerisation temperature. Pore characterisation of the monolithic polymer showed unimodal pore size distributions at different radial positions with an identical modal pore size of 400 nm. Chromatographic characterisation of the polymer after functionalisation with amino groups displayed a persistent dynamic binding capacity of 15.5 mg of plasmid DNA/mL. The maximum pressure drop recorded was only 0.12 MPa at a flow rate of 10 mL/min. The polymer demonstrated rapid separation ability by fractionating Escherichia coli DH5α-pUC19 clarified lysate in only 3 min after loading. The plasmid sample collected after the fast purification process was tested to be a homogeneous supercoiled plasmid with DNA electrophoresis and restriction analysis.

Degradation of hydroxycinnamic acid mixtures in aqueous sucrose solutions by the Fenton process

The degradation efficiencies and behaviors of caffeic acid (CaA), p-coumaric acid (pCoA) and ferulic acid (FeA) in aqueous sucrose solutions containing the mixture of these hydroxycinnamic acids (HCAs) mixtures were studied by the Fenton oxidation process. Central composite design and multi-response surface methodology were used to evaluate and optimize the interactive effects of process parameters. Four quadratic polynomial models were developed for the degradation of each individual acid in the mixture and the total HCAs degraded. Sucrose was the most influential parameter that significantly affected the total amount of HCA degraded. Under the conditions studied there was < 0.01% loss of sucrose in all reactions. The optimal values of the process parameters for a 200 mg/L HCA mixture in water (pH 4.73, 25.15 °C) and sucrose solution (13 mass%, pH 5.39, 35.98 °C) were 77% and 57% respectively. Regression analysis showed goodness of fit between the experimental results and the predicted values. The degradation behavior of CaA differed from those of pCoA and FeA, where further CaA degradation is observed at increasing sucrose and decreasing solution pH. The differences (established using UV/Vis and ATR-FTIR spectroscopy) were because, unlike the other acids, CaA formed a complex with Fe(III) or with Fe(III) hydrogen-bonded to sucrose, and coprecipitated with lepidocrocite, an iron oxyhydroxide.

The applicability of the Precaution Adoption Process Model for understanding self-regulatory driving behaviour among older drivers

1. Introduction The success of self-regulation, in terms of enhancing older drivers’ safety and maintaining their mobility, depends largely upon older drivers’ awareness of the declines in their driving abilities. Therefore, interventions targeted at increasing older drivers’ safety should aim to enhance their awareness of their physical, sensory and cognitive limitations. Moreover, previous research suggests that driving behaviour change may occur through stages and that interventions and feedback may be perceived differently at each stage. 2. Study aims To further understand the process of driving self-regulation among older adults by exploring their perceptions and experiences of self-regulation, using the PAPM as a framework. To investigate the possible impact of feedback on their driving on their decision making process. 3. Methodology Research tool: Qualitative focus groups (n=5 sessions) Recruitment: Posters, media, newspaper advertisement and emails Inclusion criteria: Aged 70 or more, English-speaking, current drivers Participants: Convenience sample of 27 men and women aged 74 to 90 in the Sunshine Coast and Brisbane city, Queensland, Australia. 4. Analysis Thematic analysis was conducted following the process outlined by Braun and Clarke (2006) to identify, analyse and report themes within the data. Four main themes were identified.

Determination of effective moisture diffusivity of banana using thermogravimetric analysis

Drying has been extensively used as a food preservation procedure. The longer life attained by drying is however accompanied by huge energy consumption and deterioration of quality. Moisture diffusivity is an important factor that is considered essential to understand for design, analysis, and optimization of drying processes for food and other materials. Without an accurate value of moisture diffusivity, drying kinetics, energy consumption, quality attributes such as shrinkage, texture, and microstructure cannot be predicted properly. However, moisture diffusivities differ due to variation of composition and microstructure of foodstuff and drying variables. For a particular food, it changes with many factors including moisture content, water holding capacity, process variables and physiochemical attributes of food. Published information on moisture diffusivities of banana is inadequate and sometimes inconsistent due to lack of precise repeatable analysis techniques. In this work, the effective moisture diffusivity of banana was determined by Thermogravimetric Analysis (TGA), which ensures precise measurements and reproduction of experiments. A TGA Q500 V20.13 Build 39 was deployed to obtain the drying curve of the food material. It was found that effective moisture diffusivity ranged from 6.63 x10-10 to 1.03 x10-9 and 1.34 x10-10 to 6.60 x10-10 for isothermal at 70 0C and non-isothermal process respectively.These values are consistent with the value of moisture diffusivity found in the literature.

Using conversation analysis for understanding children’s talk about traumatic events

This chapter provides a theoretical overview of literature that uses conversation analysis (CA) to explore children’s interactions related to trauma and associated mental health matters. The relatively new approach of using CA to understand trauma reveals the importance of talk in the process of recovery, and also how the participants co-construct talk about traumatic experiences. The chapter will explore literature using a CA approach to investigate children’s trauma talk with professionals as well as literature specifically discussing children’s talk about their traumatic experiences with people who are not qualified therapists or psychiatrists. We conclude by calling for more research using a CA approach for investigating children’s traumatic experiences due to the insight it provides into each child’s personal sense making of traumatic events with a range of people.

Single voxel Magnetic Resonance Spectroscopic (1H-MRS) analysis of deep brain structures after traumatic prefrontal brain injury (TBI) in rat

Chronic difficulties arising from mild brain injury (TBI) are difficult to predict because the processes underlying changes after TBI are poorly understood. In mild brain injury the extent of neuropsychiatric and cognitive symptoms correspond poorly to overt tissue loss (Barth 1983; Liu 2010). Cellular, immune and hormonal cascades occurring after injury and continuing during the healing process may impact uninjured brain regions sensitive to the effects of physiological and emotional stress, which receive projections from the injury site. Changes in these most basic properties due to injury or disease have profound implications for virtually every aspect of brain function through disruption of neurotransmitter, neuroendocrine and metabolic systems. In order to screen for changes in transmitter and metabolic activity, in this study we developed Single voxel proton Magnetic Resonance Spectroscopy (1H-MRS) for use in both injured and control animals. We first evaluated if 1H-MRS could be used to evaluate in vivo, alterations in brain metabolism and catabolism of the prefrontal cortex, amygdala and ventral hippocampus in both control and injured animals after controlled cortical impact injury to the rat prefrontal cortex. We found that metabolite measurements for Myo-Inositol, Choline, creatine, Glutamate+Glutamine, and N-acetyl-acetate are attainable in deep brain structures in vivo in injured and controls rats. We next seek to evaluate longitudinally, in vivo, alterations in brain metabolism and catabolism of the prefrontal cortex, amygdala and ventral hippocampus during the first month after controlled cortical impact injury to the rat prefrontal cortex. These ongoing studies will provide data on the changes in transmitters and metabolites over time in injured and non-injured subjects. These studies address some of the fundamental questions about how mild brain injury has such diverse effects on overall brain health and function.

Regional aviation and economic growth : cointegration and causality analysis in Australia

Air transport is a critical link to regional, rural and remote communities in Australia. Air services provide important economic and social benefits but very little research has been done on assessing the value of regional aviation. This research provides the first empirical evidence that there is short and long run causality between regional aviation and economic growth. The authors analysed 88 regional airports in Australia over a period of 1985–86 to 2010–11 to determine the catalytic impacts of regional air transport on regional economic growth. The analysis was conducted using annual data related to total airport passenger movements – for the level of airport activity, and real aggregate taxable income – to represent economic growth. A significant bi-directional relationship was established: airports have an impact on regional economic growth and the economy directly impacts regional air transport. The economic significance of regional air transport confirms the importance of the airport as infrastructure for regional councils and the need for them to maintain and develop local airports. Funding should be targeted at airports directly to support regional development.

Latent class analysis reveals distinct subgroups of patients based on symptom occurrence and demographic and clinical characteristics

Context Cancer patients experience a broad range of physical and psychological symptoms as a result of their disease and its treatment. On average, these patients report ten unrelieved and co-occurring symptoms. Objectives To determine if subgroups of oncology outpatients receiving active treatment (n=582) could be identified based on their distinct experience with thirteen commonly occurring symptoms; to determine whether these subgroups differed on select demographic, and clinical characteristics; and to determine if these subgroups differed on quality of life (QOL) outcomes. Methods Demographic, clinical, and symptom data from one Australian and two U.S. studies were combined. Latent class analysis (LCA) was used to identify patient subgroups with distinct symptom experiences based on self-report data on symptom occurrence using the Memorial Symptom Assessment Scale (MSAS). Results Four distinct latent classes were identified (i.e., All Low (28.0%), Moderate Physical and Lower Psych (26.3%), Moderate Physical and Higher Psych (25.4%), All High (20.3%)). Age, gender, education, cancer diagnosis, and presence of metastatic disease differentiated among the latent classes. Patients in the All High class had the worst QOL scores. Conclusion Findings from this study confirm the large amount of interindividual variability in the symptom experience of oncology patients. The identification of demographic and clinical characteristics that place patients are risk for a higher symptom burden can be used to guide more aggressive and individualized symptom management interventions.

Sectional analysis of stormwater treatment performance of a constructed wetland

Constructed wetlands are among the most common Water Sensitive Urban Design (WSUD) measures for stormwater treatment. These systems have been extensively studied to understand their performance and influential treatment processes. Unfortunately, most past studies have been undertaken considering a wetland system as a lumped system with a primary focus on the reduction of the event mean concentration (EMC) values of specific pollutant species or total pollutant load removal. This research study adopted an innovative approach by partitioning the inflow runoff hydrograph and then investigating treatment performance in each partition and their relationships with a range of hydraulic factors. The study outcomes confirmed that influenced by rainfall characteristics, the constructed wetland displays different treatment characteristics for the initial and later sectors of the runoff hydrograph. The treatment of small rainfall events (<15 mm) is comparatively better at the beginning of runoff events while the trends in pollutant load reductions for large rainfall events (>15 mm) are generally lower at the beginning and gradually increase towards the end of rainfall events. This highlights the importance of ensuring that the inflow into a constructed wetland has low turbulence in order to achieve consistent treatment performance for both, small and large rainfall events.

Segmenting Australian high school students : two-step cluster analysis preliminary results

A one size fits all approach dominates alcohol programs in school settings (Botvin et al., 2007), which may limit program effectiveness (Snyder et al., 2004). Programs tailored to the meet the needs and wants of adolescent groups may be more effective. Limited attention has been directed towards employing a full segmentation process. Where segmentation has been examined, the focus has remained on socio-demographic characteristics and more recently psychographic variables (Mathijssen et al., 2012). The current study aimed to identify whether the addition of behaviour could be used to identify segments. Variables included attitudes towards binge drinking (α = 0.86), behavioral intentions’ (α = 0.97), perceived behavioral control (PBC), injunctive norms (α = 0.94); descriptive norms (α = 0.87), knowledge and reported behaviour. Data was collected from five schools, n = 625 (32.96% girls). Two-Step cluster analysis produced a sample (n = 625) with a silhouette measure of cohesion and separation of 0.4. The intention measure and whether students reported previously consuming alcohol were the most distinguishing characteristics - predictor importance scores of (1.0). A four segment solution emerged. The first segment (“Male abstainers” – 37.2%) featured the highest knowledge score (M: 5.9) along with the lowest-risk drinking attitudes and intentions to drink excessively. Segment 2 (“At risk drinkers” - 11.2%) were characterised by their high-risk attitudes and high-risk drinking intentions. Injunctive (M: 4.1) and descriptive norms (M: 4.9) may indicate a social environment where drinking is the norm. Segment 3 (”Female abstainers” – 25.9%) represents young girls, who have the lowest-risk attitudes and low intentions to drink excessively. The fourth and final segment (boys = 67.4%) (“Moderate drinkers” – 25.7%) all report previously drinking alcohol yet their attitudes and intentions towards excessive alcohol consumption are lower than other segments. Segmentation focuses on identifying groups of individuals who feature similar characteristics. The current study illustrates the importance of including reported behaviour in addition to psychographic and demographic characteristics to identify unique groups to inform intervention planning and design. Key messages The principle of segmentation has received limited attention in the context of school-based alcohol education programs. This research identified four segments amongst 14-16 year high school students, each of which can be targeted with a unique, tailored program to meet the needs and wants of the target audience.

Process variability of pollutant build-up on urban road surfaces

Knowledge of the pollutant build-up process is a key requirement for developing stormwater pollution mitigation strategies. In this context, process variability is a concept which needs to be understood in-depth. Analysis of particulate build-up on three road surfaces in an urban catchment confirmed that particles <150µm and >150µm have characteristically different build-up patterns, and these patterns are consistent over different field conditions. Three theoretical build-up patterns were developed based on the size-fractionated particulate build-up patterns, and these patterns explain the variability in particle behavior and the variation in particle-bound pollutant load and composition over the antecedent dry period. Behavioral variability of particles <150µm was found to exert the most significant influence on the build-up process variability. As characterization of process variability is particularly important in stormwater quality modeling, it is recommended that the influence of behavioral variability of particles <150µm on pollutant build-up should be specifically addressed. This would eliminate model deficiencies in the replication of the build-up process and facilitate the accounting of the inherent process uncertainty, and thereby enhance the water quality predictions.

Analysis of passenger group behaviour and its impact on passenger flow using an agent-based model

Group interaction within crowds is a common phenomenon and has great influence on pedestrian behaviour. This paper investigates the impact of passenger group dynamics using an agent-based simulation method for the outbound passenger process at airports. Unlike most passenger-flow models that treat passengers as individual agents, the proposed model additionally incorporates their group dynamics as well. The simulation compares passenger behaviour at airport processes and discretionary services under different group formations. Results from experiments (both qualitative and quantitative) show that incorporating group attributes, in particular, the interactions with fellow travellers and wavers can have significant influence on passengers activity preference as well as the performance and utilisation of services in airport terminals. The model also provides a convenient way to investigate the effectiveness of airport space design and service allocations, which can contribute to positive passenger experiences. The model was created using AnyLogic software and its parameters were initialised using recent research data published in the literature.