Priority threat management of invasive animals to protect biodiversity under climate change

Climate change is a major threat to global biodiversity, and its impacts can act synergistically to heighten the severity of other threats. Most research on projecting species range shifts under climate change has not been translated to informing priority management strategies on the ground. We develop a prioritization framework to assess strategies for managing threats to biodiversity under climate change and apply it to the management of invasive animal species across one-sixth of the Australian continent, the Lake Eyre Basin. We collected information from key stakeholders and experts on the impacts of invasive animals on 148 of the region's most threatened species and 11 potential strategies. Assisted by models of current distributions of threatened species and their projected distributions, experts estimated the cost, feasibility, and potential benefits of each strategy for improving the persistence of threatened species with and without climate change. We discover that the relative cost-effectiveness of invasive animal control strategies is robust to climate change, with the management of feral pigs being the highest priority for conserving threatened species overall. Complementary sets of strategies to protect as many threatened species as possible under limited budgets change when climate change is considered, with additional strategies required to avoid impending extinctions from the region. Overall, we find that the ranking of strategies by cost-effectiveness was relatively unaffected by including climate change into decision-making, even though the benefits of the strategies were lower. Future climate conditions and impacts on range shifts become most important to consider when designing comprehensive management plans for the control of invasive animals under limited budgets to maximize the number of threatened species that can be protected.

An integrated mathematical approach to measure the capacity of MMTS

This article focusses upon multi-modal transportation systems (MMTS) and the issues surrounding the determination of system capacity. For that purpose a multi-objective framework is advocated that integrates all the different modes and many different competing capacity objectives. This framework is analytical in nature and facilitates a variety of capacity querying and capacity expansion planning.

Variable Size Population NSGA-II: VPNSGA-II

Multi-objective optimization is an active field of research with broad applicability in aeronautics. This report details a variant of the original NSGA-II software aimed to improve the performances of such a widely used Genetic Algorithm in finding the optimal Pareto-front of a Multi-Objective optimization problem for the use of UAV and aircraft design and optimsaiton. Original NSGA-II works on a population of predetermined constant size and its computational cost to evaluate one generation is O(mn^2 ), being m the number of objective functions and n the population size. The basic idea encouraging this work is that of reduce the computational cost of the NSGA-II algorithm by making it work on a population of variable size, in order to obtain better convergence towards the Pareto-front in less time. In this work some test functions will be tested with both original NSGA-II and VPNSGA-II algorithms; each test will be timed in order to get a measure of the computational cost of each trial and the results will be compared.

Hybrid game strategies for MO problems in aerospace design

Game strategies have been developed in past decades and used in the field of economics, engineering, computer science and biology due to their efficiency in solving design optimisation problems. In addition, research on Multi-Objective (MO) and Multidisciplinary Design Optimisation (MDO) has focused on developing robust and efficient optimisation method to produce quality solutions with less computational time. In this paper, a new optimisation method Hybrid Game Strategy for MO problems is introduced and compared to CMA-ES based optimisation approach. Numerical results obtained from both optimisation methods are compared in terms of computational expense and model quality. The benefits of using Game-strategies are demonstrated.

Sustainable building envelope design by considering energy cost and occupant satisfaction

The built environment is a major contributor to the world’s carbon dioxide emissions, with a considerable amount of energy being consumed in buildings due to heating, ventilation and air-conditioning, space illumination, use of electrical appliances, etc., to facilitate various anthropogenic activities. The development of sustainable buildings seeks to ameliorate this situation mainly by reducing energy consumption. Sustainable building design, however, is a complicated process involving a large number of design variables, each with a range of feasible values. There are also multiple, often conflicting, objectives involved such as the life cycle costs and occupant satisfaction. One approach to dealing with this is through the use of optimization models. In this paper, a new multi-objective optimization model is developed for sustainable building design by considering the design objectives of cost and energy consumption minimization and occupant comfort level maximization. In a case study demonstration, it is shown that the model can derive a set of suitable design solutions in terms of life cycle cost, energy consumption and indoor environmental quality so as to help the client and design team gain a better understanding of the design space and trade-off patterns between different design objectives. The model can very useful in the conceptual design stages to determine appropriate operational settings to achieve the optimal building performance in terms of minimizing energy consumption and maximizing occupant comfort level.

Multi-victim sexual assault : a case study in rural Australia

Objective: There is a paucity of information regarding cases of multi-victim sexual assault of children. The reported incidence suggests that these cases are rare. The aim of this paper is to provide practitioners with information about effective intervention strategies arising out of the direct experience of managing a case of multi-victim sexual assault in an Australian rural community. --------- Method: A descriptive, case-report methodology summarizing the investigation and intervention in a case of multi-victim sexual assault is reported. A community based intervention arising out of the disclosures of 21 male children is described. The intervention occurred at an individual, group, and community level using a coordinated multi-disciplinary team and natural helping networks. ---------- Results: The coordination of police and welfare services increased the communication flow to victims, their families, and the community. The case also demonstrated the utility in regularly briefing political and bureaucratic authorities as well as local officials about emergent issues. Coordinating political and bureaucratic responses was essential in obtaining ongoing support and sufficient researching to enable the effective delivery of services. ---------- Conclusions: Interventions were focused at an individual, group, and community level using a coordinated multi-disciplinary team and natural helping networks. This provided a choice of services which were sensitive to the case setting. Recommendations are offered for practitioners who are confronted with similar events. While this paper describes an approach for intervening in a case of multi-victim sexual assault, further empirical research is needed to enable service deliverers to efficaciously target interventions which offer choice to victims and their families.

Load–frequency control : a GA-based multi-agent reinforcement learning

The load–frequency control (LFC) problem has been one of the major subjects in a power system. In practice, LFC systems use proportional–integral (PI) controllers. However since these controllers are designed using a linear model, the non-linearities of the system are not accounted for and they are incapable of gaining good dynamical performance for a wide range of operating conditions in a multi-area power system. A strategy for solving this problem because of the distributed nature of a multi-area power system is presented by using a multi-agent reinforcement learning (MARL) approach. It consists of two agents in each power area; the estimator agent provides the area control error (ACE) signal based on the frequency bias estimation and the controller agent uses reinforcement learning to control the power system in which genetic algorithm optimisation is used to tune its parameters. This method does not depend on any knowledge of the system and it admits considerable flexibility in defining the control objective. Also, by finding the ACE signal based on the frequency bias estimation the LFC performance is improved and by using the MARL parallel, computation is realised, leading to a high degree of scalability. Here, to illustrate the accuracy of the proposed approach, a three-area power system example is given with two scenarios.

Flexible high-power multi DC-DC converters for train systems

This thesis reports on the investigations, simulations and analyses of novel power electronics topologies and control strategies. The research is financed by an Australian Research Council (ARC) Linkage (07-09) grant. Therefore, in addition to developing original research and contributing to the available knowledge of power electronics, it also contributes to the design of a DC-DC converter for specific application to the auxiliary power supply in electric trains. Specifically, in this regard, it contributes to the design of a 7.5 kW DC-DC converter for the industrial partner (Schaffler and Associates Ltd) who supported this project. As the thesis is formatted as a ‘thesis by publication’, the contents are organized around published papers. The research has resulted in eleven papers, including seven peer reviewed and published conference papers, one published journal paper, two journal papers accepted for publication and one submitted journal paper (provisionally accepted subject to few changes). In this research, several novel DC-DC converter topologies are introduced, analysed, and tested. The similarity of all of the topologies devised lies in their ‘current circulating’ switching state, which allows them to store some energy in the inductor, as extra inductor current. The stored energy may be applied to enhance the performance of the converter in the occurrence of load current or input voltage disturbances. In addition, when there is an alternating load current, the ability to store energy allows the converter to perform satisfactorily despite frequently and highly varying load current. In this research, the capability of current storage has been utilised to design topologies for specific applications, and the enhancement of the performance of the considered applications has been illustrated. The simplest DC-DC converter topology, which has a ‘current circulating’ switching state, is the Positive Buck-Boost (PBB) converter (also known as the non-inverting Buck-Boost converter). Usually, the topology of the PBB converter is operating as a Buck or a Boost converter in applications with widely varying input voltage or output reference voltage. For example, in electric railways (the application of our industrial partner), the overhead line voltage alternates from 1000VDC to 500VDC and the required regulated voltage is 600VDC. In the course of this research, our industrial partner (Schaffler and Associates Ltd) industrialized a PBB converter–the ‘Mudo converter’–operating at 7.5 kW. Programming the onboard DSP and testing the PBB converter in experimental and nominal power and voltage was part of this research program. In the earlier stages of this research, the advantages and drawbacks of utilization of the ‘current circulating’ switching state in the positive Buck-Boost converter were investigated. In brief, the advantages were found to be robustness against input voltage and current load disturbances, and the drawback was extra conduction and switching loss. Although the robustness against disturbances is desirable for many applications, the price of energy loss must be minimized to attract attention to the utilization of the PBB converter. In further stages of this research, two novel control strategies for different applications were devised to minimise the extra energy loss while the advantages of the positive Buck-Boost converter were fully utilized. The first strategy is Smart Load Controller (SLC) for applications with pre-knowledge or predictability of input voltage and/or load current disturbances. A convenient example of these applications is electric/hybrid cars where a master controller commands all changes in loads and voltage sources. Therefore, the master controller has a pre-knowledge of the load and input voltage disturbances so it can apply the SLC strategy to utilize robustness of the PBB converter. Another strategy aiming to minimise energy loss and maximise the robustness in the face of disturbance is developed to cover applications with unexpected disturbances. This strategy is named Dynamic Hysteresis Band (DHB), and is used to manipulate the hysteresis band height after occurrence of disturbance to reduce dynamics of the output voltage. When no disturbance has occurred, the PBB converter works with minimum inductor current and minimum energy loss. New topologies based on the PBB converter have been introduced to address input voltage disturbances for different onboard applications. The research shows that the performance of applications of symmetrical/asymmetrical multi-level diode-clamped inverters, DC-networks, and linear-assisted RF amplifiers may be enhanced by the utilization of topologies based on the PBB converter. Multi-level diode-clamped inverters have the problem of DC-link voltage balancing when the power factor of their load closes to unity. This research has shown that this problem may be solved with a suitable multi-output DC-DC converter supplying DClink capacitors. Furthermore, the multi-level diode-clamped inverters supplied with asymmetrical DC-link voltages may improve the quality of load voltage and reduce the level of Electromagnetic Interference (EMI). Mathematical analyses and experiments on supplying symmetrical and asymmetrical multi-level inverters by specifically designed multi-output DC-DC converters have been reported in two journal papers. Another application in which the system performance can be improved by utilization of the ‘current circulating’ switching state is linear-assisted RF amplifiers in communicational receivers. The concept of ‘linear-assisted’ is to divide the signal into two frequency domains: low frequency, which should be amplified by a switching circuit; and the high frequency domain, which should be amplified by a linear amplifier. The objective is to minimize the overall power loss. This research suggests using the current storage capacity of a PBB based converter to increase its bandwidth, and to increase the domain of the switching converter. The PBB converter addresses the industrial demand for a DC-DC converter for the application of auxiliary power supply of a typical electric train. However, after testing the industrial prototype of the PBB converter, there were some voltage and current spikes because of switching. To attenuate this problem without significantly increasing the switching loss, the idea of Active Gate Signalling (AGS) is presented. AGS suggests a smart gate driver that selectively controls the switching process to reduce voltage/current spikes, without unacceptable reduction in the efficiency of switching.

Nursing Sans Frontieres: A Three Year Case Study of Multi-State Registration of Support Nursing Practice Using Information Technology

Objective: To highlight the registration issues for nurses who wish to practice nationally, particularly those practicing within the telehealth sector. Design: As part of a national clinical research study, applications were made to every state and territory for mutual recognition of nursing registration and fee waiver for telenursing cross boarder practice for a period of three years. These processes are described using a case study approach. Outcome: The aim of this case study was to achieve registration in every state and territory of Australia without paying multiple fees by using mutual recognition provisions and the cross-border fee waiver policy of the nurse regulatory authorities in order to practice telenursing. Results: Mutual recognition and fee waiver for cross-border practice was granted unconditionally in two states: Victoria (Vic) and Tasmania (Tas), and one territory: the Northern Territory (NT). The remainder of the Australian states and territories would only grant temporary registration for the period of the project or not at all, due to policy restrictions or nurse regulatory authority (NRA) Board decisions. As a consequence of gaining fee waiver the annual cost of registration was a maximum of $145 per annum as opposed to the potential $959 for initial registration and $625 for annual renewal. Conclusions: Having eight individual nurses Acts and NRAs for a population of 265,000 nurses would clearly indicate a case for over regulation in this country. The structure of regulation of nursing in Australia is a barrier to the changing and evolving role of nurses in the 21st century and a significant factor when considering workforce planning.

Vibrational spectroscopy of the multi anion mineral zykaite Fe4(AsO4)(SO4)(OH)•15H2O – implications for arsenate removal

Some minerals are colloidal and are poorly diffracting . Vibrational spectroscopy offers one of the few methods for the assessment of the structure of these types of minerals. Among this group of minerals is zykaite with formula Fe4(AsO4)(SO4)(OH)•15H2O. The objective of this research is to determine the molecular structure of the mineral zykaite using vibrational spectroscopy. Raman and infrared bands are attributed to the AsO43-, SO42- and water stretching vibrations. The sharp band at 3515 cm-1 is assigned to the stretching vibration of the OH units. This mineral offers a mechanism for the formation of more crystalline minerals such as scorodite and bukovskyite. Arsenate ions can be removed from aqueous systems through the addition of ferric compounds such as ferric chloride. This results in the formation of minerals such as zykaite and pitticite (Fe3+,AsO4,SO4,H2O).

Raman spectroscopy of the multi-anion mineral mallestigite Pb3Sb5+(SO4)(AsO4)(OH)6•3H2O – a mineral of archaeological significance

Some minerals are formed which show poorly defined X-ray diffraction patterns. Vibrational spectroscopy offers one of the few methods for the assessment of the structure of the oxyanions in such minerals. Among this group of minerals is mallestigite with formula Pb3Sb5+(SO4)(AsO4)(OH)6•3H2O. The objective of this research is to determine the molecular structure of the mineral mallestigite using vibrational spectroscopy. Raman and infrared bands are attributed to the AsO43- , SO42- and water stretching vibrations. Mallestigite is a mineral formed in ancient waste dumps such as occurs at Mallestiger, Carinthia, Austria and as such is a mineral of archaeological significance.

Correlation-based damage detection for complicated truss bridges using multi-layer genetic algorithms

The study presents a multi-layer genetic algorithm (GA) approach using correlation-based methods to facilitate damage determination for through-truss bridge structures. To begin, the structure’s damage-suspicious elements are divided into several groups. In the first GA layer, the damage is initially optimised for all groups using correlation objective function. In the second layer, the groups are combined to larger groups and the optimisation starts over at the normalised point of the first layer result. Then the identification process repeats until reaching the final layer where one group includes all structural elements and only minor optimisations are required to fine tune the final result. Several damage scenarios on a complicated through-truss bridge example are nominated to address the proposed approach’s effectiveness. Structural modal strain energy has been employed as the variable vector in the correlation function for damage determination. Simulations and comparison with the traditional single-layer optimisation shows that the proposed approach is efficient and feasible for complicated truss bridge structures when the measurement noise is taken into account.

Maintenance optimisation of a multi-state series-parallel system considering economic dependence and state-dependent inspection intervals

This paper presents a maintenance optimisation method for a multi-state series-parallel system considering economic dependence and state-dependent inspection intervals. The objective function considered in the paper is the average revenue per unit time calculated based on the semi-regenerative theory and the universal generating function (UGF). A new algorithm using the stochastic ordering is also developed in this paper to reduce the search space of maintenance strategies and to enhance the efficiency of optimisation algorithms. A numerical simulation is presented in the study to evaluate the efficiency of the proposed maintenance strategy and optimisation algorithms. The simulation result reveals that maintenance strategies with opportunistic maintenance and state-dependent inspection intervals are more cost-effective when the influence of economic dependence and inspection cost is significant. The study further demonstrates that the optimisation algorithm proposed in this paper has higher computational efficiency than the commonly employed heuristic algorithms.

Damage diagnosis for complex steel truss bridges using multi-layer genetic algorithm

Considerate amount of research has proposed optimization-based approaches employing various vibration parameters for structural damage diagnosis. The damage detection by these methods is in fact a result of updating the analytical structural model in line with the current physical model. The feasibility of these approaches has been proven. But most of the verification has been done on simple structures, such as beams or plates. In the application on a complex structure, like steel truss bridges, a traditional optimization process will cost massive computational resources and lengthy convergence. This study presents a multi-layer genetic algorithm (ML-GA) to overcome the problem. Unlike the tedious convergence process in a conventional damage optimization process, in each layer, the proposed algorithm divides the GA’s population into groups with a less number of damage candidates; then, the converged population in each group evolves as an initial population of the next layer, where the groups merge to larger groups. In a damage detection process featuring ML-GA, as parallel computation can be implemented, the optimization performance and computational efficiency can be enhanced. In order to assess the proposed algorithm, the modal strain energy correlation (MSEC) has been considered as the objective function. Several damage scenarios of a complex steel truss bridge’s finite element model have been employed to evaluate the effectiveness and performance of ML-GA, against a conventional GA. In both single- and multiple damage scenarios, the analytical and experimental study shows that the MSEC index has achieved excellent damage indication and efficiency using the proposed ML-GA, whereas the conventional GA only converges at a local solution.

EUELC project : a multi-centre, multipurpose study to investigate early stage NSCLC, and to establish a biobank for ongoing collaboration

The European Early Lung Cancer (EUELC) project aims to determine if specific genetic alterations occurring in lung carcinogenesis are detectable in the respiratory epithelium. In order to pursue this objective, nonsmall cell lung cancer (NSCLC) patients with a very high risk of developing progressive lung cancer were recruited from 12 centres in eight European countries: France, Germany, southern Ireland, Italy, the Netherlands, Poland, Spain and the UK. In addition, NSCLC patients were followed up every 6 months for 36 months. A European Bronchial Tissue Bank was set up at the University of Liverpool (Liverpool, UK) to optimise the use of biological specimens. The molecular - pathological investigations were subdivided into specific work packages that were delivered by EUELC Partners. The work packages encompassed mutational analysis, genetic instability, methylation profiling, expression profiling utilising immunohistochemistry and chip-based technologies, as well as in-depth analysis of FHIT and RARβ genes, the telomerase catalytic subunit hTERT and genotyping of susceptibility genes in specific pathways. The EUELC project engendered a tremendous collaborative effort, and it enabled the EUELC Partners to establish protocols for assessing molecular biomarkers in early lung cancer with the view to using such biomarkers for early diagnosis and as intermediate end-points in future chemopreventive programmes. Copyright©ERS Journals Ltd 2009.