Computational experiments involving population size for FPGA-based implementation of a GA for the TSP

The feasibility of using an in-hardware implementation of a genetic algorithm (GA) to solve the computationally expensive travelling salesman problem (TSP) is explored, especially in regard to hardware resource requirements for problem and population sizes. We investigate via numerical experiments whether a small population size might prove sufficient to obtain reasonable quality solutions for the TSP, thereby permitting relatively resource efficient hardware implementation on field programmable gate arrays (FPGAs). Software experiments on two TSP benchmarks involving 48 and 532 cities were used to explore the extent to which population size can be reduced without compromising solution quality, and results show that a GA allowed to run for a large number of generations with a smaller population size can yield solutions of comparable quality to those obtained using a larger population. This finding is then used to investigate feasible problem sizes on a targeted Virtex-7 vx485T-2 FPGA platform via exploration of hardware resource requirements for memory and data flow operations.

An extravariation model for improving confidence intervals of population size estimates from removal data

We propose a new model for estimating the size of a population from successive catches taken during a removal experiment. The data from these experiments often have excessive variation, known as overdispersion, as compared with that predicted by the multinomial model. The new model allows catchability to vary randomly among samplings, which accounts for overdispersion. When the catchability is assumed to have a beta distribution, the likelihood function, which is refered to as beta-multinomial, is derived, and hence the maximum likelihood estimates can be evaluated. Simulations show that in the presence of extravariation in the data, the confidence intervals have been substantially underestimated in previous models (Leslie-DeLury, Moran) and that the new model provides more reliable confidence intervals. The performance of these methods was also demonstrated using two real data sets: one with overdispersion, from smallmouth bass (Micropterus dolomieu), and the other without overdispersion, from rat (Rattus rattus).

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.

The effects of habitat fragmentation on the demography and population genetic structure of Uromys Caudimaculatus

Habitat fragmentation can have an impact on a wide variety of biological processes including abundance, life history strategies, mating system, inbreeding and genetic diversity levels of individual species. Although fragmented populations have received much attention, ecological and genetic responses of species to fragmentation have still not been fully resolved. The current study investigated the ecological factors that may influence the demographic and genetic structure of the giant white-tailed rat (Uromys caudimaculatus) within fragmented tropical rainforests. It is the first study to examine relationships between food resources, vegetation attributes and Uromys demography in a quantitative manner. Giant white-tailed rat densities were strongly correlated with specific suites of food resources rather than forest structure or other factors linked to fragmentation (i.e. fragment size). Several demographic parameters including the density of resident adults and juvenile recruitment showed similar patterns. Although data were limited, high quality food resources appear to initiate breeding in female Uromys. Where data were sufficient, influx of juveniles was significantly related to the density of high quality food resources that had fallen in the previous three months. Thus, availability of high quality food resources appear to be more important than either vegetation structure or fragment size in influencing giant white-tailed rat demography. These results support the suggestion that a species’ response to fragmentation can be related to their specific habitat requirements and can vary in response to local ecological conditions. In contrast to demographic data, genetic data revealed a significant negative effect of habitat fragmentation on genetic diversity and effective population size in U. caudimaculatus. All three fragments showed lower levels of allelic richness, number of private alleles and expected heterozygosity compared with the unfragmented continuous rainforest site. Populations at all sites were significantly differentiated, suggesting restricted among population gene flow. The combined effects of reduced genetic diversity, lower effective population size and restricted gene flow suggest that long-term viability of small fragmented populations may be at risk, unless effective management is employed in the future. A diverse range of genetic reproductive behaviours and sex-biased dispersal patterns were evident within U. caudimaculatus populations. Genetic paternity analyses revealed that the major mating system in U. caudimaculatus appeared to be polygyny at sites P1, P3 and C1. Evidence of genetic monogamy, however, was also found in the three fragmented sites, and was the dominant mating system in the remaining low density, small fragment (P2). High variability in reproductive skew and reproductive success was also found but was less pronounced when only resident Uromys were considered. Male body condition predicted which males sired offspring, however, neither body condition nor heterozygosity levels were accurate predictors of the number of offspring assigned to individual males or females. Genetic spatial autocorrelation analyses provided evidence for increased philopatry among females at site P1, but increased philopatry among males at site P3. This suggests that male-biased dispersal occurs at site P1 and female-biased dispersal at site P3, implying that in addition to mating systems, Uromys may also be able to adjust their dispersal behaviour to suit local ecological conditions. This study highlights the importance of examining the mechanisms that underlie population-level responses to habitat fragmentation using a combined ecological and genetic approach. The ecological data suggested that habitat quality (i.e. high quality food resources) rather than habitat quantity (i.e. fragment size) was relatively more important in influencing giant white-tailed rat demographics, at least for the populations studied here . Conversely, genetic data showed strong evidence that Uromys populations were affected adversely by habitat fragmentation and that management of isolated populations may be required for long-term viability of populations within isolated rainforest fragments.

Prediction of IBD based on population history for fine gene mapping

A novel multiple regression method (RM) is developed to predict identity-by-descent probabilities at a locus L (IBDL), among individuals without pedigree, given information on surrounding markers and population history. These IBDL probabilities are a function of the increase in linkage disequilibrium (LD) generated by drift in a homogeneous population over generations. Three parameters are sufficient to describe population history: effective population size (Ne), number of generations since foundation (T), and marker allele frequencies among founders (p). IBD L are used in a simulation study to map a quantitative trait locus (QTL) via variance component estimation. RM is compared to a coalescent method (CM) in terms of power and robustness of QTL detection. Differences between RM and CM are small but significant. For example, RM is more powerful than CM in dioecious populations, but not in monoecious populations. Moreover, RM is more robust than CM when marker phases are unknown or when there is complete LD among founders or Ne is wrong, and less robust when p is wrong. CM utilises all marker haplotype information, whereas RM utilises information contained in each individual marker and all possible marker pairs but not in higher order interactions. RM consists of a family of models encompassing four different population structures, and two ways of using marker information, which contrasts with the single model that must cater for all possible evolutionary scenarios in CM.

Systematic and Day-to-Day Effects of Chemical-Derived Population Estimates on Wastewater-Based Drug Epidemiology

Population size is crucial when estimating population-normalized drug consumption (PNDC) from wastewater-based drug epidemiology (WBDE). Three conceptually different population estimates can be used: de jure (common census, residence), de facto (all persons within a sewer catchment), and chemical loads (contributors to the sampled wastewater). De facto and chemical loads will be the same where all households contribute to a central sewer system without wastewater loss. This study explored the feasibility of determining a de facto population and its effect on estimating PNDC in an urban community over an extended period. Drugs and other chemicals were analyzed in 311 daily composite wastewater samples. The daily estimated de facto population (using chemical loads) was on average 32% higher than the de jure population. Consequently, using the latter would systemically overestimate PNDC by 22%. However, the relative day-to-day pattern of drug consumption was similar regardless of the type of normalization as daily illicit drug loads appeared to vary substantially more than the population. Using chemical loads population, we objectively quantified the total methodological uncertainty of PNDC and reduced it by a factor of 2. Our study illustrated the potential benefits of using chemical loads population for obtaining more robust PNDC data in WBDE.

A Model to Estimate the Population Contributing to the Wastewater Using Samples Collected on Census Day

An important uncertainty when estimating per capita consumption of, for example, illicit drugs by means of wastewater analysis (sometimes referred to as “sewage epidemiology”) relates to the size and variability of the de facto population in the catchment of interest. In the absence of a day-specific direct population count any indirect surrogate model to estimate population size lacks a standard to assess associated uncertainties. Therefore, the objective of this study was to collect wastewater samples at a unique opportunity, that is, on a census day, as a basis for a model to estimate the number of people contributing to a given wastewater sample. Mass loads for a wide range of pharmaceuticals and personal care products were quantified in influents of ten sewage treatment plants (STP) serving populations ranging from approximately 3500 to 500 000 people. Separate linear models for population size were estimated with the mass loads of the different chemical as the explanatory variable: 14 chemicals showed good, linear relationships, with highest correlations for acesulfame and gabapentin. De facto population was then estimated through Bayesian inference, by updating the population size provided by STP staff (prior knowledge) with measured chemical mass loads. Cross validation showed that large populations can be estimated fairly accurately with a few chemical mass loads quantified from 24-h composite samples. In contrast, the prior knowledge for small population sizes cannot be improved substantially despite the information of multiple chemical mass loads. In the future, observations other than chemical mass loads may improve this deficit, since Bayesian inference allows including any kind of information relating to population size.

Weather variability, tides, and Barmah Forest Virus Disease in the Gladstone region, Australia

In this study we examined the impact of weather variability and tides on the transmission of Barmah Forest virus (BFV) disease and developed a weather-based forecasting model for BFV disease in the Gladstone region, Australia. We used seasonal autoregressive integrated moving-average (SARIMA) models to determine the contribution of weather variables to BFV transmission after the time-series data of response and explanatory variables were made stationary through seasonal differencing. We obtained data on the monthly counts of BFV cases, weather variables (e.g., mean minimum and maximum temperature, total rainfall, and mean relative humidity), high and low tides, and the population size in the Gladstone region between January 1992 and December 2001 from the Queensland Department of Health, Australian Bureau of Meteorology, Queensland Department of Transport, and Australian Bureau of Statistics, respectively. The SARIMA model shows that the 5-month moving average of minimum temperature (β = 0.15, p-value < 0.001) was statistically significantly and positively associated with BFV disease, whereas high tide in the current month (β = −1.03, p-value = 0.04) was statistically significantly and inversely associated with it. However, no significant association was found for other variables. These results may be applied to forecast the occurrence of BFV disease and to use public health resources in BFV control and prevention.

Settlement geography of African refugee communities in Southeast Queensland : an analysis of residential distribution and secondary migration

Before 2001, most Africans immigrating to Australia were white South Africans and Zimbabweans who arrived as economic and family-reunion migrants (Cox, Cooper & Adepoju, 1999). Black African communities are a more recent addition to the Australian landscape, with most entering Australia as refugees after 2001. African refugees are a particularly disadvantaged immigrant group, which the Department of Immigration and Multicultural Affairs (in the Community Relations Commission of New South Wales, 2006) suggests require high levels of settlement support (p.23). Decision makers and settlement service providers need to have settlement data on the communities so that they can be effective in planning, budgeting and delivering support where it is most needed. Settlement data are also useful for determining the challenges that these communities face in trying to establish themselves in resettlement. There has been no verification of existing secondary data sources, however, or previous formal study of African refugee settlement geography in Southeast Queensland. This research addresses the knowledge gap by using a mixed-method approach to identify and describe the distribution and population size of eight African communities in Southeast Queensland, examine secondary migration patterns in these communities and assess the relationship between these geographic features and housing, a critical factor in successful settlement. Significant discrepancies exist between the primary data gathered in the study and existing secondary data relating to population size and distribution of the communities. Results also reveal a tension between the socio-cultural forces and the housing and economic imperatives driving secondary migration in the communities, and a general lack of engagement by African refugees with structured support networks. These findings have a wide range of implications for policy and for groups that provide settlement support to these communities.

Optimisation of a class of transportation problems using genetic algorithms

This paper presents a Genetic Algorithms (GA) approach to search the optimized path for a class of transportation problems. The formulation of the problems for suitable application of GA will be discussed. Exchanging genetic information in the sense of neighborhoods will be introduced for generation reproduction. The performance of the GA will be evaluated by computer simulation. The proposed algorithm use simple coding with population size 1 converged in reasonable optimality within several minutes.

The use of ZIP and CART to model cryptosporidiosis in relation to climatic variables

This research assesses the potential impact of weekly weather variability on the incidence of cryptosporidiosis disease using time series zero-inflated Poisson (ZIP) and classification and regression tree (CART) models. Data on weather variables, notified cryptosporidiosis cases and population size in Brisbane were supplied by the Australian Bureau of Meteorology, Queensland Department of Health, and Australian Bureau of Statistics, respectively. Both time series ZIP and CART models show a clear association between weather variables (maximum temperature, relative humidity, rainfall and wind speed) and cryptosporidiosis disease. The time series CART models indicated that, when weekly maximum temperature exceeded 31°C and relative humidity was less than 63%, the relative risk of cryptosporidiosis rose by 13.64 (expected morbidity: 39.4; 95% confidence interval: 30.9–47.9). These findings may have applications as a decision support tool in planning disease control and risk management programs for cryptosporidiosis disease.

The seasonal phenology of Bactrocera tryoni (Froggatt) (Diptera: Tephritidae) in Queensland

Bactrocera tryoni is a polyphagous fruit fly, originally endemic to tropical and subtropical coastal eastern Australia, but now also widely distributed in temperate eastern Australia. In temperate parts of its range, B. tryoni populations show distinct seasonal peaks driven by changing seasonal climates, especially changing temperature. In contrast to temperate areas, the seasonal phenology of B. tryoni in subtropical and tropical parts of its range is poorly documented and the role of climate unknown. Using a large, historical (1940s and 1950s) fruit fly trapping data set, we present the seasonal phenology of B. tryoni at nine sites across Queensland for multiple (two to seven) years per site. We correlate monthly trap data for each site with monthly weather averages (temperature, rainfall and relative humidity) to investigate climatic influences. We also correlate observed population data with predicted population data generated by an existing B. tryoni population model. Supporting predictions from climate driven models, B. tryoni did show year-round breeding at most Queensland sites. However, contrary to predictions, there was a common pattern of a significant population decline in autumn and winter, followed by a rapid population increase in August and then one or more distinct peaks of abundance in spring and summer. Mean monthly fly abundance was significantly different across sites, but was not correlated with altitudinal, latitudinal or longitudinal gradients. There were very few significant correlations between monthly fly population size and weather variables for eight of the nine sites. For the southern site of Gatton fly population abundance was correlated with temperature. Results suggest that while climate factors may be influencing B. tryoni populations in southern subtropical Queensland, they appear to be having only minor or no influence in northern sub-tropical and tropical Queensland. In the discussion we focus on the role of other factors, particularly larval host plant availability, as likely drivers of B. tryoni abundance in tropical and subtropical parts of its range.

Positive correlation of male and female Bactrocera tryoni (Froggatt) (Diptera: Tephritidae) catches in orange-ammonia traps

Populations of the Queensland fruit fly, Bactrocera tryoni, are routinely monitored using cue-lure, a male-only attractant. Such monitoring provides no information about females and there is little information available to show if male and female B. tryoni numbers are correlated in the field. Using a data set of 1 148 weekly clearances of orange-ammonia baited traps, which catch both males and females, the correlation between male and female numbers was tested for 48 weeks of the year (four weeks each month) and for the combined data set. Weekly male and female trap catches were almost entirely highly correlated, regardless of mean population size or time of year. For the whole year, the correlation between male and female numbers was r = 0.722, significant at p<0.001. Results suggest that changes in the number if male B. tryoni, as detected through cue-lure sampling, will reflect changes in numbers of female B. tryoni.

Mapping Australia's philanthropic investment in women and girls

The Australian Women Donors Network (Women Donors) partnered the Australian Centre for Philanthropy and Nonprofit Studies (ACPNS) at QUT to conduct this research. No studies exist on the size or sources of philanthropic giving in Australia directed intentionally towards the needs of women and girls. The survey aims to fill this knowledge gap and create a baseline for understanding trends and views in this area. Because the survey treads some new ground, its findings raise questions as well as giving answers. Encouragingly, 100 people from across the philanthropy spectrum completed the survey, embracing 41 individual donors and other respondents from various foundation types. Given the population difference, this response compares well with the 145 respondents to the USA-based Foundation Center’s 2009 European study (reported in 2011). The survey was designed for givers generally, not just those involved in giving to women and girls specifically. It is possible, though, that people with an interest in funding this area were more likely to participate. This potential oversampling may inflate the figures on funding women and girls to some degree. Also, because the population size of Australian philanthropists is unknown, no claims can be made that this information is generalizable to all Australian funders. Nonetheless, some patterns and themes emerge from the 100 responses.

Development and characterisation of tri- and tetra-nucleotide polymorphic microsatellite markers for skipjack tuna (Katsuwonus pelamis)

Skipjack tuna (katsuwonus pelamis) (SJT) is the largest tuna fishery in all the major oceans around the world, and the largest marine fishery in Sri Lanka. Knowledge of genetic population structure and effective population size of SJT in the Indian Ocean and other major oceans, however, is still lacking for better management practices and conservation strategies. We developed microsatellite genetic markers using SJT around Sri Lanka in the Indian Ocean, and characterise one tri- and seven tetra-nucleotide microsatellite loci isolated from enriched genomic libraries from SJT, to provide tools for addressing both conservation and fisheries management questions. An analysis of these eight microsatellite markers in two populations of SJT from eastern Sri Lanka (n = 44) and the Maldives Islands (n = 53) showed that all eight microsatellites were polymorphic with an average number of alleles per locus of 11.80 (range 5-27). Expected heterozygosities at marker loci ranged from 0.450 to 0.961. These markers are being used currently to characterise population structure and extent of natural gene flow in SJT populations from the eastern and western Indian Ocean. No significant linkage disequilibrium was detected among any loci pairs.