Stochastic analysis of transit route segments' passenger load variation for capacity and quality of service assessment

This paper investigates stochastic analysis of transit segment hourly passenger load factor variation for transit capacity and quality of service (QoS) analysis using Automatic Fare Collection data for a premium radial bus route in Brisbane, Australia. It compares stochastic analysis to traditional peak hour factor (PHF) analysis to gain further insight into variability of transit route segments’ passenger loading during a study hour. It demonstrates that hourly design load factor is a useful method of modeling a route segment’s capacity and QoS time history across the study weekday. This analysis method is readily adaptable to different passenger load standards by adjusting design percentile, reflecting either a more relaxed or more stringent condition. This paper also considers hourly coefficient of variation of load factor as a capacity and QoS assessment measure, in particular through its relationships with hourly average and design load factors. Smaller value reflects uniform passenger loading, which is generally indicative of well dispersed passenger boarding demands and good schedule maintenance. Conversely, higher value may be indicative of pulsed or uneven passenger boarding demands, poor schedule maintenance, and/or bus bunching. An assessment table based on hourly coefficient of variation of load factor is developed and applied to this case study. Inferences are drawn for a selection of study hours across the weekday studied.

Stochastic analysis of transit route segments' passenger load variation for capacity & quality of service assessment

This study uses weekday Automatic Fare Collection (AFC) data on a premium bus line in Brisbane, Australia •Stochastic analysis is compared to peak hour factor (PHF) analysis for insight into passenger loading variability •Hourly design load factor (e.g. 88th percentile) is found to be a useful method of modeling a segment’s passenger demand time-history across a study weekday, for capacity and QoS assessment •Hourly coefficient of variation of load factor is found to be a useful QoS and operational assessment measure, particularly through its relationship with hourly average load factor, and with design load factor •An assessment table based on hourly coefficient of variation of load factor is developed from the case study

Ground Zero

Synopsis and review of the Australian feature film Ground Zero directed by Michael Pattinson and Bruce Myles.

Variation in parental magmas of Mt Rouse, a complex polymagmatic monogenetic volcano in the basaltic intraplate Newer Volcanics Province, southeast Australia

Monogenetic volcanoes have long been regarded as simple in nature, involving single magma batches and uncomplicated evolutions; however, recent detailed research into individual centres is challenging that assumption. Mt Rouse (Kolor) is the volumetrically largest volcano in the monogenetic Newer Volcanics Province of southeast Australia. This study presents new major, trace and Sr–Nd–Pb isotope data for samples selected on the basis of a detailed stratigraphic framework analysis of the volcanic products from Mt Rouse. The volcano is the product of three magma batches geochemically similar to Ocean–Island basalts, featuring increasing LREE enrichment with each magma batch (batches A, B and C) but no evidence of crustal contamination; the Sr–Nd–Pb isotopes define two groupings. Modelling suggests that the magmas were sourced from a zone of partial melting crossing the lithosphere–asthenosphere boundary, with batch A forming a large volume partial melt in the deep lithosphere (1.7 GPa/55.5 km); and batches B and C from similar areas within the shallow asthenosphere (1.88 GPa/61 km and 1.94 GPa/63 km, respectively). The formation and extraction of these magmas may have been due to high deformation rates in the mantle caused by edge-driven convection and asthenospheric upwelling. The lithosphere– asthenosphere boundary is important with respect to NVP volcanism. An eruption chronology involves sequential eruption of magma batches A, C and B, followed by simultaneous eruption of batches A and B. Mt Rouse is a complex polymagmatic monogenetic volcano that illustrates the complexity of monogenetic volcanism and demonstrates the importance of combining detailed stratigraphic analysis alongside systematic geochemical sampling.

Exploring variation in measurement as a foundation for statistical thinking in the elementary school

This study was based on the premise that variation is the foundation of statistics and statistical investigations. The study followed the development of fourth-grade students' understanding of variation through participation in a sequence of two lessons based on measurement. In the first lesson all students measured the arm span of one student, revealing pathways students follow in developing understanding of variation and linear measurement (related to research question 1). In the second lesson each student's arm span was measured once, introducing a different aspect of variation for students to observe and contrast. From this second lesson, students' development of the ability to compare their representations for the two scenarios and explain differences in terms of variation was explored (research question 2). Students' documentation, in both workbook and software formats, enabled us to monitor their engagement and identify their increasing appreciation of the need to observe, represent, and contrast the variation in the data. Following the lessons, a written student assessment was used for judging retention of understanding of variation developed through the lessons and the degree of transfer of understanding to a different scenario (research question 3).

Variation in the complex carbohydrate biosynthesis loci of Acinetobacter baumannii genomes

Extracellular polysaccharides are major immunogenic components of the bacterial cell envelope. However, little is known about their biosynthesis in the genus Acinetobacter, which includes A. baumannii, an important nosocomial pathogen. Whether Acinetobacter sp. produce a capsule or a lipopolysaccharide carrying an O antigen or both is not resolved. To explore these issues, genes involved in the synthesis of complex polysaccharides were located in 10 complete A. baumannii genome sequences, and the function of each of their products was predicted via comparison to enzymes with a known function. The absence of a gene encoding a WaaL ligase, required to link the carbohydrate polymer to the lipid A-core oligosaccharide (lipooligosaccharide) forming lipopolysaccharide, suggests that only a capsule is produced. Nine distinct arrangements of a large capsule biosynthesis locus, designated KL1 to KL9, were found in the genomes. Three forms of a second, smaller variable locus, likely to be required for synthesis of the outer core of the lipid A-core moiety, were designated OCL1 to OCL3 and also annotated. Each K locus includes genes for capsule export as well as genes for synthesis of activated sugar precursors, and for glycosyltransfer, glycan modification and oligosaccharide repeat-unit processing. The K loci all include the export genes at one end and genes for synthesis of common sugar precursors at the other, with a highly variable region that includes the remaining genes in between. Five different capsule loci, KL2, KL6, KL7, KL8 and KL9 were detected in multiply antibiotic resistant isolates belonging to global clone 2, and two other loci, KL1 and KL4, in global clone 1. This indicates that this region is being substituted repeatedly in multiply antibiotic resistant isolates from these clones.

Variation in the OC locus of Acinetobacter baumannii genomes predicts extensive structural diversity in the Lipooligosaccharide

Lipooligosaccharide (LOS) is a complex surface structure that is linked to many pathogenic properties of Acinetobacter baumannii. In A. baumannii, the genes responsible for the synthesis of the outer core (OC) component of the LOS are located between ilvE and aspS. The content of the OC locus is usually variable within a species, and examination of 6 complete and 227 draft A. baumannii genome sequences available in GenBank non-redundant and Whole Genome Shotgun databases revealed nine distinct new types, OCL4-OCL12, in addition to the three known ones. The twelve gene clusters fell into two distinct groups, designated Group A and Group B, based on similarities in the genes present. OCL6 (Group B) was unique in that it included genes for the synthesis of L-Rhamnosep. Genetic exchange of the different configurations between strains has occurred as some OC forms were found in several different sequence types (STs). OCL1 (Group A) was the most widely distributed being present in 18 STs, and OCL6 was found in 16 STs. Variation within clones was also observed, with more than one OC locus type found in the two globally disseminated clones, GC1 and GC2, that include the majority of multiply antibiotic resistant isolates. OCL1 was the most abundant gene cluster in both GC1 and GC2 genomes but GC1 isolates also carried OCL2, OCL3 or OCL5, and OCL3 was also present in GC2. As replacement of the OC locus in the major global clones indicates the presence of sub-lineages, a PCR typing scheme was developed to rapidly distinguish Group A and Group B types, and to distinguish the specific forms found in GC1 and GC2 isolates.

Genetic and environmental influences on neuroimaging phenotypes: A meta-analytical perspective on twin imaging studies

Because brain structure and function are affected in neurological and psychiatric disorders, it is important to disentangle the sources of variation in these phenotypes. Over the past 15 years, twin studies have found evidence for both genetic and environmental influences on neuroimaging phenotypes, but considerable variation across studies makes it difficult to draw clear conclusions about the relative magnitude of these influences. Here we performed the first meta-analysis of structural MRI data from 48 studies on >1,250 twin pairs, and diffusion tensor imaging data from 10 studies on 444 twin pairs. The proportion of total variance accounted for by genes (A), shared environment (C), and unshared environment (E), was calculated by averaging A, C, and E estimates across studies from independent twin cohorts and weighting by sample size. The results indicated that additive genetic estimates were significantly different from zero for all metaanalyzed phenotypes, with the exception of fractional anisotropy (FA) of the callosal splenium, and cortical thickness (CT) of the uncus, left parahippocampal gyrus, and insula. For many phenotypes there was also a significant influence of C. We now have good estimates of heritability for many regional and lobar CT measures, in addition to the global volumes. Confidence intervals are wide and number of individuals small for many of the other phenotypes. In conclusion, while our meta-analysis shows that imaging measures are strongly influenced by genes, and that novel phenotypes such as CT measures, FA measures, and brain activation measures look especially promising, replication across independent samples and demographic groups is necessary.

A nonconservative Lagrangian framework for statistical fluid registration - SAFIRA

In this paper, we used a nonconservative Lagrangian mechanics approach to formulate a new statistical algorithm for fluid registration of 3-D brain images. This algorithm is named SAFIRA, acronym for statistically-assisted fluid image registration algorithm. A nonstatistical version of this algorithm was implemented, where the deformation was regularized by penalizing deviations from a zero rate of strain. In, the terms regularizing the deformation included the covariance of the deformation matrices Σ and the vector fields (q). Here, we used a Lagrangian framework to reformulate this algorithm, showing that the regularizing terms essentially allow nonconservative work to occur during the flow. Given 3-D brain images from a group of subjects, vector fields and their corresponding deformation matrices are computed in a first round of registrations using the nonstatistical implementation. Covariance matrices for both the deformation matrices and the vector fields are then obtained and incorporated (separately or jointly) in the nonconservative terms, creating four versions of SAFIRA. We evaluated and compared our algorithms' performance on 92 3-D brain scans from healthy monozygotic and dizygotic twins; 2-D validations are also shown for corpus callosum shapes delineated at midline in the same subjects. After preliminary tests to demonstrate each method, we compared their detection power using tensor-based morphometry (TBM), a technique to analyze local volumetric differences in brain structure. We compared the accuracy of each algorithm variant using various statistical metrics derived from the images and deformation fields. All these tests were also run with a traditional fluid method, which has been quite widely used in TBM studies. The versions incorporating vector-based empirical statistics on brain variation were consistently more accurate than their counterparts, when used for automated volumetric quantification in new brain images. This suggests the advantages of this approach for large-scale neuroimaging studies.

Reducing structural variation to determine the genetics of white matter integrity across hemispheres - a dti study of 100 twins

Studies of cerebral asymmetry can open doors to understanding the functional specialization of each brain hemisphere, and how this is altered in disease. Here we examined hemispheric asymmetries in fiber architecture using diffusion tensor imaging (DTI) in 100 subjects, using high-dimensional fluid warping to disentangle shape differences from measures sensitive to myelination. Confounding effects of purely structural asymmetries were reduced by using co-registered structural images to fluidly warp 3D maps of fiber characteristics (fractional and geodesic anisotropy) to a structurally symmetric minimal deformation template (MDT). We performed a quantitative genetic analysis on 100 subjects to determine whether the sources of the remaining signal asymmetries were primarily genetic or environmental. A twin design was used to identify the heritable features of fiber asymmetry in various regions of interest, to further assist in the discovery of genes influencing brain micro-architecture and brain lateralization. Genetic influences and left/right asymmetries were detected in the fiber architecture of the frontal lobes, with minor differences depending on the choice of registration template.

Understanding past climate variation and environmental change for the future of an iconic landscape – K'gari Fraser Island, Queensland, Australia

The unique combination of landscapes and processes that are present and operate on Fraser Island (K'gari) create a dynamic setting that is capable of recording past environmental events, climate variations and former landscapes. Likewise, its geographic position makes Fraser Island sensitive to those events and processes. Based on optically stimulated luminescence dating, the records archived within the world's largest sand island span a period that has the potential to exceed 750 ka and contain specific records that are of extremely high resolution over the past 40,000 years. This is due to the geographic position of Fraser Island, which lies in the coastal subtropical region of Queensland Australia. Fraser Island is exposed to the open ocean currents of the Coral Sea on the east coast and the waters of Hervey Bay on its western margin and is positioned to receive moisture from the Indo-Australian monsoon, southeast trade winds and experiences occasional tropical and ex-tropical cyclones. We review literature that presents the current level of understanding of sea level change, ecological variation and environmental change on Fraser Island. The previous works illustrate the importance of Fraser Island and may link processes, environments and climates on Fraser Island with global records.

Rainfall variability drives interannual variation in N2O emissions from a humid, subtropical pasture

Variations in interannual rainfall totals can lead to large uncertainties in annual N2O emission budget estimates from short term field studies. The interannual variation in nitrous oxide (N2O) emissions from a subtropical pasture in Queensland, Australia, was examined using continuous measurements of automated chambers over 2 consecutive years. Nitrous oxide emissions were highest during the summer months and were highly episodic, related more to the size and distribution of rain events than soil water content. Over 48% of the total N2O emitted was lost in just 16% of measurement days. Interannual variation in annual N2O estimates was high, with cumulative emissions increasing with decreasing rainfall. Cumulative emissions averaged 1826.7 ± 199.9 g N2O-N ha−1 yr−1 over the two year period, though emissions from 2008 (2148 ± 273 g N2O-N ha−1 yr−1) were 42% higher than 2007 (1504 ± 126 g N2O-N ha−1 yr−1). This increase in annual emissions coincided with almost half of the summer precipitation from 2007 to 2008. Emissions dynamics were chiefly driven by the distribution and size of rain events which varied on a seasonal and annual basis. Sampling frequency effects on cumulative N2O flux estimation were assessed using a jackknife technique to inform future manual sampling campaigns. Test subsets of the daily measured data were generated for the pasture and two adjacent land-uses (rainforest and lychee orchard) by selecting measured flux values at regular time intervals ranging from 1 to 30 days. Errors associated with weekly sampling were up to 34% of the sub-daily mean and were highly biased towards overestimation if strategically sampled following rain events. Sampling time of day also played a critical role. Morning sampling best represented the 24 hour mean in the pasture, whereas sampling at noon proved the most accurate in the shaded rainforest and lychee orchard.

Resampling-based approaches to study variation in morphological modularity

Modularity has been suggested to be connected to evolvability because a higher degree of independence among parts allows them to evolve as separate units. Recently, the Escoufier RV coefficient has been proposed as a measure of the degree of integration between modules in multivariate morphometric datasets. However, it has been shown, using randomly simulated datasets, that the value of the RV coefficient depends on sample size. Also, so far there is no statistical test for the difference in the RV coefficient between a priori defined groups of observations. Here, we (1), using a rarefaction analysis, show that the value of the RV coefficient depends on sample size also in real geometric morphometric datasets; (2) propose a permutation procedure to test for the difference in the RV coefficient between a priori defined groups of observations; (3) show, through simulations, that such a permutation procedure has an appropriate Type I error; (4) suggest that a rarefaction procedure could be used to obtain sample-size-corrected values of the RV coefficient; and (5) propose a nearest-neighbor procedure that could be used when studying the variation of modularity in geographic space. The approaches outlined here, readily extendable to non-morphometric datasets, allow study of the variation in the degree of integration between a priori defined modules. A Java application – that will allow performance of the proposed test using a software with graphical user interface – has also been developed and is available at the Morphometrics at Stony Brook Web page (http://life.bio.sunysb.edu/morph/).

Geographical and morphological variation within and between colour phases in Coris julis (L. 1758), a protogynous marine fish

The possible differences between sexes in patterns of morphological variation in geographical space have been explored only in gonochorist freshwater species. We explored patterns of body shape variation in geographical space in a marine sequential hermaphrodite species, Coris julis (L. 1758), analyzing variation both within and between colour phases, through the use of geometric morphometrics and spatially-explicit statistical analyses. We also tested for the association of body shape with two environmental variables: temperature and chlorophyll a concentration, as obtained from time-series of satellite-derived data. Both colour phases showed a significant morphological variation in geographical space and patterns of variation divergent between phases. Although the morphological variation was qualitatively similar, individuals in the initial colour phase showed a more marked variation than individuals in the terminal phase. Body shape showed a weak but significant correlation with environmental variables, which was more pronounced in primary specimens.