704 resultados para Plant quality


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In tephritid fruit flies of the genus Bactrocera Macquart, a group of plant derived compounds (sensu amplo ‘male lures') enhance the mating success of males that have consumed them. For flies responding to the male lure methyl eugenol, this is due to the accumulation of chemicals derived from the male lure in the male rectal gland (site of pheromone synthesis) and the subsequent release of an attractive pheromone. Cuelure, raspberry ketone and zingerone are a second, related group of male lures to which many Bactrocera species respond. Raspberry ketone and cuelure are both known to accumulate in the rectal gland of males as raspberry ketone, but it is not known if the emitted male pheromone is subsequently altered in complexity or is more attractive to females. Using Bactrocera tryoni as our test insect, and cuelure and zingerone as our test chemicals, we assess: (i) lure accumulation in the rectal gland; (ii) if the lures are released exclusively in association with the male pheromone; and (iii) if the pheromone of lure-fed males is more attractive to females than the pheromone of lure-unfed males. As previously documented, we found cuelure was stored in its hydroxyl form of raspberry ketone, while zingerone was stored largely in an unaltered state. Small but consistent amounts of raspberry ketone and β-(4-hydroxy-3-methoxyphenyl)-propionic acid were also detected in zingerone-fed flies. Males released the ingested lures or their analogues, along with endogenous pheromone chemicals, only during the dusk courtship period. More females responded to squashed rectal glands extracted from flies fed on cuelure than to glands from control flies, while more females responded to the pheromone of calling cuelure-fed males than to control males. The response to zingerone treatments in both cases was not different from the control. The results show that male B. tryoni release ingested lures as part of their pheromone blend and, at least for cuelure, this attracts more females.

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Identifying appropriate decision criteria and making optimal decisions in a structured way is a complex process. This paper presents an approach for doing this in the form of a hybrid Quality Function Deployment (QFD) and Cybernetic Analytic Network Process (CANP) model for project manager selection. This involves the use of QFD to translate the owner's project management expectations into selection criteria and the CANP to weight the expectations and selection criteria. The supermatrix approach then prioritises the candidates with respect to the overall decision-making goal. A case study is used to demonstrate the use of the model in selecting a renovation project manager. This involves the development of 18 selection criteria in response to the owner's three main expectations of time, cost and quality.

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This research established innovative methods and a predictive model to evaluate water quality using the trace element and heavy metal concentrations of drinking water from the greater Brisbane area. Significantly, the combined use of Inductively Coupled Plasma - Mass Spectrometry and Chemometrics can be used worldwide to provide comprehensive, rapid and affordable analyses of elements in drinking water that can have a considerable impact on human health.

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Eight new dihydro-β-agarofurans, denhaminols A–H (1–8), were isolated from the leaves of the Australian rainforest tree Denhamia celastroides. The chemical structures of 1–8 were elucidated following analysis of 1D/2D NMR and MS data. The absolute configuration of denhaminol A (1) was determined by single-crystal X-ray crystallography. All compounds were evaluated for cytotoxic activity against the human prostate cancer cell line LNCaP, using live-cell imaging and metabolic assays. Denhaminols A (1) and G (7) were also tested for their effects on the lipid content of LNCaP cells. This is the first report of secondary metabolites from D. celastroides.

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This paper investigates quality of service (QoS) and resource productivity implications of transit route passenger loading and travel time. It highlights the value of occupancy load factor as a direct passenger comfort QoS measure. Automatic Fare Collection data for a premium radial bus route in Brisbane, Australia, is used to investigate time series correlation between occupancy load factor and passenger average travel time. Correlation is strong across the entire span of service in both directions. Passengers tend to be making longer, peak direction commuter trips under significantly less comfortable conditions than off-peak. The Transit Capacity and Quality of Service Manual uses segment based load factor as a measure of onboard loading comfort QoS. This paper provides additional insight into QoS by relating the two route based dimensions of occupancy load factor and passenger average travel time together in a two dimensional format, both from the passenger’s and operator’s perspectives. Future research will apply Value of Time to QoS measurement, reflecting perceived passenger comfort through crowding and average time spent onboard. This would also assist in transit service quality econometric modeling. The methodology can be readily applied in a practical setting where AFC data for fixed scheduled routes is available. The study outcomes also provide valuable research and development directions.

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This presentation investigates quality of service (QoS) and resource productivity implications of transit route passenger loading and travel time. It highlights the value of occupancy load factor as a direct passenger comfort QoS measure. Automatic Fare Collection data for a premium radial bus route in Brisbane, Australia, is used to investigate time series correlation between occupancy load factor and passenger average travel time. Correlation is strong across the entire span of service in both directions. Passengers tend to be making longer, peak direction commuter trips under significantly less comfortable conditions than off-peak. The Transit Capacity and Quality of Service Manual uses segment based load factor as a measure of onboard loading comfort QoS. This paper provides additional insight into QoS by relating the two route based dimensions of occupancy load factor and passenger average travel time together in a two dimensional format, both from the passenger’s and operator’s perspectives. Future research will apply Value of Time to QoS measurement, reflecting perceived passenger comfort through crowding and average time spent onboard. This would also assist in transit service quality econometric modeling. The methodology can be readily applied in a practical setting where AFC data for fixed scheduled routes is available. The study outcomes also provide valuable research and development directions.

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This study explores how explicit transit quality of services (TQoS) measures including service frequency, service span, and travel time ratio, along with implicit environmental predictors such as topographic grade factor influence bus ridership using a case study city of Brisbane, Australia. The primary hypothesis tested was that bus ridership is higher within suburbs with high transit quality of service than suburbs that have limited service quality. Using Multiple Linear Regression (MLR) this study identifies a strong positive relationship between route intensity (bus-km/h-km2) and bus ridership, indicating that increasing both service frequency and spatial route density correspond to higher bus ridership. Additionally, travel time ratio (in-vehicle transit travel time to in-vehicle auto travel time) is also found to have significant negative association with ridership within a suburb, reflecting a decline in transit use with increased travel time ratio. Conversely, topographic grade and service span are not found to exert any significant impact on bus ridership in a suburb. Our study findings enhance the fundamental understanding of traveller behaviour which is informative to urban transportation policy, planning and provision.

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This study investigates whether an Australian city’s suburbs having high transit Quality of Service (QoS) are associated with higher transit ridership than those having low transit QoS •We explore how QoS measures including service frequency, service span, service coverage, and travel time ratio, along with implicit environmental predictors such as topographic grade factor influence bus ridership •We applied Multiple Linear Regression (MLR) to examine the relationship between QoS and ridership •Its outcomes enhance our understanding of transit user behavior, which is informative to urban transportation policy, planning, and provision

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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.

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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