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

Assessment of the molecular structure of an intermediate member of the triplite-zwieselite mineral series: A raman and infrared study

The mineral series triplite-zwieselite with theoretical formula (Mn2+)2(PO4)(F)-(Fe2+)2(PO4)(F) from the El Criolo granitic pegmatite, located in the Eastern Pampean Ranges of Córdoba Province, was studied using electron microprobe, thermogravimetry, and Raman and infrared spectroscopy. The analysis of the mineral provided a formula of (Fe1.00, Mn0.85, Ca0.08, Mg0.06)∑2.00(PO4)1.00(F0.80, OH0.20)∑1.00. An intense Raman band at 981 cm−1 with a shoulder at 977 cm−1 is assigned to the ν1 symmetric stretching mode. The observation of two bands for the phosphate symmetric stretching mode offers support for the concept that the phosphate units in the structure of triplite-zwieselite are not equivalent. Low-intensity Raman bands at 1012, 1036, 1071, 1087, and 1127 cm−1 are assigned to the ν3 antisymmetric stretching modes. A set of Raman bands at 572, 604, 639, and 684 cm−1 are attributed to the ν4 out-of-plane bending modes. A single intense Raman band is found at 3508 cm−1 and is assigned to the stretching vibration of hydroxyl units. Infrared bands are observed at 3018, 3125, and 3358 cm−1 and are attributed to water stretching vibrations. Supplemental materials are available for this article. Go to the publisher's online edition of Spectroscopy Letters to view the supplemental file.

Tornado Damage Assessment in the aftermath of the May 20th 2013 Moore Oklahoma Tornado

This report presents observations, findings, and recommendations from an engineering reconnaissance trip following the May 20th, 2013 tornado that struck Moore, Oklahoma. A team of faculty, research scientists, professional engineers, and civil engineering students were tasked with investigating and documenting the performance of critical facility buildings and residences, (IBC Occupancy Category II, III, and IV), in Moore, OK. The Enhanced Fujita (EF) 5 tornado created a 17-mile long damage swath destroying over 12,000 buildings and killing 24 people. The total economic loss from this single event was estimated at $3 billion. The May 20th tornado was the third major tornado to hit Moore in the previous 15 years.

The primacy of vital signs – Acute care nurses’ and midwives’ use of physical assessment skills: A cross sectional study

BACKGROUND: Registered nurses and midwives play an essential role in detecting patients at risk of deterioration through ongoing assessment and action in response to changing health status. Yet, evidence suggests that clinical deterioration frequently goes unnoticed in hospitalised patients. While much attention has been paid to early warning and rapid response systems, little research has examined factors related to physical assessment skills. OBJECTIVES: To determine a minimum data set of core skills used during nursing assessment of hospitalised patients and identify nurse and workplace predictors of the use of physical assessment to detect patient deterioration. DESIGN: The study used a single-centre, cross-sectional survey design. SETTING and PARTICIPANTS: The study included 434 registered nurses and midwives (Grades 5-7) involved in clinical care of patients on acute care wards, including medicine, surgery, oncology, mental health and maternity service areas, at a 929-bed tertiary referral teaching hospital in Southeast Queensland, Australia. METHODS: We conducted a hospital-wide survey of registered nurses and midwives using the 133-item Physical Assessment Skills Inventory and the 58-item Barriers to Registered Nurses’ Use of Physical Assessment scale. Median frequency for each physical assessment skill was calculated to determine core skills. To explore predictors of core skill utilisation, backward stepwise general linear modelling was conducted. Means and regression coefficients are reported with 95% confidence intervals. A p value < .05 was considered significant for all analyses. RESULTS: Core skills used by most nurses every time they worked included assessment of temperature, oxygen saturation, blood pressure, breathing effort, skin, wound and mental status. Reliance on others and technology (F = 35.77, p < .001), lack of confidence (F = 5.52, p = .02), work area (F = 3.79, p = .002), and clinical role (F = 44.24, p < .001) were significant predictors of the extent of physical assessment skill use. CONCLUSIONS: The increasing acuity of the acute care patient plausibly warrants more than vital signs assessment; however, our study confirms nurses’ physical assessment core skill set is mainly comprised of vital signs. The focus on these endpoints of deterioration as dictated by early warning and rapid response systems may divert attention from and devalue comprehensive nursing assessment that could detect subtle changes in health status earlier in the patient's hospitalisation.

Simultaneous underwater visibility assessment, enhancement and improved stereo

Vision-based underwater navigation and obstacle avoidance demands robust computer vision algorithms, particularly for operation in turbid water with reduced visibility. This paper describes a novel method for the simultaneous underwater image quality assessment, visibility enhancement and disparity computation to increase stereo range resolution under dynamic, natural lighting and turbid conditions. The technique estimates the visibility properties from a sparse 3D map of the original degraded image using a physical underwater light attenuation model. Firstly, an iterated distance-adaptive image contrast enhancement enables a dense disparity computation and visibility estimation. Secondly, using a light attenuation model for ocean water, a color corrected stereo underwater image is obtained along with a visibility distance estimate. Experimental results in shallow, naturally lit, high-turbidity coastal environments show the proposed technique improves range estimation over the original images as well as image quality and color for habitat classification. Furthermore, the recursiveness and robustness of the technique allows implementation onboard an Autonomous Underwater Vehicle for improving navigation and obstacle avoidance performance.