890 resultados para Average waiting time


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Pigeons and other animals soon learn to wait (pause) after food delivery on periodic-food schedules before resuming the food-rewarded response. Under most conditions the steady-state duration of the average waiting time, t, is a linear function of the typical interfood interval. We describe three experiments designed to explore the limits of this process. In all experiments, t was associated with one key color and the subsequent food delay, T, with another. In the first experiment, we compared the relation between t (waiting time) and T (food delay) under two conditions: when T was held constant, and when T was an inverse function of t. The pigeons could maximize the rate of food delivery under the first condition by setting t to a consistently short value; optimal behavior under the second condition required a linear relation with unit slope between t and T. Despite this difference in optimal policy, the pigeons in both cases showed the same linear relation, with slope less than one, between t and T. This result was confirmed in a second parametric experiment that added a third condition, in which T + t was held constant. Linear waiting appears to be an obligatory rule for pigeons. In a third experiment we arranged for a multiplicative relation between t and T (positive feedback), and produced either very short or very long waiting times as predicted by a quasi-dynamic model in which waiting time is strongly determined by the just-preceding food delay.

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Fifty four purse-seine boats at Mangalore landing centre were observed during different stages of unloading fish catch. It was found that a boat takes 75% of the berthing time to unload an average quantity of 2.4 tons of fish. Further, unloading period and catch were found to be directly related where it was estimated that 5 to 7 minutes are spent in unloading about half a ton of fish to a nearby tempo by employing 9 ± 2 laborers.

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This paper presents a methodology for real-time estimation of exit movement-specific average travel time on urban routes by integrating real-time cumulative plots, probe vehicles, and historic cumulative plots. Two approaches, component based and extreme based, are discussed for route travel time estimation. The methodology is tested with simulation and is validated with real data from Lucerne, Switzerland, that demonstrate its potential for accurate estimation. Both approaches provide similar results. The component-based approach is more reliable, with a greater chance of obtaining a probe vehicle in each interval, although additional data from each component is required. The extreme-based approach is simple and requires only data from upstream and downstream of the route, but the chances of obtaining a probe that traverses the entire route might be low. The performance of the methodology is also compared with a probe-only method. The proposed methodology requires only a few probes for accurate estimation; the probe-only method requires significantly more probes.

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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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In continuum one-dimensional space, a coupled directed continuous time random walk model is proposed, where the random walker jumps toward one direction and the waiting time between jumps affects the subsequent jump. In the proposed model, the Laplace-Laplace transform of the probability density function P(x,t) of finding the walker at position at time is completely determined by the Laplace transform of the probability density function φ(t) of the waiting time. In terms of the probability density function of the waiting time in the Laplace domain, the limit distribution of the random process and the corresponding evolving equations are derived.

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Although the principle of equal access to medically justified treatment has been promoted by official health policies in many Western health care systems, practices do not completely meet policy targets. Waiting times for elective surgery vary between patient groups and regions, and growing problems in the availability of services threaten equal access to treatment. Waiting times have come to the attention of decision-makers, and several policy initiatives have been introduced to ensure the availability of care within a reasonable time. In Finland, for example, the treatment guarantee came into force in 2005. However, no consensus exists on optimal waiting time for different patient groups. The purpose of this multi-centre randomized controlled trial was to analyse health-related quality of life, pain and physical function in total hip or knee replacement patients during the waiting time and to evaluate whether the waiting time is associated with patients health outcomes at admission. This study also assessed whether the length of waiting time is associated with social and health services utilization in patients awaiting total hip or knee replacement. In addition, patients health-related quality of life was compared with that of the general population. Consecutive patients with a need for a primary total hip or knee replacement due to osteoarthritis were placed on the waiting list between August 2002 and November 2003. Patients were randomly assigned to a short waiting time (maximum 3 months) or a non-fixed waiting time (waiting time not fixed in advance, instead the patient followed the hospitals routine practice). Patients health-related quality of life was measured upon being placed on the waiting list and again at hospital admission using the generic 15D instrument. Pain and physical function were evaluated using the self-report Harris Hip Score for hip patients and a scale modified from the Knee Society Clinical Rating System for knee patients. Utilization measures were the use of home health care, rehabilitation and social services, physician visits and inpatient care. Health and social services use was low in both waiting time groups. The most common services used while waiting were rehabilitation services and informal care, including unpaid care provided by relatives, neighbours and volunteers. Although patients suffered from clear restrictions in usual activities and physical functioning, they seemed primarily to lean on informal care and personal networks instead of professional care. While longer waiting time did not result in poorer health-related quality of life at admission and use of services during the waiting time was similar to that at the time of placement on the list, there is likely to be higher costs of waiting by people who wait longer simply because they are using services for a longer period. In economic terms, this would represent a negative impact of waiting. Only a few reports have been published of the health-related quality of life of patients awaiting total hip or knee replacement. These findings demonstrate that, in addition to physical dimensions of health, patients suffered from restrictions in psychological well-being such as depression, distress and reduced vitality. This raises the question of how to support patients who suffer from psychological distress during the waiting time and how to develop strategies to improve patients initiatives to reduce symptoms and the burden of waiting. Key words: waiting time, total hip replacement, total knee replacement, health-related quality of life, randomized controlled trial, outcome assessment, social service, utilization of health services

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Fault-tolerance is due to the semiconductor technology development important, not only for safety-critical systems but also for general-purpose (non-safety critical) systems. However, instead of guaranteeing that deadlines always are met, it is for general-purpose systems important to minimize the average execution time (AET) while ensuring fault-tolerance. For a given job and a soft (transient) error probability, we define mathematical formulas for AET that includes bus communication overhead for both voting (active replication) and rollback-recovery with checkpointing (RRC). And, for a given multi-processor system-on-chip (MPSoC), we define integer linear programming (ILP) models that minimize AET including bus communication overhead when: (1) selecting the number of checkpoints when using RRC, (2) finding the number of processors and job-to-processor assignment when using voting, and (3) defining fault-tolerance scheme (voting or RRC) per job and defining its usage for each job. Experiments demonstrate significant savings in AET.

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Discrete Conditional Phase-type (DC-Ph) models are a family of models which represent skewed survival data conditioned on specific inter-related discrete variables. The survival data is modeled using a Coxian phase-type distribution which is associated with the inter-related variables using a range of possible data mining approaches such as Bayesian networks (BNs), the Naïve Bayes Classification method and classification regression trees. This paper utilizes the Discrete Conditional Phase-type model (DC-Ph) to explore the modeling of patient waiting times in an Accident and Emergency Department of a UK hospital. The resulting DC-Ph model takes on the form of the Coxian phase-type distribution conditioned on the outcome of a logistic regression model.

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PURPOSE:

To characterize willingness to pay for private operations and preferred waiting time among patients awaiting cataract surgery in Hong Kong.

METHODS:

This was a cross-sectional survey. Subjects randomly selected from cataract surgical waiting lists in Hong Kong (n = 467) underwent a telephone interview based on a structured, validated questionnaire. Data were collected on private insurance coverage, preferred waiting time, amount willing to pay for surgery, and self-reported visual function and health status.

RESULTS:

Among 300 subjects completing the interview, 144 (48.2%) were 76 years of age or older, 177 (59%) were women, and mean time waiting for surgery was 17 +/- 15 months. Among 220 subjects (73.3%) willing to pay anything for surgery, the mean amount was US$552 +/- 443. With adjustment for age, education, and monthly household income, subjects willing to pay anything were less willing to wait 12 months for surgery (OR = 4.34; P = 0.002), more likely to know someone having had cataract surgery (OR = 2.20; P = 0.03), and more likely to use their own savings to pay for the surgery (OR = 2.21; P = 0.04). Subjects considering private cataract surgery, knowing people who have had cataract surgery, using nongovernment sources to pay for surgery, and having lower visual function were willing to pay more.

CONCLUSIONS:

Many patients wait significant periods for cataract surgery in Hong Kong, and are willing to pay substantial amounts for private operations. These results may have implications for other countries with cataract waiting lists.

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The short article attempts to make some very brief reflections on the effects a lack of public policies positively discriminatory in terms of public employment retirement. In particular, the observation of the absurd contradiction between the average age of retirement at the time of death (for men and women) and the average pension time for men and women in public employment in Portugal.

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We consider the small-time behavior of interfaces of zero contact angle solutions to the thin-film equation. For a certain class of initial data, through asymptotic analyses, we deduce a wide variety of behavior for the free boundary point. These are supported by extensive numerical simulations. © 2007 Society for Industrial and Applied Mathematics