scholarly journals WAITING TIME ANALYSIS OF M^X/G/1 QUEUES WITH/WITHOUT VACATIONS UNDER RANDOM ORDER OF SERVICE DISCIPLINE

2000 ◽  
Vol 43 (4) ◽  
pp. 455-468 ◽  
Author(s):  
Norikazu Kawasaki ◽  
Hideaki Takagi ◽  
Yutaka Takahashi ◽  
Sung-Jo Hong ◽  
Toshiharu Hasegawa
Keyword(s):  
Waiting Time ◽  
Random Order ◽  
Service Discipline ◽  
Time Analysis

Random-order-of-service for heterogeneous customers: waiting time analysis

2014 ◽  
Vol 226 (1) ◽  
pp. 527-550 ◽  
Author(s):  
W. Rogiest ◽  
K. Laevens ◽  
J. Walraevens ◽  
H. Bruneel
Keyword(s):  
Waiting Time ◽  
Random Order ◽  
Time Analysis

scholarly journals Waiting time analysis for MX/G/1 priority queues with/without vacations under random order of service discipline

2000 ◽  
Vol 13 (4) ◽  
pp. 365-392 ◽  
Author(s):  
Norikazu Kawasaki ◽  
Hideaki Takagi ◽  
Yutaka Takahashi ◽  
Sung-Jo Hong ◽  
Toshiharu Hasegawa
Keyword(s):  
Waiting Time ◽  
Waiting Times ◽  
Random Order ◽  
Priority Queues ◽  
Service Discipline ◽  
Second Moments ◽  
Preemptive Resume Priority ◽  
The One ◽  
The Relationship ◽  
Stieltjes Transforms

We study MX/G/1 nonpreemptive and preemptive-resume priority queues with/without vacations under random order of service (ROS) discipline within each class. By considering the conditional waiting times given the states of the system, which an arbitrary message observes upon arrival, we derive the Laplace-Stieltjes transforms of the waiting time distributions and explicitly obtain the first two moments. The relationship for the second moments under ROS and first-come first-served disciplines extends the one found previously by Takacs and Fuhrmann for non-priority single arrival queues.

Facial Nerve EMG: Low-Tech Monitoring with a Stopwatch

2021 ◽  
Author(s):  
Julian Prell ◽  
Christian Scheller ◽  
Sebastian Simmermacher ◽  
Christian Strauss ◽  
Stefan Rampp
Keyword(s):  
Facial Nerve ◽  
Real Time ◽  
Random Order ◽  
Automated Analysis ◽  
Data Sets ◽  
Time Analysis ◽  
Frequency Pattern ◽  
Real Time Analysis ◽  
Offline Analysis ◽  
Automated Algorithms

Abstract Objective The quantity of A-trains, a high-frequency pattern of free-running facial nerve electromyography, is correlated with the risk for postoperative high-grade facial nerve paresis. This correlation has been confirmed by automated analysis with dedicated algorithms and by visual offline analysis but not by audiovisual real-time analysis. Methods An investigator was presented with 29 complete data sets measured during actual surgeries in real time and without breaks in a random order. Data were presented either strictly via loudspeaker (audio) or simultaneously by loudspeaker and computer screen (audiovisual). Visible and/or audible A-train activity was then quantified by the investigator with the computerized equivalent of a stopwatch. The same data were also analyzed with quantification of A-trains by automated algorithms. Results Automated (auto) traintime (TT), known to be a small, yet highly representative fraction of overall A-train activity, ranged from 0.01 to 10.86 s (median: 0.58 s). In contrast, audio-TT ranged from 0 to 1,357.44 s (median: 29.69 s), and audiovisual-TT ranged from 0 to 786.57 s (median: 46.19 s). All three modalities were correlated to each other in a highly significant way. Likewise, all three modalities correlated significantly with the extent of postoperative facial paresis. As a rule of thumb, patients with visible/audible A-train activity < 1 minute presented with a more favorable clinical outcome than patients with > 1 minute of A-train activity. Conclusion Detection and even quantification of A-trains is technically possible not only with intraoperative automated real-time calculation or postoperative visual offline analysis, but also with very basic monitoring equipment and real-time good quality audiovisual analysis. However, the investigator found audiovisual real-time-analysis to be very demanding; thus tools for automated quantification can be very helpful in this respect.

Waiting Time Analysis at University Hospitals Based on Visitor Psychology

2020 ◽  
pp. 212-221
Author(s):  
Shigeyoshi Iizuka ◽  
Shozo Nishii ◽  
Eriko Tanimoto ◽  
Hiro Nakazawa ◽  
Asuka Kodaka ◽  
...  
Keyword(s):  
Waiting Time ◽  
Time Analysis ◽  
University Hospitals

Patient arrival pattern and waiting time analysis for a tertiary hospital radiology department

2019 ◽  
Vol 25 (3) ◽  
pp. 136-143
Author(s):  
Felicitas Ugochinyere Idigo ◽  
Kenneth Kalu Agwu ◽  
Obinna Emmanuel Onwujekwe ◽  
Mark Chukwudi Okeji ◽  
Angel-Mary Chukwunyelu Anakwue
Keyword(s):  
Waiting Time ◽  
Tertiary Hospital ◽  
Radiology Department ◽  
Time Analysis ◽  
Patient Arrival ◽  
Arrival Pattern

Convex ordering of the attained waiting times in single-server queues and related problems

1991 ◽  
Vol 28 (02) ◽  
pp. 433-445 ◽  
Author(s):  
Masakiyo Miyazawa ◽  
Genji Yamazaki
Keyword(s):  
Waiting Time ◽  
Time Distribution ◽  
Waiting Times ◽  
Service Discipline ◽  
Single Server ◽  
Waiting Time Distribution ◽  
Convex Order ◽  
Convex Ordering ◽  
Single Server Queues ◽  
Service Disciplines

The attained waiting time of customers in service of the G/G/1 queue is compared for various work-conserving service disciplines. It is proved that the attained waiting time distribution is minimized (maximized) in convex order when the discipline is FCFS (PR-LCFS). We apply the result to characterize finiteness of moments of the attained waiting time in the GI/GI/1 queue with an arbitrary work-conserving service discipline. In this discussion, some interesting relationships are obtained for a PR-LCFS queue.

scholarly journals The Effect of First-Step Techniques from the Staggered Stance in American Football

2017 ◽  
Vol 01 (02) ◽  
pp. E69-E73
Author(s):  
Nikolas Knudsen ◽  
Thomas Andersen
Keyword(s):  
Reaction Time ◽  
Random Order ◽  
American Football ◽  
Football Players ◽  
Ground Reaction Forces ◽  
Time Analysis ◽  
Sprint Time ◽  
Reaction Forces ◽  
Force Profiles ◽  
Time Linear

AbstractThe purpose of this study was to evaluate 3 different starting techniques from the staggered stance with regards to sprint time, reaction time, linear impulse and power. 11 male amateur American football players volunteered to participate in a testing session consisting of twelve 5 m sprints, 4 in each technique (normal (NORM), backwards false step (BFS) and forwards false step (FFS)) in random order. Sprint starts were performed on force plates to investigate ground reaction forces, reaction time and total sprint time. Analysis showed significant differences in sprint times, with NORM (1.77±0.10 s) being faster than FFS (1.81±0.12 s) and BFS (2.01±0.13 s), and FFS being faster than BFS, although no differences were found in reaction time. In terms of mean force and power, NORM (331.1±39.2N, 542.2±72.3W) and FFS (320.8±43.2N, 550.9±81.4W) were significantly larger than BFS (256.9±36.2N, 443.5±61.1W). This indicates that when starting from a staggered stance, the BFS is inferior to the others and should be avoided. However, since the force profiles of the NORM and the FFS were similar, the differences in sprint time could arise from a technique bias towards the NORM start.

scholarly journals ANALYSIS OF THE WAITING TIME IN M^/G/1 QUEUES WITH GATED RANDOM ORDER OF SERVICE

1996 ◽  
Vol 39 (4) ◽  
pp. 459-474
Author(s):  
Norikazu Kawasaki ◽  
Hideaki Takagi ◽  
Yutaka Takahashi ◽  
Toshiharu Hasegawa
Keyword(s):  
Waiting Time ◽  
Random Order
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