A Simulation-Based Study on the Throughput Capacity of Topology Control in CSMA/CA Networks

2006 ◽  
Author(s):  
D.M. Blough ◽  
C. Harvesf ◽  
G. Resta ◽  
G. Riley ◽  
P. Santi
Keyword(s):  
Topology Control ◽  
Throughput Capacity ◽  
Simulation Based

scholarly journals DETERMINATION OF THE THROUGHPUT CAPACITY OF STOP POINTS OF URBAN PUBLIC TRANSPORT

2020 ◽  
Vol 17 (2) ◽  
pp. 248-261
Author(s):  
A. I. Fadeev ◽  
E. V. Fomin ◽  
S. Alhusseini
Keyword(s):  
Mathematical Models ◽  
Transport System ◽  
Public Transport ◽  
Throughput Capacity ◽  
Public Transport System ◽  
Simulation Based ◽  
Urban Public Transport ◽  
Random Nature ◽  
Stop Point

Introduction. One of the most important parameters of the transport system is the capacity of line, which in urban public transport system is usually determined by the stop points throughput capacity. When determining the throughput capacity of stop points, it is necessary to consider the random nature of the transport flows at the stop and the process of boarding and alighting passengers. In this work, the stop point is considered as a multi-channel single-phase queuing system (QS). On this basis, an approach to determining the throughput capacity of stop points in urban passenger transport is proposed and justified.Materials and methods. Two mathematical models of a stop point as QS are considered: analytical and simulation. Based on the obtained analysis results from these models, recommendations are offered for calculating the actual throughput capacity of a stop point.Results. In this article, as example a specific stop points are taken, to evaluate the performance of the proposed mathematical models and formulate recommendations to determine its throughput capacity.Discussion and conclusion. The proposed procedure for determining the stop points throughput capacity, consisting of identifying critical stop points with the highest passengers traffic, determining the service process parameters of fleet, and calculating the probability of queue occurrence, allows to set the maximum traffic intensity for the lines of urban public transport.Financial transparency: the authors have no financial interest in the presented materials or methods. There is no conflict of interest.

Managing operating performance at an ophthalmology clinic

2019 ◽  
Vol 25 (12) ◽  
pp. 1-13
Author(s):  
Harshal Lowalekar ◽  
N Ravichandran
Keyword(s):  
Waiting Times ◽  
Operating Performance ◽  
Central India ◽  
Throughput Capacity ◽  
Operating Parameters ◽  
Operational Parameters ◽  
Scheduling System ◽  
Simulation Based ◽  
System Improvement ◽  
Overall Performance

This article documents the experience of addressing improvements in the operational parameters of an ophthalmology clinic in central India. Shortcomings in the operating parameters of throughput, capacity use and patient waiting times required the immediate attention of senior management. Based on elementary data analysis, the present study advocates a scheduling system that improves all three of these operating parameters. Several administrative options to improve the overall performance of the system were identified. For a deeper understanding of the design issues, a simulation-based model was developed. The results of the simulation model were used to prioritise action areas that needed system improvement.

Inquiry Learning

2009 ◽  
Vol 23 (2) ◽  
pp. 117-127 ◽  
Author(s):  
Astrid Wichmann ◽  
Detlev Leutner
Keyword(s):  
Scientific Reasoning ◽  
Inquiry Learning ◽  
Instructional Support ◽  
Test Results ◽  
Knowledge Test ◽  
Simulation Based ◽  
Scientific Experiments ◽  
Specific Support ◽  
Science Classes

Seventy-nine students from three science classes conducted simulation-based scientific experiments. They received one of three kinds of instructional support in order to encourage scientific reasoning during inquiry learning: (1) basic inquiry support, (2) advanced inquiry support including explanation prompts, or (3) advanced inquiry support including explanation prompts and regulation prompts. Knowledge test as well as application test results show that students with regulation prompts significantly outperformed students with explanation prompts (knowledge: d = 0.65; application: d = 0.80) and students with basic inquiry support only (knowledge: d = 0.57; application: d = 0.83). The results are in line with a theoretical focus on inquiry learning according to which students need specific support with respect to the regulation of scientific reasoning when developing explanations during experimentation activities.

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