Simulation Scripts Research Articles

An Overview of Packet Reordering in Transmission Control Protocol (TCP): Problems, Solutions, and Challenges

Transmission control protocol (TCP) is the most popular transport layer protocol for the Internet. Due to various reasons, such as multipath routing, route fluttering, and retransmissions, packets belonging to the same flow may arrive out of order at a destination. Such packet reordering violates the design principles of some traffic control mechanisms in TCP and, thus, poses performance problems. In this paper, we provide a comprehensive and in-depth survey on recent research on packet reordering in TCP. The causes and problems for packet reordering are discussed. Various representative algorithms are examined and compared by computer simulations. The ported program codes and simulation scripts are available for download. Some open questions are discussed to stimulate further research in this area

Trait and state predictors of error detection accuracy in a simulated quality control task

This study examined whether personality traits or mood and workload states are more predictive of detection accuracy in a simulated quality control task mimicking prescription checking in a pharmacy. Ninety-one undergraduate volunteers checked 80 simulated scripts for accuracy in a basic task environment with error rates ranging from 26% to 38%. Four dimensions of perceived workload and three task-related mood factors were assessed multiple times during the task, and several theoretically relevant personality traits were measured before the simulation. Performance was measured using hit rate and false alarm ratios. Results suggested that trait factors were more predictive of hit ratios in this study than were measures of task-related mood and perceived workload. Implications and future research directions are discussed.

Self-regulation, engagement, motivation, and performance in a simulated quality control task.

This study examined how self-regulation and task-related motivation were related to the accuracy of error detection and task engagement in a simulated quality control task that mimicked prescription-checking behavior in a pharmacy. Ninety-one participants completed measures of self-regulation, task engagement, and task-related motivation and then checked 80 simulated scripts with inserted error ratios ranging from 26% to 38%. Motivation and task engagement were assessed at the beginning of the task, the midpoint of the task, and after the task was over. Performance was measured in terms of sensitivity (error detections) and specificity (false alarm responses). Results indicated that motivation was correlated with higher sensitivity, while self-regulation was correlated with lower specificity. Higher mid-task motivation and higher self-regulation were also predictive of greater task engagement at the midpoint of the task only. Results are discussed and future research directions are proposed.

Simulator-independent representation of ionic conductance models with ChannelDB

ChannelDB is an implementation of a database of ionic conductance models, stored in simulator-independent NeuroML format, in order to share channel models between different neural simulators. At present, ChannelDB is implemented as a stand-alone module with its own graphical user interface to the database, which is implemented with MySQL. The NeuroML development kit parser is used to create Java objects from the NeuroML format (XML) files stored in the database. These are then accessed with Java software to create simulation scripts for the particular simulator. ChannelDB with all source code and documentation may be downloaded from http://www.modelersworkspace.org.

Simulator-independent representation of ionic conductance models with ChannelDB

ChannelDB is an implementation of a database of ionic conductance models, stored in simulator-independent NeuroML format, in order to share channel models between different neural simulators. At present, ChannelDB is implemented as a stand-alone module with its own graphical user interface to the database, which is implemented with MySQL. The NeuroML development kit parser is used to create Java objects from the NeuroML format (XML) files stored in the database. These are then accessed with Java software to create simulation scripts for the particular simulator. ChannelDB with all source code and documentation may be downloaded from http://www.modelersworkspace.org.

A multidisciplinary approach to teach responses to weapons of mass destruction and terrorism using combined simulation modalities

Study objective To reinforce concepts presented in the lectures; understand the complexity and speed of casualty and information generation during a Weapons of Mass Destruction and Terrorism (WMD/T) event; experience the novelty of combined weapons' effects; recognize the time course of the various chemical, biological, and radiation agents; and make challenging decisions with incomplete and conflicting information. Settings Two environments simulated simultaneously: one a major trauma center emergency room (ER) with two patient simulators and several human actors; the other an Emergency Operations Command Center (EOC). Target audience Students for this course included: clinicians, scientists, military and intelligence officers, lawyers, administrators, and logistic personnel whose jobs involve planning and executing emergency response plans to WMD/T. Simulation script A WMD/T attack in Washington, D.C., has occurred. Clinical students performed in their real life roles in the simulated ER, while nonclinical students did the same in the simulated EOC. Six ER casualties with combined WMD/T injuries were presented and treated over 40 minutes. In the EOC, each person was given his or her role title with identification tag. The EOC scenario took cues from the action in the ER via two television (TV) news feeds and telephone calls from other Emergency Operations Assets. Performance expectations Students were expected to actively engage in their roles. Student performances were self-evaluated during the debriefing. Debriefing The two groups were reunited and debriefed utilizing disaster crisis resource management tools. Assessment of effectiveness Students answered an 18-point questionnaire to help evaluate the usefulness and acceptance of multimodality patient simulation. Lessons learned Large-scale multimodality patient simulation can be used to train both clinicians and nonclinicians for future events of WMD/T. Students accepted the simulation experience and thought that scenario was appropriately realistic, complex, and overwhelming. Difficulties include the extensive man-hours involved in designing and presenting the live simulations. EOC-only sessions could be staged with only a few video cassette recorders, TVs, telephones, and callers.

Signal probability effects on error detection performance in a quality control task.

This study was designed to examine the influence of signal probability on detection accuracy in a quality control task patterned after the checking of prescriptions in a pharmacy. Signal probability was defined as the ratio of erroneous scripts to correct scripts. 91 participants were either exposed to signal probabilities ranging from 26 to 30% ("low probability") or 34 to 38% ("high probability") while checking two sets of 40 simulated scripts for accuracy. Wrong item, wrong number, and wrong label information errors were possible, and performance was measured in terms of sensitivity (percentage of errors detected) and specificity (percentage of correct orders accepted). Analysis indicated that sensitivity was significantly worse for participants in the high signal probability condition, but specificity was not significantly different between signal probability conditions. Connections to existing theory and future directions for research are discussed.

A simulation approach to the design of dynamic feedback scheduling algorithms for time-shared computer systems

The goal of a scheduling algorithm for a time-shared computer system is to provide acceptable request response time and resource utilization through effective resource allocation. In order to do this, it is necessary for the algorithm to be capable of adjusting itself to handle the various situations, precipitated by the set of active user requests and the computing system's status, which may occur. An effort is now underway to design the structural framework of a scheduling algorithm which will dynamically formulate its resource allocation policies and adjust its policy formulation depending on the success or failure of those policies. Once designed, the framework will then be used to construct the scheduling algorithm for a given time-shared computer system.The approach chosen for determining the practicability of the algorithm design is the inclusion of the algorithm in the simulation of a swapping time-shared computer system model. The simulation contains four basic activities: processor, input/output, swapper, each of which contains a resource allocation policy for determining a priority ordering among those requests which have asked for the respective resource, and the user activity. User activities follow one of a number of simulated scripts which may be composed of several types of requests. Each request is given behavioral traits dependent on its type.While the complete algorithm is not yet implemented within the simulation, preliminary results suggest that some improvement in response time may be possible.