Contributors:John Palmieri, Kevin Spiegler, Kevin Pang, Catherine Myers
The purpose of this experiment was to compare between-strain differences regarding avoidance behavior. Active avoidance data were collected from 40 Wistar-Kyoto (WKY) and 40 Sprague Dawley (SD) rats during a foot-shock experiment. The experiment consisted of 12 sessions each composed of 25 trials. Each trial consisted of a warning period, a possible shock period (if avoidance did not occur), and an intertrial interval (ITI) representing a safety period. During warning periods, rats had an option to press a lever in order to avoid future shock. However, if a lever press did not occur, then a shock period began during which the rats could press the lever to escape the foot-shock. After an escape or an avoidance, a 180-second safe period began (ITI). The maximum time of a warning period and a shock were both 72-seconds each if the rat failed to lever press during both periods. Every session also began with a 60-second habituation period during which the rat could familiarize itself with the experimental cage.
Lever presses during ITI periods were classified as inter-trial responses (ITRs) whereas lever presses during the habituation periods were classified as anticipatory responses (ARs). Lever presses during shock periods were labeled as escapes “E” whereas lever presses during warning periods were labeled as avoidances “A”.
Lever press data were discretized to 12-second periods. Frequency counts for lever presses during each 12-second timestep can be found in the raw data files (located in the “ratRL_datafiles_Model_InputOutput” folder). An example name of this file is “S09.csv” representing an SD rat or “W09.csv” representing a WKY rat.
A reinforcement learning, actor-critic model was applied to this raw data to determine different learning parameters for each rat. The code for this model fitting can be found in the folder titled “ratRL_ModelFitting_Code”.
Based on learning parameters determined from the reinforcement learning model code, a simulation of theoretical rats running through the experimental protocol was created. This simulation can be found in the “ratRL_Simulation_Code” folder. Inputs to this simulation are found in the “ratRL_codedTrials_Sim_InputOutput” folder. The summaries for each experimental rat’s performance during the foot-shock avoidance experiment are labeled “Sum” files (ie S09sum.csv) and the learning parameters for each rat are stored in the file called “parm_listfile.csv”. Output files are labeled “out” (ie S09out.csv) and represent the performance of a theoretical simulated rat.
The differences in learning parameters between the two rat strains can provide insight into anxiety-disorders as the WKY rats have been used as an animal model for anxiety. Learning parameters can also be mapped to certain brain regions in order to explore possible neurological differences in the two strains in future experiments.
The high sensitivity of COVID-19 and the need for high accuracy calculations necessitate collecting the required data sets from reliable sources. Thus, all information was collected and categorized from reputable sources such as WHO (World Health Organization) and worldometers site (www.worldometers.info). The worldometers site contains information such as daily mortality statistics, recovery, and newly confirmed cases.
Research data including observation data is obtained from a collection of Iranian samples’ reports in three parts (i.e. death, confirmed and recovered). This countrywide daily information is confirmed by the WHO. It should be noted that the relevant data was collected between February 19 and May 16, 2020.
In this work we have presented the state of the art of requirements engineering in methodologies used for the development of HRIS system web-based. To achieve this purpose, we started describing the structure of the requirements engineering process and the most common techniques used in such a process in the classic software
development for HRIS Traditionally non-web applications in an organization. During this study, various stages of the system development life cycle were executed. Understanding the problem statement, literature survey, analysis, interpretation, design and implementation gave the thorough idea of a study life-cycle. The objective behind designing of a new HRIS was to eliminate manual work to some extent and also help employees to access their own HRIS, and also sharing information with other departments in the organization. The process developed by this HRIS web-based system includes. First, I have main activities: capture requirements, definition and Analysis of requirements. The techniques most frequently used to perform these activities are among others, use cases, scenarios. I have used a combination of three requirements elicitation methodobservation, interviews and brainstorming. In the interview process, I have interviewed three potential customers. And finally, after brainstorming, I have used use case technique to specify the user requirements. Based on the user requirements, I have designed class attributes and activity diagram. In a second step I have designed the class diagram to get the clear picture of the future HRIS system. I have designed an outline of the methodologies for the web describing how these approaches cover the aspects related to requirements engineering, I use struts MVC framework and J2EE standard platform technology, a new flexible, high-effect, expandable enterprise human resources management system framework is designed and then implemented. This system not only can successfully resolve a large number of practical problems the enterprise human resources management faced to improve human resources management efficiency, but also the information system is simple and easy to implement, has strong features such as easy to expand, easy to maintain, flexible and secure. Finally, I have also designed GUI prototypes. This study can be further extended in implementation of other modules of HRIS like recruitment and selection, training and development, compensation, benefits and payroll. Can be used another framework for developing this application for example (ASP.NET MVC &Spring MVC) easily. Reason the persistence layer and business logic layer it will not be affected, thus the system flexibility and maintainability.
This dataset contains the Psi and Delta spectra obtained from the spectroscopic ellipsometer. Also, it contains the data after modelling with B-spline and Psemi_Tri general oscillator model. Furthermore, absorption and transmission spectra obtained from the UV-Vis spectrometer is also included in the data set. We calculate the transmission spectra from the obtained optical properties from the SE modelling. Research data_calculate T_DIB file has the calculated transmission spectra for different thicknesses sample.