Description:

The primary objective of this project is to develop an mHealth app to directly provide caregivers with evidence-based content and peer-moderated support they can easily access and use to improve outcomes for their children and families. The app, currently called “FMF Connect,” is derived from our work on the scientifically-validated Families Moving Forward (FMF) Program that has shown promising results for child and caregiver outcomes in three trials with families raising children with FASD.

Study Participation:

Contact our study coordinator, Jennifer Parr, at jennifer_parr@urmc.rochester.edu.

Privacy Policy:

Multiple procedures are implemented to protect participant confidentiality and data collected through the app. In terms of data storage and management, all hard copy data (e.g., notes during qualitative interviews), will be secured in locked file cabinets within locked offices, available only to program staff. Electronic data (e.g., digital audio recordings and transcripts from interviews) will be stored in secured servers, and only program staff with knowledge of the password will be able to access the data. Forms with identifying information will be separated from the data collected and only subject numbers will be retained in data analysis files.

All information obtained for research will be kept strictly confidential (as allowed by law) by research staff. Participants will be told about all exceptions (e.g., child/dependent adult abuse, harm to self or others) to confidentiality during the consent process. Research staff and the Peer Moderator will be closely supervised by Dr. Petrenko and instructed on confidentiality, including what information is confidential, the limits of confidentiality, and to whom to report concerns. If maltreatment is suspected, staff will first discuss their concerns with the caregiver and inform him/her that, as indicated in the consent form that she/he had previously signed, our staff are ethically and legally obligated to file a report with Child Protective Services. We have found that this approach conveys respect for the family and mitigates parental anger that might otherwise emanate from filing a report. In our experience, when situations requiring filing a maltreatment report are handled sensitively and framed as stemming from concern for the welfare of the entire family, caregivers often perceive the process as being helpful to them.

Description:

This code extends the ns-3.20 energy framework by adding the concept of energy harvester. In addition to the definition of the general interfaces and helper classes, this code includes a basic energy harvester implementation, that provides a time-varying amount of energy, distributed according to a user-definable random variable. An example that shows how to set up the extended framework is also provided.

Credits:

Cristiano Tapparello.

Download:

Source code included in ns-3.21 (and later)

References:

C. Tapparello, H. Ayatollahi and W. Heinzelman, Energy Harvesting Framework for Network Simulator 3 (ns-3), 2nd International Workshop on Energy Neutral Sensing Systems (ENSsys), Memphis, TN, USA. November 6, 2014.

Description:

This code implements the Stochastic Shortest Path (SSP) solver proposed in [SSP] and is based on the Focused Real Time Dynamic Programming algorithm of [FRTDP]. This is an optimized algorithm to solve SSP problems having a very large state space (successful tests have been run for up to 1 billion of states). The code below can be used to obtain the results of [SSP] and can be easily modified to solve any other SSP problem. Our implementation uses the Boost C++ Libraries, which must be installed to successfully compile the code.

Credits:

Cristiano Tapparello and Michele Rossi.

Download:

Source code

References:

[SSP] Michele Rossi, Cristiano Tapparello and Stefano Tomasin, On Optimal Cooperator Selection Policies for Multi-Hop Ad Hoc Networks, IEEE Transactions on Wireless Communications,Vol. 10, No. 2, February 2011, pp. 506-518.

[FRTDP] T. Smith and R. G. Simmons, Focused real-time dynamic programming for MDPs: squeezing more out of a heuristic, in Proc. National Conference on Artificial Intelligence, Boston, MA, July 2006.