Beginners Guide to TikTok for Search - Rachel Pearson - We are Tilt __ Bright...
Wirn2013
1. ENVIRONMENTAL RISK
ASSESSMENT OF
GENETICALLY MODIFIED
ORGANISMS
F. Camastra, A. Ciaramella, V. Giovannelli, M.
Lener, V. Rastelli, A. Staiano, G. Staiano, A.
Starace
WIRN 2013 - XXIII Italian Workshop on Neural Networks
Vietri sul Mare, Salerno, ItalyMay 23rd 2013
2. May 23rd 2013 WIRN 2013 – Vietri sul Mare, Salerno, Italy
Contents
Introduction
Environmental Risk Assessment (ERA)
Fuzzy Decision Support System
System Validation
Conclusions and Future Works
3. May 23rd 2013 WIRN 2013 – Vietri sul Mare, Salerno, ItalyMay 23rd 2013
Introduction
Genetically Modified Organism (GMO)
organism altered using genetic engineering
techniques
ADVANTAGES
GM Crop plant
resistant to herbicide, pests, diseases or
environmental conditions
with improved nutritional or pharmaceutical properties
4. May 23rd 2013 WIRN 2013 – Vietri sul Mare, Salerno, ItalyMay 23rd 2013
BUT …
Risks that might impact on:
Consumers
Man, Animals (e.g., butterflies)
Natural environment
Natural habitats, Soil
European directives assess and manage GMO
risks
The notifier, i.e., the person who requests the
GMO release, must perform an ERA
5. May 23rd 2013 WIRN 2013 – Vietri sul Mare, Salerno, ItalyMay 23rd 2013
Environmental Risk
Assessment (ERA)
1. Preliminary identification of risks
available scientific database and literature
2. Effects on non-target species
BT toxin and butterflies
3. Effects on natural environment
Compatible wild plants
4. Management of the risk
mitigation measures
6. May 23rd 2013 WIRN 2013 – Vietri sul Mare, Salerno, ItalyMay 23rd 2013
The Conceptual Model
7. May 23rd 2013 WIRN 2013 – Vietri sul Mare, Salerno, ItalyMay 23rd 2013
Overview of ERA process
8. May 23rd 2013 WIRN 2013 – Vietri sul Mare, Salerno, ItalyMay 23rd 2013
System architecture
9. May 23rd 2013 WIRN 2013 – Vietri sul Mare, Salerno, ItalyMay 23rd 2013
The Electronic Questionnaire
As a web application
Different kinds of questions:
Textual
Numerical
Date
Linguistic
Multiple choice
10. May 23rd 2013 WIRN 2013 – Vietri sul Mare, Salerno, ItalyMay 23rd 2013
Textual question
11. May 23rd 2013 WIRN 2013 – Vietri sul Mare, Salerno, ItalyMay 23rd 2013
Numerical question
12. May 23rd 2013 WIRN 2013 – Vietri sul Mare, Salerno, ItalyMay 23rd 2013
Date question
13. May 23rd 2013 WIRN 2013 – Vietri sul Mare, Salerno, ItalyMay 23rd 2013
Linguistic question
14. May 23rd 2013 WIRN 2013 – Vietri sul Mare, Salerno, ItalyMay 23rd 2013
Multiple choice question
15. May 23rd 2013 WIRN 2013 – Vietri sul Mare, Salerno, ItalyMay 23rd 2013
The Fuzzy Decision Support
System
(FDSS)
16. May 23rd 2013 WIRN 2013 – Vietri sul Mare, Salerno, ItalyMay 23rd 2013
Fuzzifier
17. May 23rd 2013 WIRN 2013 – Vietri sul Mare, Salerno, ItalyMay 23rd 2013
Fuzzy rule base
IF vegetative cycle duration IS
High
AND cultural cycle duration IS
Low
THEN phenological risk IS Low
18. May 23rd 2013 WIRN 2013 – Vietri sul Mare, Salerno, ItalyMay 23rd 2013
Inference engine
IF vegetative cycle duration IS
High
AND cultural cycle duration IS
Low
THEN phenological risk IS Low
IF vegetative cycle duration IS
High
AND cultural cycle duration IS
High
THEN phenological risk IS High
…
19. May 23rd 2013 WIRN 2013 – Vietri sul Mare, Salerno, ItalyMay 23rd 2013
Defuzzifier
IF vegetative cycle duration IS
High
AND cultural cycle duration IS
Low
THEN phenological risk IS Low
IF vegetative cycle duration IS
High
AND cultural cycle duration IS
High
THEN phenological risk IS High
…
20. May 23rd 2013 WIRN 2013 – Vietri sul Mare, Salerno, ItalyMay 23rd 2013
Report
21. May 23rd 2013 WIRN 2013 – Vietri sul Mare, Salerno, ItalyMay 23rd 2013
Open source library
http://jfuzzylogic.sourceforge.net/html/index.html
Fuzzy Control Language (FCL)
specification
Aggregation, Activation and Accumulation
methods
Supports continue, discrete or custom
membership functions
Flexible and extensible FDSS
jFuzzyLogic
22. May 23rd 2013 WIRN 2013 – Vietri sul Mare, Salerno, ItalyMay 23rd 2013
System validation
The Fuzzy Decision Support System has been
tested
by producing about 150 ERAs
by submitting the produced inferences to a pool of
ISPRA experts
not involved in the rule definition
to assess the consistency and completeness of
the system
23. May 23rd 2013 WIRN 2013 – Vietri sul Mare, Salerno, ItalyMay 23rd 2013
Scenario 1
24. May 23rd 2013 WIRN 2013 – Vietri sul Mare, Salerno, ItalyMay 23rd 2013
Scenario 2
25. May 23rd 2013 WIRN 2013 – Vietri sul Mare, Salerno, ItalyMay 23rd 2013
Conclusions and Future Works
Fuzzy Decision Support System
identify potential impacts that can achieve one
or more receptors through a set of migration
paths
validated on Bt-maize1 and Brassica napus2
by the human experts of ISPRA
Future works
Machine learning algorithms to learn the
FDSS knowledge base1 GM maize
2 GM colza
Editor's Notes
Good morning. I’m Alfredo Starace and I’m going to present a joint research project performed by University of Naples Parthenope and ISPRA that is InstitutoSuperiore per la Protezione e la ricerca ambientale which is the part of Ministero dell’ambiente devoted to the control of Genetically modified organism.The research project was develop within the European community life program.
We will firstly take a short introduction to the problem highlighting the motivationsof the research.Later, we will describe the Environmental Risk Assessment processand our Fuzzy Decision Support System to facilitate this task.After we’ll show the results obtained with the supervision of field experts.And Finally, we’ll drowsome conclusions