<|begin_of_text|><|start_header_id|>system<|end_header_id|>You are an expert on climate change, human mobility and migration. Answer the question truthfully.<|eot_id|><|start_header_id|>user<|end_header_id|>Here are the title and abstract of a scientific paper probably related to climate change, human mobility and migration..
Title:"Construction of Optimal Membership Functions for a Fuzzy Routing Scheme in Opportunistic Mobile Networks."
Abstract:"This article proposes FRIMF, a fuzzy routing scheme for opportunistic mobile networks (OMNs). In FRIMF, we exploit the pairwise intercontact times to evaluate the connection strength between two nodes. Instead of assuming a random movement model, in the present case we consider node contact processes in OMNs as bursty events. Consequently, we introduce a burstiness parameter to characterize the variability in the dynamics of pairwise interactions. This variance metric, along with the statistical mean of pairwise intercontact times, is used to define a single FRIMF routing metric called closeness through a fuzzy inference system. This reflects the tie strength of the pair nodes. To improve the transmission environment, we further propose a method to develop optimal membership functions for the FRIMF's fuzzy parameters based on the contact information. Particularly, we leverage the membership function elicitation techniques commonly used in collective opinion aggregations based on a direct rating process to establish the relevancy between vagueness estimates of the routing parameters and statistical distributions of the pairwise intercontact times in a way that eventually presents asymmetric triangular fuzzy numbers. In turn, these TFNs are used to properly define the fuzzy sets of the FRIMF's parameters. Through simulations in the real human mobility environments, we show that FRIMF utilizing the enhanced asymmetric TFNs can outperform that using the typical symmetric TFNs developed based on our subjective preferences. Lastly, comparing with several algorithm benchmarks, we confirm the efficiency of FRIMF in transmission cost and delay.."
I have six questions you need to answer.
Q1: Is this article is about 'human migration mobility': 1 = yes; 0 = no. Answer with a number. Do not give a textual explanation.

Q2: What drivers of migration and mobility does the article study? 0 = 'other', 1 = 'climate', 2 = 'environment', 3 = 'economic', 4 = 'conflict', 5 = 'political', 6 = 'social', 7 = 'family'. Answer with a number or a sequence of numbers separated by commas. Do not give a textual explanation. 

Q3: What is the overall sentiment of the article?:0 = 'neutral', 1 = 'negative', 2 = 'positive'. Answer with a number. Do not give a textual explanation.

Q4: What type of climate or environmental keywords are explicitly mentioned?:  0 = 'none', 1 = 'algal blooms', 2 = 'infestation', 3 = 'infectious diseases', 4 = 'ocean acidification', 5 = 'sea ice', 6 = 'acid rain', 7 = 'drought', 8 = 'cold wave', 9 = 'heat wave', 10 = 'glacial', 11 = 'wildfire', 12 = 'air pollution', 13 = 'biodiversity loss', 14 = 'coastal erosion', 15 = 'land degradation', 16 = 'deforestation', 17 = 'desertification', 18 = 'water pollution', 19 = 'diebacks', 20 = 'permafrost loss', 21 = 'sand mining', 22 = 'sea level rise', 23 = 'earthquake', 24 = 'tsunami', 25 = 'volcanic', 26 = 'flood', 27 = 'avalanche', 28 = 'landslide', 29 = 'downburst', 30 = 'thunderstorm', 31 = 'sandstorm', 32 = 'haze', 33 = 'storm', 34 = 'cyclone', 35 = 'blizzard', 36 = 'hail', 37 = 'snow', 38 = 'tornado', 39 = 'wind', 40 = 'hurricane', 41 = 'typhoon', 42 = 'other'. Answer with a number or a sequence of numbers separated by commas. Do not give a textual explanation. 

Q5: What discipline does the article belong to?: 0 = 'other', 1 = 'social science', 2 = 'natural science', 3 = 'arts and humanities', 4 = 'multidiscipline and interdiscipline'. Answer with a number. Do not give a textual explanation.

Q6: Does the article apply quantitative and/or statistical analysis?: 0 = 'no', 1 = 'yes'. Answer with a number. Do not give a textual explanation.

Use the following template: 

Q1 = [];
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Q6 = [].<|eot_id|><|start_header_id|>assistant<|end_header_id|>Answer:
Q1 = [0];
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Q4 = [];
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Q6 = [1].
Construction of Optimal Membership Functions for a Fuzzy Routing Scheme in Opportunistic Mobile Networks
This article proposes FRIMF, a fuzzy routing scheme for opportunistic mobile networks (OMNs). In FRIMF, we exploit the pairwise intercontact times to evaluate the connection strength between two nodes. Instead of assuming a random movement model, in the present case we consider node contact processes in OMNs as bursty events. Consequently, we introduce a burstiness parameter to characterize the variability in the dynamics of pairwise interactions. This variance metric, along with the statistical mean of pairwise intercontact times, is used to define a single FRIMF routing metric called closeness through a fuzzy inference system. This reflects the tie strength of the pair nodes. To improve the transmission environment, we further propose a method to develop optimal membership functions for the FRIMF's fuzzy parameters based on the contact information. Particularly, we leverage the membership function elicitation techniques commonly used in collective opinion aggregations based on a direct rating process to establish the relevancy between vagueness estimates of the routing parameters and statistical distributions of the pairwise intercontact times in a way that eventually presents asymmetric triangular fuzzy numbers. In turn, these TFNs are used to properly define the fuzzy sets of the FRIMF's parameters. Through simulations in the real human mobility environments, we show that FRIMF utilizing the enhanced asymmetric TFNs can outperform that using the typical symmetric TFNs developed based on our subjective preferences. Lastly, comparing with several algorithm benchmarks, we confirm the efficiency of FRIMF in transmission cost and delay.