<|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:"Fairness Analysis of Routing in Opportunistic Mobile Networks."
Abstract:"Multicopy utility-based forwarding algorithms are popular in opportunistic mobile networks. They aim to gain high system throughput while keeping the cost low. However, most of them ignore the fairness issue on the successful delivery rate among users. In this paper, we analyze the fairness evaluation of the success rate distribution, and we propose a new fair packet-forwarding strategy based on packet priority. We formulate the opportunistic packet-forwarding process as a discrete-time Markov chain and deduce a stationary probability distribution vector. Instead of taking the hill-climbing heuristic on utility comparison, we introduce a lower utility tolerance mechanism for the decision-making process of each node, and we theoretically demonstrate that the proposed mechanism may be used to control the success rate of packet delivery by changing a simple parameter. In addition, we adopt a message-duplication restricting mechanism to adjust the number of replications based on packet priority. Our proposed protocol can work as a plug-in for traditional utility-based forwarding algorithms. The performance is compared with several well-known opportunistic routing protocols via both a synthetic model and real human mobility traces. The results show that our protocol improves the fairness in successful delivery rates among users while maintaining almost the same system throughput. Moreover, it reduces the cost of traditional utility-based forwarding algorithms, rendering our proposed scheme not only fair but efficient as well.."
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 = [];
Q2 = [];
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Q4 = [];
Q5 = [];
Q6 = [].<|eot_id|><|start_header_id|>assistant<|end_header_id|>Answer:
Q1 = [0];
Q2 = [];
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Q5 = [];
Q6 = [1].
Fairness Analysis of Routing in Opportunistic Mobile Networks
Multicopy utility-based forwarding algorithms are popular in opportunistic mobile networks. They aim to gain high system throughput while keeping the cost low. However, most of them ignore the fairness issue on the successful delivery rate among users. In this paper, we analyze the fairness evaluation of the success rate distribution, and we propose a new fair packet-forwarding strategy based on packet priority. We formulate the opportunistic packet-forwarding process as a discrete-time Markov chain and deduce a stationary probability distribution vector. Instead of taking the hill-climbing heuristic on utility comparison, we introduce a lower utility tolerance mechanism for the decision-making process of each node, and we theoretically demonstrate that the proposed mechanism may be used to control the success rate of packet delivery by changing a simple parameter. In addition, we adopt a message-duplication restricting mechanism to adjust the number of replications based on packet priority. Our proposed protocol can work as a plug-in for traditional utility-based forwarding algorithms. The performance is compared with several well-known opportunistic routing protocols via both a synthetic model and real human mobility traces. The results show that our protocol improves the fairness in successful delivery rates among users while maintaining almost the same system throughput. Moreover, it reduces the cost of traditional utility-based forwarding algorithms, rendering our proposed scheme not only fair but efficient as well.