<|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:"Performance modelling of opportunistic forwarding under heterogenous mobility."
Abstract:"The delay tolerant networking paradigm aims to enable communications in disconnected environments where traditional protocols would fail. Opportunistic networks are delay tolerant networks whose nodes are typically the users' personal mobile devices. Communications in an opportunistic network rely on the mobility of users: each message is forwarded from node to node, according to a hop-by-hop decision process that selects the node that is better suited for bringing the message closer to its destination. Despite the variety of forwarding protocols that have been proposed in the recent years, there is no reference framework for the performance modelling of opportunistic forwarding. In this paper we start to fill this gap by proposing an analytical model for the first two moments of the delay and the number of hops experienced by messages when delivered in an opportunistic fashion. This model seamlessly integrates both social-aware and social-oblivious single-copy forwarding protocols, as well as different hypotheses for user contact dynamics. More specifically, the parameters of model can be solved in a closed form in the case of exponential and Pareto inter-meeting times, two popular cases emerged from the literature on human mobility analysis. In order to exemplify how the proposed framework can be used, we discuss its application to two case studies with different mobility settings. Then, we discuss how the framework can be also extended to accommodate inter-meeting times following a hyper-exponential distribution. This case is particularly relevant as hyper-exponential distributions are able to approximate the large class of high-variance distributions (distributions with coefficient of variation greater than one), which are those more challenging, e.g., from the delay standpoint. Finally, we provide a validation for the framework with both ideal contacts (i.e., exactly following a given distribution) and contacts extracted from a real mobility trace. This evaluation highlights the strength of the framework in terms of its ability both to provide very accurate predictions under ideal mobility and to effectively approximate the behaviour of the delay moments under real mobility. (C) 2014 Elsevier B.V. All rights reserved.."
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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: 

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Q6 = [].<|eot_id|><|start_header_id|>assistant<|end_header_id|>Answer:
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Q6 = [1].
Performance modelling of opportunistic forwarding under heterogenous mobility
The delay tolerant networking paradigm aims to enable communications in disconnected environments where traditional protocols would fail. Opportunistic networks are delay tolerant networks whose nodes are typically the users' personal mobile devices. Communications in an opportunistic network rely on the mobility of users: each message is forwarded from node to node, according to a hop-by-hop decision process that selects the node that is better suited for bringing the message closer to its destination. Despite the variety of forwarding protocols that have been proposed in the recent years, there is no reference framework for the performance modelling of opportunistic forwarding. In this paper we start to fill this gap by proposing an analytical model for the first two moments of the delay and the number of hops experienced by messages when delivered in an opportunistic fashion. This model seamlessly integrates both social-aware and social-oblivious single-copy forwarding protocols, as well as different hypotheses for user contact dynamics. More specifically, the parameters of model can be solved in a closed form in the case of exponential and Pareto inter-meeting times, two popular cases emerged from the literature on human mobility analysis. In order to exemplify how the proposed framework can be used, we discuss its application to two case studies with different mobility settings. Then, we discuss how the framework can be also extended to accommodate inter-meeting times following a hyper-exponential distribution. This case is particularly relevant as hyper-exponential distributions are able to approximate the large class of high-variance distributions (distributions with coefficient of variation greater than one), which are those more challenging, e.g., from the delay standpoint. Finally, we provide a validation for the framework with both ideal contacts (i.e., exactly following a given distribution) and contacts extracted from a real mobility trace. This evaluation highlights the strength of the framework in terms of its ability both to provide very accurate predictions under ideal mobility and to effectively approximate the behaviour of the delay moments under real mobility. (C) 2014 Elsevier B.V. All rights reserved.