<|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:"Risk Diffusion Modeling and Vulnerability Quantification on Japanese Human Mobility Network from Complex Network Analysis Point of View."
Abstract:"The human mobility networks are vital infrastructure in recent social systems. Many efforts have been made to keep the healthy human mobility flows to maintain sustainable development of recent well-connected society. However, the inter-connectivity sometimes raises unintended diffusion and amplification of the intrinsic risks, which is difficult to forecast because of the complexity of the underlying networks. Therefore, it is believed that modeling and simulation of the risk diffusion in the human mobility networks are suggestive and meaningful. Also, recent improvement of usability of individual-level human mobility data and capabilities of high-performance computing technologies enable us to employ the data-driven approaches. In this paper, the risk diffusion dynamics is modeled based on the SIS epidemic model and the vulnerability index is defined to quantify the node-level easiness of suffering risks. We also conduct the link removal test to find the better risk mitigation methods.."
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 = [];
Q3 = [];
Q4 = [];
Q5 = [];
Q6 = [].<|eot_id|><|start_header_id|>assistant<|end_header_id|>Answer:
Q1 = [1];
Q2 = [];
Q3 = [2];
Q4 = [0];
Q5 = [];
Q6 = [1].
Risk Diffusion Modeling and Vulnerability Quantification on Japanese Human Mobility Network from Complex Network Analysis Point of View
The human mobility networks are vital infrastructure in recent social systems. Many efforts have been made to keep the healthy human mobility flows to maintain sustainable development of recent well-connected society. However, the inter-connectivity sometimes raises unintended diffusion and amplification of the intrinsic risks, which is difficult to forecast because of the complexity of the underlying networks. Therefore, it is believed that modeling and simulation of the risk diffusion in the human mobility networks are suggestive and meaningful. Also, recent improvement of usability of individual-level human mobility data and capabilities of high-performance computing technologies enable us to employ the data-driven approaches. In this paper, the risk diffusion dynamics is modeled based on the SIS epidemic model and the vulnerability index is defined to quantify the node-level easiness of suffering risks. We also conduct the link removal test to find the better risk mitigation methods.