<|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:"Location prediction and mobility modelling for enhanced localization solution."
Abstract:"Current and future mobile applications massively exploit the knowledge of the user's location to improve the offered services. However, user localization is by far one of the oldest and most difficult issues, due to its dynamism and to unavailability of some technologies in indoor environments. The enhanced localization solution (ELS) proposed in this paper is an innovative self adaptive solution that smartly combines standard location tracking techniques (e.g., GPS, GSM and WiFi localization), newly built-in technologies, as well as human mobility modelling and machine learning techniques. The main purposes of this solution are: to reduce the impact the service has, on the mobile device resources usage (mainly the battery consumption), when it is asked to provide a continuous localization; to help in preserving the privacy of the user, by running the whole system on the mobile device, without relying on a back-end server; and furthermore, to offer an ubiquitous coverage. The aspects mainly explored in this paper are: location prediction and mobility modelling, required to optimally estimate the current location with ELS. We are finding that people tend to move, for most of the time, among a limited set of places and that this can be modelled with a user prediction graph, which is further used to predict the next movement. Performing experiments on real users data, we show that the proposed prediction and the mobility model method of ELS are able to successfully predict the next location, even if we do not account for time features.."
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 = [0];
Q3 = [];
Q4 = [0];
Q5 = [];
Q6 = [1].
Location prediction and mobility modelling for enhanced localization solution
Current and future mobile applications massively exploit the knowledge of the user's location to improve the offered services. However, user localization is by far one of the oldest and most difficult issues, due to its dynamism and to unavailability of some technologies in indoor environments. The enhanced localization solution (ELS) proposed in this paper is an innovative self adaptive solution that smartly combines standard location tracking techniques (e.g., GPS, GSM and WiFi localization), newly built-in technologies, as well as human mobility modelling and machine learning techniques. The main purposes of this solution are: to reduce the impact the service has, on the mobile device resources usage (mainly the battery consumption), when it is asked to provide a continuous localization; to help in preserving the privacy of the user, by running the whole system on the mobile device, without relying on a back-end server; and furthermore, to offer an ubiquitous coverage. The aspects mainly explored in this paper are: location prediction and mobility modelling, required to optimally estimate the current location with ELS. We are finding that people tend to move, for most of the time, among a limited set of places and that this can be modelled with a user prediction graph, which is further used to predict the next movement. Performing experiments on real users data, we show that the proposed prediction and the mobility model method of ELS are able to successfully predict the next location, even if we do not account for time features.