<|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:"TSVNet: Combining Time-Series and Opportunistic Sensing by Transfer Learning for Dynamic Thermal Sensation Estimation."
Abstract:"The development of sensing technologies has enabled the estimation of human thermal sensation, which helps efficient heating, ventilation, and air conditioning control. Recently, machine learning techniques using physiological and environmental sensors have been proposed. However, in most existing literature, the personal thermal sensation is estimated using only opportunistic data as features, and evaluations are conducted in a static environment. In real situations, we are exposed to a dynamic environment, owing to human mobility and changes in airflow. Therefore, it is necessary to estimate personal thermal sensation in a dynamic surrounding environment. In this study, we propose TSVNet, a deep-learning-based method reflecting time-series changes to address the dynamic environment. We collected data for a total of 123 days, which included the dynamic environment data from 21 subjects, for the evaluation. The results indicate that our method improves F1-score by 5.8% compared with a baseline method. We also design a data balancing method for regression problems on imbalanced datasets, including time-series data. In addition, the result of the lookback time evaluation shows that the use of physiological information in the previous 10 minutes improves the performance of the method.."
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 = [];
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Q5 = [];
Q6 = [].<|eot_id|><|start_header_id|>assistant<|end_header_id|>Answer:
Q1 = [0];
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Q6 = [1].
TSVNet: Combining Time-Series and Opportunistic Sensing by Transfer Learning for Dynamic Thermal Sensation Estimation
The development of sensing technologies has enabled the estimation of human thermal sensation, which helps efficient heating, ventilation, and air conditioning control. Recently, machine learning techniques using physiological and environmental sensors have been proposed. However, in most existing literature, the personal thermal sensation is estimated using only opportunistic data as features, and evaluations are conducted in a static environment. In real situations, we are exposed to a dynamic environment, owing to human mobility and changes in airflow. Therefore, it is necessary to estimate personal thermal sensation in a dynamic surrounding environment. In this study, we propose TSVNet, a deep-learning-based method reflecting time-series changes to address the dynamic environment. We collected data for a total of 123 days, which included the dynamic environment data from 21 subjects, for the evaluation. The results indicate that our method improves F1-score by 5.8% compared with a baseline method. We also design a data balancing method for regression problems on imbalanced datasets, including time-series data. In addition, the result of the lookback time evaluation shows that the use of physiological information in the previous 10 minutes improves the performance of the method.