Informational inefficiency on bitcoin futures

This paper investigates the dynamics and drivers of informational inefficiency in the Bitcoin futures market. To quantify the adaptive pattern of informational inefficiency, we leverage two groups of statistics which measure long memory and fractal dimension to construct a global-local market inefficiency index. Our findings validate the adaptive market hypothesis, and the global and local inefficiency exhibits different patterns and contributions. Regarding the driving factors of the time-varying inefficiency, our results suggest that trading activity of retailers (hedgers) increases (decreases) informational inefficiency. Compared to hedgers and retailers, the role played by speculators is more likely to be affected by the COVID-19 crisis. Extremely bullish and bearish investor sentiment has more significant impact on the local inefficiency. Arbitrage potential, funding liquidity, and the pandemic exert impacts on the global and local inefficiency differently. No significant evidence is found for market liquidity and policy uncertainty related to cryptocurrency.

Multizone Modelling of SARS-CoV-2 Airborne Transmission in Mechanically Ventilated Buildings

This study contributes to a better understanding of the airborne transmission risks in multizone, mechanically ventilated buildings and how to reduce infection risk. A novel modeling approach combining the Wells-Riley and the US National Institute of Standards and Technology (NIST) CONTAM models was applied to a multizone whole building to simulate exposure and assess the effectiveness of different mitigation measures. A case study for the US Department of Energy large office prototype building was conducted to illustrate the approach. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

Volatility forecasting with Hybrid-long short-term memory models: Evidence from the COVID-19 period

Volatility forecasting, a central issue in financial risk modelling and management, has attracted increasing attention after several major financial market crises. In this article, we draw upon the literature on volatility forecasting and hybrid models to construct the Hybrid-long short-term memory (LSTM) models to forecast the intraday realized volatility in three major US stock indexes. We construct the hybrid models by combining one or multiple traditional time series models with the LSTM model, and incorporating either the estimated parameters, or the predicted volatility, or both from the statistical models as additional input values into the LSTM model. We perform the out-of-sample test of our Hybrid-LSTM models in volatility forecasting during the coronavirus disease 2019 (COVID-19) period. Empirical results show that the Hybrid-LSTM models can still significantly improve the volatility forecasting performance of the LSTM model during the COVID-19 period. By analysing how the construction methods may influence the forecasting performance of the Hybrid-LSTM models, we provide some suggestions on their design. Finally, we identify the optimal Hybrid-LSTM model for each stock index and compare its performance with the LSTM model on each day during our sample period. We find that the Hybrid-LSTM models' great capability of capturing market dynamics explains their good performance in forecasting. © 2023 John Wiley & Sons Ltd.

Me, my child and Covid-19: Parents' reflections on their child's experiences of lockdown in the UK and China

Over the past 2 years, the world has been living through the unprecedented Covid-19 pandemic. Children have had to adapt to online classrooms and lessons of some sort, and many parents have been forced to work from home while supervising their child's home learning activities. We used participatory visual methods to understand how children and their parents have coped during this time, engaging parents as co-researchers to ask their child to photograph and/or draw pictures that represent their daily lived experiences over the lockdown period. We then asked parents to interview their children (24 in total, 13 in the UK and 11 in China), using the children's artwork as prompts, and finally we interviewed parents. Through the data collection process, parents captured their children's experiences and feelings since the coronavirus struck. The data was analysed using Foucault's theory of discourse to provide unique and comparative insights into children's experiences in the UK and China during this exceptional time. Ours is the first study to integrate parents' and children's views of Covid-19, drawing on parents as co-researchers. We argue that combining the data collection methods and drawing on parents as co-researchers enabled parents to gain insights into an understanding of their child's lived experiences throughout the pandemic that might otherwise have been unknown. These insights were often unexpected for parents, and have been grouped around themes of parental relief, anxiety and understanding.

[Clinical characteristics and vaccination status of SARS-CoV-2 Omicron variant infected children].

Objective: To investigate the clinical characteristics and vaccination status of SARS-CoV-2 Omicron variant infected children. Methods: A total of 105 children infected with Omicron variant admitted to Tianjin Haihe Hospital (designated referral hospital for SARS-CoV-2 infection in Tianjin) from January 8, 2022 to February 3 were included for a retrospective study. The cases were divided into pneumonia group and non-pneumonia group according to chest imaging. Based on the doses of inactivated SARS-CoV-2 vaccine, the children who completed SARS-CoV-2 antibody test within 3 days after hospitalization were divided into 2 dose group and<2 dose group.Rank sum test and Chi-square test were used for the comparison between the groups. Results: The age of these 105 children was 10 (8, 11) years on admission, 53 children were males and 52 were females. Eighty-seven cases (82.9%) had mild symptoms, 13 cases (12.4%) had common symptoms and 5 cases (4.8%) were asymptomatic. Ninety-one cases (86.7%) completed 2 doses vaccination. The clinical symptoms were characterized by cough (74 cases, 70.5%), fever (58 cases, 55.2%), sore or dry throat (34 cases, 32.4%), nasal congestion (28 cases, 26.7%), rhinorrhea (23 cases, 21.9%). None of the children received antivirals, steroids, immunosuppressant or oxygen therapy. Seventy-six cases(72.4%) received traditional Chinese medicine treatment. The pneumonia group had a higher rate of positive SARS-CoV-2 IgG within 1 day after admission (13/13 vs. 87.0% (80/92), χ2=42.81, P<0.001) than the non-pneumonia group. Among the 62 children who had serial SARS-CoV-2 antibody tests within 3 days after hospitalization, Compared to the<2 dose group, the 2 dose group had a higher rate of nucleic acid conversion within 16 days after onset and a higher rate of positive SARS-CoV-2 IgG 1 day after admission and 3 days after hospitalization (96.4% (54/56) vs. 4/6, 100.0% (56/56) vs. 2/6, 100.0% (56/56) vs. 3/6, all P<0.05). Conclusions: Most children infected with Omicron variant have mild symptoms, mainly respiratory infection symptoms. The proportion of SARS-CoV-2 antibody IgG positive in children who have received 2 doses of inactivated SARS-CoV-2 vaccines is higher,and the time of whose nucleic acid conversion may be shortened.

Periodic Weather-Aware LSTM with Event Mechanism for Parking Behavior Prediction

There are plenty of parking spaces in big cities, but we often find nowhere to park. The reason is the lack of prediction of parking behavior. If we could provide parking behavior in advance, we can ease this parking problem that affects human well-being. We observe that parking lots have periodic parking patterns, which is an important factor for parking behavior prediction. Unfortunately, existing work ignores such periodic parking patterns in parking behavior prediction, and thus incurs low accuracy. To solve this problem, we propose PewLSTM, a novel periodic weather-aware LSTM model that successfully predicts the parking behavior based on historical records, weather, environments, weekdays, and events. PewLSTM consists of two parts: a periodic weather-aware LSTM prediction module and an event prediction module, for predicting parking behaviors in regular days and events. Based on 910,477 real parking records in 904 days from 13 parking lots, PewLSTM yields 93.84% parking prediction accuracy, which is about 30% higher than the state-of-the-art parking behavior prediction method. We have also analyzed parking behaviors in events like holidays and COVID-19;PewLSTM can also handle parking behavior prediction in events and reaches 90.68% accuracy. IEEE

Predicting the trend of the COVID-19 outbreak and timely grading the current risk level of epidemic based on moving average prediction limits

Objective : To establish a practical data-driven method that helps predict the evolutionary trend of the coronavirus disease 2019 (COVID-19) epidemic, track and prejudge the current risk classification of the epidemic area, and provide a quantitative evidence for precision prevention and control strategies. Methods ¡¤ A moving average prediction limit (MAPL) method was established based on the moving average method. The previous severe acute respiratory syndrome (SARS) epidemic data was used to verify the practicability of the MAPL method for predicting epidemic trends and quantitative risk. By tracking the COVID-19 outbreak epidemic data publicly reported since January 16, 2020, the MAPL method was used for timely epidemic trend prediction and the risk classification. Results ¡¤ According to the MAPL analysis, the na-tional epidemic of COVID-19 peaked in early February 2020. After active prevention and control in early stages, the overall epidemic situation in the country showed a downward trend from mid-February to mid-March. Compared with Hubei Province, the number of new cases in non-Hubei region declined rapidly in mid-February, but then increased slightly. The analysis of imported cases since March showed that there was a medium to high level of epidemic import risk in the near future. It is recommended to take corresponding prevention and control measures to prevent the epidemic from spreading again. Conclusion ¡¤ The MAPL method can assist in judging the epidemic trend of emerging infectious diseases and predicting the risk levels in a timely manner. Each epidemic district may implement a differentiated precision prevention and control strategies according to the local classification of epidemic risk. Since March, attention should be paid to the prevention and control of imported risks.