Postoperative awake prone position in geriatric patients with hip fractures: a protocol for a randomized controlled trial on the efficacy of postoperative prone position in reducing pulmonary complications and improving oxygenation.

INTRODUCTION: Postoperative pulmonary complications (PPCs) are prevalent in geriatric patients with hip fractures. Low oxygen level is one of the most important risk factors for PPCs. Prone position has been proven efficacy in improving oxygenation and delaying the progress of pulmonary diseases, especially in patients with acute respiratory distress syndrome induced by multiple etiologies. The application of awake prone position (APP) has also attracted widespread attention in recent years. A randomized controlled trial (RCT) will be carried out to measure the effect of postoperative APP in a population of geriatric patients undergoing hip fracture surgery. METHODS: This is an RCT. Patients older than 65 years old admitted through the emergency department and diagnosed with an intertrochanteric or femoral neck fracture will be eligible for enrollment and assigned randomly to the control group with routine postoperative management of orthopedics or APP group with an additional prone position for the first three consecutive postoperative days (PODs). Patients receiving conservative treatment will not be eligible for enrollment. We will record the difference in the patient's room-air-breathing arterial partial pressure of oxygen (PaO2) values between the 4th POD (POD 4) and emergency visits, the morbidity of PPCs and other postoperative complications, and length of stay. The incidence of PPCs, readmission rates, and mortality rates will be followed up for 90 PODs. DISCUSSION: We describe the protocol for a single-center RCT that will evaluate the efficacy of postoperative APP treatment in reducing pulmonary complications and improving oxygenation in geriatric patients with hip fractures. ETHICS AND DISSEMINATION: This protocol was approved by the independent ethics committee (IEC) for Clinical Research of Zhongda Hospital, Affiliated to Southeast University, and is registered on the Chinese Clinical Trial Registry. The findings of the trial will be disseminated through peer-reviewed journals. ETHICS APPROVAL NUMBER: 2021ZDSYLL203-P01 TRIAL REGISTRATION: ChiCTR ChiCTR2100049311 . Registered on 29 July 2021. TRIAL STATUS: Recruiting. Recruitment is expected to be completed in December 2024.

Identifying informative tweets during a pandemic via a topic-aware neural language model.

Every epidemic affects the real lives of many people around the world and leads to terrible consequences. Recently, many tweets about the COVID-19 pandemic have been shared publicly on social media platforms. The analysis of these tweets is helpful for emergency response organizations to prioritize their tasks and make better decisions. However, most of these tweets are non-informative, which is a challenge for establishing an automated system to detect useful information in social media. Furthermore, existing methods ignore unlabeled data and topic background knowledge, which can provide additional semantic information. In this paper, we propose a novel Topic-Aware BERT (TABERT) model to solve the above challenges. TABERT first leverages a topic model to extract the latent topics of tweets. Secondly, a flexible framework is used to combine topic information with the output of BERT. Finally, we adopt adversarial training to achieve semi-supervised learning, and a large amount of unlabeled data can be used to improve inner representations of the model. Experimental results on the dataset of COVID-19 English tweets show that our model outperforms classic and state-of-the-art baselines.

Extreme quantile spillovers and drivers among clean energy, electricity and energy metals markets

This paper examines return and volatility spillover effects among the clean energy (electric vehicles, solar and wind), electricity and 8 energy metals (silver, tin, nickel, cobalt, lead, zinc, aluminum and copper) markets and their drivers under the conditions of the mean and extreme quantiles. The results show moderate spillovers among the clean energy, electricity and energy metals markets, and greater connectivity among the three markets under extreme quantile conditions. Among them, the clean energy markets always play the role of the transmitter, and the electricity market always plays the role of the receiver of spillover effects. In addition, the return and volatility spillovers among the three markets have remarkable time-varying features, and they increase dramatically when extreme events occur, especially under extreme quantile conditions. Finally, we reveal the drivers of return and volatility spillovers among these markets by the OLS and quantile regression methods. The COVID-19 and the Arca Tech 100 (PSE) index are found to be important drivers.

Partisan Conflict, National Security Policy Uncertainty and Tourism

This study investigates the spillover effects among partisan conflict, national security policy uncertainty and tourism (i.e., tourist arrivals, exports, and stock) in the U.S. by using the TVP-VAR-based connectedness measures. Specifically, we discuss the association strength, spillover direction and dynamic linkages among the three under this framework. The results show that partisan conflict and national security policy uncertainty are net transmitters of spillovers to tourism, and those effects are more potent for inbound tourism demand than tourism stock performance. Moreover, the magnitude of spillovers among the three is time-varying and increases significantly in times of crisis, especially during the 9/11 attacks, the global financial crisis and the COVID-19 pandemic. Our results have important implications for tourism managers to develop sustainable development strategies to buffer or adapt to the uncertainty impact.

Realized higher-order moments spillovers between commodity and stock markets: Evidence from China

Using 5-min data of Chinese stock market index and eight Chinese commodity futures (soybean, wheat, corn, gold, silver, copper and aluminum, crude oil) from March 26, 2018 to October 22, 2020, we analyze the dynamic spillover connectedness of returns and realized moments, including realized volatility, realized skewness, and realized kurtosis, during various shock periods via a time-varying parameter vector autoregression (TVP-VAR) connectedness approach. The results show that spillover effects between stock and commodity markets intensify during shock periods such as ‘Trade disputes between China and the United States’ and ‘COVID-19’. Volatility spillovers are relatively stronger;however, higher-order moment spillovers contain additional information of stock-commodity spillovers that cannot be observed from volatility spillovers. Shocks from the silver market influence all three realized moments of the entire financial markets. Soybean, corn, aluminum, and oil markets are easily affected by other markets. The contribution of wheat to the system of spillovers between stock and commodity markets is only observed at higher-order moments. Further analyses involving OLS and quantile regressions show that total spillovers are generally affected by the US stock market and economic uncertainties as well as the COVID epidemic. We construct daily realized volatility, skewness, and kurtosis using 5-min data of eight Chinese commodity futures and the Chinese stock market index from March 26, 2018 to October 22, 2020, then analyse the dynamic spillovers of realized moments among these markets. The results show that the spillover effects between commodity and stock markets intensify during shock periods such as ‘trade disputes between China and the United States’ and ‘COVID-19’. Volatility spillovers are relatively stronger than spillovers in skewness or spillovers in kurtosis;however, spillovers in higher-order moments seem to contain additional information. Shocks from the silver market influence realized moments of other markets. Soybean, corn, aluminium, and oil markets are affected by other markets. The contribution of wheat as a net transmitter to the system of spillovers between stock and commodity markets is only observed at higher-order realized moments. The results from OLS and quantile regressions show that the total spillovers are generally affected by the US stock market, economic uncertainties, and the COVID-19 outbreak.

Is Bitcoin a better hedging and safe-haven investment than traditional assets against currencies? Evidence from the time-frequency domain approach

Many studies have discussed hedges and safe havens against stocks, but few studies focus on the hedging/safe-haven performance of assets against the currency market over different time horizons. This paper studies the connectedness, hedging and safe-haven properties of Bitcoin/gold/crude oil/commodities against six currencies across multiple investment horizons, placing a particular focus on the performance of these assets during the recent COVID-19 outbreak. Our findings suggest that the overall dependence between assets and the currency market is the strongest in the short term, and Bitcoin is the least dependent across all investment horizons. The dynamic relationships between the four assets and the currency market vary with timescales. Bitcoin offers better hedging capability in the long term and commodities emerge as the most favorable option for the optimal portfolio of currency over all time horizons. Further analysis shows that assets are better at helping investments reduce risk in the initial stages of the pandemic, and gold is an effective and robust safe haven for currencies.

Identifying informative tweets during a pandemic via a topic-aware neural language model

Every epidemic affects the real lives of many people around the world and leads to terrible consequences. Recently, many tweets about the COVID-19 pandemic have been shared publicly on social media platforms. The analysis of these tweets is helpful for emergency response organizations to prioritize their tasks and make better decisions. However, most of these tweets are non-informative, which is a challenge for establishing an automated system to detect useful information in social media. Furthermore, existing methods ignore unlabeled data and topic background knowledge, which can provide additional semantic information. In this paper, we propose a novel Topic-Aware BERT (TABERT) model to solve the above challenges. TABERT first leverages a topic model to extract the latent topics of tweets. Secondly, a flexible framework is used to combine topic information with the output of BERT. Finally, we adopt adversarial training to achieve semi-supervised learning, and a large amount of unlabeled data can be used to improve inner representations of the model. Experimental results on the dataset of COVID-19 English tweets show that our model outperforms classic and state-of-the-art baselines.

The time-varying effects of trade policy uncertainty and geopolitical risks shocks on the commodity market prices: Evidence from the TVP-VAR-SV approach

The paper focuses on investigating the time-varying influence of geopolitical risks (GPR) and trade policy uncertainty (TPU) on commodity prices by using time-varying parameter vector autoregressive model with stochastic volatility (TVP-VAR-SV). We find that (i) TPU and GPR have significant time-varying effects on the aggregate (classified) commodity market, and the former is a short-term effect before 2006, and it becomes a medium-to-long-term effect after 2006, while the latter is mainly a short-term effect;(ii) TPU shock has a significant positive and time-varying impact on GPR, and the relatively long-term impact is more obvious before 2017, while the short-term impact will dominate after 2017. Additionally, the GPR shock has a short-term negative impact on TPU, and a medium- and long-term positive impact except for the period from 2002 to 2006;(iii) the impact of geopolitical threats (GPT) and geopolitical acts (GPA) on aggregate commodity market have positive and negative alternating shock effects with time variability and a significant short periods impact;(iv) there is a certain degree of heterogeneity in the response of different commodity prices, and the response of individual commodities is related to specific external shocks, such as the COVID-19 pandemic.

Spillover effects from news to travel and leisure stocks during the COVID-19 pandemic: Evidence from the time and frequency domains

The impact of the COVID-19 pandemic on tourism has received general attention in the literature, while the role of news during the pandemic has been ignored. Using a time-frequency connectedness approach, this paper focuses on the spillover effects of COVID-19-related news on the return and volatility of four regional travel and leisure (T&L) stocks. The results in the time domain reveal significant spillovers from news to T&L stocks. Specifically, in the return system, T&L stocks are mainly affected by media hype, while in the volatility system, they are mainly affected by panic sentiment. This paper also finds two risk contagion paths. The contagion index and Global T&L stock are the sources of these paths. The results in the frequency domain indicate that the shocks in the T&L industry are mainly driven by short-term fluctuations. The spillovers from news to T&L stocks and among these T&L stocks are stronger within 1 month.

Research on the dynamic impact of external uncertain factors on tourism

In recent years, a variety of uncertain factors have occurred frequently, such as international financial crisis, geographic conflicts, Sino-US trade disputes, and COVID-19, which have brought obvious unconventional fluctuations to China's tourism industry. By combing the uncertain events, this paper divides the uncertain factors into three categories. The first is Economic Policy Uncertainty (EPU), which refers to the uncertainty of future tourism development and unpredictable effects of tourism policy. The second is geopolitical risk (GPR), which refers to the risks related to armed conflicts or tensions between countries, which are more exogenous than economic and have a huge impact on inbound and outbound tourism. The third is financial stress (FS), which is concentrated to reflect the uncertainty of changes in the financial system to market, which is more likely to cause uncertain effects on the financial aspects of tourism companies' such as investment and cash flow. A comprehensive discussion of these three types of external uncertainties' impact mechanisms on tourism would help tourism companies to prevent and deal with risk events, and is significant for promoting the upgrade of supply-side transformation of the tourism industry. Based on the existing research, the documents provide good academic value about the impacts of uncertain factors on tourism, but only qualitatively or statically. Therefore, there is still a lack of dynamic research. To this end, We introduces a time-varying parameter vector autoregressive model (TVP-SVAR-SV), which extends the constant parameters of the classic SVAR to the stochastic volatility parameters. This model could capture the time-varying changes of variables caused by external shocks, including gradual changes or potential structural mutations, without the need to split the time series into sub-sequences, that makes it possible to study the characteristics of heteroscedasticity, clustering, asymmetry, and periodic effects of tourism variables. Therefore, based on the advantages of the TVP-SVAR-SV, we studies the impact of EPU, GPR and FS on tourism companies in different intervals or at specific points in time, which will help tourism companies better deal with challenges, seize opportunities, and maintain sustainable development.In summary, this paper exploits the financial data of tourism companies to analyze the dynamic impact of three uncertain factors, EPU, GPR, and FS on China's tourism companies. The results show that the economic policy uncertainty has the greatest impact on the scenic enterprises, especially the uncertainty brought by the SARS epidemic, which has a long-term significant negative effect;The geographical risk will have an obvious downward impact on the travel agency enterprises, which tends to increase in the near future, but has a certain positive impact on the scenic and hotel enterprises;The aggravation of financial pressure will bring strong adverse effects to scenic spots and travel agency enterprises, while the alleviation of financial pressure has a positive effect on the development of the two types of enterprises;After the outbreak of highly uncertain events, tourism enterprises will show obvious time-varying lag response, usually more than one year. The conclusion of this study is helpful to improve the understanding of the uncertain factors in the tourism industry, and also provides policy implications for how to deal with the complex and changeable external environment.

COVID-19-related government interventions and travel and leisure stock

COVID-19-related government interventions have significantly affected tourism, while the impact of government interventions on the tourism financial market remains essentially unexplored. This paper comprehensively evaluates how COVID-19-related government interventions affected the travel and leisure stock markets based on a panel quantile regression model. Three government interventions (stringency index, containment and health index and economic support index) and two important stock market features (return and volatility) are discussed. The results reveal that the three government interventions are beneficial to the travel and leisure stock market, especially when the market is under adverse conditions. Specifically, containment and health measures lead to an increase in stock returns. Stringency measures and economic support measures promote stock return and restrain stock market volatility. This study provides significant insights for protecting and recovering the travel and leisure stock market by considering when and which government interventions should be implemented.

The role of coronavirus news in the volatility forecasting of crude oil futures markets: Evidence from China.

Based on the high-frequency heterogeneous autoregressive (HAR) model, this paper investigates whether coronavirus news (in China and globally) contains incremental information to predict the volatility of China's crude oil, and studies which types of coronavirus news can better forecast China's crude oil volatility. Considering the information overlap among various coronavirus news items and making full use of the information in various coronavirus news items, this paper uses two prevailing shrinkage methods, lasso and elastic nets, to select coronavirus news items and then uses the HAR model to predict China's crude oil volatility. The results show that (i) coronavirus news can be utilized to significantly predict China's crude oil volatility for both in-sample and out-of-sample analyses; (ii) the Panic Index (PI) and the Country Sentiment Index (CSI) have a greater impact on China's crude oil volatility. Additionally, China's Fake News Index (FNI) have a significant impact on China's crude oil volatility forecast; and (iii) global coronavirus news provides more incremental information than China's coronavirus news for predicting the volatility of China's crude oil market, which indicates that global coronavirus news is also a key factor to consider when predicting the market volatility of China's crude oil.