Tail risk connectedness in clean energy and oil financial market.

This research investigates the connectedness and the tail risk spillover between clean energy and oil firms, from January 2011 to October 2021. To this, we use the Tail-Event driven NETworks (TENET) risk model. This approach allows for a measurement of the dynamics of tail-risk spillover for each sector and firm. Hence, we can provide a detailed picture of the existing extreme relationships within these markets. We find that the total connection between the markets varies during the period analysed, showing how the uncertainty in oil price plays a critical role in the risk dynamics for oil companies. Also, we find that relationships between energy firms tend to be intrasectoral; that is, each sector receives (emits) risk from (to) itself. These results can have important practical implications for risk management and policymakers.

Automated liquid handling robot for rapid lateral flow assay development.

The lateral flow assay (LFA) is one of the most popular technologies on the point-of-care diagnostics market due to its low cost and ease of use, with applications ranging from pregnancy to environmental toxins to infectious disease. While the use of these tests is relatively straightforward, significant development time and effort are required to create tests that are both sensitive and specific. Workflows to guide the LFA development process exist but moving from target selection to an LFA that is ready for field testing can be labor intensive, resource heavy, and time consuming. To reduce the cost and the duration of the LFA development process, we introduce a novel development platform centered on the flexibility, speed, and throughput of an automated robotic liquid handling system. The system comprises LFA-specific hardware and software that enable large optimization experiments with discrete and continuous variables such as antibody pair selection or reagent concentration. Initial validation of the platform was demonstrated during development of a malaria LFA but was readily expanded to encompass development of SARS-CoV-2 and Mycobacterium tuberculosis LFAs. The validity of the platform, where optimization experiments are run directly on LFAs rather than in solution, was based on a direct comparison between the robotic system and a more traditional ELISA-like method. By minimizing hands-on time, maximizing experiment size, and enabling improved reproducibility, the robotic system improved the quality and quantity of LFA assay development efforts.

Feverish sentiment and global equity markets during the COVID-19 pandemic.

This paper proposes a new approach to estimating investor sentiments and their implications for the global financial markets. Contextualising the COVID-19 pandemic, we draw on the six behavioural indicators (media coverage, fake news, panic, sentiment, media hype and infodemic) of the 17 largest economies and data from 1 st January 2020 to 3 rd February 2021. Our key findings, obtained using a time-varying parameter-vector auto-regression (TVP-VAR) model, indicate the total and net connectedness for the new index, entitled 'feverish sentiment'. This index provides us insight into economies that send or receive the sentiment shocks. The construction of the network structures indicates that the United Kingdom, China, the United States and Germany became the epicentres of the sentimental shocks that were transmitted to other economies. Furthermore, we also explore the predictive power of the newly constructed index on stock returns and volatility. It turns out that investor sentiment positively (negatively) predicts the stock volatility (return) at the onset of COVID-19. This is the first study of its kind to assess international feverish sentiments by proposing a novel approach and its impacts on the equity market. Based on empirical findings, the study also offers some policy directions to mitigate the fear and panic during the pandemic.

Carbon emissions determinants and forecasting: Evidence from G6 countries.

We examine the explanatory and forecasting power of economic growth, financial development, trade openness and FDI for CO2 emissions in major developed economies within the context of the debate on curbing CO2 emissions Post-Paris Agreement (COP21). Using data from G-6 countries from 1978 to 2014 and employing a set of empirical approaches, we find weak evidence of the Environmental Kuznets Curve, while economic growth, capital market expansion, and trade openness are found to be major drivers of carbon emissions. Carbon emissions are also weakly and negatively affected by stock market capitalization and FDI. Moreover, the forecasting performance is quite good, particularly by augmenting the model with energy consumption and oil prices. With respect to climate commitments, our empirical findings reveal important policy implications.

Preventing crash in stock market: The role of economic policy uncertainty during COVID-19.

This paper investigates the impact of economic policy uncertainty (EPU) on the crash risk of US stock market during the COVID-19 pandemic. To this end, we use the GARCH-S (GARCH with skewness) model to estimate daily skewness as a proxy for the stock market crash risk. The empirical results show the significantly negative correlation between EPU and stock market crash risk, indicating the aggravation of EPU increase the crash risk. Moreover, the negative correlation gets stronger after the global COVID-19 outbreak, which shows the crash risk of the US stock market will be more affected by EPU during the epidemic.

The impact of carbon emissions on country risk: Evidence from the G7 economies.

This paper empirically investigates the effect of carbon emissions on sovereign risk? To answer this question, we use fixed effects model by using annual data from G7 advanced economies, which includes Canada, France, Germany, Italy, Japan, UK and USA, for the period from 1996 to 2014. We employ a novel extreme value theory to measure sovereign risk. The results indicate that climate change (carbon emissions) are likely to increase sovereign risk significantly. We also expand our analysis to some specific sectors, as some of the sectors emit more carbon than others. Specifically, we take top three polluting sectors namely: transportation, electricity and industry and show that they are more likely to increase the sovereign risk. Our results are robust to change in risk measures, estimation in differences and dynamic version of econometric models. Therefore, we have robust consideration that the carbon emissions significantly explain the sovereign risk.

Environmental consequences of population, affluence and technological progress for European countries: A Malthusian view.

This paper examines the short-run and long-run effects of economic, sociological and energy factors on environmental degradation in 28 European countries. In so doing, we employ Panel Vector Autoregressive (PVAR) and Fully Modified OLS (FMOLS) approaches on data from 1990 to 2014 in a STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) framework. Key empirical results indicate that these factors may contribute to environmental improvement in the short run; however, there are adverse implications in the long-run. Specifically, economic factors including economic growth, trade openness and foreign direct investment cause environmental degradation in the under-analysis economies. The sociological factors as measured by the population growth and the level of urbanization also show a negative impact on the environmental degradation in the short-run but in the long run, both population size and urbanization increase environmental degradation. These findings are in line with the concerns raised by Thomas Robert Malthus in his Essay on the Principle of Population. With regards to the energy factors, it indicates that the renewable energies help the European environment by reducing the level of carbon dioxide emissions whereas the higher energy intensity is an ecological threat. Our results remain robust in the EKC framework.

Role of financial development, economic growth & foreign direct investment in driving climate change: A case of emerging ASEAN.

In the context of remarkable economic growth and financial development in the emerging economies of East Asia, this paper attempts to shed light on the ecological consequences (CO2 emission) of economic growth, foreign direct investment and financial development in the selected ASEAN-5 economies. Drawing on the data from 1982 to 2014, we employed a set of quantitative techniques for panel data analysis which entailed Dynamic Ordinary Least Squares (DOLS) and Fully Modified OLS (FMOLS) approaches. Our findings indicate that financial and economic development, as well as FDI, have a statistically significant long-run co-integrating relationship with environmental degradation (CO2 emissions) in the under analysis economies. It showed that in ASEAN-5 countries, economic growth, financial development and FDI leads to an increase in environmental degradation. The quadratic term for economic growth showed a negative impact on environmental degradation i.e. Environmental Kuznets Curve (EKC). Our key findings manifest and emphasise the importance of appropriate policies for more inclusive economic and financial development and sustainable foreign direct investment which does not impede on the environment.

An Integration of Decision Tree and Visual Analysis to Analyze Intracranial Pressure.

In Traumatic Brain Injury (TBI), elevated Intracranial Pressure (ICP) causes severe brain damages due to hemorrhage and swelling. Monitoring ICP plays an important role in the treatment of TBI patients because ICP is considered a strong predictor of neurological outcome and a potentially amenable method to treat patients. However, it is difficult to predict and measure accurate ICP due to the complex nature of patients' clinical conditions. ICP monitoring for severe TBI patient is a challenging problem for clinicians because traditionally known ICP monitoring is an invasive procedure by placing a device inside the brain to measure pressure. Therefore, ICP monitoring might have a high infection risk and cause medical complications. In here, an ICP monitoring using texture features is proposed to overcome this limitation. The combination of image processing methods and a decision tree algorithm is utilized to estimate ICP of TBI patients noninvasively. In addition, a visual analytics tool is used to conduct an interactive visual factor analysis and outlier detection.

Improving overtriage of aeromedical transport in trauma: a regional process improvement initiative.

BACKGROUND: Aeromedical transport (AMT) is an effective but costly means of rescuing critically injured patients. Although studies have shown that it improves survival to hospital discharge compared with ground transportation, an efficient threshold or universal criteria for this mode of transport remains to be established. Herein, we examined the effect of implementing a Trauma Advisory Committee (TAC) initiative focused on reducing AMT overtriage (OT) rates. METHODS: TAC outreach coordinators implemented a process improvement (PI) initiative and collected data prospectively from January 2007 to December 2011. OT was defined as patients who were airlifted from scene and later discharged from the emergency department. Serving as liaisons to surrounding counties, TAC outreach coordinators conducted quarterly PI meetings with local emergency medical service agencies. Patients were grouped into those who were airlifted from TAC counties versus counties outside TAC's jurisdiction (non-TAC). Standard statistical methods were used. RESULTS: From 2007 to 2011, 3,349 patients were airlifted from 30 counties, 1,427 (43%) from TAC counties and 1,922 (57%) from non-TAC counties. The OT rates from TAC counties declined compared with non-TAC counties each year and reached statistical significance in 2008 (17% vs. 23%, p < 0.05), 2009 (11% vs. 17%m p < 0.05), and 2011 (6% vs. 12%, p < 0.05). The reduction in OT continued over the study duration, with improvement in TAC counties compared with previous years. CONCLUSION: Implementation of a regional TAC PI initiative focused on OT issues led to a more efficient use of AMT. LEVEL OF EVIDENCE: Prognostic study, level III; therapeutic study, level IV.

Wasted hospital days impair the value of length-of-stay variables in the quality assessment of trauma care.

Hospital length of stay (LOS) is frequently used to evaluate the quality of trauma care but LOS may be impacted by nonmedical factors as well. We reviewed our experience with delays in patient discharge to determine its financial consequences and its impact on LOS. We performed an analysis of linked trauma registry and "delayed discharge" databases. Actual LOS (A-LOS) values were compared with calculated ideal LOS (I-LOS) values, and the per cent increase in LOS was calculated. Linear regression analysis was used to identify significant predictors of prolonged LOS. One thousand, five hundred and seventeen patients were studied, with an A-LOS of 6.54 days. Seven per cent of patients experienced discharge delays, resulting in 580 excess hospital days. Calculated I-LOS was 6.15 days, 6.34 per cent lower than A-LOS. Other I-LOS estimates were as much as 25 per cent lower than A-LOS. Estimated excess patient charges associated with delayed discharges were $4,000,000 to $15,000,000. Discharge delays are an infrequent, although costly, occurrence that has a significant impact on LOS. LOS therefore may not be an appropriate metric for assessing the quality of trauma care, and should only be used if it has been corrected for discharge delays. Concerted efforts should be directed towards identifying and correcting the factors responsible for delayed discharge in trauma patients.

Removal of erythropoietin from anaemia trauma practice guideline does not increase red blood cell transfusions and decreases hospital utilization costs.

INTRODUCTION: We previously demonstrated that utilization of erythropoietin (r-EPO) did not significantly reduce blood utilization in trauma patients. We undertook this study to analyze blood utilization 1 year after r-EPO removal from our trauma service anaemia practice management guideline. METHODS: Electronic records of patients admitted to the trauma service were retrospectively reviewed for units of packed red blood cells (pRBCs) transfused and for units of r-EPO administered 12 months before the initiation of an anaemia practice guideline (PRE), 12 months during the use of an anaemia guideline (GUIDE), and 12 months following removal of r-EPO from the guideline (POST). Hospital acquisition cost was also reviewed for the respective time periods. Nominal data were analyzed using chi-squared or Fisher's exact tests, and interval data were compared using ANOVA followed by Tukey's test where appropriate. Results were considered significant for P<0.05. RESULTS: Over the 3-year study period, 4881 patients were admitted to the trauma service and included in this study. The hospital length of stay, intensive care unit length of stay, and units of pRBC transfused were similar among all three groups. Group I (PRE) received a total of 228 doses of r-EPO at a cost of $102,600. Group II (GUIDE) received a total of 410 doses at a cost of $184,500. Group III (POST) received 28 doses of r-EPO at a cost of $12,600. CONCLUSION: Removal of erythropoietin from our trauma service anaemia practice management guideline did not result in increased blood utilization. However, it yielded a hospital acquisition cost savings of $171,900.

Utility of neurosurgical consultation for mild traumatic brain injury.

Trauma patients presenting with a Glasgow Coma Scale (GCS) score of 14-15 are considered to have mild traumatic brain injury (TBI) with overall good neurologic outcomes. Current practice consists of initial stabilization, followed by a head CT, and neurosurgical consultation. Aside from serial neurologic examinations, patients with a GCS of 15 rarely require neurosurgical intervention. In this study, we examined the added value of neurosurgical consultation in the care of patients after TBI with a GCS of 15. We retrospectively reviewed the medical records of patients presenting after blunt trauma with an abnormal head CT and GCS of 15 between January 2004 and January 2005. Patients with a normal head CT and <48 hours hospital stay were excluded. Data included demographics, mechanisms of injury, Injury Severity Score, the radiologists' dictated interpretations of the head CT, and neurosurgical interventions. Fifty-six patients met the inclusion criteria. The mean age was 41+/-2.3 years, and the mean Injury Severity Scores was 10.2 +/-0.6. Mechanisms of injury included 64 per cent motor vehicle crash, 16 per cent motorcycle crash, 13 per cent fall, and 7 per cent all-terrain vehicle crash. The initial CT scans showed 43 per cent parenchymal contusions, 38 per cent subarachnoid hemorrhage, 14 per cent subdural hematomas, and 5 per cent epidural hematomas. All patients received a routine follow-up head CT, and 16 per cent showed changes (five improved and four were worse compared with initial CT scans). None of these patients received a neurosurgical intervention, and two were transferred to a rehabilitation service. In this era of limited resources, trauma patients who present with a GCS score of 15 after mild TBI can be safely managed without neurosurgical consultation, even in the presence of an abnormal head CT scan.