Bruusklitgaard6088

We study whether the trading behavior of corporate insiders provides additional information to the market, after controlling for the public information integrated by sophisticated investors. First, we establish that insiders and option market participants trade in the same direction on average. Second, we show that insidertrading is relatively more informed when the option market sentiment is positive. The marginal information content of insider trades is higher for firms with higher levels of information asymmetry and during time periods when future economic conditions are less certain.The rapidly spreading COVID-19 pandemic has affected many people worldwide. Due to the high infectivity, countries make calls to stay at home or take measures such as lockdowns to ensure that people are least affected by the virus. Meanwhile, infected people are getting treatments people who are slightly affected are quarantined at home, and those who are heavily affected are treated in hospitals. Hence there is an excessive increase in the hospital workload. This causes physical fatigue in healthcare professionals. Along with the increasing workload, the fear of being infected and infecting the environment causes psychological problems in healthcare professionals. It is important to protect healthcare professionals and provide them with suitable working conditions. For this reason, besides the provision of protective equipment such as gloves, overalls, mask, and glasses that are necessary for the protection of healthcare workers from the virus, healthcare services should also be planned very carefully. One of the critical issues is planning the shift schedules of the physicians. In this study, we handle the preparation of a physician shift schedule of a hospital in Turkey during the COVID-19 pandemic. The hospital has established three new COVID-19 related departments and the aim is to provide continuous service in the new departments while maintaining the workload in the existing departments. We propose a mixed integer programming (MIP) model to address the shift scheduling problem and transform it into a decision support system (DSS). The resulting schedules minimize the exposure of the physicians to the virus with a balanced workload while maintaining the healthcare service in all departments.National and local societies all around the world are fighting the most dramatic global public health emergency of our time, which has soon become an economic, social and human crisis touching all key dimensions of our lives. Within an inevitable revamping attention on the need for government intervention to face the challenges raised by the Covid19 pandemic, industrial policy is appearing as a central piece of the puzzle. As production dynamics in every country is highly affected by the crisis, industrial policy is considered part of the response to solve dramatic economic and social problems deriving by extraordinary levels of unemployment, deprivation and poverty. In this paper, we argue that a turning point on the connection between industrial policy, sustainability and development has been reached, highlighting the need to rethink its theoretical foundations as well as its governance and implementation processes for a new role in our post-Covid 19 societies. Therefore, the research question underlying this paper deals primarily with the nexus between the debate on industrial policy and its effects in terms of human development, social cohesion and sustainability. For this reason, we attempt at closing the gap between different strands of literature, whose integrated connection leads to a new analytical framework with real-world implications on the role of industrial policy, not only as tool for productive dynamics, but also as a leverage for sustainable human development. All in all, we aim at contributing to the debate on our post-Covid19 economies and societies in two ways firstly, by providing a new integrated analytical framework on industrial policy to steer a sustainable structural change of our economies and societies towards sustainable human development; secondly, by identifying preliminary implications on industrial policy governance and implementation, investing in the accurate and transparent design of industrial policy in the post-Covid19 era.COVID-19 and climate change share several striking similarities in terms of causes and consequences. For instance, COVID-19 and climate change affect deprived and vulnerable communities the most, which implies that effectively designed policies that mitigate these risks may also reduce the widening inequalities that they cause. Both problems can be characterized as low-probability-high consequence (LP-HC) risks, which are associated with various behavioral biases that imply that individual behavior deviates from rational risk assessments by experts and optimal preparedness strategies. One could view the COVID-19 pandemic as a rapid learning experiment about how to cope more effectively with climate change and develop actions for reducing its impacts before it is too late. However, the ensuing question relates to whether the COVID-19 crisis and its aftermath will speed up climate change mitigation and adaptation policies, which depends on how individuals perceive and take action to reduce LP-HC risks. Using insights into behavioral biases in individual decisions about LP-HC risks based on decades of empirical research in psychology and behavioral economics, we illustrate how parallels can be drawn between decision-making processes about COVID-19 and climate change. see more In particular, we discuss six important risk-related behavioral biases in the context of individual decision making about these two global challenges to derive lessons for climate policy. We contend that the impacts from climate change can be mitigated if we proactively draw lessons from the pandemic, and implement policies that work with, instead of against, an individual's risk perceptions and biases. We conclude with recommendations for communication policies that make people pay attention to climate change risks and for linking government responses to the COVID-19 crisis and its aftermath with environmental sustainability and climate action.Several variables and practices affect the evolution and geographic spread of COVID-19. Some of these variables pertain to policy measures such as social distancing, quarantines for specific areas, and testing availability. In this paper, I analyze the effect that lockdown and testing policies had on new contagions in Chile, especially focusing on potential heterogeneity given by population characteristics. Leveraging a natural experiment in the determination of early quarantines, I use an Augmented Synthetic Control Method to build counterfactuals for high and lower-income areas that experienced a lockdown during the first two months of the pandemic. I find substantial differences in the impact that quarantine policies had for different populations While lockdowns were effective in containing and reducing new cases of COVID-19 in higher-income municipalities, I find no significant effect of this measure for lower-income areas. To further explain these results, I test for difference in mobility during quarantine for high and lower-income municipalities, as well as delays in test results and testing availability. These findings are consistent with previous results, showing that differences in the effectiveness of lockdowns could be partially attributed to heterogeneity in quarantine compliance in terms of mobility, as well as differential testing availability for higher and lower-income areas.Accurate motion tracking of the left ventricle is critical in detecting wall motion abnormalities in the heart after an injury such as a myocardial infarction. We propose an unsupervised motion tracking framework with physiological constraints to learn dense displacement fields between sequential pairs of 2-D B-mode echocardiography images. Current deep-learning motion-tracking algorithms require large amounts of data to provide ground-truth, which is difficult to obtain for in vivo datasets (such as patient data and animal studies), or are unsuccessful in tracking motion between echocardiographic images due to inherent ultrasound properties (such as low signal-to-noise ratio and various image artifacts). We design a U-Net inspired convolutional neural network that uses manually traced segmentations as a guide to learn displacement estimations between a source and target image without ground-truth displacement fields by minimizing the difference between a transformed source frame and the original target frame. We then penalize divergence in the displacement field in order to enforce incompressibility within the left ventricle. We demonstrate the performance of our model on synthetic and in vivo canine 2-D echocardiography datasets by comparing it against a non-rigid registration algorithm and a shape-tracking algorithm. Our results show favorable performance of our model against both methods.Nanomedicine has seen a significant rise in the development of new research tools and clinically functional devices. In this regard, significant advances and new commercial applications are expected in the pharmaceutical and orthopedic industries. For advanced orthopedic implant technologies, appropriate nanoscale surface modifications are highly effective strategies and are widely studied in the literature for improving implant performance. It is well-established that implants with nanotubular surfaces show a drastic improvement in new bone creation and gene expression compared to implants without nanotopography. Nevertheless, the scientific and clinical understanding of mixed oxide nanotubes (MONs) and their potential applications, especially in biomedical applications are still in the early stages of development. This review aims to establish a credible platform for the current and future roles of MONs in nanomedicine, particularly in advanced orthopedic implants. We first introduce the concept of MONs and then discuss the preparation strategies. This is followed by a review of the recent advancement of MONs in biomedical applications, including mineralization abilities, biocompatibility, antibacterial activity, cell culture, and animal testing, as well as clinical possibilities. To conclude, we propose that the combination of nanotubular surface modification with incorporating sensor allows clinicians to precisely record patient data as a critical contributor to evidence-based medicine.We examine the prevalence of SARS-CoV-2 infections among patients admitted to a Parisian psychiatric University Hospital Group (GHU). A total of 548 patients were admitted to the GHU's full-time psychiatric wards between April 6 and May 3 2020. More than 80% were tested. A total of 7 patients tested positive for the SARS-Cov-2 (1.3%), with 5 patients (in 92, 5.4%) testing positive in the first week. GHU patients presented a low prevalence of SARS-CoV-2, even if all patients live in the hardest hit region in France. Social isolation and loneliness, as well as self-isolation of patients with symptoms could explain our results.

As COVID-19 spreads rapidly, this global pandemic has not only brought the risk of death but also spread unbearable psychological pressure to people around the world. The aim of this study was to explore (a) the mediating role of rumination in the association between stressors of COVID-19 and stress consequences of college students, and (b) the moderating role of psychological support in the indirect relationship between stressors of COVID-19 and stress consequences of college students.

Eight hundred and forty-one Chinese college students (Mage = 19.50 years, SD = 1.580) completed the measures of stressors of COVID-19, stress consequences, rumination, and psychological support.

Stressors of COVID-19 were significantly positively associated with stress consequences, and mediation analyses indicated that rumination partially mediated this association. Moderated mediation analysis further revealed that psychological support buffered the relation between stressors of COVID-19 and rumination, as well as the relation between rumination and stress consequences.