IPO-related organizational change and long-term performance

Mainstream literature on long-term performance of initial public offerings focuses on long-term underperformance. Because underperformance is an anomalous phenomenon, many authors search for explanations based on financial market imperfections. More recently, however, the attention shifts from underperformance to long-term performance in general. This induces the search for other than financial market imperfections in explaining under- or outperformance. This article presents the idea that in many companies the preparation for the IPO and the IPO itself may bring organizational change. It searches for IPO-related organizational change in The Netherlands with interviews of Dutch corporate officers. The research shows that an IPO primarily changes financial management and financial reporting, but that other types of organizational change may also be relevant. Moreover, long-term stock market performance was on average higher in companies where IPO-related organizational changes were reported than in companies where the changes were not reported.

Pediatric oncology as a Learning Health System: Ethical implications for best available treatment protocols

Introduction Pediatric oncology is often considered as a field in which research and care are highly integrated. We believe that this integration can be seen as a so‐called Learning Health System, a system in which research is considered an important means to continuously improve the practice of care. In order to substantiate our assumption of pediatric oncology as an LHS, we will analyze so‐called “best available treatment protocols.” These protocols always contain research elements, even if themain goal of these protocols is to treat children diagnosed with cancer. Methods We will analyze the implications for ethical review and informed consent if these protocols had to function as exponents of pediatric oncology an LHS. Results An analysis of best available treatment protocols teaches us how these protocols integrate care and research and how these protocols can be seen as exponents of a system where care and research need no longer be sharply distinct practices. Discussion Further intervention in the field of pediatric oncology is essential to also meet the requirements for an ethically responsible LHS. Conclusion Best available treatment protocols, which combine research and care, can be seen as examples of pediatric oncology as an LHS. However, in order to prevent that research elements in these protocols will be overlooked, we will have to find new ways to accommodate for the oversight of these protocols, such as multifaceted review and risk‐adapted approaches. Moreover, informed consent process must be changed in order for patients to understand how care and research are integrated in these protocols

Machine learning in Huntington’s disease:exploring the Enroll-HD dataset for prognosis and driving capability prediction

Background: In biomedicine, machine learning (ML) has proven beneficial for the prognosis and diagnosis of different diseases, including cancer and neurodegenerative disorders. For rare diseases, however, the requirement for large datasets often prevents this approach. Huntington’s disease (HD) is a rare neurodegenerative disorder caused by a CAG repeat expansion in the coding region of the huntingtin gene. The world’s largest observational study for HD, Enroll-HD, describes over 21,000 participants. As such, Enroll-HD is amenable to ML methods. In this study, we pre-processed and imputed Enroll-HD with ML methods to maximise the inclusion of participants and variables. With this dataset we developed models to improve the prediction of the age at onset (AAO) and compared it to the well-established Langbehn formula. In addition, we used recurrent neural networks (RNNs) to demonstrate the utility of ML methods for longitudinal datasets, assessing driving capabilities by learning from previous participant assessments. Results: Simple pre-processing imputed around 42% of missing values in Enroll-HD. Also, 167 variables were retained as a result of imputing with ML. We found that multiple ML models were able to outperform the Langbehn formula. The best ML model (light gradient boosting machine) improved the prognosis of AAO compared to the Langbehn formula by 9.2%, based on root mean squared error in the test set. In addition, our ML model provides more accurate prognosis for a wider CAG repeat range compared to the Langbehn formula. Driving capability was predicted with an accuracy of 85.2%. The resulting pre-processing workflow and code to train the ML models are available to be used for related HD predictions at: https://github.com/JasperO98/hdml/tree/main . Conclusions: Our pre-processing workflow made it possible to resolve the missing values and include most participants and variables in Enroll-HD. We show the added value of a ML approach, which improved AAO predictions and allowed for the development of an advisory model that can assist clinicians and participants in estimating future driving capability.</p

Evolution and Flare Activity of Delta-Sunspots in Cycle 23

The emergence and magnetic evolution of solar active regions (ARs) of beta-gamma-delta type, which are known to be highly flare-productive, were studied with the SOHO/MDI data in Cycle 23. We selected 31 ARs that can be observed from their birth phase, as unbiased samples for our study. From the analysis of the magnetic topology (twist and writhe), we obtained the following results. i) Emerging beta-gamma-delta ARs can be classified into three topological types as "quasi-beta", "writhed" and "top-to-top". ii) Among them, the "writhed" and "top-to-top" types tend to show high flare activity. iii) As the signs of twist and writhe agree with each other in most cases of the "writhed" type (12 cases out of 13), we propose a magnetic model in which the emerging flux regions in a beta-gamma-delta AR are not separated but united as a single structure below the solar surface. iv) Almost all the "writhed"-type ARs have downward knotted structures in the mid portion of the magnetic flux tube. This, we believe, is the essential property of beta-gamma-delta ARs. v) The flare activity of beta-gamma-delta ARs is highly correlated not only with the sunspot area but also with the magnetic complexity. vi) We suggest that there is a possible scaling-law between the flare index and the maximum umbral area

Modeling the Subsurface Structure of Sunspots

While sunspots are easily observed at the solar surface, determining their subsurface structure is not trivial. There are two main hypotheses for the subsurface structure of sunspots: the monolithic model and the cluster model. Local helioseismology is the only means by which we can investigate subphotospheric structure. However, as current linear inversion techniques do not yet allow helioseismology to probe the internal structure with sufficient confidence to distinguish between the monolith and cluster models, the development of physically realistic sunspot models are a priority for helioseismologists. This is because they are not only important indicators of the variety of physical effects that may influence helioseismic inferences in active regions, but they also enable detailed assessments of the validity of helioseismic interpretations through numerical forward modeling. In this paper, we provide a critical review of the existing sunspot models and an overview of numerical methods employed to model wave propagation through model sunspots. We then carry out an helioseismic analysis of the sunspot in Active Region 9787 and address the serious inconsistencies uncovered by \citeauthor{gizonetal2009}~(\citeyear{gizonetal2009,gizonetal2009a}). We find that this sunspot is most probably associated with a shallow, positive wave-speed perturbation (unlike the traditional two-layer model) and that travel-time measurements are consistent with a horizontal outflow in the surrounding moat.Comment: 73 pages, 19 figures, accepted by Solar Physic

Sociotechnische meetinstrumenten

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