Contributions in Merlin

With the rapid growth of healthcare waste and improvement of people’s environment awareness, the sustainability of health-care waste management is becoming increasingly important. However, there is no research on the identification of key factors that influence sustainable healthcare waste management using the quantitative methods in the existing literature. Based on an in-depth analysis of the existing literature, we found out 15 factors influencing sustainable healthcare waste management. Then we used the interval-valued fuzzy DEMATEL (Decision Making Trial and Evaluation Laboratory) method to analyze these factors. Finally, we presented a case study to demonstrate the effectiveness of the proposed method. We found that sound legal framework supporting sustainability, strengthening sustainable management training or education programs and positive attitude and perception towards sustainable healthcare waste management are the top three most important factors. This study contributes to the literature on sustainable healthcare waste management and helps to improve the sustainability of healthcare waste management.

We study the assignment of discrete resources in a general model encompassing a wide range of applied environments, such as school choice, course allocation, and refugee resettlement. We allow single-unit and general multi-unit demands and any linear constraints. We prove the Second Welfare Theorem for these environments and a strong version of the First Welfare Theorem. In this way, we establish an equivalence between strong efficiency and decentralization through prices in discrete environments. Showing that all strongly efficient outcomes can be implemented through pseudomarkets, we provide a foundation for using pseudomarkets in market design.

This paper uses a contest setting to analyze the provision of intertemporal incentives in organizations. Should a principal repeatedly award small prizes or give a large prize that takes past performance into account? A simple theoretical model predicts higher efforts in the latter case. An experiment confirms this prediction, but the size of the effect is smaller than expected. This result reflects two observations of independent interest. First, there is a revenge effect for laggards in repeated contests: Laggards exert higher efforts than leaders with the same first-period effort level. Second, there is an intimidation effect for laggards in the single-prize case: Laggards exert lower efforts than leaders with the same first-period effort level. Moreover, we observe polarization in laggard behavior.

While chronic and acute stress are often associated with negative health, the sense of coherence-revised (SOC-R) is proposed to facilitate coping with stress and promote health. However, research is lacking on the specific mechanisms. Therefore, the current study aimed to investigate potential mediating and moderating mechanisms of SOC-R in the relationship between stress and health. Using a cross-sectional design, standardized questionnaires assessed SOC-R, acute (perceived) stress, early-life adversity (ELA; indicator for early-life chronic stress), mental and physical health, and satisfaction with life. Mediation and moderation analyses were conducted with N = 531 Irish adults (mean age: 59.5 years; 58.4% female). Regarding acute (perceived) stress, results showed that SOC-R and its Manageability subscale significantly mediated the association between perceived stress and mental health, and satisfaction with life. SOC-R and its Manageability subscale also significantly moderated the association between perceived stress and mental health. Regarding ELA, the Manageability subscale significantly mediated the association between ELA and mental health, and satisfaction with life; and the Balance subscale significantly mediated the association between ELA and physical health. SOC-R may provide a useful focus for stress-related research, with future longitudinal studies needed to examine SOC-R as a long-term modulating pathway between stress and health.

Dans le monde académique, les courants dominants en économie et finance brillent par leur discrétion en ce qui concerne le dérèglement climatique et ses désastreuses conséquences économiques et sociales, affirme Marc Chesney, professeur à l’Université de Zurich

Ontologies are becoming a key component of numerous applications and research fields. But knowledge captured within ontologies is not static. Some ontology updates potentially have a wide ranging impact; others only affect very localised parts of the ontology and their applications. Investigating the impact of the evolution gives us insight into the editing behaviour but also signals ontology engineers and users how the ontology evolution is affecting other applications. However, such research is in its infancy. Hence, we need to investigate the evolution itself and its impact on the simplest of applications: the materialisation. In this work, we define impact measures that capture the effect of changes on the materialisation. In the future, the impact measures introduced in this work can be used to investigate how aware the ontology editors are about consequences of changes. By introducing five different measures, which focus either on the change in the materialisation with respect to the size or on the number of changes applied, we are able to quantify the consequences of ontology changes. To see these measures in action, we investigate the evolution and its impact on materialisation for nine open biomedical ontologies, most of which adhere to the description logic. Our results show that these ontologies evolve at varying paces but no statistically significant difference between the ontologies with respect to their evolution could be identified. We identify three types of ontologies based on the types of complex changes which are applied to them throughout their evolution. The impact on the materialisation is the same for the investigated ontologies, bringing us to the conclusion that the effect of changes on the materialisation can be generalised to other similar ontologies. Further, we found that the materialised concept inclusion axioms experience most of the impact induced by changes to the class inheritance of the ontology and other changes only marginally touch the materialisation.

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This paper injects factor structure into the estimation of time-varying, large-dimensional covariance matrices of stock returns. Existing factor models struggle to model the covariance matrix of residuals in the presence of time-varying conditional heteroskedasticity in large universes. Conversely, rotation-equivariant estimators of large-dimensional time-varying covariance matrices forsake directional information embedded in market-wide risk factors. We introduce a new covariance matrix estimator that blends factor structure with time-varying conditional heteroskedasticity of residuals in large dimensions up to 1000 stocks. It displays superior all-around performance on historical data against a variety of state-of-the-art competitors, including static factor models, exogenous factor models, sparsity-based models, and structure-free dynamic models. This new estimator can be used to deliver more efficient portfolio selection and detection of anomalies in the cross-section of stock returns.

This paper focuses on portfolio optimization with respect to cryptocurrencies and shows that they significantly differ from each other in their technological attributes but also value proposition. Additionally, it is illustrated that cryptocurrencies are extremely volatile assets with leptokurtic, skewed and heavy tailed return distributions. The correlation amongst different coins is extremely large, whereas the correlation strongly depends on the state of the market. Moreover, a semi-parametric bootstrapping framework for portfolio optimization with respect to the Value-at-Risk (VaR) and Expected Shortfall (ES) is introduced and compared to the classical multiperiod mean-variance optimization. The three optimizations are executed for constant and non-rebalancing portfolios that are restricted to stocks, bonds and cash exclusively and portfolios that additionally contain cryptocurrencies. Overall, the results did not clearly indicate the existence of a portfolio rebalancing bonus for the analyzed investment horizon. However, they showed that including cryptocurrencies in a portfolio can significantly increase the return to risk ratio, independent of the optimization technique. Furthermore, the mean-ES optimization delivers the best performance compared to optimizations with other risk measures.

I study the performance of 50 buy-and-build private equity funds with vintage years up to 2018 using the dataset of Preqin. A total of 778 platform and 2430 add-on deals are evaluated. I find an outperfor-mance of 33 per cent (average) and 23 per cent (median) compared to the S&P 500 over a fund’s lifetime, resulting in an annual outperformance of at least 3.83 per cent. I derive a series of value creation drivers through the case study with Ufenau Capital Partners that are unique to the buy-and-build strategy and explain outperformance.