Payment cards offer a simple and convenient method for making purchases. Owing to the increase in the usage of payment cards, especially in online purchases, fraud cases are on the rise. The rise creates financial risk and uncertainty, as in the commercial sector, it incurs billions of losses each year. However, real transaction records that can facilitate the development of effective predictive models for fraud detection are difficult to obtain, mainly because of issues related to confidentially of customer information. In this paper, we apply a total of 13 statistical and machine learning models for payment card fraud detection using both publicly available and real transaction records. The results from both original features and aggregated features are analyzed and compared. A statistical hypothesis test is conducted to evaluate whether the aggregated features identified by a genetic algorithm can offer a better discriminative power, as compared with the original features, in fraud detection. The outcomes positively ascertain the effectiveness of using aggregated features for undertaking real-world payment card fraud detection problems.In many real world situations, the design of social rankings over agents or items from a given raking over groups or coalitions, to which these agents or items belong to, is of big interest. With this aim, we revise the lexicographic excellence solution and introduce two novel solutions which, moreover, take into account the size of the groups. We present some desirable axioms which are interpreted in this context. Next, a comparable axiomatization of these three solutions is established, revealing the main differences among the two new social rankings and the lexicographic excellence solution. Finally, we apply the three social rankings under study to a real scenario. Specifically, the performance of some football players of Paris Saint-Germain during the UEFA Champions League according to these three rules is analyzed.This paper introduces new methods to study the changing dynamics of COVID-19 cases and deaths among the 50 worst-affected countries throughout 2020. First, we analyse the trajectories and turning points of rolling mortality rates to understand at which times the disease was most lethal. We demonstrate five characteristic classes of mortality rate trajectories and determine structural similarity in mortality trends over time. Next, we introduce a class of virulence matrices to study the evolution of COVID-19 cases and deaths on a global scale. Finally, we introduce three-way inconsistency analysis to determine anomalous countries with respect to three attributes countries' COVID-19 cases, deaths and human development indices. We demonstrate the most anomalous countries across these three measures are Pakistan, the United States and the United Arab Emirates.Q-learning is a regression-based approach that is widely used to formalize the development of an optimal dynamic treatment strategy. Finite dimensional working models are typically used to estimate certain nuisance parameters, and misspecification of these working models can result in residual confounding and/or efficiency loss. We propose a robust Q-learning approach which allows estimating such nuisance parameters using data-adaptive techniques. We study the asymptotic behavior of our estimators and provide simulation studies that highlight the need for and usefulness of the proposed method in practice. We use the data from the "Extending Treatment Effectiveness of Naltrexone" multi-stage randomized trial to illustrate our proposed methods.As individuals undergoing a developmental process characterized by identity exploration, Jewish young adults are particularly vulnerable to the disruption of social connections related to the COVID-19 pandemic. https://www.selleckchem.com/products/gdc-0068.html Recent research has demonstrated that young adults, including young Jews, have experienced higher rates of mental health difficulties than older individuals during the pandemic. Using data from a survey of Jewish young adults who applied to participate in Birthright Israel summer 2020 trips but were unable to participate due to the pandemic, we examined the factors contributing to young adults' mental health difficulties. We found that loneliness, rather than financial worries or concerns about the health impacts of COVID-19, was the single most important driver of reported emotional or mental health difficulties. Results also suggested that simply increasing the frequency of contacts between individuals is unlikely to reduce loneliness, unless these are positive, substantial connections, such as those among members of a "social support network." Building and rebuilding deep, meaningful social connections between Jewish young adults should be a top priority for Jewish organizations going forward.Social scientists routinely rely on methods of interpolation to adjust available data to their research needs. Spatial data from different sources often are based on different geographies that need to be reconciled, and some boundaries (e.g., administrative or political boundaries) change frequently. This study calls attention to the potential for substantial error in efforts to harmonize data to constant boundaries using standard approaches to areal and population interpolation. The case in point is census tract boundaries in the United States, which are redefined before every decennial census. Research on neighborhood effects and neighborhood change rely heavily on estimates of local area characteristics for a consistent area of time, for which they now routinely use estimates based on interpolation offered by sources such as the Neighborhood Change Data Base (NCDB) and Longitudinal Tract Data Base (LTDB). We identify a fundamental problem with how these estimates are created, and we reveal an alarming leveare of tracts that experienced complex boundary changes.Monthly, high-resolution (∼2 km) ammonia (NH3) column maps from the Infrared Atmospheric Sounding Interferometer (IASI) were developed across the contiguous United States and adjacent areas. Ammonia hotspots (95th percentile of the column distribution) were highly localized with a characteristic length scale of 12 km and median area of 152 km2. Five seasonality clusters were identified with k-means++ clustering. The Midwest and eastern United States had a broad, spring maximum of NH3 (67% of hotspots in this cluster). The western United States, in contrast, showed a narrower midsummer peak (32% of hotspots). IASI spatiotemporal clustering was consistent with those from the Ammonia Monitoring Network. CMAQ and GFDL-AM3 modeled NH3 columns have some success replicating the seasonal patterns but did not capture the regional differences. The high spatial-resolution monthly NH3 maps serve as a constraint for model simulations and as a guide for the placement of future, ground-based network sites.