An indicator-based short-term forecast for quarterly GDP in the euro area.

This paper discusses an approach to estimate euro area GDP quarterly growth over two quarters ahead. The estimates are derived from separate single equations for each quarter to be forecast using OLS including a moving error term. The explanatory variables describe real economic activity or its assessment in opinion surveys, and financial variables, both of the euro area and the US. The euro area opinion survey variables are the present business situation in the retail sector and the construction confidence indicator, while the US National Association of Purchasing Managers index of the manufacturing industry reflects the importance of international economic links. There are two financial variables. First, the relative yield spread between the euro area and the US. Second, the real effective exchange rate is an indication of the competitive position of euro area exporterseuro, quaterly forecast, GDP

Integration of Skyline Queries into Spark SQL

Skyline queries are frequently used in data analytics and multi-criteria decision support applications to filter relevant information from big amounts of data. Apache Spark is a popular framework for processing big, distributed data. The framework even provides a convenient SQL-like interface via the Spark SQL module. However, skyline queries are not natively supported and require tedious rewriting to fit the SQL standard or Spark's SQL-like language. The goal of our work is to fill this gap. We thus provide a full-fledged integration of the skyline operator into Spark SQL. This allows for a simple and easy to use syntax to input skyline queries. Moreover, our empirical results show that this integrated solution of skyline queries by far outperforms a solution based on rewriting into standard SQL

Pharmacotherapeutic intervention in impulsive preschool children: The need for a comprehensive therapeutic approach

Impulsive and aggressive behaviour symptoms often are serious problems in children, even already at preschool age. Thus, effective treatment approaches are requested. In this comment pharmacotherapeutic treatment approaches, first of all risperidone, their limitations and alternative psychotherapeutic approaches are outlined

Integration von Skyline Anfragen in Spark SQL

Arbeit an der Bibliothek noch nicht eingelangt - Daten nicht geprüftAbweichender Titel nach Übersetzung der Verfasserin/des VerfassersSkyline Anfragen werden üblicherweise von Datenanalysten und Datenanalystinnen verwendet, um "interessante" Punkte in Datenbanken oder ähnlichen Datensammlungen zu finden. Sie können in Empfehlungsmaschinen oder für die Visualisierung von "interessanten" Punkten verwendet werden. Die von Skyline Anfragen verarbeiteten Datenmengen sind häufig groß und in verteilten Datenbanksystemen gespeichert. Spark ist ein mächtiges, vereinheitlichtes Analyse-System, das gut zur Verarbeitung von großen verteilten Datenmengen geeignet ist. Die Komponente Spark SQL erlaubt es, SQL-ähnliche Datenabfragen als Zeichenkette oder als API-Zugriff zu schreiben. Skyline Anfragen werden in Spark nicht direkt unterstützt. Daher setzen wir uns die Integration von Skyline Anfragen in Spark zum Ziel. Dadurch kombinieren wir die Vorteile von Skyline Anfragen und Spark SQL. Dies erlaubt uns, Skyline Anfragen einfach als eine Erweiterung von SQL zu schreiben und diese mithilfe von Spark auszuführen. Um dies zu erreichen, zeigen wir, wie Skyline Anfragen integriert werden, sodass sowohl Anfragezeichenketten als auch Zugriffe per DataFrame/DataSet API ausgeführt werden können. Dies beinhaltet auch die PySpark Integration, sodass Skyline Anfragen auch in Python Skripten, die Spark verwenden, benutzt werden können. Wir implementieren unterschiedliche Algorithmen und Optimierungen, um die Leistungsfähigkeit zu steigern. Dies beinhaltet einen Block-Nested-Loop Algorithmus, einen verteilten Algorithmus auf Basis eines MapReduce-Ansatzes und einen spezialisierten verteilten Algorithmus für Skylines auf Basis von Datenmengen mit fehlenden Werten. Abschließend führen wir Benchmarks sowohl in einer lokalen Umgebung als auch in einer verteilten Cluster-Umgebung durch, um die Leistungsfähigkeit unserer Implementierung zu erheben. Aus diesen Benchmarks können wir eine gute Leistungsfähigkeit ablesen. Diese ist besser als jene von vergleichbaren Anfragen, die auf reines SQL setzen um dieselben Resultate zu berechnen.Skyline queries are commonly used by data scientist for finding "interesting" data points in databases or other collections of data. They can also be used in recommendation engines or to visualize "interesting" parts of datasets. The datasets processed by skyline queries are often large and stored in a distributed manner. Spark is a powerful unified analytics engine that is good at handling large, distributed datasets. The Spark SQL component of Spark also allows writing SQL-like queries as strings and using an API to access the data. We, therefore, set the integration of skyline queries into Spark as our main goal. Thereby, we combine the advantages of skyline queries and Spark SQL. This allows us to easily write skyline queries as an extension of SQL and run the query using Spark. To achieve this, we show how skyline queries can be integrated into both query strings and the DataFrame/DataSet API of Spark. This includes integration into PySpark such that skyline queries can also be executed in Python scripts that use Spark. We implement different algorithms and optimizations to boost performance. This includes a block-nested-loop algorithm, a distributed algorithm based on common MapReduce approaches to compute skyline queries, and a specialized distributed algorithm which can handle skylines on incomplete datasets. Lastly, we run benchmarks on both a local and a clustered environment to get a better understanding of the performance of our implementation. From these benchmarks, we can see that the integration gives us good performance figures which are much better than those of equivalent "plain" SQL queries which calculate the same results.19

Only Small Effects of Mindfulness-Based Interventions on Biomarker Levels of Inflammation and Stress: A Preregistered Systematic Review and Two Three-Level Meta-Analyses

Mindfulness-based interventions (MBIs) have a positive effect on biomarkers of inflammation and stress in patients with psychiatric disorders and physical illnesses. Regarding subclinical populations, results are less clear. The present meta-analysis addressed the effects of MBIs on biomarkers in psychiatric populations and among healthy, stressed, and at-risk populations. All available biomarker data were investigated with a comprehensive approach, using two three-level meta-analyses. Pre–post changes in biomarker levels within treatment groups (k = 40 studies, total N = 1441) and treatment effects compared to control group effects, using only RCT data (k = 32, total N = 2880), were of similar magnitude, Hedges g = −0.15 (95% CI = [−0.23, −0.06], p < 0.001) and g = −0.11 (95% CI = [−0.23, 0.001], p = 0.053). Effects increased in magnitude when including available follow-up data but did not differ between type of sample, MBI, biomarker, and control group or duration of the MBI. This suggests that MBIs may ameliorate biomarker levels in both psychiatric and subclinical populations to a small extent. However, low study quality and evidence of publication bias may have impacted on the results. More large and preregistered studies are still needed in this field of research