A new comparative approach to macroeconomic modeling and policy analysis

In the aftermath of the global financial crisis, the state of macroeconomic modeling and the use of macroeconomic models in policy analysis has come under heavy criticism. Macroeconomists in academia and policy institutions have been blamed for relying too much on a particular class of macroeconomic models. This paper proposes a comparative approach to macroeconomic policy analysis that is open to competing modeling paradigms. Macroeconomic model comparison projects have helped produce some very influential insights such as the Taylor rule. However, they have been infrequent and costly, because they require the input of many teams of researchers and multiple meetings to obtain a limited set of comparative findings. This paper provides a new approach that enables individual researchers to conduct model comparisons easily, frequently, at low cost and on a large scale. Using this approach a model archive is built that includes many well-known empirically estimated models that may be used for quantitative analysis of monetary and fiscal stabilization policies. A computational platform is created that allows straightforward comparisons of models’ implications. Its application is illustrated by comparing different monetary and fiscal policies across selected models. Researchers can easily include new models in the data base and compare the effects of novel extensions to established benchmarks thereby fostering a comparative instead of insular approach to model development

One-Year Randomized Controlled Trial and Follow-Up of Integrated Neurocognitive Therapy for Schizophrenia Outpatients

Objective: Cognitive remediation (CR) approaches have demonstrated to be effective in improving cognitive functions in schizophrenia. However, there is a lack of integrated CR approaches that target multiple neuro- and social-cognitive domains with a special focus on the generalization of therapy effects to functional outcome. Method: This 8-site randomized controlled trial evaluated the efficacy of a novel CR group therapy approach called integrated neurocognitive therapy (INT). INT includes well-defined exercises to improve all neuro- and social-cognitive domains as defined by the Measurement And Treatment Research to Improve Cognition in Schizophrenia (MATRICS) initiative by compensation and restitution. One hundred and fifty-six outpatients with a diagnosis of schizophrenia or schizoaffective disorder according to DSM-IV-TR or ICD-10 were randomly assigned to receive 15 weeks of INT or treatment as usual (TAU). INT patients received 30 bi-weekly therapy sessions. Each session lasted 90min. Mixed models were applied to assess changes in neurocognition, social cognition, symptoms, and functional outcome at post-treatment and at 9-month follow-up. Results: In comparison to TAU, INT patients showed significant improvements in several neuro- and social-cognitive domains, negative symptoms, and functional outcome after therapy and at 9-month follow-up. Number-needed-to-treat analyses indicate that only 5 INT patients are necessary to produce durable and meaningful improvements in functional outcome. Conclusions: Integrated interventions on neurocognition and social cognition have the potential to improve not only cognitive performance but also functional outcome. These findings are important as treatment guidelines for schizophrenia have criticized CR for its poor generalization effect

WIRKSAMKEIT INTEGRIERTER THERAPIEANSÄTZEN BEI SCHIZOPHREN ERKRANKTEN

This article outlines two examples of cognitive-behavior group therapy approaches for an integrative treatment of psychosis: the Integrated Psychological Therapy (IPT) and the Integrated Neurocognitive Therapy (INT). We present their theoretical background, content and conceptualization as well as recent empirical evidence for their efficacy. The article concludes that adapting these approaches to early intervention for people with high risk of psychosis seems promising.Dieser Artikel stellt beispielhaft zwei kognitiv-behaviorale Gruppentherapieansätze für die integrierte Psychosebehandlung vor: Das Integrierte Psychologische Therapieprogramm (IPT) und die Integrierte Neurokognitive Therapie (INT). Ihr theoretischer Hintergrund, Inhalt und Konzeption sowie aktuelle Wirksamkeitsnachweise werden dargestellt. Der Artikel schließt mit dem Ausblick, dass eine Anpassung dieser Ansätze einen vielversprechenden Beitrag für die Frühintervention leisten könnte

Model and Discretization Error Adaptivity within Stationary Gas Transport Optimization

The minimization of operation costs for natural gas transport networks is studied. Based on a recently developed model hierarchy ranging from detailed models of instationary partial differential equations with temperature dependence to highly simplified algebraic equations, modeling and discretization error estimates are presented to control the overall error in an optimization method for stationary and isothermal gas flows. The error control is realized by switching to more detailed models or finer discretizations if necessary to guarantee that a prescribed model and discretization error tolerance is satisfied in the end. We prove convergence of the adaptively controlled optimization method and illustrate the new approach with numerical examples

Fully Bayesian Inference for Structural MRI: Application to Segmentation and Statistical Analysis of T2-Hypointensities.

Aiming at iron-related T2-hypointensity, which is related to normal aging and neurodegenerative processes, we here present two practicable approaches, based on Bayesian inference, for preprocessing and statistical analysis of a complex set of structural MRI data. In particular, Markov Chain Monte Carlo methods were used to simulate posterior distributions. First, we rendered a segmentation algorithm that uses outlier detection based on model checking techniques within a Bayesian mixture model. Second, we rendered an analytical tool comprising a Bayesian regression model with smoothness priors (in the form of Gaussian Markov random fields) mitigating the necessity to smooth data prior to statistical analysis. For validation, we used simulated data and MRI data of 27 healthy controls (age: [Formula: see text]; range, [Formula: see text]). We first observed robust segmentation of both simulated T2-hypointensities and gray-matter regions known to be T2-hypointense. Second, simulated data and images of segmented T2-hypointensity were analyzed. We found not only robust identification of simulated effects but also a biologically plausible age-related increase of T2-hypointensity primarily within the dentate nucleus but also within the globus pallidus, substantia nigra, and red nucleus. Our results indicate that fully Bayesian inference can successfully be applied for preprocessing and statistical analysis of structural MRI data

Inverse-Tunable Red Luminescence and Electronic Properties of Nitridoberylloaluminates Sr2-xBax[BeAl3N5]:Eu2+ (x=0-2)

The nitridoberylloaluminate Ba-2[BeAl3N5]:Eu2+ and solid solutions Sr2-xBax[BeAl3N5]:Eu2+ (x=0.5, 1.0, 1.5) were synthesized in a hot isostatic press (HIP) under 50 MPa N-2 atmosphere at 1200 degrees C. Ba-2[BeAl3N5]:Eu2+ crystallizes in tri-clinic space group P (1) over bar (no. 2) (Z=2, a=6.1869(10), b= 7.1736(13), c= 8.0391(14) angstrom, alpha = 102.754(8), beta = 112.032(6), gamma = 104.765(7)degrees), which was determined from single-crystal X-ray diffraction data. The lattice parameters of the solid solution series have been obtained from Rietveld refinements and show a nearly linear dependence on the atomic ratio Sr:Ba. The electronic properties and the band gaps of M-2[BeAl3N5](M=Sr, Ba) have been investigated by a combination of soft X-ray spectroscopy and density functional theory (DFT) calculations. Upon irradiation with blue light (440-450 nm), the nitridoberylloaluminates exhibit intense orange to red luminescence, which can be tuned between 610 and 656 nm (fwhm =1922-2025 cm(-1) (72-87 nm)). In contrast to the usual trend, the substitution of the smaller Sr2+= by larger Ba leads to an inverse-tunable luminescence to higher wavelengths. Low-temperature luminescence measurements have been performed to exclude anomalous emission

Towards a New Science of a Clinical Data Intelligence

In this paper we define Clinical Data Intelligence as the analysis of data generated in the clinical routine with the goal of improving patient care. We define a science of a Clinical Data Intelligence as a data analysis that permits the derivation of scientific, i.e., generalizable and reliable results. We argue that a science of a Clinical Data Intelligence is sensible in the context of a Big Data analysis, i.e., with data from many patients and with complete patient information. We discuss that Clinical Data Intelligence requires the joint efforts of knowledge engineering, information extraction (from textual and other unstructured data), and statistics and statistical machine learning. We describe some of our main results as conjectures and relate them to a recently funded research project involving two major German university hospitals.Comment: NIPS 2013 Workshop: Machine Learning for Clinical Data Analysis and Healthcare, 201

Extensive sequential polymorphic interconversion in the solid-state: Two hydrates and ten anhydrous phases of hexamidine diisethionate

Crystal polymorphism and solvent inclusion is a dominant research area in the pharmaceutical industry and continues to unveil complex systems. Here, we present the solid-state system of hexamidine diisethionate (HDI), an antiseptic drug compound forming a dimorphic dihydrate as well as ten anhydrous polymorphs. The X-ray and neutron crystal structures of the hydrated crystal forms and related interaction energies show no direct interaction between the cation and water but very strong interactions between cation and anion, and anion and water. This is observed macroscopically as high stability of the hydrate against dehydration by temperature and humidity. The anhydrous polymorphs reveal a rare case of sequential and reversible polymorphic transformations, which are characterized by thermal analysis and variable-temperature powder X-ray diffraction (PXRD). While most transitions are accompanied by significant structural changes, the low-energy transitions can only be detected as slight changes in the reflection positions with temperature. HDI thus represents a model compound to investigate polymorphic transitions with small structural changes