Die mittelalterliche Stadt digital erschließen. Der Einsatz von Smartphone-Apps in der mediävistischen Schul- und Hochschullehre

Die fortschreitende Digitalisierung verändert die historische Wissensvermittlung an Schulen und Hochschulen. Der digitale Wandel muss in der Lehre aufgegriffen und dafür Konzepte zu seiner Implementierung entwickelt werden. Der Beitrag schlägt ein mögliches praxisorientiertes Konzept vor und stellt es zur Diskussion: Die Verwendung von Smartphones – hier am Beispiel einer Stadtführungs-App – zur Begleitung digitaler Lehr-Lern-Prozesse im urbanen Raum. Die Erstellung digitaler Stadtführungen soll die Lernenden dabei unterstützen, den urbanen Raum aus einem historischen Blickwinkel zu erschließen. Angewandt und überprüft wurde das Konzept in Rom im Rahmen eines Kooperationsprojekts zwischen der Albert-Ludwigs-Universität und Pädagogischer Hochschule Freiburg

frömde selczame ding in the traveler’s gaze. The Construction of Intercultural Spaces in Konrad Grünemberg’s Late Medieval Account of his Pilgrimage to Jerusalem

In der zweiten Hälfte des 15. Jahrhunderts machten sich zahlreiche christliche Pilger auf die beschwerliche und gefährliche Reise nach Jerusalem. Das dabei zu überquerende Mittelmeer, konkret die Überfahrt von Venedig und Jaffa, wird in den Berichten der Pilger als zu überwindender Gefahren-, aber auch als interkultureller Begegnungsraum wahrgenommen und narrativ konstruiert. Der vorliegende Beitrag analysiert die Konstruktion dieses interkulturellen Raumes am Beispiel des Pilgerberichts Konrad Grünembergs, der 1486 nach Jerusalem reiste. Die Analyse zeigt, dass die Pilger je nach ihrem Bildungsgrad und ihrer Sozialisation unterschiedliche interkulturelle Raumschichten wahrnehmen und konstruieren, die sich hinsichtlich ihres Abstraktionsgrads unterscheiden.In the second half of the 15th century, numerous Christian pilgrims set out on the arduous and dangerous journey to Jerusalem. The Mediterranean Sea that had to be crossed, specifically the crossing from Venice and Jaffa, is perceived in the pilgrims’ accounts as a space of danger to be overcome, but also as an intercultural space of encounter, and is constructed in narrative terms. This article analyses the construction of this intercultural space using the example of the pilgrimage account of Konrad Grünemberg, who travelled to Jerusalem in 1486. The analysis shows that the pilgrims, depending on their level of education and their respective socialisation, perceive and construct different intercultural spatial layers that differ in terms of their degree of abstraction

Defining Early Positive Response to Psychotherapy: An Empirical Comparison Between Clinically Significant Change Criteria and Growth Mixture Modeling

Several different approaches have been applied to identify early positive change in response to psychotherapy so as to predict later treatment outcome and length as well as use this information for outcome monitoring and treatment planning. In this study, simple methods based on clinically significant change criteria and computationally demanding growth mixture modeling (GMM) are compared with regard to their overlap and uniqueness as well as their characteristics in terms of initial impairment, therapy outcome, and treatment length. The GMM approach identified a highly specific subgroup of early improving patients. These patients were characterized by higher average intake impairments and higher pre- to-posttreatment score differences. Although being more specific for the prediction of treatment success, GMM was much less sensitive than clinically significant and reliable change criteria. There were no differences between the groups with regard to treatment length. Because each of the approaches had specific advantages, results suggest a combination of both methods for practical use in routine outcome monitoring and treatment planning

Disclosure, Enforcement and the Valuation of Equity

Empirical evidence shows that higher levels of disclosure and enforcement do not consistently translate into higher firm valuations. This observation implies a real-life setting in which a richer information environment can shift investors’ risk premiums upwards or downwards. However economic literature does not convincingly explain why adverse effects can occur. To fill this gap in research, we provide a model beyond the standard principal-agent framework that can explain valuation effects from augmented disclosure and enforcement regulation. We also show that country differences in regulatory effectiveness do not align with the legal system, but instead with structural strengths or difficulties of an economy. In a six country setting, investigating Canada, France, Germany, Japan, the UK and the U.S., we systematically capture regulatory changes and document varying valuation effects from mandatory disclosure regulation. Our analysis shows that valuation effects are driven by the share of “bad news” firms, which is higher in economies with structural difficulties. Effects from higher levels of disclosure are thus neither generalizable across economies nor dependent on the legal system as previously hypothesized. Keywords: Equity Valuation; Disclosure; Enforcement; Panel data analysis. DOI: 10.7176/RJFA/10-8-04 Publication date: April 30th 201

Late Onset Postpartum Eclampsia: It is Really Never Too Late—A Case of Eclampsia 8 Weeks after Delivery

Introduction. Eclampsia is the combination of preeclampsia and seizures. Approximately one-half of all cases of eclampsia occur postpartum. Thereby late onset postpartum eclampsia is defined by its onset more than 48 hours after delivery. Summary of Case. We report a postpartum eclampsia occurring 8 weeks after delivery, which is the latest onset ever described. The course was complicated by an intracerebral hemorrhage (ICH). Conclusion. A late onset postpartum eclampsia even several weeks after delivery should be considered as possible diagnosis, since early treatment initiation with magnesium sulphate and antihypertensive medication prevents severe complications and reduces mortality

Withdrawal ruptures in adolescents with borderline personality disorder psychotherapy are marked by increased speech pauses-can minimal responses be automatically detected?

Alliance ruptures of the withdrawal type are prevalent in adolescents with borderline personality disorder (BPD). Longer speech pauses are negatively perceived by these patients. Safran and Muran's rupture model is promising but its application is very work intensive. This workload makes research costly and limits clinical usage. We hypothesised that pauses can be used to automatically detect one of the markers of the rupture model i.e. the minimal response marker. Additionally, the association of withdrawal ruptures with pauses was investigated. A total of 516 ruptures occurring in 242 psychotherapy sessions collected in 22 psychotherapies of adolescent patients with BPD and subthreshold BPD were investigated. Trained observers detected ruptures based on video and audio recordings. In contrast, pauses were automatically marked in the audio-recordings of the psychotherapy sessions and automatic speaker diarisation was used to determine the speaker-switching patterns in which the pauses occur. A random forest classifier detected time frames in which ruptures with the minimal response marker occurred based on the quantity of pauses. Performance was very good with an area under the ROC curve of 0.89. Pauses which were both preceded and followed by therapist speech were the most important predictors for minimal response ruptures. Research costs can be reduced by using machine learning techniques instead of manual rating for rupture detection. In combination with other video and audio derived features like movement analysis or automatic facial emotion detection, more complete rupture detection might be possible in the future. These innovative machine learning techniques help to narrow down the mechanisms of change of psychotherapy, here specifically of the therapeutic alliance. They might also be used to technologically augment psychotherapy training and supervision

Publisher Correction: Coherent diffractive imaging of single helium nanodroplets with a high harmonic generation source

In the original version of this Article, the affiliation for Luca Poletto was incorrectly given as ‘European XFEL GmbH, Holzkoppel 4, 22869 Schenefeld, Hamburg, Germany’, instead of the correct ‘CNR, Istituto di Fotonica e Nanotecnologie Padova, Via Trasea 7, 35131 Padova, Italy’. This has now been corrected in both the PDF and HTML versions of the Article

Machine Learning Facial Emotion Classifiers in Psychotherapy Research: A Proof-of-Concept Study.

BACKGROUND New advances in the field of machine learning make it possible to track facial emotional expression with high resolution, including micro-expressions. These advances have promising applications for psychotherapy research, since manual coding (e.g., the Facial Action Coding System), is time-consuming. PURPOSE We tested whether this technology can reliably identify in-session emotional expression in a naturalistic treatment setting, and how these measures relate to the outcome of psychotherapy. METHOD We applied a machine learning emotion classifier to video material from 389 psychotherapy sessions of 23 patients with borderline personality pathology. We validated the findings with human ratings according to the Clients Emotional Arousal Scale (CEAS) and explored associations with treatment outcomes. RESULTS Overall, machine learning ratings showed significant agreement with human ratings. Machine learning emotion classifiers, particularly the display of positive emotions (smiling and happiness), showed medium effect size on median-split treatment outcome (d = 0.3) as well as continuous improvement (r = 0.49, p < 0.05). Patients who dropped out form psychotherapy, showed significantly more neutral expressions, and generally less social smiling, particularly at the beginning of psychotherapeutic sessions. CONCLUSIONS Machine learning classifiers are a highly promising resource for research in psychotherapy. The results highlight differential associations of displayed positive and negative feelings with treatment outcomes. Machine learning emotion recognition may be used for the early identification of drop-out risks and clinically relevant interactions in psychotherapy