Export market exit and firm survival: Theory and first evidence

This paper deploys a dynamic extension of the Melitz (2003) model to generate predictions on export market exit and firm survival in a setting where firms endogenously make exit decisions. The central driver of the model dynamics is the inclusion of exogenous economy wide technological progress. The model predicts - inter alia - that a higher relative productivity not only increases the likelihood of exporting, but also the chances of firm survival and continued export market engagements. We relate these predictions to the empirical stylized facts of export market exit and firm survival based on Danish firm-level data. We find strong evidence that firms experience a decline in market share prior to export market exit and prior to death and that the firms discontinuing their exporting activity or closing down tend to be small. Overall, our empirical results support the central predictions from the model

A New Resistive Adaptive Gate-Driving Concept with Automated Identification of Operational Parameters

This paper proposes a new adaptive gate-driving concept based on parallel-connected resistive driving stages, which allows the modification of the effective gate-resistance for every turn-on and turn-off event during operation. By selecting the appropriate gate-resistance, the switching behavior can be optimized individually for each specific operating point (Vsw, Isw, Tj). As a result, higher efficiency under partial load can be achieved. The selection of effective gate-resistance is based on the results of a here introduced automatic optimization method, which takes constraints such as dv/dt- and di/dt-limits into account. Subject of this paper is also the comparison of the new approach with the widely used single-stage resistive driver

Raum - Perspektive - Medium 2: Wahrnehmung im Blick

Vorwort zu "Raum - Perspektive - Medium 2: Wahrnehmung im Blick"Preface to 'Raum - Perspektive - Medium 2: Wahrnehmung im Blick

Nonmechanical parfocal and autofocus features based on wave propagation distribution in lensfree holographic microscopy

Performing long-term cell observations is a non-trivial task for conventional optical microscopy, since it is usually not compatible with environments of an incubator and its temperature and humidity requirements. Lensless holographic microscopy, being entirely based on semiconductor chips without lenses and without any moving parts, has proven to be a very interesting alternative to conventional microscopy. Here, we report on the integration of a computational parfocal feature, which operates based on wave propagation distribution analysis, to perform a fast autofocusing process. This unique non-mechanical focusing approach was implemented to keep the imaged object staying in-focus during continuous long-term and real-time recordings. A light-emitting diode (LED) combined with pinhole setup was used to realize a point light source, leading to a resolution down to 2.76 μm. Our approach delivers not only in-focus sharp images of dynamic cells, but also three-dimensional (3D) information on their (x, y, z)-positions. System reliability tests were conducted inside a sealed incubator to monitor cultures of three different biological living cells (i.e., MIN6, neuroblastoma (SH-SY5Y), and Prorocentrum minimum). Altogether, this autofocusing framework enables new opportunities for highly integrated microscopic imaging and dynamic tracking of moving objects in harsh environments with large sample areas

Substance Abuse-Related Self-Stigma in Women with Substance Use Disorder and Comorbid Posttraumatic Stress Disorder

Background: Self-stigma is a result of internalizing negative stereotypes by the affected person. Research on self-stigma in substance use disorders (SUD) is still scarce, especially regarding the role of childhood trauma and subsequent posttraumatic disorders. Objectives: The present study investigated the progressive model of self-stigma in women with SUD and posttraumatic stress disorder (PTSD), and the predictive value of PTSD severity and childhood trauma experiences on self-stigma. Method: In a cross-sectional study with 343 women with SUD and PTSD, we used the Self-Stigma in Alcohol Dependency Scale, the Childhood Trauma Questionnaire (CTQ), the PTSD Symptom Scale Interview (PSS-I), and to control for SUD severity and depression, the Addiction Severity Index Lite and the Beck Depression Inventory-II. Hierarchical regression analyses were conducted for each stage of self-stigma (aware-agree-apply-harm). Results: The interrelated successive stages of self-stigma were largely confirmed. In the regression models, no significant effects of the PSS-I- and the CTQ-scores were observed at any stage of self-stigma. Agreeing with negative stereotypes was solely predicted by younger age, applying these stereotypes to oneself was higher in women with younger age, higher depression and SUD severity, and suffering from the application (harm) was only predicted by depression. Conclusions: The progressive model of self-stigma could be confirmed in women with SUD and PTSD, but PTSD severity and childhood trauma did not directly affect this process. Self-stigma appears to be related to depression in a stronger way than PTSD is related to women with SUD and PTSD

Plasma Metabolome Alterations Discriminate between COVID-19 and Non-COVID-19 Pneumonia

Pneumonia is a common cause of morbidity and mortality and is most often caused by bacterial pathogens. COVID-19 is characterized by lung infection with potential progressive organ failure. The systemic consequences of both disease on the systemic blood metabolome are not fully understood. The aim of this study was to compare the blood metabolome of both diseases and we hypothesize that plasma metabolomics may help to identify the systemic effects of these diseases. Therefore, we profiled the plasma metabolome of 43 cases of COVID-19 pneumonia, 23 cases of non-COVID-19 pneumonia, and 26 controls using a non-targeted approach. Metabolic alterations differentiating the three groups were detected, with specific metabolic changes distinguishing the two types of pneumonia groups. A comparison of venous and arterial blood plasma samples from the same subjects revealed the distinct metabolic effects of pulmonary pneumonia. In addition, a machine learning signature of four metabolites was predictive of the disease outcome of COVID-19 subjects with an area under the curve (AUC) of 86 ± 10 %. Overall, the results of this study uncover systemic metabolic changes that could be linked to the etiology of COVID-19 pneumonia and nonCOVID-19 pneumonia

Continuous Live-Cell Culture Imaging and Single-Cell Tracking by Computational Lensfree LED Microscopy

Continuous cell culture monitoring as a way of investigating growth, proliferation, and kinetics of biological experiments is in high demand. However, commercially available solutions are typically expensive and large in size. Digital inline-holographic microscopes (DIHM) can provide a cost-effective alternative to conventional microscopes, bridging the gap towards live-cell culture imaging. In this work, a DIHM is built from inexpensive components and applied to different cell cultures. The images are reconstructed by computational methods and the data are analyzed with particle detection and tracking methods. Counting of cells as well as movement tracking of living cells is demonstrated, showing the feasibility of using a field-portable DIHM for basic cell culture investigation and bringing about the potential to deeply understand cell motility