Concentration, Coagglomeration and Spillovers: The Geography of New Market Firms in Germany

The Neuer Markt, launched in 1997 by Deutsche Börse, the German stock exchange, is Europe’s closest equivalent to the Nasdaq, the US high-tech oriented stock market. Although the New Economy in Germany is not restricted to Neuer Markt firms one may argue that these firms and their employees form the spearhead of Germany’s New Economy. In the current paper we employ the ‘dartboard approach’ pioneered by Ellison and Glaeser to analyse the spatial concentration of New Economy employment in Germany, the coagglomeration of firms belonging to different sub-sectors of Neuer Markt and the (intraregional) spillovers between different high tech industries. We refine the analysis by differentiating between Neuer Markt firms in general and New Economy firms in a more narrow sense, and we compare their spatial distribution with the structure of the ‘traditional economy’ as well as with the spatial distribution of other innovative activities such as patent applications or R&D. Key Words: Geographic concentration, New Economy, Neuer Markt, Dartboard Approach JEL Classification: G19, O30, O18, R11

Concentration, Coagglomeration and Spillovers: The Geography of New Market Firms in Germany

The Neuer Markt, launched in 1997 by Deutsche Börse, the German stock exchange, is Europe’s closest equivalent to the Nasdaq, the US high-tech oriented stock market. Although the New Economy in Germany is not restricted to Neuer Markt firms one may argue that these firms and their employees form the spearhead of Germany’s New Economy. In the current paper we employ the ‘dartboard approach’ pioneered by Ellison and Glaeser to analyse the spatial concentration of New Economy employment in Germany, the coagglomeration of firms belonging to different sub-sectors of Neuer Markt and the (intraregional) spillovers between different high tech industries. We refine the analysis by differentiating between Neuer Markt firms in general and New Economy firms in a more narrow sense, and we compare their spatial distribution with the structure of the ‘traditional economy’ as well as with the spatial distribution of other innovative activities such as patent applications or R&D. Key Words: Geographic concentration, New Economy, Neuer Markt, Dartboard Approach JEL Classification: G19, O30, O18, R1

Robustness and Generalization Performance of Deep Learning Models on Cyber-Physical Systems: A Comparative Study

Deep learning (DL) models have seen increased attention for time series forecasting, yet the application on cyber-physical systems (CPS) is hindered by the lacking robustness of these methods. Thus, this study evaluates the robustness and generalization performance of DL architectures on multivariate time series data from CPS. Our investigation focuses on the models' ability to handle a range of perturbations, such as sensor faults and noise, and assesses their impact on overall performance. Furthermore, we test the generalization and transfer learning capabilities of these models by exposing them to out-of-distribution (OOD) samples. These include deviations from standard system operations, while the core dynamics of the underlying physical system are preserved. Additionally, we test how well the models respond to several data augmentation techniques, including added noise and time warping. Our experimental framework utilizes a simulated three-tank system, proposed as a novel benchmark for evaluating the robustness and generalization performance of DL algorithms in CPS data contexts. The findings reveal that certain DL model architectures and training techniques exhibit superior effectiveness in handling OOD samples and various perturbations. These insights have significant implications for the development of DL models that deliver reliable and robust performance in real-world CPS applications.Comment: Accepted at the IJCAI 2023 Workshop of Artificial Intelligence for Time Series Analysis (AI4TS

Long-Term Efficacy and Safety of Chronic Globus Pallidus Internus Stimulation in Different Types of Primary Dystonia

Background: Deep brain stimulation (DBS) of the globus pallidus internus (GPi) offers a very promising therapy for medically intractable dystonia. However, little is known about the long-term benefit and safety of this procedure. We therefore performed a retrospective long-term analysis of 18 patients (age 12-78 years) suffering from primary generalized (9), segmental (6) or focal (3) dystonia (minimum follow-up: 36 months). Methods: Outcome was assessed using the Burke-Fahn-Marsden (BFM) scores (generalized dystonia) and the Tsui score (focal/segmental dystonia). Follow-up ranged between 37 and 90 months (mean 60 months). Results: Patients with generalized dystonia showed a mean improvement in the BFM movement score of 39.4% (range 0 68.8%), 42.5% (range -16.0 to 81.3%) and 46.8% (range-2.7 to 83.1%) at the 3- and 12-month, and long-term follow-up, respectively. In focal/ segmental dystonia, the mean reduction in the Tsui score was 36.8% (range 0-100%), 65.1% (range 16.7-100%) and 59.8% (range 16.7-100%) at the 3- and 12-month, and long-term follow-up, respectively. Local infections were noted in 2 patients and hardware problems (electrode dislocation and breakage of the extension cable) in 1 patient. Conclusion: Our data showed Gpi-DBS to offer a very effective and safe therapy for different kinds of primary dystonia, with a significant long-term benefit in the majority of cases. Copyright (c) 2008 S. Karger AG, Base

Entwicklung elektrochemischer Biosensoren für die Tumordiagnostik

Die vorliegende Arbeit befasst sich mit der Entwicklung und Anwendung elektrochemischer Biosensoren zur Erweiterung oder zum Ersatz herkömmlicher Diagnostikverfahren. Als Basis für die Biosensoren wurden Elektrodenarraychips entworfen und im Reinraum gefertigt. Die als 9WPtE bezeichneten Elektrodenarrays waren aus 3 x 3 Elektrodenpaaren im 96-well-Maßstab (ANSI-Standard) aufgebaut. Jedes Elektrodenpaar bestand aus einer kreisrunden Arbeitselektrode mit einem Durchmesser von 1,9 mm und einer Gegenelektrode als offenem Kreisring um die Arbeitselektrode mit einem Durchmesser von 7 mm. Außerhalb des Reinraums wurden separate Messkammern und Ag/AgCl-Referenzelektroden integriert. Sowohl das Referenzsystem als auch die Signalqualität der 9WPtE-Elektrodenarraychips wurden mittels Zyklovoltammetrie, Impedanzspektroskopie und Rasterkraftmikroskopie analysiert und anhand dieser Untersuchungen optimiert. Das Augenmerk lag hierbei auf den Produktionsprozessen zur Herstellung der Elektrodenarraychips, auf den Elektrolytbedingungen für die elektrochemischen Messungen und auf der Recyclebarkeit der Chips. Die Funktionalisierung der Arbeitselektroden der 9WPtE-Chips erfolgte mit sich selbst-organisierenden Schichten aus Thiolen. An die Thiole wurden mittels Chemoligation die biologischen Erkennungskomponenten kovalent gekoppelt. Mit dem 9WPtE-Elektrodenarray wurde auf diese Weise ein funktionsfähiger kompetitiver Immunosensoren gegen den Tumormarker Tenascin C entwickelt. Außerdem wurden der 9WPtE-Chip und ein zusätzlich entwickelter Durchflusssensor, basierend auf dem Prinzip des 9WPtE, genutzt, um die Möglichkeit der Detektion ganzer eukaryotischer Zellen zu untersuchen

Mammographic breast density and hormonal, proliferative and stromal histopathologic markers in benign and malignant tissue of different ethnicities

Analysis of the association of mammographic brast density with histopathologic markers

Inflammatory markers in a 2-year soy intervention among premenopausal women

Epidemiologic evidence supports a role of soy foods in breast cancer etiology. Because chronic inflammation appears to be a critical component in carcinogenesis, we examined the potential anti-inflammatory effects of soy foods.The original 2-year dietary intervention randomized 220 premenopausal women of whom 183 women (90 in the intervention group and 93 in the control group) were included in the current investigation; 40% were of Asian ancestry. The intervention group consumed two daily soy servings containing 50 mg of isoflavones (aglycone equivalents), whereas the controls maintained their regular diet. Five serum samples obtained at month 0, 3, 6, 12, and 24 were analyzed for interleukin (IL)-6, C-reactive protein (CRP), leptin, and adiponectin by ELISA. For statistical analysis, mixed models were applied to incorporate the repeated measurements.Results:The levels of all analytes were lower in Asian than Caucasian women. Overweight women had significantly higher levels of CRP, IL-6, and leptin and lower levels of adiponectin than normal weight women. We did not observe a significant effect of soy foods on the four markers, but leptin increased in the control and not in the intervention group (p = 0.20 for group-time effect); this difference was significant for Asian (p = 0.01) and obese women (p = 0.005).During this 2-year intervention, soy foods did not modify serum levels of CRP, IL-6, leptin, and adiponectin in premenopausal women although leptin levels remained stable among women in the intervention group who were obese or of Asian ancestry. Further studies with diverse markers of inflammation are necessary to clarify the specific effect of soy on immune responses

Controlling the behavior of single live cells with high density arrays of microscopic OLEDs

This work was supported in part by the Scottish Funding Council (via SUPA) and the Human Frontier Science Program (HFSP). The authors thank Andrew Morton (U St Andrews) for fruitful discussions.Microarrays of OLEDs are used to ­optically control the locomotion of individual cells in real time. Close contact (<2 μm) between OLEDs and the aqueous cell culture medium is facilitated by high-performance thin-film encapsulation. This work paves the way for using OLED light sources to control and study neuronal activity and cell signaling with extreme spatial and temporal resolution.PostprintPeer reviewe

Diagnosis driven Anomaly Detection for CPS

In Cyber-Physical Systems (CPS) research, anomaly detection (detecting abnormal behavior) and diagnosis (identifying the underlying root cause) are often treated as distinct, isolated tasks. However, diagnosis algorithms require symptoms, i.e. temporally and spatially isolated anomalies, as input. Thus, anomaly detection and diagnosis must be developed together to provide a holistic solution for diagnosis in CPS. We therefore propose a method for utilizing deep learning-based anomaly detection to generate inputs for Consistency-Based Diagnosis (CBD). We evaluate our approach on a simulated and a real-world CPS dataset, where our model demonstrates strong performance relative to other state-of-the-art models

AI-assisted study of auxetic structures

In this study, the viability of using machine learning models to predict stress-strain curves of auxetic structures based on geometry-describing parameters is explored. Given the computational cost and time associated with generating these curves through numerical simulations, a machine learning-based approach promises a more efficient alternative. A range of machine learning models, including Artificial Neural Networks, k-Nearest Neighbors Regression, Support Vector Regression, and XGBoost, is implemented and compared regarding the aptitude to predict stress-strain curves under quasi-static compressive loading. Training data is generated using validated finite element simulations. The performance of these models is rigorously tested on data not seen during training. The Feed-Forward Artificial Neural Network emerged as the most proficient model, achieving a Mean Absolute Percentage Error of 0.367 ± 0.230