Workshop zur Physik der Energiespeicherung

Das Thema erneuerbare Energien wird heutzutage in der Schule intensiv behandelt. Ein Beispiel dafür ist das Wahlpflichtfach Naturwissenschaften und Technik an Thüringer Schulen. Ein wichtiges Thema hierbei ist die Speicherung von Energie, da die elektrische Energie oft nicht dann zum Einsatz kommen kann, wenn sie gewonnen wird. Für die Speicherung gibt es eine Reihe von Möglichkeiten, welche aber unterschiedliche Wirkungsgrade oder technische Umsetzungsmöglichkeiten besitzen. In einem Experimentier-Workshop für das 2. MINT-Festival Jena sollen die physikalischen Hintergründe so aufbereitet werden, dass Schülerinnen und Schüler der Sekundarstufe II die verschiedenen Möglichkeiten selbst testen und unter die Lupe nehmen können. Erneuerbare Energien müssen nicht nur gewonnen, sondern auch gespeichert werden. Wir stellen in Konzepten für Stationen vor, wie sich die Themen experimentell in einem Workshop umsetzen lassen, da Experimente zum Thema schnell einen Rahmen annehmen, der die Möglichkeiten im Unterricht übersteigt

Workshop zur Physik der Energiespeicherung

Das Thema erneuerbare Energien wird heutzutage in der Schule intensiv behandelt. Ein Beispiel dafür ist das Wahlpflichtfach Naturwissenschaften und Technik an Thüringer Schulen. Ein wichtiges Thema hierbei ist die Speicherung von Energie, da die elektrische Energie oft nicht dann zum Einsatz kommen kann, wenn sie gewonnen wird. Für die Speicherung gibt es eine Reihe von Möglichkeiten, welche aber unterschiedliche Wirkungsgrade oder technische Umsetzungsmöglichkeiten besitzen. In einem Experimentier-Workshop für das 2. MINT-Festival Jena sollen die physikalischen Hintergründe so aufbereitet werden, dass Schülerinnen und Schüler der Sekundarstufe II die verschiedenen Möglichkeiten selbst testen und unter die Lupe nehmen können. Erneuerbare Energien müssen nicht nur gewonnen, sondern auch gespeichert werden. Wir stellen in Konzepten für Stationen vor, wie sich die Themen experimentell in einem Workshop umsetzen lassen, da Experimente zum Thema schnell einen Rahmen annehmen, der die Möglichkeiten im Unterricht übersteigt

Development of a Class Framework for Flood Forecasting

Aus der Einleitung: The calculation and prediction of river flow is a very old problem. Especially extremely high values of the runoff can cause enormous economic damage. A system which precisely predicts the runoff and warns in case of a flood event can prevent a high amount of the damages. On the basis of a good flood forecast, one can take action by preventive methods and warnings. An efficient constructional flood retention can reduce the effects of a flood event enormously.With a precise runoff prediction with longer lead times (>48h), the dam administration is enabled to give order to their gatekeepers to empty dams and reservoirs very fast, following a smart strategy. With a good timing, that enables the dams later to store and retain the peak of the flood and to reduce all effects of damage in the downstream. A warning of people in possible flooded areas with greater lead time, enables them to evacuate not fixed things like cars, computers, important documents and so on. Additionally it is possible to use the underlying rainfall-runoff model to perform runoff simulations to find out which areas are threatened at which precipitation events and associated runoff in the river. Altogether these methods can avoid a huge amount of economic damage.:List of Symbols and Abbreviations S. III 1 Introduction S. 1 2 Process based Rainfall-Runoff Modelling S. 5 2.1 Basics of runoff processes S. 5 2.2 Physically based rainfall-runoff and hydrodynamic river models S. 15 3 Portraying Rainfall-Runoff Processes with Neural Networks S. 21 3.1 The Challenge in General S. 22 3.2 State-of-the-art Approaches S. 24 3.3 Architectures of neural networks for time series prediction S. 26 4 Requirements specification S. 33 5 The PAI-OFF approach as the base of the system S. 35 5.1 Pre-Processing of the Input Data S. 37 5.2 Operating and training the PoNN S. 47 5.3 The PAI-OFF approach - an Intelligent System S. 52 6 Design and Implementation S. 55 6.1 Design S. 55 6.2 Implementation S. 58 6.3 Exported interface definition S. 62 6.4 Displaying output data with involvement of uncertainty S. 64 7 Results and Discussion S. 69 7.1 Evaluation of the Results S. 69 7.2 Discussion of the achieved state S. 75 8 Conclusion and FutureWork S. 77 8.1 Access to real-time meteorological input data S. 77 8.2 Using further developed prediction methods S. 79 8.3 Development of a graphical user interface S. 80 Bibliography S. 8

Relating the orientation of cortical traveling waves and co-occurring spike patterns

To study information processing in the cerebral cortex, multiple complementary approaches exist to characterize the coordinated population dynamics. One approach is to investigate the correlated spiking activity of individual neurons. Another approach is to analyze the local field potential (LFP) as an aggregate signature of the neuronal population dynamics. However, it is an open question how these two scales of observation relate to each other.The LFP activity in the motor cortex exhibits functionally relevant oscillations in the beta frequency band (e.g. [1]). It has been shown that the phases of beta oscillations typically form propagating waves [2, 3]. These are commonly observed as planar waves that travel across the primary motor cortex, preferably on a rostral-caudal axis [3]. Significant patterns of precise synchronous spiking (on a ms scale) that have been identified in the motor cortex [4] also display a preferred spatial orientation [5]. Indeed, estimated functional connectivity measured from spike trains using a Granger causality approach occurs in a directed manner that aligns with the mean propagation axis of LFP waves [6]. These findings raise the question of a direct relation between a single spike pattern and a co-occurring LFP wave.To investigate this question, we analyzed multi-electrode-array (Utah array) recordings of the motor cortex (MI/PMd) from a macaque monkey during an instructed reach-to-grasp task [7]. In the beta-band LFP recordings (15-25 Hz), we identified wave directions and planarity based on the gradient of the instantaneous phase using an automated analysis pipeline approach (Cobrawap) [8,9]. Independently, we detected all repeating synchronous spike patterns in the same data sets using the SPADE method [10, 11]. We identified the dominant spatial axis of the synchronous spike pattern as the first eigenvector of a principal component analysis (PCA) over the electrode grid coordinates of the involved neurons. We show that this axis tends to be perpendicular to the propagation direction of simultaneously occurring planar waves (cf. Fig.). This relationship does not only appear on average as suggested by previous work [5,6] but also on a pattern-by-pattern basis. Finally, we discuss extensions of this analysis approach to non-synchronous spike patterns.References:[1]: Kilavik et al. (2012). doi:10.1093/cercor/bhr299[2]: Denker et al. (2018). doi:10.1038/s41598-018-22990-7[3]: Rubino et al. (2006). doi:10.1038/nn1802[4]: Riehle et al. (1997). doi:10.1126/science.278.5345.1950[5]: Torre et al. (2016). doi:10.1523/JNEUROSCI.4375-15.2016[6]: Takahashi et al. (2015). doi:10.1038/ncomms8169[7]: Brochier et al. (2018). doi:10.1038/sdata.2018.55[8]: Gutzen et al. (2021). doi:10.12751/NNCN.BC2020.0030[9]: Capone et al. (2022). doi:10.48550/arXiv.2104.07445[10]: Torre et al. (2013). doi:10.3389/fncom.2013.00132[11]: Stella et al. (2022). doi:10.1523/ENEURO.0505-21.2022Acknowledgements:Founded by EU Grant 785907 (HBP SGA2), EU Grant 945539 (HBP SGA3), ANR Grant GRASP (France), Helmholtz IVF Grant ZT-I-0003 (HAF), and the Joint-Lab “Supercomputing and Modeling for the Human Brain”

Relating the orientation of cortical traveling waves and co-occurring spike patterns

The collective population dynamics of the cerebral cortex can be studied at different levels. One option is to study individual neurons' collective correlated spiking activity. A complementary approach on the mesoscopic scale is to analyze the local field potential (LFP) as an aggregate signature of the neuronal population activity. However, the exact relation between these two observation levels remains an open research question.The LFP activity in the motor cortex exhibits functionally relevant oscillations in the beta frequency band (e.g., [1]). It has been shown that the phases of beta oscillations typically form traveling waves [2]. While different spatial patterns of such waves are identified [3], the most common are planar waves that travel across the primary motor cortex, predominantly along the rostral-caudal axis [2].There are several indications of spatio-temporal organization of motor cortex activity in different signal types. Repeating patterns of precise synchronous spiking (on a ms scale) identified in the motor cortex [4] also display a preferred spatial orientation [5]. Correlated spiking activity measured by functional connectivity occurs in the same direction as the average propagation axis of LFP waves [6]. In more local recordings, it was found that the spiking activity phase locks to beta LFP oscillations. The phase locking is even more pronounced for spikes involved in significant synchronous spiking as identified by Unitary Events [7].To investigate the direct relation of synchronous spike patterns to beta LFP phase waves, we analyze multi-electrode array (Utah array) recordings of the motor cortex (M1/PMd) from a macaque monkey during an instructed reach-to-grasp task [8]. We analyze the LFP In the beta band (15-30 Hz) for wave directions and their planarity based on the gradient of the instantaneous phase using an automated analysis pipeline approach (Cobrawap) [9,10]. Independently, we detect repeating synchronous spike patterns in the same data sets using the SPADE method [11, 12]. We show that the average pattern orientation axis tends to be perpendicular to the propagation direction of simultaneously occurring planar waves, as suggested by previous work [5,6]. Moreover, this relation is observed pattern-by-pattern, most prominently during movement preparation. These findings provide direct evidence of how spatially organized oscillatory LFP activity can be interpreted in the context of precisely coordinated spike patterns.References:[1] Kilavik et al. (2012). doi:10.1093/cercor/bhr299[2] Rubino et al. (2006). doi:10.1038/nn1802[3] Denker et al. (2018). doi:10.1038/s41598-018-22990-7[4] Riehle et al. (1997). doi:10.1126/science.278.5345.1950[5] Torre et al. (2016). doi:10.1523/JNEUROSCI.4375-15.2016[6] Takahashi et al. (2015). doi:10.1038/ncomms8169[7] Denker (2011). doi:10.1093/cercor/bhr040[8] Brochier et al. (2018). doi:10.1038/sdata.2018.55[9] Gutzen et al. (2022). doi:10.48550/arXiv.2211.08527 RRID:SCR_022966[10] Capone et al. (2022). doi:10.48550/arXiv.2104.07445[11] Torre et al. (2013). doi:10.3389/fncom.2013.00132[12] Stella et al. (2022). doi:10.1523/ENEURO.0505-21.2022Acknowledgments:Funded by EU Grant 785907 (HBP SGA2), EU Grant 945539 (HBP SGA3), ANR Grant GRASP (France), Helmholtz IVF Grant ZT-I-0003 (HAF), the Joint-Lab “Supercomputing and Modeling for the Human Brain”, and the Ministry of Culture and Science of the State of North Rhine-Westphalia, Germany (NRW-network 'iBehave', grant number: NW21-049)