Perfusion‐Dependent Cerebral Autoregulation Impairment in Hemispheric Stroke

Objective: Loss of cerebral autoregulation (CA) plays a key role in secondary neurologic injury. However, the regional distribution of CA impairment after acute cerebral injury remains unclear because, in clinical practice, CA is only assessed within a limited compartment. Here, we performed large-scale regional mapping of cortical perfusion and CA in patients undergoing decompressive surgery for malignant hemispheric stroke. Methods: In 24 patients, autoregulation over the affected hemisphere was calculated based on direct, 15 to 20-minute cortical perfusion measurement with intraoperative laser speckle imaging and mean arterial blood pressure (MAP) recording. Cortical perfusion was normalized against noninfarcted tissue and 6 perfusion categories from 0% to >100% were defined. The interaction between cortical perfusion and MAP was estimated using a linear random slope model and Pearson correlation. Results: Cortical perfusion and CA impairment were heterogeneously distributed across the entire hemisphere. The degree of CA impairment was significantly greater in areas with critical hypoperfusion (40-60%: 0.42% per mmHg and 60-80%: 0.46% per mmHg) than in noninfarcted (> 100%: 0.22% per mmHg) or infarcted (0-20%: 0.29% per mmHg) areas (*p 100% (r = 0.36; *p < 0.05). Tissue integrity had no impact on the degree of CA impairment. Interpretation: In hemispheric stroke, CA is impaired across the entire hemisphere to a variable extent. Autoregulation impairment was greatest in hypoperfused and potentially viable tissue, suggesting that precise localization of such regions is essential for effective tailoring of perfusion pressure-based treatment strategies. ANN NEUROL 202

Effective Verification for Low-Level Software with Competing Interrupts

Interrupt-driven software is difficult to test and debug, especially when interrupts can be nested and subject to priorities. Interrupts can arrive at arbitrary times, leading to an exponential blow-up in the number of cases to consider. We present a new formal approach to verifying interrupt-driven software based on symbolic execution. The approach leverages recent advances in the encoding of the execution traces of interacting, concurrent threads. We assess the performance of our method on benchmarks drawn from embedded systems code and device drivers, and experimentally compare it to conventional approaches that use source-to-source transformations. Our results show that our method significantly outperforms these techniques. To the best of our knowledge, our work is the first to demonstrate effective verification of low-level embedded software with nested interrupt

Abstracts from the 8th International Conference on cGMP Generators, Effectors and Therapeutic Implications

This work was supported by a restricted research grant of Bayer AG

Impact of dynamic component parameters on the reliability analysis of power systems

Zusammenfassung in englischer SpracheAbweichender Titel nach Übersetzung der Verfasserin/des VerfassersModerne Monitoringsysteme erlauben im elektrischen Energienetz durch steigende Rechenkapazitäten die Berechnung von detaillierten Echtzeit-Zustandsinformationen über die überwachten Betriebsmittel. Durch diese Informationen ist in der Betriebsführung eine bessere Ausnutzung der betroffenen Netzkomponente, ohne diese zu gefährden, möglich. Dies erlaubt einen wirtschaftlicheren Betrieb des Betriebsmittels. In Betriebssituationen, in denen Komponenten ausgefallen sind, ist dadurch eine zusätzliche Stützung des Netzbetriebs möglich. Für die Zuverlässigkeitsanalyse ist eine Berücksichtigung der Fähigkeiten von diesen Monitoringsystemen von Interesse, da dadurch eine positive Beeinflussung der Versorgungssicherheit von Netzkunden möglich ist. In dieser Arbeit werden die Auswirkungen von Betriebsarten untersucht, welche durch den Einsatz von Monitoringsystemen ermöglicht werden. Es soll analysiert werden, welchen Effekt die Berücksichtigung von kontinuierlichen Kennlinien für Thermal Rating und den kurzzeitigen Notbetrieb auf die Zuverlässigkeitsanalyse besitzen. Für die Untersuchungen wird ein thermisches Modell für Transformatoren verwendet. Die Zuverlässigkeitsanalyse wird nach dem Verfahren der Zustandsenumeration durchgeführt. Es werden Methoden zur generellen Einbindung thermischer Komponentenmodelle für Thermal Rating und Notbetrieb entwickelt. Die Entwicklung eines Verfahrens zur Berücksichtigung von Lastzuständen vor dem untersuchten Fehlerzustand wird angeführt. Sämtliche entwickelten Verfahren werden an einem einfachen Testnetz analysiert. Alle umgesetzten Methoden zur Implementierung thermischer Modelle in der Zuverlässig keitsanalyse zeigen eine Reduzierung der Nichtverfügbarkeit gegenüber einer Referenzimplementierung ohne Berücksichtigung der Modelle. Der kurzzeitige Notbetrieb zeigt verglichen mit Thermal Rating für die berücksichtigten Notbetriebskennlinien eine stärkere Reduktion der Nichtverfügbarkeit. Beide Verfahren weisen eine geringe Empfindlichkeit auf die Anzahl der Häufigkeitsklassen der Umgebungstemperatur auf. Das Ergebnis der Nichtverfügbarkeit sinkt mit steigender Anzahl der Häufigkeitsklassen geringfügig. Für die Umgebungstemperatur sind Häufigkeitsverteilungen mit einer geringen Anzahl an Klassen ausreichend. Wenn Transformatoren im Testnetz Thermal Rating oder Notbetrieb verwenden, wird in der Zuverlässigkeitsanalyse die erwartete Nichtverfügbarkeit der Versorgung der Last reduziert.Developments in computing capacity enable component monitoring systems for real-time calcuation of conditions in electricity grids. This information provides an optimized utility use without an increase of failures. Consequently, an economical benefit is expected. If a component fails, a backup for operating the grid is available. Considering the potential of monitoring sytems in realiability analyses is of interest. It is expected that those appliances positively affect the security of supply for grid customers. This thesis analyzes the different modes of operation which are enabled by modern component monitoring systems. The effects of thermal rating and short time overloading are studied with continuous characteristic curves; a thermal transformer model is used. The reliability analyses uses the state enumeration method for calculations. For implementing thermal models in the state enumeration method, a general method that is not only applicable to transformers is preferred. All developed approaches are analyzed by calculating a simple electricity grid. For all methods, it is shown that using monitoring-based operation optimization leads to a lower expected downtime for the system. Short time overloading enables a larger decrease in system downtime compared to thermal rating. Both strategies are less sensitive to the number of temperature frequency classes as to the number of load frequency classes. Using thermal rating or short time overloading reduces the expected system downtime calculated by the reliability analyses.6

earthobservations/wetterdienst: Release 0.9.0

Large refactoring Make period type in DWDObservationData and cli optional Activate SQL querying again by using DuckDB 0.2.2.dev254. Thanks, @Mytherin! Fix coercion of integers with nans Fix problem with storing IntegerArrays in HDF Rename DWDStationRequest to DWDObservationData Add DWDObservationSites API wrapper to acquire station information Move discover_climate_observations to DWDObservationMetadata.discover_parameters Add PDF-based DWDObservationMetadata.describe_fields() Upgrade Docker images to Python 3.8.6 Move intermediate storage of HDF out of data collection Fix bug with date filtering for empty/no station data for a given parameter Radar data: Add non-RADOLAN data acquisitio

earthobservations/wetterdienst: Improve MOSMIX subsystem and various refactorings

CLI: Obtain "--tidy" argument from command line Extend MOSMIX support to equal the API of observations DWDObservationSites now filters for those stations which have a file on the server DWDObservationData now also takes an individual parameter independent of the pre-configured DWD datasets by using DWDObservationParameter or similar names e.g. "precipitation_height" Newly introduced coexistence of DWDObservationParameter and DWDObservationParameterSet to address parameter sets as well as individual parameters Imports are changed to submodule thus now one has to import everything from wetterdienst.dwd Renaming of time_resolution to resolution, period_type to period, several other relabel

earthobservations/wetterdienst: Enhance documentation, add test for example notebook, fix bug with TimeResolution enumeration

Add test for Jupyter notebook Add function to discover available climate observations (time resolution, parameter, period type) Make the CLI work again and add software tests to prevent future havocs Use Sphinx Material theme for documentation Fix typo in enumeration for TimeResolution.MINUTES_1

earthobservations/wetterdienst: Fix Mosmix station locations

DWD: Add missing radar site "Emden" (EMD, wmo=10204) Mosmix stations: fix longitudes/latitudes to be decimal degrees (before they were degrees and minutes) Change key STATION_HEIGHT to HEIGHT, LAT to LATITUDE, LON to LONGITUDE Rename "Data" classes to "Values" Make arguments singula