Die Einführung des digitalen Bauantragsverfahrens ermöglicht, die Prüfung des Standsicherheitsnachweises vollständig papierlos durchzuführen. Dadurch können sowohl statische Berechnung als auch Konstruktionspläne medienbruchfrei geprüft werden, weshalb eine Beschleunigung des Verfahrens erwartet werden kann. Der Übergang von Papier zu PDF stellt einen deutlichen Fortschritt für die beteiligten Parteien dar, kann aber hinsichtlich des Grads der Digitalisierung nur ein Anfang sein. Der größtmögliche Grad der Digitalisierung wird durch die Verwendung von BIM (BIM: Building Information Modeling) in Verbindung mit den notwendigen Datenumgebungen (CDE) erreicht. Da der Einsatz von BIM bei Planung, Errichtung und Betrieb von Gebäuden erhebliche Vorteile bietet, ist die weitere Verbreitung absehbar bzw. wird von der Bundes- und den Landesregierungen die Anwendung teilweise bereits vorgeschrieben. Auswirkungen auf die Prüfung und Genehmigung von Bauwerken sind daher zu erwarten. In diesem Aufsatz wird untersucht, wie digitalisierte Prüfprozesse gestaltet werden können. Die Prozesse werden vorgestellt und hinsichtlich des Reifegrads der Digitalisierung bewertet.

Kaum hat sich der Mensch an Text- und Bildgeneratoren gewöhnt, veröffentlichen OpenAI, Google, Microsoft und Meta ihre multimodalen Modelle, die beide Welten vereinen. Das ermöglicht praktischen KI-Anwendungen und sogar Robotern ein umfassenderes Verständnis der Welt.

Background: Abnormalities in electrocortical parameters and persistence of afterimage after visual stimulation are known to occur in migraine patients. The results of studies on Contingent Negative Variation (CNV) and afterimage persistence in migraine patients suggest a link between these two phenomena and a connection to the pathomechanism of migraine.

Objectives: To date, no studies have investigated both afterimage duration and CNV parameters in the same subjects. The aim of this study was to investigate the relationship between the early component of CNV (iCNV) and the duration of the afterimage in migraine patients.

Methods: Sixty seven migraine patients from the headache center of the University of Rostock Medical Center were examined for iCNV amplitude, iCNV habituation and afterimage duration. The subjects also completed questionnaires developed for this study and the MIDAS (Migraine Disability Assessment) questionnaire.

Results: Associations were found between iCNV amplitude and afterimage duration and between habituation capacity and afterimage duration. A deficit in habituation capacity correlated with a significantly prolonged afterimage duration. Increased iCNV amplitude and prolonged afterimage duration were also significantly correlated.

Conclusion: Conclusions about the pathophysiology of migraine can be drawn from the results of this study. The results support the hypothesis of cortical hyperexcitability as a consequence of a low pre-activation level, which may be a possible contributory cause of migraine. Furthermore, they allow assessment of whether the afterimage examination, which is easier and quicker to perform than the CNV examination, can be used as a diagnostic tool or as a parameter to monitor the course of therapy in people with migraine.

Lead-Management ist eine der großen Herausforderungen im Vertrieb, da die Neukundengewinnung immer bedeutsamer wird. Dieses essential erläutert, wie durch den parallelen Ausbau der drei Basisdimensionen: Prozesse, Menschen und Daten Lead-Exzellenz erreicht werden kann. Hierzu sind zunächst die Rahmenbedingungen zu analysieren und zu bewerten. Anschließend müssen die Prozesse in eine Prozesslandkarte eingebunden, die Kompetenzprofile den neuen Anforderungen angepasst und die Datenbasis vervollständigt werden.
Eine umfangreiche Checkliste zeigt auf, wie Unternehmen bei der Einführung oder Überarbeitung ihres bereits existierenden Lead-Managements sinnvollerweise vorgehen sollten.

Modern DRAM is susceptible to fault attacks that undermine the entire system’s security. The most well-studied disturbance effect is Rowhammer, where an attacker repeatedly opens and closes (i.e., hammers) different rows, which can lead to bitflips in adjacent rows. Different hammering strategies include double-sided, hammering two rows sandwiching a victim row, and one-location, hammering a single row. One-location Rowhammer requires no physical address information, as any location in memory is mapped to a DRAM row, and no relation between rows is required for hammering. The recently discovered Rowpress differs from Rowhammer by not hammering rows but keeping them open longer, evident by a disjoint set of affected memory locations.

In this paper, we examine the differences between four attack variants: one-location Rowhammer, a one-location Rowpress variant we developed, double-sided Rowhammer, and double-sided Rowpress on a set of 12 DDR4 modules. Our methodology is to hammer and press the exact same set of physical memory locations in all attack variants. Surprisingly, our results show that on 4 out of 12 DDR4 modules, we were only able to reproduce double-sided Rowhammer but none of the other attack variants. On 2 DDR4 modules, we were able to reproduce all attack variants. We find that the number of unique bitflip locations ranges from 161 to 15 612, when hammering the exact same set of physical memory locations. Our one-location Rowhammer attack induces roughly the same amount of bitflips as double-sided Rowhammer, however, only 61.8 % of bitflip locations overlap. We explain this by one-location Rowhammer inducing bitflips due to the Rowhammer as well as the Rowpress effect, making the differentiation of both methods difficult, therefore, calling it Presshammer. Based on our observed bitflips, we develop the first end-to-end one-location Rowpress attack. One-location Rowpress requires only minimal physical address information that an attacker can acquire through a same-row same-bank side-channel attack. Our end-to- end attack escalates to kernel privileges within less than 10 minutes.

Large language models (LLMs) have garnered significant attention, but the definition of “large” lacks clarity. This paper focuses on medium-sized language models (MLMs), defined as having at least six billion parameters but less than 100 billion. The study evaluates MLMs regarding zero-shot generative question answering in German and English language, which requires models to provide elaborate answers without external document retrieval (RAG). The paper introduces an own test dataset and presents results from human evaluation. Results show that combining the best answers from different MLMs yielded an overall correct answer rate of 82.7% which is better than the 60.9% of ChatGPT. The best English MLM achieved 71.8% and has 33B parameters, which highlights the importance of using appropriate training data for fine-tuning rather than solely relying on the number of parameters. The best German model also surpasses ChatGPT for the equivalent dataset. More fine-grained feedback should be used to further improve the quality of answers. The open source community is quickly closing the gap to the best commercial models.