A Model Based Framework for Testing Safety and Security in Operational Technology Environments

Todays industrial control systems consist of tightly coupled components allowing adversaries to exploit security attack surfaces from the information technology side, and, thus, also get access to automation devices residing at the operational technology level to compromise their safety functions. To identify these concerns, we propose a model-based testing approach which we consider a promising way to analyze the safety and security behavior of a system under test providing means to protect its components and to increase the quality and efficiency of the overall system. The structure of the underlying framework is divided into four parts, according to the critical factors in testing of operational technology environments. As a first step, this paper describes the ingredients of the envisioned framework. A system model allows to overview possible attack surfaces, while the foundations of testing and the recommendation of mitigation strategies will be based on process-specific safety and security standard procedures with the combination of existing vulnerability databases

Industrial robotics in factory automation: from the early stage to the Internet of Things

Robotics is a surprisingly old discipline, and robots have shaped industry and the various industrial revolutions for many decades. This paper covers topics relevant to the IES Technical Committee on Factory Automation, focusing in particular on the evolution of industrial robotics. After providing a historical perspective on the topic, the paper addresses current and future trends, revealing the close link between the progress in industrial robotics and the parallel evolution of industrial communication systems, which represent an enabling technology for modern industrial robotics.Peer ReviewedPostprint (author's final draft

Machine Learning Meets Communication Networks: Current Trends and Future Challenges

The growing network density and unprecedented increase in network traffic, caused by the massively expanding number of connected devices and online services, require intelligent network operations. Machine Learning (ML) has been applied in this regard in different types of networks and networking technologies to meet the requirements of future communicating devices and services. In this article, we provide a detailed account of current research on the application of ML in communication networks and shed light on future research challenges. Research on the application of ML in communication networks is described in: i) the three layers, i.e., physical, access, and network layers; and ii) novel computing and networking concepts such as Multi-access Edge Computing (MEC), Software Defined Networking (SDN), Network Functions Virtualization (NFV), and a brief overview of ML-based network security. Important future research challenges are identified and presented to help stir further research in key areas in this direction

Grünlandenergie Havelland

Im Rahmen des Projekts „Grünlandenergie Havelland“ untersuchte das Deutsche Biomasseforschungszentrum in Kooperation mit dem Leibniz-Institut für Agrartechnik Potsdam-Bornim e. V. und der Bosch & Partner GmbH am Beispiel der Modellregion Havelland (Landkreis Havelland und umliegende Gebiete) mögliche Konversionspfade zur Energiegewinnung von halmgutartigem Grüngut. Im Fokus stand die Verwertung von überschüssigem Gras von extensiv bewirtschafteten Grünlandflächen sowie von halmgutartiger Biomasse aus der Gewässerunterhaltung und Biotoppflege. Als Reststoffe lässt die energetische Nutzung dieser Substrate eine besonders gute Treibhausgasbilanz erwarten. Aufgrund der stofflichen Eigenschaften sowie der dezentralen und häufig sehr heterogenen Aufkommen ist die energetische Nutzung dieser Substrate jedoch mit besonderen technischen und logistischen Herausforderungen verbunden. Ziel des Projekts war die Entwicklung von übertragbaren Konzepten zur Nutzung der betrachteten Grüngutsortimente für die Wärme- und Stromerzeugung. Ausgehend von der Analyse der entsprechenden Biomassepotenziale sowie geeigneter Standorte und Technologien wurden vollständige Bereistellungsketten verschiedener Nutzungskonzepte untersucht. Die abschließende Bewertung der Nutzungskonzepte erfolgt anhand der Parameter: Wirtschaftlichkeit, Treibhausgasemissionsminderungspotenzial und Umsetzbarkeit. Im Ergebnis werden für die regionalen Akteure anwendungsreife Analysemethoden bereitgestellt, Empfehlungen für einzelne Nutzungskonzepte ausgesprochen und weitergehender Forschungsbedarf benannt. [... aus der Zusammenfassung

Persister cell phenotypes contribute to poor patient outcomes after neoadjuvant chemotherapy in PDAC

Neoadjuvant chemotherapy can improve the survival of individuals with borderline and unresectable pancreatic ductal adenocarcinoma; however, heterogeneous responses to chemotherapy remain a significant clinical challenge. Here, we performed RNA sequencing (n = 97) and multiplexed immunofluorescence (n = 122) on chemo-naive and postchemotherapy (post-CTX) resected patient samples (chemoradiotherapy excluded) to define the impact of neoadjuvant chemotherapy. Transcriptome analysis combined with high-resolution mapping of whole-tissue sections identified GATA6 (classical), KRT17 (basal-like) and cytochrome P450 3A (CYP3A) coexpressing cells that were preferentially enriched in post-CTX resected samples. The persistence of GATA6hi and KRT17hi cells post-CTX was significantly associated with poor survival after mFOLFIRINOX (mFFX), but not gemcitabine (GEM), treatment. Analysis of organoid models derived from chemo-naive and post-CTX samples demonstrated that CYP3A expression is a predictor of chemotherapy response and that CYP3A-expressing drug detoxification pathways can metabolize the prodrug irinotecan, a constituent of mFFX. These findings identify CYP3A-expressing drug-tolerant cell phenotypes in residual disease that may ultimately inform adjuvant treatment selection