Datenbankenunterstützung für das Protein-Protein-Docking: ein effizienter und robuster Feature-Index

In der vorliegenden Arbeit schlagen wir eine Architektur für ein Dockingsystem vor, das zu einem gegebenen Anfrageprotein alle möglichen Dockingpartner und deren zugehörige Konstellationen in einer Proteindatenbank sucht. Die einzelnen Filter- und Bewertungsschritte dieses Dockingsystems sollen durch den Einsatz räumlicher Zugriffsstrukturen unterstützt werden. Hier behandeln wir insbesondere die Verwendung eines effizienten und robusten Feature-Index. Dieser dient dazu, aus dem Raum aller möglichen Konstellationen von Partnerproteinen aus der Datenbank gute Kandidatenvorschläge zu ermitteln. Dazu werden im Vorfeld die Proteinoberflächen regionalisiert, in einem k-dimensionalen Raum von Kennzahlen beschrieben und in einer k-dimensionalen räumlichen Indexstruktur verwaltet. Für die Docking-Suche wird das Anfrageprotein analog regionalisiert, und zusätzlich werden seine Kennzahlen komplementiert, so daß zum Komplement ähnliche Regionen als mögliche Docking-Stellen im Index gefunden werden können. In den nachfolgenden Filterschritten des Dockingsystems wird unter anderem das räumliche Passen genauer überprüft

Automotive Intelligence Embedded in Electric Connected Autonomous and Shared Vehicles Technology for Sustainable Green Mobility

The automotive sector digitalization accelerates the technology convergence of perception, computing processing, connectivity, propulsion, and data fusion for electric connected autonomous and shared (ECAS) vehicles. This brings cutting-edge computing paradigms with embedded cognitive capabilities into vehicle domains and data infrastructure to provide holistic intrinsic and extrinsic intelligence for new mobility applications. Digital technologies are a significant enabler in achieving the sustainability goals of the green transformation of the mobility and transportation sectors. Innovation occurs predominantly in ECAS vehicles’ architecture, operations, intelligent functions, and automotive digital infrastructure. The traditional ownership model is moving toward multimodal and shared mobility services. The ECAS vehicle’s technology allows for the development of virtual automotive functions that run on shared hardware platforms with data unlocking value, and for introducing new, shared computing-based automotive features. Facilitating vehicle automation, vehicle electrification, vehicle-to-everything (V2X) communication is accomplished by the convergence of artificial intelligence (AI), cellular/wireless connectivity, edge computing, the Internet of things (IoT), the Internet of intelligent things (IoIT), digital twins (DTs), virtual/augmented reality (VR/AR) and distributed ledger technologies (DLTs). Vehicles become more intelligent, connected, functioning as edge micro servers on wheels, powered by sensors/actuators, hardware (HW), software (SW) and smart virtual functions that are integrated into the digital infrastructure. Electrification, automation, connectivity, digitalization, decarbonization, decentralization, and standardization are the main drivers that unlock intelligent vehicles' potential for sustainable green mobility applications. ECAS vehicles act as autonomous agents using swarm intelligence to communicate and exchange information, either directly or indirectly, with each other and the infrastructure, accessing independent services such as energy, high-definition maps, routes, infrastructure information, traffic lights, tolls, parking (micropayments), and finding emergent/intelligent solutions. The article gives an overview of the advances in AI technologies and applications to realize intelligent functions and optimize vehicle performance, control, and decision-making for future ECAS vehicles to support the acceleration of deployment in various mobility scenarios. ECAS vehicles, systems, sub-systems, and components are subjected to stringent regulatory frameworks, which set rigorous requirements for autonomous vehicles. An in-depth assessment of existing standards, regulations, and laws, including a thorough gap analysis, is required. Global guidelines must be provided on how to fulfill the requirements. ECAS vehicle technology trustworthiness, including AI-based HW/SW and algorithms, is necessary for developing ECAS systems across the entire automotive ecosystem. The safety and transparency of AI-based technology and the explainability of the purpose, use, benefits, and limitations of AI systems are critical for fulfilling trustworthiness requirements. The article presents ECAS vehicles’ evolution toward domain controller, zonal vehicle, and federated vehicle/edge/cloud-centric based on distributed intelligence in the vehicle and infrastructure level architectures and the role of AI techniques and methods to implement the different autonomous driving and optimization functions for sustainable green mobility.publishedVersio

Colorimetric nanofibers as optical sensors

Sensors play a major role in many applications today, ranging from biomedicine to safety equipment, where they detect and warn us about changes in the environment. Nanofibers, characterized by high porosity, flexibility, and a large specific surface area, are the ideal material for ultrasensitive, fastresponding, and user-friendly sensor design. Indeed, a large specific surface area increases the sensitivity and response time of the sensor as the contact area with the analyte is enlarged. Thanks to the flexibility of membranes, nanofibrous sensors cannot only be applied in high-end analyte detection, but also in personal, daily use. Many different nanofibrous sensors have already been designed; albeit, the most straightforward and easiest-to-interpret sensor response is a visual change in color, which is of particular interest in the case of warning signals. Recently, many researchers have focused on the design of so-called colorimetric nanofibers, which typically involve the incorporation of a colorimetric functionality into the nanofibrous matrix. Many different strategies have been used and explored for colorimetric nanofibrous sensor design, which are outlined in this feature article. The many examples and applications demonstrate the value of colorimetric nanofibers for advanced optical sensor design, and could provide directions for future research in this area

Accelerated Active Ageing Test on SiC JFETs Power Module with Silver Joining Technology for High Temperature Application

ESREF 2009 - 20th European Symposium on the Reliability of Electron Devices, Failure Physics and Analysis, Bordeaux, FRANCE, 05-/10/2009 - 09/10/2009This paper presents the accelerated active power cycling test (APCT) results on SiC JFETs power module dedicated to operate at high temperature. This study partly focuses on the new chip joining technology (LTJT), which permit to use SiC JFETs transistors at higher temperatures. We present the different die attachments tested with high temperature lead solder and silver sintering joining technologies. Active power cycling results for high junction temperature Tjmax=175°C with "Tj=80°K to perform an evaluation of main damages during active test are carried out and a comparison between lead and silver chip joining technologies is presented