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Optimization-based process synthesis by phenomena-based building blocks and an MINLP framework featuring structural screening
No full textAn existing approach for optimization-based process synthesis with abstracted phenomena-based building blocks (PBB) is extended by implementing it into a novel MINLP framework with structural screening. Consistency across the multilayer MINLP framework is guaranteed by creating a MathML/XML data model and subsequently exporting the code to the different program parts. The novel framework focuses both on fidelity by implementing thermodynamically sound models and on generality by employing a state-space superstructure that spans a large search space. In order to retain tractability, we insert a structural screening layer which pre-screens based on binary decision variables of the superstructure by graph- and rule-based analyses, penalizing non-physical instances without solution of the underlying MINLP. The MINLP framework is successfully applied on two challenging synthesis tasks to determine the separation of the feed streams of benzene and toluene, as well as of n-pentane, n-hexane, and n-heptane utilizing superstructures with two, respectively four PBB
Unique Information Through the Lens of Channel Ordering: An Introduction and Review
No full textThe problem of constructing information measures with a well-defined interpretation is of fundamental significance in information theory. A good definition of an information measure entails certain desirable properties while also providing answers to operational problems. In this work, we investigate the properties of the unique information, an information measure that quantifies a deviation from the Blackwell order. Beyond providing an accessible introduction to the topic from a channel ordering perspective, we present a novel resource-theoretic characterization of unique information in a cryptographic task related to secret key agreement. Our operational view of unique information entails rich physical intuition that leads to new insights into secret key agreement in the context of non-negative decompositions of the mutual information into redundant and synergistic contributions. Through this lens, we illuminate new directions for research in partial information decompositions and information-theoretic cryptography
Special issue editorial: advanced partial least squares structural equation modeling (PLS-SEM) applications in business research
No full textCost analysis of kerosene production from power-based syngas via the Fischer-Tropsch and methanol pathway
No full textCurrent estimates for power-based kerosene production costs are up to ten times higher than conventional, fossil fuel-based kerosene prices. Therefore, successful market integration necessitates a thorough understanding of the cost structure and the key factors influencing kerosene production costs. This paper provides an extensive cost analysis of power-based kerosene production comparing two different plant concepts, one using the Fischer-Tropsch synthesis and hydrotreatment (FT pathway), the other applying direct methanol synthesis with downstream dehydration and oligomerization (MeOH pathway). Two cost allocation methods are applied to address uncertainties associated with unpredictable by-product revenues: allocating costs solely to the kerosene fraction, without considering by-product revenues, establishes the upper cost limit, while allocating costs at the total fuel fraction, defines the lower cost boundary. For these two cases, possible cost ranges are evaluated by varying technical and economic frame conditions. For the “total fuel allocation”, the FT pathway yields lower kerosene production cost than the methanol pathway (FT: 3,630 €/t, MeOH: 4,240 €/t). But contrarily for the “kerosene allocation”, the MeOH pathway shows lower cost (FT: 5,070 €/t, MeOH: 4,660 €/t). By-product revenue variation indicates benefits for the FT pathway if naphtha prices above 30 % of the kerosene production cost can be achieved. In all cases, costs are mainly affected by the supply of H2 and CO2; thus, feedstock conversion efficiency is the most important factor determining the production costs besides feedstock prices. While variations in the H2 price (3 to 7 €/kg) significantly influence kerosene production costs for both pathways (ca. ± 25 %), CO2 prices at the level of CO2 supply costs from DAC (1,000 €/t) can lead to even higher cost increases of up to 75 % compared to CO2 prices related to carbon capture costs from point sources (150 €/t). Thus, this analysis provides novel insights into the cost composition and the most important influencing parameters for the two most widely discussed production pathways for power-based kerosene production and enables comparison and assessment of production costs under different framework conditions
Envy-Free Dynamic Pricing Schemes
No full textIn combinatorial markets, the goal is typically to determine a pair of pricing and allocation of items that results in an efficient distribution of resources or maximizes the seller’s profit. In dynamic pricing schemes, agents arrive in an unspecified sequential order, and the prices can be updated between agent arrivals, which makes the concept of fairness of dynamic prices highly nontrivial. In markets with expected price deflation, a typical agent follows the prices prior to their purchase and become price insensitive after, whereas the opposite happens in markets with expected price inflation. To properly address these differences, we study the existence of optimal dynamic prices under fairness constraints in unit-demand markets. We propose five possible notions of envy freeness, depending on the period over which agents compare themselves to others: the entire time horizon, only the past, only the future, a mixture of the two, or only the present. For social welfare maximization, we give polynomial-time algorithms that always find envy-free optimal dynamic prices. For revenue maximization, we show that the corresponding problems are APX-hard if the ordering of the agents is fixed but are tractable when the seller can choose the ordering
Compressive behavior and connecting topology of monolithic nanoporous niobium
No full textWith an eye on the role of structure size and topology, this study explores the mechanical behavior of nanoporous (NP) Nb made by liquid-metal dealloying. Results from X-ray nanotomography and macro-compression tests confirm that coarsening degrades the yield strength and that Young's modulus deviates from scaling laws developed for NP Au made by dealloying in aqueous media. We find that the scaled genus of NP Nb is lower than what has been reported for NP Au, and this low connectivity provides an obvious explanation for the low modulus. Furthermore, the structural dispersion implies that additional structural descriptors should be acknowledged
Deep Learning for High Speed Optical Coherence Elastography with a Fiber Scanning Endoscope
No full textTissue stiffness is related to soft tissue pathologies and can be assessed through palpation or via clinical imaging systems, e.g., ultrasound or magnetic resonance imaging. Typically, the image based approaches are not suitable during interventions, particularly for minimally invasive surgery. To this end, we present a miniaturized fiber scanning endoscope for fast and localized elastography. Moreover, we propose a deep learning based signal processing pipeline to account for the intricate data and the need for real-time estimates. Our elasticity estimation approach is based on imaging complex and diffuse wave fields that encompass multiple wave frequencies and propagate in various directions. We optimize the probe design to enable different scan patterns. To maximize temporal sampling while maintaining three-dimensional information we define a scan pattern in a conical shape with a temporal frequency of 5.05 kHz. To efficiently process the image sequences of complex wave fields we consider a spatio-temporal deep learning network. We train the network in an end-to-end fashion on measurements from phantoms representing multiple elasticities. The network is used to obtain localized and robust elasticity estimates, allowing to create elasticity maps in real-time. For 2D scanning, our approach results in a mean absolute error of 6.31 ± 5.76 kPa compared to 11.33 ± 12.78 kPa for conventional phase tracking. For scanning without estimating the wave direction, the novel 3D method reduces the error to 4.48 ± 3.63 kPa compared to 19.75 ± 21.82 kPa for the conventional 2D method. Finally, we demonstrate feasibility of elasticity estimates in ex-vivo porcine tissue
Data-driven, non-linear ship response prediction based on time series of irregular, long-crested sea states amidships
No full textThe accurate prediction of vessel responses in waves is crucial for decision-making and contribute to the operational safety and risk minimization. Short-term predictions can be carried out by estimating the vessel's motions and loads based on incident waves. Existing model-based approaches either require computationally intensive simulations that compromise real-time capability or use simplified models affecting the accuracy of the prediction. Therefore, this study explores the feasibility of using neural networks for mapping time signals of surface elevation data and a set of corresponding ship responses, i.e. the heave and pitch motions as well as the vertical bending moment. The approach followed here is built on the assumption that the wave profile amidships is known. A synthetic dataset was generated using a time-domain strip theory solver with considerations of non-linear effects on motions and loads due to large amplitude waves in a variety of irregular, long-crested sea state conditions. We propose two different neural network models, a multi-layer perceptron (MLP) and a fully convolutional neural network (FCNN), and compare their performances on measurement data obtained from model tests in a seakeeping basin. The evaluations also include the freak wave reproduction of the ‘new year wave’. The proposed networks are able to estimate the motions and bending moment accurately for a wide range of sea state conditions, surpassing current state-of-the-art models on the given data sets
Quantitative analysis of periprocedural thrombus fragmentation using an automated optical detection system in a comprehensive stroke intervention training platform
No full textMechanical thrombectomy for occluded large brain vessels in acute ischemic stroke has proven highly effective, but periprocedural thrombus fragmentation (PTF) remains a significant challenge that can lead to downstream embolization and incomplete recanalization. While laboratory methods exist for detailed PTF analysis, practical solutions are needed for quantitative assessment of simulated thrombectomy procedures performed on physical training models. We present a novel measurement system that enables automated detection and quantification of thrombus fragmentation events during simulated thrombectomy procedures on the HANNES neurointerventional simulator. The system employs a six-channel measurement chamber with integrated UV illumination and an optical detection setup to track fluorescent thrombus fragments. Fragments are automatically detected, assigned IDs, and measured in real-time using computer vision techniques. Validation studies comparing the system's measurements to microscopic analysis demonstrated strong correlation (Pearson's r=0.9939, p<0.001). In controlled testing, the system achieved 100% sensitivity and specificity for single fragment detection, successfully measuring fragments as small as 642 μm in radius. While adhering fragments are currently treated as single entities, the system's ability to automatically track and quantify PTF events in real-time provides an objective basis for evaluating procedural performance and comparing thrombectomy techniques in training scenarios. This novel measurement system represents a practical advance for studying thrombus fragmentation in educational settings. Integration into comprehensive training platforms like HANNES could improve understanding and management of PTF risks, potentially leading to better patient outcomes through enhanced operator training. Future studies correlating PTF metrics with physician experience and clinical results may establish performance benchmarks to help quantify individual proficiency and predict clinical competency
Technologien zur Kraftstoffbereitstellung
No full textIm neuen Monitoringbericht „Erneuerbare Energien im Verkehr“ wird der Status quo der Energiewende im Verkehr dargestellt. Dabei stehen erneuerbare biomasse und strombasierte Kraft stoffe sowie erneuerbarer Strom im Kontext sich ändernder Rahmenbedingungen im Fokus. Einführend wird ein Ausblick auf die Energiewende im Verkehr beschrieben, dabei werden der zu künftige Bedarf an erneuerbaren Energieträgern und deren Bereitstellung gegenübergestellt. Darüber hinaus werden der aktuelle Stand im Verkehr und dessen Infrastruktur sowie der derzeitige rechtliche Rahmen und die grundlegenden politischen Zielstellungen für erneuerbare Energien sowie die wesentlichen Schritte in der Bereitstellungs und Nutzungskette von erneuerbaren Energien dargelegt, gefolgt von einer ökologischen und ökonomischen Einordnung. Der Bericht ist eine Fortsetzung und Erweiterung der bisherigen DBFZ Reports Nr. 11 (Monitoring Biokraftstoffsektor [Naumann (2019)] und Nr. 44 (Monitoring erneuerbarer Energien im Verkehr) [Schröder (2022)] in neuem Layout. Der Monitoringbericht wird nur digital bereitgestellt, einzelne Abbildungen können zusätzlich von der zugehörigen Webseite heruntergeladen werden