AIT Austrian Institute of Technology

Antiresonant hollow-core fibers are a novel type of optical fiber with unparalleled characteristics, unattainable with solid-core fibers. In particular, antiresonant hollow-core fibers are characterized by vacuum-like propagation speed, suppressed dispersion and nonlinear optical effects and, recently, even ultra-low propagation loss. In recent years, the development of a type of antiresonant hollow-core fiber, called Nested Antiresonant Nodeless Fiber (NANF) has seen tremendous improvements, and applications in both classical and quantum communications have been suggested. While encouraging results over meter long distances have been shown, the distribution of polarized entangled photons over inner-city distances through antiresonant hollow-core fibers has not been demonstrated yet. This could be a game changer for the development of quantum networks, which leverage entanglement for the distribution of secret keys, and more in the long term, in a quantum internet scenario. In this work, an experimental investigation of entanglement distribution through a NANF with an overall length of 7.72 km is presented. Remarkably, substantial reduction of latency (about 13μs) and suppressed chromatic dispersion (about one order of magnitude) of the studied NANF compared to a telecom single-mode fiber (Corning's SMF28) of equal length are measured for different bandwidths of the distributed entangled photons. Moreover, by encoding entanglement in polarization, high fidelity (>95%) distribution of narrow-bandwidth entangled photons is demonstrated. This result paves the way to the exploitation of NANF as a superior transmitting medium for quantum technology applications relying on the distribution of entanglement encoded in polarization over inner-city distances.

Systematically including dynamically changing waypoints as desired discrete actions, for instance, resulting from Systematically including dynamically changing waypoints as desired discrete actions, for instance, resulting from superordinate task planning, has been challenging for online model predictive trajectory optimization with short planning horizons. This paper presents a novel waypoint model predictive control (wMPC) concept for online replanning tasks. The main idea is to split the planning horizon at the waypoint when it becomes reachable within the current planning horizon and reduce the horizon length towards the waypoints and goal points. This approach keeps the computational load low and provides flexibility in adapting to changing conditions in realtime. The presented approach achieves competitive path lengths and trajectory durations compared to (global) offline RRTtype planners, VP-STO, and tracking MPC in a multi-waypoint scenario. Moreover, the ability of wMPC to dynamically replan tasks online is experimentally demonstrated on a KUKA LBR iiwa 14 R820 robot in a dynamic pick-and-place scenario.

Innovation networks play a key role in advancing knowledge transfer, collaboration, and technological progress across sectors and regions. Central to the understanding of the mechanisms driving such networks is their dynamic evolution and structure. Much of the literature explores spatial and socio-economic drivers of innovation networks, focusing on geographic, institutional, and cultural influences. However, many of these studies tend to overlook the intricate properties that govern the behavior and dynamics of these networks. This study seeks to address this research gap, delving deeper by investigating Chinese intercity innovation networks between 2007 and 2018. Specifically, we examine: (i) the preferential attachment dynamics within intercity innovation networks, (ii) transitivity effects that underscore the interconnectedness of these networks, and (iii) the persistence and recurrence of connections. We find that cities show indeed a remarkable tendency to form ties with others that already have numerous connections. Such transitivity effects are important in highlighting the formation of innovation clusters. Moreover, the influence of link memory suggests that past collaborations significantly determine future partnerships, similar to the persistent nature of relationships in agglomeration theories.

Microorganisms interact with plant roots through colonization of the root surface, i.e., the rhizoplane or the surrounding soil, i.e., the rhizosphere. Beneficial rhizosphere bacteria such as Pseudomonas spp. can promote plant growth and protect against pathogens by producing a range of bioactive compounds, including specialized metabolites like cyclic lipopeptides (CLPs) known for their biosurfactant and antimicrobial activities. However, the role of CLPs in natural soil systems during bacteria-plant interactions is underexplored. Here, Pseudomonas fluorescens SBW25, producing the CLP viscosin, was used to study the impact of viscosin on bacterial root colonization and microbiome assembly in two cultivars of winter wheat (Heerup and Sheriff). We inoculated germinated wheat seeds with SBW25 wild type or a viscosin-deficient mutant and grew the plants in agricultural soil. After 2 weeks, enhanced root colonization of SBW25 wild type compared to the viscosin-deficient mutant was observed, while no differences were observed between wheat cultivars. In contrast, the impact on root-associated microbial community structure was plant-genotype-specific, and SBW25 wild type specifically reduced the relative abundance of an unclassified oomycete and Phytophthora in Sheriff and Heerup, respectively. This study provides new insights into the natural role of viscosin and specifically highlights the importance of viscosin in wheat root colonization under natural soil conditions and in shaping the root microbial communities associated with different wheat cultivars. Furthermore, it pinpoints the significance of microbial microdiversity, plant genotype, and microbe-microbe interactions when studying colonization of plant roots. IMPORTANCE Understanding parameters governing microbiome assembly on plant roots is critical for successfully exploiting beneficial plant-microbe interactions for improved plant growth under low-input conditions. While it is well-known from in vitro studies that specialized metabolites are important for plant-microbe interactions, e.g., root colonization, studies on the ecological role under natural soil conditions are limited. This might explain the often-low translational power from laboratory testing to field performance of microbial inoculants. Here, we showed that viscosin synthesis potential results in a differential impact on the microbiome assembly dependent on wheat cultivar, unlinked to colonization potential. Overall, our study provides novel insights into factors governing microbial assembly on plant roots, and how this has a derived but differential effect on the bacterial and protist communities.

Nitrogen (N) is a vital nutrient and an essential component of biological macromolecules, such as nucleic acids and proteins. Microorganisms represent major drivers of N-cycling processes in all ecosystems, including the soil and plant environment. The availability of N is a major growth limiting factor for plants and it is significantly affected by the plant microbiome. Plants and microorganisms form complex interaction networks resulting in molecular signaling, nutrient exchange and other distinct metabolic responses. In these networks, microbial partners influence growth and N use efficiency of plants either positively or negatively. Harnessing the beneficial effects of specific players within crop microbiomes is a promising strategy to counteract the emerging threats for human and planetary health due to the overuse of industrial N fertilizers. However, in addition to N-providing activities (e.g. the well-known symbiosis of legumes and Rhizobium bacteria), other plant-microorganism interactions must be considered to obtain a complete picture of how microbial driven N-transformations might affect plant nutrition. For this, we review recent insights into the tight interplay between plants and N-cycling microorganisms focusing on microbial N-transformation processes representing N sources and sinks that ultimately shape the plant N acquisition.

Recognizing multiple residents’ activities is a pivotal domain within active and assisted living technologies, where the diversity of actions in a multi-occupant home poses a challenge due to their uneven distribution. Frequent activities contrast with those occurring sporadically, necessitating adept handling of class imbalance to ensure the integrity of activity recognition systems based on raw sensor data. While deep learning has proven its merit in identifying activities for solitary residents within balanced datasets, its application to multi-resident scenarios requires careful consideration. This study provides a comprehensive survey on the issue of class imbalance and explores the efficacy of Long Short-Term Memory and Bidirectional Long Short-Term Memory networks in discerning activities of multiple residents, considering both individual and aggregate labeling of actions. Through rigorous experimentation with data-level and algorithmic strategies to address class imbalances, this research scrutinizes the explicability of deep learning models, enhancing their transparency and reliability. Performance metrics are drawn from a series of evaluations on three distinct, highly imbalanced smart home datasets, offering insights into the models’ behavior and contributing to the advancement of trustworthy multi-resident activity recognition systems.

Die Unvorhersehbarkeit von Zufallszahlen findet in verschiedenen Bereichen Anwendung, z. B. bei Lotterien, wissenschaftlichen Simulationen und grundlegenden physikalischen Tests. Am offensichtlichsten ist ihre Anwendung jedoch in kryptografischen Protokollen, die zwangsläufig Zufallszahlengeneratoren zur Erzeugung von Seeds, anfänglichen Zufallswerten, Nonces (Salts), Blinding-Werten und Padding-Bytes enthalten. Um für solche Aufgaben verwendet werden zu können, müssen Zahlengeneratoren bestimmte Kriterien erfüllen, um die Sicherheit des kryptografischen Protokolls zu gewährleisten. Dies bezieht sich in erster Linie auf die Unvorhersehbarkeit der generierten Zahlenwerte, selbst wenn der Angreifer das Design des Zufallszahlengenerators kennt. Im Gegensatz zu deterministischen Zufallszahlengeneratoren, die Zufallswerte mit einer Entropie erzeugen, die durch die Entropie des anfänglichen Seeds begrenzt ist, betrachten wir in diesem Kapitel nichtdeterministische Zufallszahlengeneratoren, die sich bei der Erzeugung von Zufallszahlen auf den Quantenzustand der Materie verlassen. Nichtdeterministische Zufallszahlengeneratoren verwenden verschiedene Techniken wie radioaktiven Zerfall, Schrotrauschen in Halbleitern, Photonen und andere

Diese Einführung beschreibt eines der wichtigsten Elemente für die Erzeugung quantenmechanischer Zufallszahlen – den Strahlteiler (BS). Ein BS ist ein passives Element zur Aufteilung von Lichtstrahlen (z. B. Laserstrahlen) in zwei ausgehende Strahlen.

Titanium alloys with high stiffness are crucial for aerospace engineering and are often fabricated using additive manufacturing (AM) methods like arc or laser techniques. These high energy processes alter the microstructure and mechanical properties. To enhance stiffness, TiC and B4C are added to Ti–6Al–4V and Ti–8Al–1Mo–1V alloys via powder hot extrusion. The resulting metal matrix composites (MMCs) are analyzed in both their as‐extruded and heat‐treated states for microstructure and mechanical properties. To simulate an AM process, samples are remelted using a gas tungsten arc‐welding (GTAW) torch and examined. In the results, it is shown that TiC and B4C increased mechanical properties up to 2 GPa cm³ g⁻¹, with the highest increase observed in heat‐treated B4C samples, achieving specific stiffnesses of 34.6 GPa cm³ g⁻¹ (Ti–6Al–4V) and 32.3 GPa cm³ g⁻¹ (Ti–8Al–1Mo–1V). Powder hot extrusion proves effective in producing Ti–MMCs with high stiffness even with reactive ceramic additions. However, GTAW remelting leads to the decomposition of TiC‐reinforced Ti–MMCs, significantly altering morphology and reducing stiffness below that of the base alloy.

Hydrogels are of great importance for functionalizing sensors and microfluidics, and poly(ethylene glycol) dimethacrylate (PEG-DMA) is often used as a viscosifier for printable hydrogel precursor inks. In this study, 1–10 kDa PEG-DMA based hydrogels were characterized by gravimetric and electrochemical methods to investigate the diffusivity of small molecules and proteins. Swelling ratios (SRs) of 14.43–9.24, as well as mesh sizes ξ of 3.58–6.91 nm were calculated, and it was found that the SR correlates with the molar concentration of PEG-DMA in the ink (MCI) (SR = 0.1127 × MCI + 8.3256, R2 = 0.9692) and ξ correlates with the molecular weight (Mw) (ξ = 0.3382 × Mw + 3.638, R2 = 0.9451). To investigate the sensing properties, methylene blue (MB) and MB-conjugated proteins were measured on electrochemical sensors with and without hydrogel coating. It was found that on sensors with 10 kDa PEG-DMA hydrogel modification, the DPV peak currents were reduced to 92 % for MB, 73 % for MB-BSA, and 23 % for MB-IgG. To investigate the diffusion properties of MB(-conjugates) in hydrogels with 1–10 kDa PEG-DMA, diffusivity was calculated from the current equation. It was found that diffusivity increases with increasing ξ. Finally, the release of MB-BSA was detected after drying the MB-BSA-containing hydrogel, which is a promising result for the development of hydrogel-based reagent reservoirs for biosensing.

State of the art classical and quantum communications rely on standard optical fibers with solid cores to transmit light over long distances. However, recent advances have led to the emergence of antiresonant hollow-core optical fibers (AR-HCFs), which, due to the novel fiber geometry, show remarkable optical guiding properties, which are not as limited by the material properties as solid-core fibers. In this paper, we explore the transmission of entangled photons through a novel 7.7 km AR-HCF in a laboratory environment at 1550 nm, presenting the first successful demonstration of entanglement distribution via a long AR-HCF. In addition to showing these novel fibers are compatible with long distance quantum communication, we highlight the low latency and low chromatic dispersion intrinsic to AR-HCF, which can increase the secure key rate in time-bin-based quantum key distribution protocols.

Vascular ageing is the deterioration of arterial structure and function which occurs naturally with age, and which can be accelerated with disease. Measurements of vascular ageing are emerging as markers of cardiovascular risk, with potential applications in disease diagnosis and prognosis, and for guiding treatments. However, vascular ageing is not yet routinely assessed in clinical practice. A key step towards this is the development of technologies to assess vascular ageing. In this Roadmap, experts discuss several aspects of this process, including: measurement technologies; the development pipeline; clinical applications; and future research directions. The Roadmap summarises the state of the art, outlines the major challenges to overcome, and identifies potential future research directions to address these challenges.

Small cities and towns often struggle to provide high-quality public transport services to daily commuters. This is reflected in the modal split, where the share of car users dominates. Such a problem requires a modern solution, where transport planners can verify the impact of potential transport network improvements on the travel behavior of the residents before the changes are actually deployed. This study aims to demonstrate the usefulness of employing an agent-based simulation tool in the decision process for redesigning an express service regional bus route connecting a network of small cities and towns. The model was initially developed as a Mobility as a Service simulation solution for suburban areas of European metropolises. The model is adapted and applied to a case study for the region of Agder, Norway, simulating the impact of nine different scenarios on the patronage of a specific bus route. The simulation model proposes to upgrade the classic agent structure to a persona profile designed specifically for the case study. The main objective of this research is to identify the scenario that maximizes patronage while minimizing total route travel time and additional costs. The results suggest that the proposed model can be successfully adapted from suburban metropolitan areas to the realities of the considered case study, and potentially other similar regions. Specifically, out of the nine proposed scenarios, the model identified four promising ones. One of the four scenarios also fits the cost constraints imposed by the transport provider. The model provides a solid approach for analyzing complex transport systems that are practically impossible to consider in detail if the analysis is done without computer support. Thus, the results can be used as a decision support system for public transport planning and operations in networks of small cities and towns.