Room-temperature generation of heralded single photons on silicon chip with switchable orbital angular momentum

In quantum optics, orbital angular momentum (OAM) is very promising to achieve high-dimensional quantum states due to the nature of infinite and discrete eigenvalues, which is quantized by the topological charge of l. Here, a heralded single-photon source with switchable OAM modes is proposed and demonstrated on silicon chip. At room-temperature, the heralded single photons with 11 OAM modes (l=2~6, -6~-1) have been successfully generated and switched through thermo-optical effect. We believe that such an integrated quantum source with multiple OAM modes and operating at room-temperature would provide a practical platform for high-dimensional quantum information processing. Moreover, our proposed architecture can also be extended to other material systems to further improve the performance of OAM quantum source.

Investigation of the influence of varying tumbling strategies on a tumbling self-piercing riveting process

The increasing demands for the reduction of carbon dioxide emission require intensified efforts to increase resource efficiency. Especially in the mobility sector with large moving masses, resource savings can contribute enormously to the reduction of emissions. One possibility is to reduce the weight of the vehicles by using lightweight technologies. A frequently used method is the implementation of multi-material systems. These consist of dissimilar materials such as steel, aluminium or plastics. In the production of these systems, the joining of the different materials and geometries is a central challenge. Due to the increasing demands on the joints, the challenges for the joining processes itself are also increasing. Since conventional joining processes are rather rigid and can only react to a limited extent to disturbance variables or changing process variables, new methods and technologies are required. A widely used conventional joining method with these properties is self-piercing riveting. Because of the rigid tool combination and the fact that the rivet geometry that can be used is related to the tools, the joining of multi-material systems requires tool and rivet changes during the process. In order to extend the process window of joining with self-piercing rivet elements, the process is enhanced with a tumbling kinematic of the punch. The integration of tumbling results in a significant increase in the adjustable process parameters. This enables a higher material flow control in the joining process through a specific tumbling strategy. The materials investigated are a steel and an aluminium alloy, which differ significantly in their mechanical properties and have many applications in automotive engineering, especially for structural car body components. The steel material is a galvanized HCT590X+Z dual-phase steel, which is characterised by a low yield strength, combined with high tensile strength and a good hardening behaviour. The aluminium alloy is an EN AW-6014. The precipitation-hardening alloy consists of aluminium, magnesium and silicon with a high strength and energy absorption capability. The objective of this work is to obtain a fundamental knowledge of the new tumbling self-piercing riveting process. With different mechanical properties and different sheet thicknesses of the joining partners, the influences of these parameters on the tumbling strategy of the riveting process are analysed. Such a tumbling strategy is based on the tumbling angle, the tumbling onset and the tumbling kinematics. These parameters are investigated in the context of the work for selected combinations of multi-material systems consisting of HCT590X+Z and EN AW-6014. With the variation of the parameters, the versatility of the process can be investigated and influences of the tumbling on the self-piercing riveting process can be identified. To illustrate the results, force–displacement curves from the joining process of the individual joints are compared and the geometry of the rivet undercut and rivet heads are geometrically measured. Furthermore, micrographs allow the analysis of the characteristic joint parameters interlock, residual sheet thickness and end position of the rivet head.

Progress in simulating human geography: Assemblage theory and the practice of multi-agent artificial intelligence modeling

Over the last few years, there has been an explosion of interest in assemblage theory among human geographers. During this same period, a growing number of scholars in the field have utilized computational methodologies to simulate the complex adaptive systems they study. However, very little attention has been paid to the connections between these two developments. This article outlines those connections and argues that more explicitly integrating assemblage theory and computer modeling can encourage a more robust philosophical understanding of both and facilitate progress in scientific research on the ways in which complex socio-material systems form and transform.

METAPHYSICS AND INTERDISCIPLINARY MODELS

Metaphysical numerical methods of self-organization of natural systems of Nature, their interdisciplinary connections and models are investigated. They are confirmed by a number of examples and facts, predict informational beginnings and the sequence of formation of material systems of Nature. The facts relate to the chemical elements of the Universe, plants and living systems in health and disease. Their structural periods of self-organization coincide or have common roots. Systems have a “end-to-end” similarity of everything with everything on the basis of the principle of self-similarity and unlimited two-way connection of structural parameters. It is shown that the Abelian Group, the basis of self-organization of systems, allows you to systematize models based on the unity of their origins. The concept of natural self-organization of systems predicts the chemical elements of the Universe and the existence (or appearance) of other civilizations in the world under similar external conditions.

On the Foundation of Space and Time by Quantum-Events

The true nature of space and time has been a topic of natural philosophy, passed down since the presocratic era. In modern times reflection has particularly been inspired by the physical theories of Newton and Einstein and, more recently, by the quest for a theory of quantum gravity. In this paper we want to specify the idea that material systems and their spatio-temporal distances emerge from quantum-events. We will show a mechanism, by which quantum-events induce a metric field between material systems, which is governed by Einstein's equation including a cosmological constant.

Features of the MBE growth of nanowires with quantum dots on the silicon surface

The development of a new semiconductor element base is necessary to create a new generation of applications. At present time, the synthesis of high-quality hybrid nanostructures based on III-V quantum dots in the body of nanowires of a wide range of material systems is an urgent and important task. In work hybrid III-V nanostructures based on QDs in the body of NWs in GaP/GaAs and AlGaP/InGaP material systems were synthesized in on silicon substrates and their physical properties were investigated.

Rapid and flexible segmentation of electron microscopy data using few-shot machine learning

Automatic segmentation of key microstructural features in atomic-scale electron microscope images is critical to improved understanding of structure–property relationships in many important materials and chemical systems. However, the present paradigm involves time-intensive manual analysis that is inherently biased, error-prone, and unable to accommodate the large volumes of data produced by modern instrumentation. While more automated approaches have been proposed, many are not robust to a high variety of data, and do not generalize well to diverse microstructural features and material systems. Here, we present a flexible, semi-supervised few-shot machine learning approach for segmentation of scanning transmission electron microscopy images of three oxide material systems: (1) epitaxial heterostructures of SrTiO3/Ge, (2) La0.8Sr0.2FeO3 thin films, and (3) MoO3 nanoparticles. We demonstrate that the few-shot learning method is more robust against noise, more reconfigurable, and requires less data than conventional image analysis methods. This approach can enable rapid image classification and microstructural feature mapping needed for emerging high-throughput characterization and autonomous microscope platforms.