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Strain Engineered Electrically Pumped SiGeSn Microring Lasers on Si
SiGeSn holds great promise for enabling fully group-IV integrated photonics operating at wavelengths extending in the mid-infrared range. Here, we demonstrate an electrically pumped GeSn microring laser based on SiGeSn/GeSn heterostructures. The ring shape allows for enhanced strain relaxation, leading to enhanced optical properties, and better guiding of the carriers into the optically active region. We have engineered a partial undercut of the ring to further promote strain relaxation while maintaining adequate heat sinking. Lasing is measured up to 90 K, with a 75 K T0. Scaling of the threshold current density as the inverse of the outer circumference is linked to optical losses at the etched surface, limiting device performance. Modeling is consistent with experiments across the range of explored inner and outer radii. These results will guide additional device optimization, aiming at improving electrical injection and using stressors to increase the bandgap directness of the active material.
Collaborative annotation and semantic enrichment of 3D media
A new FOSS (free and open source software) toolchain and associated workflow is being developed in the context of NFDI4Culture, a German consortium of research- and cultural heritage institutions working towards a shared infrastructure for research data that meets the needs of 21st century data creators, maintainers and end users across the broad spectrum of the digital libraries and archives field, and the digital humanities. This short paper and demo present how the integrated toolchain connects: 1) OpenRefine - for data reconciliation and batch upload; 2) Wikibase - for linked open data (LOD) storage; and 3) Kompakkt - for rendering and annotating 3D models. The presentation is aimed at librarians, digital curators and data managers interested in learning how to manage research datasets containing 3D media, and how to make them available within an open data environment with 3D-rendering and collaborative annotation features.
Meetings and Mood-Related or Not? Insights from Student Software Projects
[Background:] Teamwork, coordination, and communication are a prerequisite for the timely completion of a software project. Meetings as a facilitator for coordination and communication are an established medium for information exchange. Analyses of meetings in software projects have shown that certain interactions in these meetings, such as proactive statements followed by supportive ones, influence the mood and motivation of a team, which in turn affects its productivity. So far, however, research has focused only on certain interactions at a detailed level, requiring a complex and fine-grained analysis of a meeting itself. [Aim:] In this paper, we investigate meetings from a more abstract perspective, focusing on the polarity of the statements, i.e., whether they appear to be positive, negative, or neutral. [Method:] We analyze the relationship between the polarity of statements in meetings and different social aspects, including conflicts as well as the mood before and after a meeting. [Results:] Our results emerge from 21 student software project meetings and show some interesting insights: (1) Positive mood before a meeting is both related to the amount of positive statements in the beginning, as well as throughout the whole meeting, (2) negative mood before the meeting only influences the amount of negative statements in the first quarter of the meeting, but not the whole meeting, and (3) the amount of positive and negative statements during the meeting has no influence on the mood afterwards. [Conclusions:] We conclude that the behaviour in meetings might rather influence short-term emotional states (feelings) than long-term emotional states (mood), which are more important for the project.
Green transition, investment horizon, and dynamic portfolio decisions
This paper analyzes the implications of investors’ short-term oriented asset holding and portfolio decisions (or short-termism), and its consequences on green investments. We adopt a dynamic portfolio model, which contrary to conventional static mean-variance models, allows us to study optimal portfolios for different decision horizons. Our baseline model contains two assets, one asset with fluctuating returns and another asset with a constant risk-free return. The asset with fluctuating returns can arise from fossil-fuel based sectors or from clean energy related sectors. We consider different drivers of short-termism: the discount rate, the nature of discounting (exponential vs. hyperbolic), and the decision horizon of investors itself. We study first the implications of these determinants of short-termism on the portfolio wealth dynamics of the baseline model. We find that portfolio wealth declines faster with a higher discount rate, with hyperbolic discounting, and with shorter decision horizon. We extend our model to include a portfolio of two assets with fluctuating returns. For both model variants, we explore the cases where innovation efforts are spent on fossil fuel or clean energy sources. Detailing dynamic portfolio decisions in such a way may allow us for better pathways to empirical tests and may provide guidance to some online financial decision making.
Melt Electrowriting of Graded Porous Scaffolds to Mimic the Matrix Structure of the Human Trabecular Meshwork
The permeability of the human trabecular meshwork (HTM) regulates eye pressure via a porosity gradient across its thickness modulated by stacked layers of matrix fibrils and cells. Changes in HTM porosity are associated with increases in intraocular pressure and the progress of diseases such as glaucoma. Engineered HTMs could help to understand the structure-function relation in natural tissues and lead to new regenerative solutions. Here, melt electrowriting (MEW) is explored as a biofabrication technique to produce fibrillar, porous scaffolds that mimic the multilayer, gradient structure of native HTM. Poly(caprolactone) constructs with a height of 125-500 μm and fiber diameters of 10-12 μm are printed. Scaffolds with a tensile modulus between 5.6 and 13 MPa and a static compression modulus in the range of 6-360 kPa are obtained by varying the scaffold design, that is, the density and orientation of the fibers and number of stacked layers. Primary HTM cells attach to the scaffolds, proliferate, and form a confluent layer within 8-14 days, depending on the scaffold design. High cell viability and cell morphology close to that in the native tissue are observed. The present work demonstrates the utility of MEW for reconstructing complex morphological features of natural tissues.
TinyGenius: Intertwining natural language processing with microtask crowdsourcing for scholarly knowledge graph creation
As the number of published scholarly articles grows steadily each year, new methods are needed to organize scholarly knowledge so that it can be more efficiently discovered and used. Natural Language Processing (NLP) techniques are able to autonomously process scholarly articles at scale and to create machine readable representations of the article content. However, autonomous NLP methods are by far not sufficiently accurate to create a high-quality knowledge graph. Yet quality is crucial for the graph to be useful in practice. We present TinyGenius, a methodology to validate NLP-extracted scholarly knowledge statements using microtasks performed with crowdsourcing. The scholarly context in which the crowd workers operate has multiple challenges. The explainability of the employed NLP methods is crucial to provide context in order to support the decision process of crowd workers. We employed TinyGenius to populate a paper-centric knowledge graph, using five distinct NLP methods. In the end, the resulting knowledge graph serves as a digital library for scholarly articles.
Comparative Verification of the Digital Library of Mathematical Functions and Computer Algebra Systems
Digital mathematical libraries assemble the knowledge of years of mathematical research. Numerous disciplines (e.g., physics, engineering, pure and applied mathematics) rely heavily on compendia gathered findings. Likewise, modern research applications rely more and more on computational solutions, which are often calculated and verified by computer algebra systems. Hence, the correctness, accuracy, and reliability of both digital mathematical libraries and computer algebra systems is a crucial attribute for modern research. In this paper, we present a novel approach to verify a digital mathematical library and two computer algebra systems with one another by converting mathematical expressions from one system to the other. We use our previously developed conversion tool (referred to as ) to translate formulae from the NIST Digital Library of Mathematical Functions to the computer algebra systems Maple and Mathematica. The contributions of our presented work are as follows: (1) we present the most comprehensive verification of computer algebra systems and digital mathematical libraries with one another; (2) we significantly enhance the performance of the underlying translator in terms of coverage and accuracy; and (3) we provide open access to translations for Maple and Mathematica of the formulae in the NIST Digital Library of Mathematical Functions.
Data Protection Impact Assessments in Practice: Experiences from Case Studies
In the context of the project A Data Protection Impact Assessment (DPIA) Tool for Practical Use in Companies and Public Administration an operationalization for Data Protection Impact Assessments was developed based on the approach of Forum Privatheit. This operationalization was tested and refined during twelve tests with startups, small- and medium sized enterprises, corporations and public bodies. This paper presents the operationalization and summarizes the experience from the tests.
PowerDuck: A GOOSE Data Set of Cyberattacks in Substations
Power grids worldwide are increasingly victims of cyberattacks, where attackers can cause immense damage to critical infrastructure. The growing digitalization and networking in power grids combined with insufficient protection against cyberattacks further exacerbate this trend. Hence, security engineers and researchers must counter these new risks by continuously improving security measures. Data sets of real network traffic during cyberattacks play a decisive role in analyzing and understanding such attacks. Therefore, this paper presents PowerDuck, a publicly available security data set containing network traces of GOOSE communication in a physical substation testbed. The data set includes recordings of various scenarios with and without the presence of attacks. Furthermore, all network packets originating from the attacker are clearly labeled to facilitate their identification. We thus envision PowerDuck improving and complementing existing data sets of substations, which are often generated synthetically, thus enhancing the security of power grids.