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Sapphire-Based Planar Bragg Grating Devices
No full textThis study reports on the long-term stability and the high-temperature capability of sapphire-based photonic crystal waveguides with integrated Bragg gratings. Furthermore, their Bragg grating reflectivity as well as their temperature sensitivity is quantified
UV-ultrashort pulsed laser ablation of fused silica
No full textThe authors report on ultraviolet ultrashort pulsed laser ablation of fused silica and compare the achievable micromachining results to those obtained by using the fundamental emission wavelength in infrared. Ablation in ultraviolet reveals a stable efficiency for increasing fluences, whereas using an infrared beam exhibits a decreasing trend of the ablation efficiency at higher and increasing fluences. In addition, a significant improvement in the surface quality is found by using an ultraviolet wavelength in a fluence range up to 20 J/cm2 compared to infrared, e.g., revealing an Ra of down to 0.45 μm on using the ultraviolet wavelength compared to Ra = 0.56 μm on using infrared at fluences up 15 J/cm2. Moreover, taking advantage of the high available pulse energy, the authors compare the achievable ablation efficiency and surface roughness using a conventionally focused ultraviolet beam and a defocused ultraviolet beam, finding that the defocused ultraviolet beam possesses a processing quality comparable to that of the focused beam. Finally, the authors exemplify the potential of ultraviolet ultrashort pulsed laser ablation by using a Tesla mixer for microfluidic integration of fused silica
Optimization of Mechanical Properties and Evaluation of Fatigue Behavior of Selective Laser Sintered Polyamide-12 Components
No full textIn this paper, a comprehensive study of the mechanical properties of selective laser sintered polyamide components is presented, for various different process parameters as well as environmental testing conditions. For the optimization of the static and dynamic mechanical load behavior, different process parameters, e.g., laser power, scan speed, and build temperature, were varied, defining an optimal parameter combination. First, the influence of the different process parameters was tested, leading to a constant energy density for different combinations. Due to similarities in mechanical load behavior, the energy density was identified as a decisive factor, mostly independent of the input parameters. Thus, secondly, the energy density was varied by the different parameters, exhibiting large differences for all levels of fatigue behavior. An optimal parameter combination of 18 W for the laser power and a scan speed of 2666 mm/s was determined, as a higher energy density led to the best results in static and dynamic testing. According to this, the variation in build temperature was investigated, leading to improvements in tensile strength and fatigue strength at higher build temperatures. Furthermore, different ambient temperatures during testing were evaluated, as the temperature-dependent behavior of polymers is of high importance for industrial applications. An increased ambient temperature as well as active cooling during testing was examined, having a significant impact on the high cycle fatigue regime and on the endurance limit
Optimization of mechanical properties of additive manufactured IN 718 parts combining LPBF and in-situ high-speed milling
No full textAutoML based workflow for design of experiments (DOE) selection and benchmarking data acquisition strategies with simulation models
No full textDesign of experiments (DOE) is an established method to allocate resources for efficient parameter space exploration. Model based active learning (AL) data sampling strategies have shown potential for further optimization. This paper introduces a workflow for conducting DOE comparative studies using automated machine learning. Based on a practical definition of model complexity in the context of machine learning, the interplay of systematic data generation and model performance is examined considering various sources of uncertainty: this includes uncertainties caused by stochastic sampling strategies, imprecise data, suboptimal modeling, and model evaluation. Results obtained from electrical circuit models with varying complexity show that not all AL sampling strategies outperform conventional DOE strategies, depending on the available data volume, the complexity of the dataset, and data uncertainties. Trade-offs in resource allocation strategies, in particular between identical replication of data points for statistical noise reduction and broad sampling for maximum parameter space exploration, and their impact on subsequent machine learning analysis are systematically investigated. Results indicate that replication oriented strategies should not be dismissed but may prove advantageous for cases with non-negligible noise impact and intermediate resource availability. The provided workflow can be used to simulate practical experimental conditions for DOE testing and DOE selection
Central bank losses and commercial bank profits - unexpected and unfair?
No full textThe Eurosystem and the Deutsche Bundesbank will incur substantial losses in 2023 that are likely to persist for several years. Due to the massive purchases of securities in the last 10 years, especially of government bonds, the banks' excess reserves have risen sharply. The resulting high interest payments to the banks since the turnaround in monetary poli-cy, with little income for the large-scale securities holdings, led to massive criticism. The banks were said to be making "unfair" profits as a result, while the fiscal authorities had to forego the previously customary transfers of central bank profits. Populist demands to limit bank profits by, for example, drastically increasing the minimum reserve ratios in the Eurosystem to reduce excess reserves are creating new severe problems and are neither justified nor helpful. Ultimately, the EU member states have benefited for a very long time from historically low interest rates because of the Eurosystem's extraordinary loose monetary policy and must now bear the flip side consequences of the massive expansion of central bank balance sheets during the necessary period of monetary policy normalisa-tion
How to obtain an integrated picture of the molecular networks involved in adaptation to microgravity in different biological systems?
No full textPeriodically, the European Space Agency (ESA) updates scientific roadmaps in consultation with the scientific community. The ESA SciSpacE Science Community White Paper (SSCWP) 9, “Biology in Space and Analogue Environments”, focusses in 5 main topic areas, aiming to address key community-identified knowledge gaps in Space Biology. Here we present one of the identified topic areas, which is also an unanswered question of life science research in Space: “How to Obtain an Integrated Picture of the Molecular Networks Involved in Adaptation to Microgravity in Different Biological Systems?” The manuscript reports the main gaps of knowledge which have been identified by the community in the above topic area as well as the approach the community indicates to address the gaps not yet bridged. Moreover, the relevance that these research activities might have for the space exploration programs and also for application in industrial and technological fields on Earth is briefly discussed
The Moderating Role of Relative Performance Information in Reducing Algorithm Aversion In The Adoption Of AI-Based Decision Support Systems in Insolvency Prediction Tasks
No full textThe integration of Artificial Intelligence (AI) into decision-making processes emerges as a pivotal strategy for enhancing organizational performance. The paper delves into the criticality of trust in AI-based Decision Support Systems (DSSs), similar to the trust required for other (Accounting) information systems to integrate them efficiently. We explore the disruptive phenomenon known as "algorithm aversion" - a significant barrier to the trust and acceptance of AI. Although AI recommendations outperform human recommendations in different decision-making fields, there exists a tendency among individuals to underweight AI-based DSSs recommendations relative to those from human decision-makers. This underutilization is attributed to the lack of trust in AI.
We conducted a laboratory experiment designed to investigate the role of AI recommendations in a workplace-related task in the field of financial accounting. The study is twofold: firstly, it examines how AI trust mediates and algorithm aversion adversely impacts decision-making performance, while also considering the moderating role of technical competence. Secondly, it investigates the potential of gamification by using means of Relative Performance Information (RPI) as a strategy to mitigate the effects of algorithm aversion.
Through this experiment, we provide empirical evidence on methods to enhance decision-making performance in the context of AI recommendations. Additionally, we identify and propose counterstrategies to combat algorithm aversion, thereby facilitating the broader adoption and integration of AI-based DSSs in accounting and auditing settings. This study contributes to the accounting and auditing research community by offering insights into how AI can be more effectively incorporated into decision-making processes, addressing both psychological and technical barriers to its acceptance