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Integrating artificial intelligence (AI) into decision-making processes is key to improving organizational performance. However, trust in AI-based decision support systems (DSSs), similar to other information systems, is important for successful integration. A disruptive phenomenon, “algorithm aversion”, can impede AI trust and, thus, acceptance. Although AI recommendations outperform human recommendations in different decision-making fields, individuals underweight recommendations from AI-based DSSs compared to human decision-makers due to a lack of AI trust. We conducted a lab experiment to investigate the role of AI recommendations in workplace-related tasks, first focusing on the mediating effect of AI trust and the negative impact of algorithm aversion on decision-making performance and the moderating effect of technical competence. Second, we analyzed the ability of gamification to reduce this phenomenon. We provide evidence regarding how to enhance decision-making performance when AI recommendations are deployed and identify countermeasures against algorithm aversion to facilitate the adoption of AI-based DSSs.
Modellierung des Antriebsstrangs einer Textilmaschine zum Zweck der modellbasierten Steuerung
(2024)
Zur Produktion von gewirkten Textilien werden mehrere Nadeln auf eine Legebarre gesetzt, die durch Servoantriebe positioniert werden. Aufgrund der erzwungenen Bewegung kann der Antriebsstrang bei hohen Drehzahlen zur Schwingung angeregt werden. Die richtige Wahl der Steuerkurven ist daher eine sehr wichtige und anspruchsvolle Aufgabe, die durch ein Antriebsstrangmodell unterstützt werden soll. Dafür wird am Beispiel eines Teststands ein Modell des Antriebsstranges in MATLAB/Simulink® entwickelt. Für das Antriebsstrangmodell müssen eine geeignete Modellordnung und Modellparameter gewählt werden. Die unbekannten Modellparameter werden durch eine Parameteridentifikation ermittelt. Mit einer Validierung wird ein geeignetes Anregungsspektrum für die Bestimmung der Modellordnung und die Parameteridentifikation ermittelt.
Krisen gab es schon immer. Aktuell hat sich jedoch ein ganzes Spektrum an Krisen entfaltet – es gibt politische und militärische Krisen, es brechen Energiemärke und Lieferketten weg, wir sehen ganze Branchen gefährdet, auch die Gesundheit macht uns Sorgen, vom Klimawandel ganz zu schweigen. In den Veranstaltungen der Ringvorlesung „Krisen und Auswege“, die im Wintersemester 2022/2023 stattfand, wurden Phänomene, Konzepte und Lösungen aus vielfältiger Perspektive erschlossen und damit die Komplexität des Gegenstands aufgezeigt. Herausforderungen für Unternehmen und Privatpersonen wurden identifiziert sowie der Einfluss auf technologische und organisatorische Entwicklungswege dargelegt.
In dieser Publikation wurden die einzelnen Vorträge der Ringvorlesung von den Autorinnen zusammengefasst.
Im Sommer des Jahres 2022 hatte der Bayerische Landtag das neue Hochschulinnovationsgesetz (BayHIG) verabschiedet. Darin ist erstmals der Wissenstransfer als die dritte, neue Säule der Hochschulaufgaben enthalten.
Tatsächlich hat sich das IMI bereits seit 15 Jahren dem Wissenstransfer Hochschule - Wirtschaft verschrieben und zahlreiche Projekte mit unterschiedlichem Fokus erfolgreich durchgeführt. In der vorliegenden Publikation werden die bearbeiteten Themen im Wissenstransfer, die zahlreichen verschiedenen Veranstaltungsformate sowie die Drittmittelprojekte beleuchtet.
Der technische Fortschritt von IT-Systemen, wie zum Beispiel der sogenannten „Künstlichen Intelligenz“ (KI) oder den „Common Data Spaces“ (CDSs), bringt die Frage nach der Akzeptanz dieser Systeme mit sich. Sowohl die aktive (ökonomische) Akzeptanz in Form des Erwerbs und der Nutzung, als auch die passive (soziale) Akzeptanz in Form der Duldung der aktiven Nutzung durch Dritte, sind für den Erfolg von IT-Systemen ausschlaggebend. Eine geringe Akzeptanz der Systeme und ein mangelndes Vertrauen in dieselben wird in einen Zusammenhang gebracht: Man fragt nach der Gestaltung „zuverlässiger und vertrauenswürdiger Systeme“, da diese besonders akzeptiert werden.
In diesem Beitrag werden „Vertrauen“ und dessen Zusammenhang mit „Zuverlässigkeit“ und „Verstehen“ im Kontext von IT-Systemen erörtert.
We report on laser drilling borehole arrays using ultrashort pulsed lasers with a particular focus on reducing the inadvertent heat accumulation across the workpiece by optimizing the drilling sequence. For the optimization, evolutionary algorithms are used and their results are verified by thermal simulation using Comsol and experimentally evaluated using a thermal imaging camera. To enhance process efficiency in terms of boreholes drilled per second, multi-spot approaches are employed using a spatial light modulator. However, as higher temperatures occur across the workpiece when using simultaneous multi-spot drilling as compared to a single-spot process, a subtle spatial distribution and sequence of the multi-spot approach has to be selected in order to limit the
resulting local heat input over the processing time. Different optimization approaches based on evolutionary algorithms aid to select those drilling sequences which allow for the combination of a high efficiency of multi-spot profiles, a low-generated process temperature and a high-component quality. In particular, using a 4 x 4 laser spot array allows for the drilling of 40,000 boreholes in less than 76 s (526 boreholes/s) with a reduced temperature increase by about 35%, as compared to a single spot process when employing an optimized drilling sequence.
Multi-Bessel Beams Generated by an Axicon and a Spatial Light Modulator for Drilling Applications
(2023)
We report on an optical setup to generate multi-Bessel beam profiles combining a refractive axicon and a spatial light modulator. Based on their particular beam profile, Bessel beams offer advantageous properties for micro drilling processes and internal volume processing, especially for transparent materials. In addition, the laser power of industrial, ultrashort pulsed lasers has increased significantly over the last few years, offering the possibility for highly efficient processes using multi-spot profiles. Our optical concept combines the dynamic possibilities of beam splitting using a spatial light modulator with the benefits of Bessel beams, which facilitates multi-Bessel beam processing. Beside the simulation and experimental evaluation of the generated multi-Bessel beams, we exemplify the applicability of the developed module for the perforation of thin metal foils by micro drilling.
We report on a comprehensive study of the mechanical properties of maraging steel
body-centred cubic lattice structures fabricated by a hybrid additive manufacturing technology that combines laser powder bed fusion with in situ high-speed milling. As the mechanical properties of additive manufactured components are inferior to, e.g., cast components, surface modifications can improve the mechanical behaviour. Different hybrid additive manufacturing technologies have been designed using additive and subtractive processes, improving process quality. Following this, mechanical testing is performed with respect to static tensile properties and dynamic stress, hardness, and porosity, comparing specimens manufactured by laser powder bed fusion only to those manufactured by the hybrid approach. In addition, the influence of different heat-treatment techniques on the mechanical behaviour of the lattice structures is investigated, namely solution and aging treatment as well as hot isostatic pressing. Thus, the influence of the superior surface quality due to the hybrid approach is evaluated, leading to, e.g., an offset of about 14–16% for the static testing of HIP lattice structures. Furthermore, the dynamic load behaviour can be improved with a finished surface, heading to a shift of the different zones of fatigue behaviour in the testing of hybrid-built specimens.
Axicon generated Bessel beams are a popular tool for high aspect ratio precision laser drilling. Spot diameter and working distance are given by the geometric parameters of the axicon and the wavelength used. Thus, it is difficult to manipulate the beam shape of a Bessel beam for a given setup. Spatial light modulators (SLMs) overcome limitations in flexibility. However, due to the limited phase shift of SLMs, only Bessel beams with flat cone angles and large focal length can be generated. In this contribution, an approach for generating Bessel beams with a shorter, but tunable focal length is presented. A physical axicon was combined with an SLM. A holographic image of a negative axicon is put on the SLM to generate a ring beam, which is focused by a subsequent physical axicon to get a small focal diameter. Thus, different sized high aspect ratio micro holes can be drilled without using any moving components.
In recent years, the demand for sustainable and degradable materials and electronic devices has increased significantly.
Among a range of biodegradable polymers, poly(lactic acid) (PLA) is a good alternative to conventional petrol-based polymers because of its attractive mechanical properties and its easy processability. Recently, PLA has also been described as a promising dielectric material with piezoelectric and electret properties. We expect that PLA—after further optimization— will play an important role as a material for environmentally friendly sensors in the future, where first applications such as air filters and pressure sensors have already been proposed. However, degradation under normal ambient conditions is very slow, and an accelerated and controllable degradation process is highly desirable for any type of PLA-based sensors.
Enzymatic hydrolysis with embedded enzymes has been proposed as an approach to accelerate and control degradation. In this work, we investigate the properties of PLA in terms of dielectric and mechanical properties with a focus on its ability to store charges after the enzyme proteinase K (Trit. album) has been incorporated. Results reveal that proteinase K has a positive effect on the charge stability of solvent-cast PLA electrets after complete evaporation of the solvent. Furthermore, we observed a concentration-dependent acceleration of mass loss in a Tris-HCl buffer. A fast degradation within only one day occurred at a concentration of 6 wt% proteinase K.
Two different control concepts for the automatic track guidance of forklifts are
proposed. Both approaches are based on Reinforcement Learning (RL), a method of Artificial
Intelligence (AI), and are able to take into account time-variant parameters, such as the vehicle velocity, and to reduce the influence of the path curvature, the most important disturbance variable of lateral vehicle control. In the first approach, both, the path curvature and the vehicle velocity signal, are provided to the controller in addition to the state variables of the controlled system. By varying the corresponding parameters in the training process, both signals can be considered and the control parameters can be optimized accordingly. In the second approach, several controllers (multi-model concept) considering the path curvature are used and the varying vehicle velocity is taken into account using a gain-scheduling concept. Considering time-variant vehicle parameters and the influence of the disturbance variable during operation, a stable track guidance is guaranteed within the whole speed range of the industrial trucks.
Rechtswidrigkeit einer Vorkaufssatzung zur Sicherung städtebaulicher Entwicklung, Urteilsbesprechung
(2000)
Schuldrecht BT
(2022)
BGB AT
(2023)
Schuldrecht AT
(2023)
The creation of aligning layers for the uniform orientation of liquid crystals is significant for both research and the application of liquid crystals. For all applications, the creation of aligning layers possessing controllable characteristics such as azimuthal and polar anchoring energies, easy-axis of director alignment and pretilt angle, in the same way as it is achieved by using photoaligning layers processed by light, is very important. Here, aligning properties of hybrid aligning layers created on the basis of sapphire surfaces additionally coated by photoaligning layer of PVCN-F are studied. These hybrid layers possess the properties of the nano-structured sapphire layer and the photosensitive PVCN-F layer, and complement each other. The irradiation time dependence of the azimuthal anchoring energy of the hybrid layers is studied. By using certain experimental conditions during irradiation of hybrid layers, e.g., polarization of light and irradiation time, a minimum value of the azimuthal anchoring energy, close to zero, was obtained. Atomic force microscope studies of the irradiated hybrid layers were also carried out. It was found that the behavior of the contact angle of nematic droplets placed on treated sapphire surfaces are in good agreement with properties of hybrid aligning layers and parameters of structuring surface obtained from AFM images.
内燃机测量技术和试验台架
(2023)
We report on a comprehensive study of laser percussion microvia drilling of Ajinomoto build-up film (ABF) material using an ultrashort pulsed laser in MHz burst mode. After laser processing, microvia drilling quality is being evaluated by the fabricated diameter and taper using laser scanning microscopy and metallography. The influences of the incubation effect, heat accumulation and shielding effects as a result of pulse to pulse interactions are being discussed on the ablation threshold, penetration depth and laser microvia drilling quality. We find that an increasing heat accumulation in MHz burst mode processing is responsible for the void formation and delamination of the insulating ABF layer. Therefore, the parameter clearance is introduced to evaluate these effects on the microvia sidewalls. For a comparable clearance, applying 2 intra-burst pulses achieves an average reduced taper of down to 19.5% compared to single pulse mode. At the same time, a reduced laser drilling time of 16.7% per microvia highlights the enormous potential of the MHz burst mode for laser drilling of ABF material in printed circuit board fabrication.
This article discusses the process of the laser turning of rotational symmetric, cylindrical components using ultrashort laser pulses with respect to the geometrical conditions and the resulting energy distribution during the laser turning process. As a result, process predictions and potential process optimizations are feasible. Particular attention is drawn to the laser spot formation on the cylindrical surface of the work piece in conjunction with the positioning of the laser beam relative to the rotation axis of the specimen. Based on fundamental calculations and experimental results, an optimum processing strategy is discussed, whereat the use of a trepanning optic in the laser turning process and the forming of a particular surface structure is additionally being issued.
We report on ultraviolet ultrashort pulsed laser processing of silicon carbide. Laser ablated cavities are evaluated with respect to their ablation rates, surface roughness and overall quality by varying the laser pulse fluence. Using this type of laser, high edge quality and surface roughness down to 107 nm can be achieved with an ablation efficiency of up to 0.228 mm 3 W −1 min −1. Functional surfaces are produced by generating laser induced periodic surface structures. The comparison with infrared wavelength underlines the advantages of the ultraviolet wavelength for laser micro machining this material. Significant differences with respect to the measured ablation depths and roughness as well as generated micro-and nanostructures appear. While infrared ablation is dominated by a chipping mechanism above a critical fluence of 9.93 Jcm −2 , higher ablation rates are observed with strong quality losses at the same time. In comparison to the infrared emission wavelength, in general , a significantly higher processing quality is achieved with the ultraviolet emission wavelength. In addition, the influence of spot size and repetition rate in UV processing is investigated. By increasing these parameters, a process acceleration without quality losses is enabled. The generation of a sophisticated microstructure exemplifies the advantages of processing silicon carbide with the UV laser reported here.
We report on the acceleration of femtosecond reductive laser sintering of copper (II) oxide on the surface of transparent cyclic olefin copolymers (COC) by varying the focus size to generate metallic copper (Cu) layers. By using different focusing conditions, different process speeds are achieved, and the structural accuracies are controlled. The formation of Cu lines is observed for different focus sizes at various scan speeds, which is attributed to laser exposure duration and chemical reaction time. The formed lines are categorized and measured by optical microscopy and provide the basis for two-dimensional Cu surfaces. By hatching, high quality conductive films are produced with an increased focus diameter of up to 400 µm at a scan speed of 400 mm/s and a hatch distance of 250 µm resulting in a multiple increased process speed by a factor of thirteen compared to previous research. Using 4-tip measurement , a low sheet resistance of 0.165 Ω/sq was validated on these Cu films, which have an atomic Cu content of 89.5%.
In this contribution we deal with the problem of producing “reasonable” data, when considering recorded energy consumption data, which are at certain sections incomplete and/or erroneous. This task is important, when energy providers employ prediction models for expected energy consumption, which are based on past recorded consumption data, which then of course should be reliable and valid. In a related contribution Yilmaz (2022), GAN-based methods for producing such “artificial data” have been investigated. In this contribution, we describe an alternative and complementary method based on signal inpainting, which has been successfully applied to audio processing Lieb and Stark (2018). After giving a short overview of the theory of proximity-based convex optimization, we describe and adapt an iterative inpainting scheme to our problem. The usefulness of this approach is demonstrated by analyzing real-world-data provided by a German energy supplier.
A wavelet-based sparse row-action method for image reconstruction in magnetic particle imaging
(2021)
Purpose
Magnetic particle imaging (MPI) is a preclinical imaging technique capable of visualizing the spatio-temporal distribution of magnetic nanoparticles. The image reconstruction of this fast and dynamic process relies on efficiently solving an ill-posed inverse problem. Current approaches to reconstruct the tracer concentration from its measurements are either adapted to image characteristics of MPI but suffer from higher computational complexity and slower convergence or are fast but lack in the image quality of the reconstructed images.
Methods
In this work we propose a novel MPI reconstruction method to combine the advantages of both approaches into a single algorithm. The underlying sparsity prior is based on an undecimated wavelet transform and is integrated into a fast row-action framework to solve the corresponding MPI minimization problem.
Results
Its performance is numerically evaluated against a classical FISTA (Fast Iterative Shrinkage-Thresholding Algorithm) approach on simulated and real MPI data. The experimental results show that the proposed method increases image quality with significantly reduced computation times.
Conclusions
In comparison to state-of-the-art MPI reconstruction methods, our approach shows better reconstruction results and at the same time accelerates the convergence rate of the underlying row-action algorithm.
This contribution demonstrates and discusses the preparation of finely dispersed copper(II) oxide nanosuspensions as precursors for reductive laser sintering (RLS). Since the presence of agglomerates interferes with the various RLS sub-processes, fine dispersion is required, and oversized particles must be identified by a measurement methodology. Aside from the established method of scanning electron microscopy for imaging individual dried particles, this work applies the holistic and statistically more significant laser diffraction in combination with dynamic image analysis in wet dispersion. In addition to direct ultrasonic homogenization, high-energy ball milling is introduced for RLS, to produce stable nanosuspensions with a high fine fraction, and, above all, the absence of oversize particles. Whereas ultrasonic dispersion stagnates at particle sizes between 500 nm and 20 μm, even after 8 h, milled suspension contains a high proportion of finest particles with diameters below 100 nm, no agglomerates larger than 1 μm and a trimodal particle size distribution with the median at 50 nm already, after 100 min of milling. The precursor layers produced by doctor blade coating are examined for their quality by laser scanning microscopy. The surface roughness of such a dry film can be reduced from 1.26 μm to 88 nm by milling. Finally, the novel precursor is used for femtosecond RLS, to produce homogeneous, high-quality copper layers with a sheet resistance of 0.28 Ω/sq and a copper mass concentration of 94.2%.
We demonstrate and discuss the integration of Bragg gratings in aerosol-jetted polymer optical waveguides, produced in the optical assembly and connection technology for component-integrated bus systems (OPTAVER) process. By using a femtosecond laser and adaptive beam shaping, an elliptical focal voxel generates different types of single pulse modification by nonlinear absorption in the waveguide material, which are arranged periodically to form Bragg gratings. Integration of a single grating structure or, alternatively, an array of Bragg grating structures in the multimode waveguide yields a pronounced reflection signal with typical multimodal properties, i.e., a number of reflection peaks with non-Gaussian shapes. However, the main wavelength of reflection, located around 1555 nm, is evaluable by means of an appropriate smoothing algorithm. When loaded by mechanical bending, a pronounced Bragg wavelength shift of this reflected peak up to 160 pm is detected. This demonstrates that the additively manufactured waveguides can be used not only for signal transmission but also as a sensor.
We report on an experimental study of laser microvia percussion drilling of Ajinomoto build-up film (ABF) material using an ultrashort pulsed laser in the ultraviolet region. In order to optimize the drilling quality in terms of the fabricated taper and microvia diameter, the
MHz burst mode is used in combination with both a Gaussian and a top-hat laser intensity distribution. To evaluate the influence of the top-hat beam shape on defocusing, microvias were drilled at different z-positions in the range of 200 μm evenly distributed around the focal plane. The drilling quality was evaluated by laser scanning microscopy and metallography. When using a top-hat beam shape, a reduced taper of down to 26.8 % on average is achieved compared to a Gaussian beam. For the application of 3 intra-burst pulses, we find an improvement of the taper by 11.6% compared to the single pulse mode due to the increasing heat accumulation. Using a top-hat beam shape in combination with the MHz burst mode, an 18.2 % reduction in laser drilling time for a microvia with a similar taper is remarkable, demonstrating the high potential for scaling throughput in electronics manufacturing.
We report on the laser ablation of cyclic olefin copolymer using an amplified ultrashort pulsed laser in the ultraviolet spectral range. In addition to a high ablation depth per laser-structured layer up to 74 μm at a fluence of 22 J cm−2, an excellent mean roughness 𝑅𝑎
of laser-patterned surfaces down to 0.5 μm is demonstrated. Furthermore, with increasing fluence, increasing ablation efficiencies up to 2.5 mm3 W−1 min−1 are determined. Regarding the quality of the ablation, we observed steep ablation flanks and low debris formation, though for fluences above 10.5 J cm−2 the formation of troughs was observed, being attributed to multiple reflections on the ablation flanks. For comparison, laser ablation was performed under identical conditions with an infrared laser wavelength. The results highlight that UV ablation exhibits significant advantages in terms of ablation efficiency, surface roughness and quality. Moreover, our results show that a larger UV focus spot accelerates the ablation process with comparable quality, paving the way for high-power UV ultrashort pulsed lasers towards an efficient and qualitative tool for the laser machining of cyclic olefin copolymer. The production of complex microfluidics further underlines the suitability of this type of laser.
Evaluation of an ultrashort pulsed laser robot system for flexible and large-area micromachining
(2023)
We report for the first time on the realization, characterization and application of an ultrashort pulsed laser robot system for flexible and large-area 2D and 3D laser micromachining with 6 articulated axes. To characterize the dynamic positioning of the laser beam during and after axes movement, CMOS image sensors were integrated into the beam path. A method introduced for the alignment of the optical axes allows a reduction of the deviations in laser beam positioning to less than 141.8 ± 92.9 μm within a 110° rotation range of axis 4. In addition, a high laser beam positioning repeatability of less than 102.2 μm is demonstrated over a total period of 14 h for a movement of axis 5 within a range of 0° to 90°. Initial laser cutting, laser structuring and laser marking applications on automotive dashboards and glass substrates are presented for flexible and large area 2D and 3D manufacturing. By applying a special laser cutting strategy for processing AF 32 eco thin glass, high cutting quality is achieved with a taper of up to 96.3% without the generation of cracks, demonstrating the innovative potential of the high-precision laser robot system. Nonetheless, different identified inherent influences of each axis 1–5 during robot axis movement demand for an innovative beam stabilization concept to achieve high precision in laser beam positioning.
Reflective coatings for astronomical X-ray optics were developed at the “Aschaffenburg Competence Center for Astronomical and Space Instrumentation” (ACCASI) since several years. As part of a Bavarian-Czech cooperation between the Technical University of Aschaffenburg and the Czech Technical University of Prague, now two mechanically identical telescopes were built. One telescope optic was equipped with conventional gold-coated mirrors, manufactured by the Czech project partners. The 34 X-ray mirrors of the second telescope use an innovative coating system made of chromium and iridium, which was applied at the Aschaffenburg coating laboratory. Both telescopes are designed according to the bionic principle of a reflecting lobster eye. The optics works with two consecutive reflections on mutually perpendicular mirror surfaces. This enables a large field of view with many square degrees in diameter, which, however, comes at the price of a reduced angular resolution. An extensive X-ray characterization of these telescopes was carried out at the PANTER test facility of MPE, which simulates parallel starlight incident on the telescopes. The telescopes have an angular resolution of about 4 arc minutes in X-rays and a focal length of about 2 meters. Furthermore, the used X-ray mirrors reflect and focus visible light as well and this functionality in the optical regime was checked in laboratory tests. Now another test campaign was done to examine the telescope resolution for real objects of the visible sky and the imaging properties for star constellations. Such functional tests by observing astronomical objects of the visible sky may simplify and accelerate the development of X-ray telescopes for satellite applications.
Predicting Autonomous Driving Behavior through Human Factor Considerations in Safety-Critical Events
(2024)
This paper investigates the ability of autonomous driving systems to predict outcomes by
considering human factors like gender, age, and driving experience, particularly in the context of
safety-critical events. The primary objective is to equip autonomous vehicles with the capacity to
make plausible deductions, handle conflicting data, and adjust their responses in real-time during
safety-critical situations. A foundational dataset, which encompasses various driving scenarios
such as lane changes, merging, and navigating complex intersections, is employed to enable vehicles
to exhibit appropriate behavior and make sound decisions in critical safety events. The deep
learning model incorporates personalized cognitive agents for each driver, considering their distinct
preferences, characteristics, and requirements. This personalized approach aims to enhance the
safety and efficiency of autonomous driving, contributing to the ongoing development of intelligent
transportation systems. The efforts made contribute to advancements in safety, efficiency, and overall
performance within autonomous driving systems. To describe the causal relationship between external
factors like weather conditions and human factors, and safety-critical driver behaviors, various
data mining techniques can be applied. One commonly used method is regression analysis. Additionally,
correlation analysis is employed to reveal relationships between different factors, helping to
identify the strength and direction of their impact on safety-critical driver behavior.
Keywords: car following; decision making; driving behavior; naturalistic driving studies; safety-critical
events; cognitive vehicles
1. Introduction
Despite the increasing prevalence of vehicle automation, the persistently high number
of car crashes remains a concern. Safety-critical events in human-driven scenarios have
become more intricate and partially uncontrollable due to unforeseen circumstances. Investigating
human driving behavior is imperative to establish traffic baselines for mixed
traffic, encompassing traditional, automated, and autonomous vehicles (AVs). Various
factors, such as weather conditions affecting visibility in longitudinal car-following (CF)
behavior [1,2], influence human driving behavior [3].
Car-following behavior, illustrating how a following vehicle responds to the lead
vehicle in the same lane, is a crucial aspect. Existing car-following models often make
assumptions about homogeneous drivers, neglecting significant heterogeneity in driving
experience, gender, character, emotions, and sociological, psychological, and physiological
traits. Failing to account for this heterogeneity hampers a comprehensive understanding of
car-following behavior, limiting model accuracy and applicability. In the development of
more realistic car-following models for mixed traffic, acknowledging the diversity among
drivers is crucial. By including individual variations such as risk-taking tendencies, reaction
times, decision-making processes, and driving styles, the modeling of real-world
driving complexities can be improved. Simplifying drivers into a few categories overlooks
the richness and variety of their characteristics, prompting the need for a more comprehensive
approach to capture nuances within different driver profiles. To address these
Smart Cities
This contribution quantifies the birefringence within injection-molded cyclic olefin copolymer plates and discusses its impact on the mechanical properties of the plates. It also focuses on the impact of birefringence on integrated waveguides and Bragg gratings and provides fabrication guidelines for such structures. The anisotropy in all three dimensions of the workpiece is examined by means of polarimetry and a prism coupler. It is found that the birefringence is inhomogenously distributed within the workpieces, whereas the maximum birefringence not only varies locally, but also depends on the observation direction. Overall, a maximum birefringence of 10 × 10−4 is found at the plate’s surface near the injection gate. The anisotropy then reduces exponentially towards the center of the workpiece and saturates at 1.8 × 10−4, in a depth of 0.4 mm. Thus, the birefringence strongly affects near-surface photonic structures. It is found that, depending on their orientation and the local birefringence of the substrate, waveguides and Bragg gratings fabricated with comparable parameters behave completely differently in terms of polarization-dependent optical attenuation, cross-sectional intensity distribution and Bragg reflection signal. For example, the support of the TM mode can vary between total loss and an optical attenuation of 0.9 dB × cm−1. In consequence, this study underlines the importance of quantifying the birefringent state of an injection-molded cyclic olefin copolymer workpiece if it is supposed to serve as a substrate for integrated photonic structures. The study furthermore demonstrates that birefringence effects can be omitted by burying the photonic structures deeper into the volume of the thermoplastic.
Planning adaptive learning paths for students’ progress throughout a course can be a challenging task, although it can be helpful for their learning progress. Within the HASKI-System, students should be able to get their own, personalized learning paths. In this paper, we present an approach towards the learning path sequencing problem. This idea is based on a novel proposal for arranging learning objects in a multi-dimensional space, bringing the relationship and similarities of these objects into a new relationship. We show, that we can use both, the Ant Colony Optimization Algorithm and the Genetic Algorithm with the idea of the Traveling-Salesman-Problem and get results, that are comparable with a proposed literature-based adaption mechanism. Nevertheless, the learning paths are all personalized based on the Felder & Silverman Learning Style Model and the hyperspace model will allow us later on to include more dimensions for other influencing factors.
Progress in mechanobiology allowed us to better understand the important role of mechanical forces in the regulation of biological processes. Space research in the field of life sciences clearly showed that gravity plays a crucial role in biological processes. The space environment offers the unique opportunity to carry out experiments without gravity, helping us not only to understand the effects of gravitational alterations on biological systems but also the mechanisms underlying mechanoperception and cell/tissue response to mechanical and gravitational stresses. Despite the progress made so far, for future space exploration programs it is necessary to increase our knowledge on the mechanotransduction processes as well as on the molecular mechanisms underlying microgravity-induced cell and tissue alterations. This white paper reports the suggestions and recommendations of the SciSpacE Science Community for the elaboration of the section of the European Space Agency roadmap “Biology in Space and Analogue Environments” focusing on “How are cells and tissues influenced by gravity and what are the gravity perception mechanisms?” The knowledge gaps that prevent the Science Community from fully answering this question and the activities proposed to fill them are discussed.
Machine learning algorithms make predictions by fitting highly parameterized nonlinear
functions to massive amounts of data. Yet those models are not necessarily consistent with physical
laws and offer limited interpretability. Extending machine learning models by introducing scientific
knowledge in the optimization problem is known as physics-based and data-driven modelling. A
promising development are physics informed neural networks (PINN) which ensure consistency to
both physical laws and measured data. The aim of this research is to model the time-dependent
temperature profile in bulk materials following the passage of a moving laser focus by a PINN. The
results from the PINN agree essentially with finite element simulations, proving the suitability of the
approach. New perspectives for applications in laser material processing arise when PINNs are
integrated in monitoring systems or used for model predictive control.
Bridging the gap between physics-based modeling and data-driven machine learning promises to reduce the amount of training data required and to improve explainability in predictive maintenance applications. For a small fleet of industrial forklift trucks, we develop a physically inspired framework for predicting remaining useful life (RUL) for selected components by integrating physically motivated feature extraction, degradation modelling and machine learning. The discussed approach is promising for situations of limited data availability or large data heterogeneity, which often occurs in fleets of customized vehicles optimized for particular tasks.
The substitution of expensive non-destructive material testing by data-based process monitoring is intensively explored in quality assurance for additive manufactured components. Machine learning show promising results for defect detection but require conceptual adaption to layer wise manufacturing and line scanning patterns in laser powder bed fusion. A multi-layer approach to co-register µ-computer tomography measurements with process monitoring data is developed and a workflow for automatic data set generation is implemented. The objective of this research is to benchmark the volumetric multi-layer approach and specifically selected deep learning methods for defect detection. The volumetric approach shows superior results compared to single slice monitoring. All investigated structured neural network topologies deliver similar performance.
Limited process control can cause metallurgical defect formation and inhomogeneous relative density in laser powder bed fusion manufactured parts. This study shows that process monitoring, based on optical melt-pool signal analysis is capable of tracing relative density variations: Unsupervised machine learning, applied to cluster multiple-slice monitoring data, reveals characteristic patterns in this noisy time-series signal, which can be co-registered with geometrical positions in the build part. For cylindrical 15–5 PH stainless steel specimens, manufactured under constant process parameters and post-analyzed by µ-computer tomography, correlations between such patterns and an increased local relative density at the edge have been observed. Finite element method (FEM) modeling of thermal histories at exemplary positions close to the edge suggest pre-heating effects caused by neighboring laser scan trajectories as possible reasons for the increased melt pool intensity at the edge.
Heart disease, also known as cardiovascular disease, encompasses a variety of heart conditions that can result in sudden death for many people. Examples include high blood pressure, ischaemia, irregular heartbeats and pericardial effusion. Electrocardiogram (ECG) signal analysis is frequently used to diagnose heart diseases, providing crucial information on how the heart functions. To analyse ECG signals, quantile graphs (QGs) is a method that maps a time series into a network based on the time-series fluctuation proprieties. Here, we demonstrate that the QG methodology can differentiate younger and older patients. Furthermore, we construct networks from the QG method and use machine-learning algorithms to perform the automatic diagnosis, obtaining high accuracy. Indeed, we verify that this method can automatically detect changes in the ECG of elderly and young subjects, with the highest classification performance for the adjacency matrix with a mean area under the receiver operating characteristic curve close to one. The findings reported here confirm the QG method’s utility in deciphering intricate, nonlinear signals like those produced by patient ECGs. Furthermore, we find a more significant, more connected and lower distribution of information networks associated with the networks from ECG data of the elderly compared with younger subjects. Finally, this methodology can be applied to other ECG data related to other diseases, such as ischaemia.
Heart disease, also known as cardiovascular disease, encompasses a variety of heart conditions that can result in sudden death for many people. Examples include high blood pressure, ischaemia, irregular heartbeats and pericardial effusion. Electrocardiogram (ECG) signal analysis is frequently used to diagnose heart diseases, providing crucial information on how the heart functions. To analyse ECG signals, quantile graphs (QGs) is a method that maps a time series into a network based on the time-series fluctuation proprieties. Here, we demonstrate that the QG methodology can differentiate younger and older patients. Furthermore, we construct networks from the QG method and use machine-learning algorithms to perform the automatic diagnosis, obtaining high accuracy. Indeed, we verify that this method can automatically detect changes in the ECG of elderly and young subjects, with the highest classification performance for the adjacency matrix with a mean area under the receiver operating characteristic curve close to one. The findings reported here confirm the QG method’s utility in deciphering intricate, nonlinear signals like those produced by patient ECGs. Furthermore, we find a more significant, more connected and lower distribution of information networks associated with the networks from ECG data of the elderly compared with younger subjects. Finally, this methodology can be applied to other ECG data related to other diseases, such as ischaemia.
Lithium-ion battery (LIB) manufacturing requires a pilot stage that optimizes its characteristics. However, this process is costly and time-consuming. One way to overcome this is to use a set of computational models that act as a digital twin of the pilot line, exchanging information in real-time that can be compared with measurements to correct parameters. Here we discuss the parameters involved in each step of LIB manufacturing, show available computational modeling approaches, and discuss details about practical implementation in terms of software. Then, we analyze these parameters regarding their criticality for modeling set-up and validation, measurement accuracy, and rapidity. Presenting this in an understandable format allows identifying missing aspects, remaining challenges, and opportunities for the emergence of pilot lines integrating digital twins. Finally, we present the challenges of managing the data produced by these models. As a snapshot of the state-of-the-art, this work is an initial step towards digitalizing battery manufacturing pilot lines, paving the way toward autonomous optimization.
There is a growing interest in the medical use of psychedelic substances, as preliminary studies using them for psychiatric disorders have shown positive results. In particular, one of these substances is N, N-dimethyltryptamine (DMT), an agonist serotonergic psychedelic that can induce profound alterations in the state of consciousness. In this work, we use an exploratory tool to reveal DMT-induced changes in brain activity using EEG data and provide new insights into the mechanisms of action of this psychedelic substance. We used a two-class classification based on (A) the connectivity matrix or (B) complex network measures derived from it as input to a support vector machine. We found that both approaches could detect changes in the brain's automatic activity, with case (B) showing the highest AUC (89%), indicating that complex network measurements best capture the brain changes that occur due to DMT use. In the second step, we ranked the features that contributed the most to this result. For case (A), we found that differences in the high alpha, low beta, and delta frequency bands were most important in distinguishing between the state before and after DMT inhalation, which is consistent with the results described in the literature. Further, the connection between the temporal (TP8) and central cortex (C3) and between the precentral gyrus (FC5) and the lateral occipital cortex (P8) contributed most to the classification result. The connection between regions TP8 and C3 has been found in the literature associated with finger movements that might have occurred during DMT consumption. However, the connection between cortical areas FC5 and P8 has not been found in the literature and is presumably related to the volunteers' emotional, visual, sensory, perceptual, and mystical experiences during DMT consumption. For case (B), closeness centrality was the most crucial complex network measure. Furthermore, we discovered larger communities and longer average path lengths when DMT was used and the converse when not, showing that the balance between functional segregation and integration had been disrupted. These findings support the idea that cortical brain activity becomes more entropic under psychedelics. Overall, a robust computational workflow has been developed here with interpretability of how DMT (or other psychedelics) modify brain networks and insights into their mechanism of action. Finally, the same methodology applied here may help interpret EEG time series from patients who consumed other psychedelic drugs.
Das Eurosystem und auch die Deutsche Bundesbank werden 2023 Verluste erzielen, die einige Jahre anhalten dürften. Aufgrund der massiven Käufe von Wertpapieren in den letzten 10 Jahren, vor allem Staatsanleihen, sind die Überschussreserven der Banken stark angestiegen. Die dadurch bedingten hohen Zinszahlungen an die Banken seit der geldpolitischen Kehrtwende, bei geringen Einnahmen für die hohen Wertpapierbestände, führten zu massiver Kritik. Die Banken würden dadurch „ungerechte“ Gewinne erzielen, während die Fiskalbehörden auf die früher üblichen Überweisungen der Zentralbankgewinne verzichten müssen. Populistische Forderungen nach einer Begrenzung der Bankengewinne durch z.B. eine pauschale, drastische Erhöhung der Mindestreservesätze im Eurosystem zur Reduzierung der Überschussreserven bergen neue Probleme und sind nicht hilfreich. Letztlich haben die Staaten sehr lange von historisch niedrigen Zinsen infolge der expansiven Geldpolitik des Eurosystems profitiert und müssen nun die Folgen der massiven Aufblähung der Zentralbankbilanzen während des Zeitraums der Normalisierung der Geldpolitik tragen.