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Complexes like iron (II)-triazoles exhibit spin crossover behavior at ambient temperature and are often considered for possible application. In previous studies, we implemented complexes of this type into polymer nanofibers and first polymer-based optical waveguide sensor systems. In our current study, we synthesized complexes of this type, implemented them into polymers and obtained composites through drop casting and doctor blading. We present that a certain combination of polymer and complex can lead to composites with high potential for optical devices. For this purpose, we used two different complexes [Fe(atrz)3](2 ns)2 and [Fe(atrz)3]Cl1.5(BF4)0.5 with different polymers for each composite. We show through transmission measurements and UV/VIS spectroscopy that the optical properties of these composite materials can reversibly change due to the spin crossover effect.
Compounds that exhibit the spin crossover effect are known to show a change of spin states through external stimuli. This reversible switching of spin states is accompanied by a change of the properties of the compound. Complexes, like iron (II)-triazole complexes, that exhibit this behavior at ambient temperature are often discussed for potential applications. In previous studies we synthesized iron (II)-triazole complexes and implemented them into electrospun nanofibers. We used Mössbauer spectroscopy in first studies to prove a successful implementation with maintaining spin crossover properties. Further studies from us showed that it is possible to use different electrospinning methods to either do a implementation or a deposition of the synthesized solid SCO material into or onto the polymer nanofibers. We now used a solvent in which both, the used iron (II)-triazole complex [Fe(atrz)3](2 ns)2 and three different polymers (Polyacrylonitrile, Polymethylmethacrylate and Polyvinylpyrrolidone), are soluble. This shall lead to a higher homogeneous distribution of the complex along the nanofibers. Mössbauer spectroscopy and other measurements are therefore in use to show a successful implementation without any significant changes to the complex.
Powder bed-based additive manufacturing processes offer an extended freedom in design and enable the processing of metals, ceramics, and polymers with a high level of relative density. The latter is a prevalent measure of process and component quality, which depends on various input variables. A key point in this context is the condition of powder beds. To enhance comprehension of their particle-level formation and facilitate process optimization, simulations based on the Discrete Element Method are increasingly employed in research. To generate qualitatively as well as quantitatively reliable simulation results, an adaptation of the contact model parameterization is necessary. However, current adaptation methods often require the implementation of models that significantly increase computational effort, therefore limiting their applicability. To counteract this obstacle, a sophisticated formula-based adaptation and evaluation method is presented in this research. Additionally, the developed method enables accelerated parameter determination with limited experimental effort. Thus, it represents an integrative component, which supports further research efforts based on the Discrete Element Method by significantly reducing the parameterization effort. The universal nature of deducting this method also allows its adaptation to similar parameterization problems and its implementation in other fields of research.
Integrated Risk and Opportunity Management (IROM) goes far beyond what is found in organizations today. However, it offers the best opportunity not only to keep pace with the VUCA world, but to actually profit from it. Accordingly, the introduction of opportunity-based thinking in addition to risk-based thinking is part of the design specification for ISO 9000 and ISO 9001. The prerequisite for the successful design of an IROM is the individual definition, control and integration of risk and opportunity management processes, considering eight success factors, the "8 C". Top management benefits directly from the result: better, coordinated decision memos enable faster and more appropriate decisions.
Renewable energy production is one of the strongest rising markets and further extreme growth can be anticipated due to desire of increased sustainability in many parts of the world. With the rising adoption of renewable power production, such facilities are increasingly attractive targets for cyber attacks. At the same time higher requirements on a reliable production are raised. In this paper we propose a concept that improves monitoring of renewable power plants by detecting anomalous behavior. The system does not only detect an anomaly, it also provides reasoning for the anomaly based on a specific mathematical model of the expected behavior by giving detailed information about various influential factors causing the alert. The set of influential factors can be configured into the system before learning normal behaviour. The concept is based on multidimensional analysis and has been implemented and successfully evaluated on actual data from different providers of wind power plants.
Parametric study of piezoresistive structures in continuous fiber reinforced additive manufacturing
(2024)
Recent advancements in fiber reinforced additive manufacturing leverage the piezoresistivity of continuous carbon fibers. This effect enables the fabrication of structural components with inherent piezoresistive properties suitable for load measurement or structural monitoring. These are achieved without necessitating additional manufacturing or assembly procedures. However, there remain unexplored variables within the domain of continuous fiber-reinforced additive manufacturing. Crucially, the roles of fiber curvature radii and sensing fiber bundle counts have yet to be comprehensively addressed. Additionally, the compression-sensitive nature of printed carbon fiber-reinforced specimens remains a largely unexplored research area. To address these gaps, this study presents experimental analyses on tensile and three-point flexural specimens incorporating sensing carbon fiber strands. All specimens were fabricated with three distinct curvature radii. For the tensile specimens, the number of layers was also varied. Sensing fiber bundles were embedded on both tensile and compression sides of the flexural specimens. Mechanical testing revealed a linear-elastic behavior in the specimens. It was observed that carbon fibers supported the majority of the load, leading to brittle fractures. The resistance measurements showed a dependence on both the number of sensing layers and the radius of curvature, and exhibited a slight decreasing trend in the cyclic tests. Compared with the sensors subjected to tensile stress, the sensors embedded on the compression side showed a lower gauge factor.
Training and evaluating deep learning models on road graphs for traffic prediction using SUMO
(2024)
The escalation of traffic volume in urban areas poses multifaceted challenges including increased accident risks, congestion, and prolonged travel times. Traditional approaches of expanding road infrastructure face limitations such as space constraints and the potential exacerbation of traffic issues.
Intelligent Transport Systems (ITS) present an alternative strategy to alleviate traffic problems by leveraging data-driven solutions. Central to ITS is traffic prediction, a process vital for applications like Traffic Management and Navigation Systems.
Recent advancements in traffic prediction have witnessed a surge of interest, particularly in deep learning methods optimized for graph-based data processing, being considered the most promising avenue presently.
These methods typically rely on real-life datasets containing traffic sensor data such as METR-LA and PeMS. However, the finite nature of real-life data prompts exploration into augmenting training and testing datasets with simulated traffic data.
This thesis explores the potential of utilizing traffic simulations, employing the microscopic traffic simulator SUMO, to train and test deep learning models for traffic prediction. A framework integrating PyTorch and SUMO is proposed for this purpose, aiming to elucidate the feasibility and effectiveness of using simulated traffic data for enhancing predictive models in traffic management systems.
Background
The eResearch system “Prospective Monitoring and Management App (PIA)” allows researchers to implement questionnaires on any topic and to manage biosamples. Currently, we use PIA in the longitudinal study ZIFCO (Integrated DZIF Infection Cohort within the German National Cohort) in Hannover (Germany) to investigate e.g. associations of risk factors and infectious diseases. Our aim was to assess user acceptance and compliance to determine suitability of PIA for epidemiological research on transient infectious diseases.
Methods
ZIFCO participants used PIA to answer weekly questionnaires on health status and report spontaneous onset of symptoms. In case of symptoms of a respiratory infection, the app requested participants to self-sample a nasal swab for viral analysis. To assess user acceptance, we implemented the System Usability Scale (SUS) and fitted a linear regression model on the resulting score. For investigation of compliance with submitting the weekly health questionnaires, we used a logistic regression model with binomial response.
Results
We analyzed data of 313 participants (median age 52.5 years, 52.4% women). An average SUS of 72.0 reveals good acceptance of PIA. Participants with a higher technology readiness score at the beginning of study participation also reported higher user acceptance. Overall compliance with submitting the weekly health questionnaires showed a median of 55.7%. Being female, of younger age and being enrolled for a longer time decreased the odds to respond. However, women over 60 had a higher chance to respond than women under 60, while men under 40 had the highest chance to respond. Compliance with nasal swab self-sampling was 77.2%.
Discussion
Our findings show that PIA is suitable for the use in epidemiologic studies with regular short questionnaires. Still, we will focus on user engagement and gamification for the further development of PIA to help incentivize regular and long-term participation.
Subclinical mastitis in heifers during early lactation affects udder health, future milk production and, therefore, the risk of premature culling. The aim of this cross-sectional study was to identify pre- and post-partum risk factors associated with a high heifer mastitis rate (HMR), and to find out which period (either pre- or post-partum) contains more risk factors and consequently should be the focus of mastitis control in heifers. A total of 77 herds were included in this study and the potential animal- and farm-related risk factors were recorded during a one-time farm visit. The HMR was provided by the dairy herd improvement test (DHI) as the annual average of the past 11 DHIs. For this study, data were analyzed in two models using generalized linear models. Each model examined the association between possible risk factors and HMR, one including only prepartum risk factors and the other one only post-partum risk factors. One identified pre-partum risk factor was the proportion of udder-healthy cows in the herd. Post-partum risk factors were the type of teat cleaning procedure before milking, teat disinfection, treatment of mastitis in heifers, a body condition score (BCS) of >3.0 in fresh heifers, and the combination of a teat cleaning procedure with a teat disinfectant. The results show the importance of the period shortly after calving for udder health in heifers, as four of the five significant risk factors were identified in this period and three of them were related to the milking process. However, further research with a higher number of herds is needed to minimize individual herd effects.
In this paper, we present a novel approach for real-time rendering of soft eclipse shadows cast by spherical, atmosphereless bodies. While this problem may seem simple at first, it is complicated by several factors. First, the extreme scale differences and huge mutual distances of the involved celestial bodies cause rendering artifacts in practice. Second, the surface of the Sun does not emit light evenly in all directions (an effect which is known as limb darkening). This makes it impossible to model the Sun as a uniform spherical light source. Finally, our intended applications include real-time rendering of solar eclipses in virtual reality, which require very high frame rates. As a solution to these problems, we precompute the amount of shadowing into an eclipse shadow map, which is parametrized so that it is independent of the position and size of the occluder. Hence, a single shadow map can be used for all spherical occluders in the Solar System. We assess the errors introduced by various simplifications and compare multiple approaches in terms of performance and precision. Last but not least, we compare our approaches to the state-of-the-art and to reference images. The implementation has been published under the MIT license.