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Print path-dependent contact temperature dependency for 3D printing using fused filament fabrication
(2022)
This paper focuses on the effects of different time spans and thus different contact temperatures when a molten strand contacts an adjacent already solidified strand in a plane during 3D printing with fused filament fabrication. For this purpose, both the manufacturing parameters and the geometry of the component are systematically varied and the effect on morphology and mechanical properties is investigated. The results clearly show that even with identical printing parameters, the transitions between the individual layers are much more visible with long time spans until fusion and lead to low mechanical properties. In contrast, short spans lead to hardly visible welds and high mechanical properties. Transferring the findings to different component sizes ultimately verifies that the average temperature at the time of contact between the already solidified and the currently deposited strand is decisive for component quality. In order to generate high component qualities, this finding must therefore be taken into account in the future in the path generation strategy, i.e., in so-called slicing.
Methods for predicting Henry's law constants Hij are important as experimental data are scarce. We introduce a new machine learning approach for such predictions: matrix completion methods (MCMs) and demonstrate its applicability using a data base that contains experimental Hij values for 101 solutes i and 247 solvents j at 298 K. Data on Hij are only available for 2661 systems i + j. These Hij are stored in a 101 × 247 matrix; the task of the MCM is to predict the missing entries. First, an entirely data-driven MCM is presented. Its predictive performance, evaluated using leave-one-out analysis, is similar to that of the Predictive Soave-Redlich-Kwong equation-of-state (PSRK-EoS), which, however, cannot be applied to all studied systems. Furthermore, a hybrid of MCM and PSRK-EoS is developed in a Bayesian framework, which yields an unprecedented performance for the prediction of Hij of the studied data set.
Based on conservation of resources theory, this paper examines the mediating mechanisms in the relationship between digital affordances and employee corporate entrepreneurship participation likelihood. Findings from an experimental study with 207 employees show a statistically significant and positive indirect effect of digital affordances on employee corporate entrepreneurship participation likelihood through employee-perceived information technology support for innovation and a statistically significant and—contrary to our expectations—positive indirect effect through employee-perceived work overload. Results are corroborated by insights from in-depth interviews with senior managers. They provide support for digital affordances as action potentials that are associated with resource gains that in turn foster employee corporate entrepreneurship participation likelihood.
Overexpression of the vacuolar sugar transporter TST1 in Arabidopsis leads to higher seed lipid levels and higher total seed yield per plant. However, effects on fruit biomass have not been observed in crop plants like melon, strawberry, cotton, apple, or tomato with increased tonoplast sugar transporter (TST) activity. Thus, it was unclear whether overexpression of TST in selected crops might lead to increased fruit yield, as observed in Arabidopsis. Here, we report that constitutive overexpression of TST1 from sugar beet in the important crop species Camelina sativa (false flax) resembles the seed characteristics observed for Arabidopsis upon increased TST activity. These effects go along with a stimulation of sugar export from source leaves and not only provoke optimised seed properties like higher lipid levels and increased overall seed yield per plant, but also modify the root architecture of BvTST1 overexpressing Camelina lines. Such mutants grew longer primary roots and showed an increased number of lateral roots, especially when developed under conditions of limited water supply. These changes in root properties result in a stabilisation of total seed yield under drought conditions. In summary, we demonstrate that increased vacuolar TST activity may lead to optimised yield of an oil-seed crop species with high levels of healthy ω3 fatty acids in storage lipids. Moreover, since BvTST1 overexpressing Camelina mutants, in addition, exhibit optimised yield under limited water availability, we might devise a strategy to create crops with improved tolerance against drought, representing one of the most challenging environmental cues today and in future.
Living systems incessantly engage in the regulation of their cellular processes to fulfill their biological functions. Beyond development-related adjustments or cell cycle oscillations, environmental fluctuations compel the system to reorganize metabolic pathways, structural components, or molecular repair and reconstitution mechanisms. These responses manifest across diverse temporal scales, necessitating an intricate regulatory orchestration. Time series experiments have become increasingly popular for charting the chronological order and elucidating the underlying mechanisms. In the era of high-throughput technologies, the majority of cellular molecules can be analyzed in one fell swoop, generating a comprehensive snapshot of the status quo of most present molecules. Methodological advancements also permit the monitoring not only of molecular abundances but also the functional status of transcripts and proteins. However, due to the still high efforts associated with such experiments, the number of measured time points and the replication of measurements remains limited. Resulting datasets contain signals from thousands of molecules, yet they are sparse in temporal resolution and are often imprecise due to biological variability and technical measurement inaccuracies.
This thesis explores the complexities arising from the examination of short time series data and introduces pioneering tools that offer fresh insights into the realm of biological time series analysis. The broad spectrum of analytic possibilities ranges from a molecule-centric investigation of individual time courses to a holistic aggregation of the system’s response to its main characteristics. By creating a modeling framework that applies domain-specific constraints, time-course signals can be transformed from a series of discrete data points into a continuous curve. These curves align with current biological conjectures about molecule kinetics being smooth and devoid of superfluous oscillations. Noise present at individual time points is judiciously accounted for during curve fitting, mitigating the impact of time points with high variance on the curve. Subsequent classification is based on the features of these curves (extreme points and inflection points) and ensures a reduction in data amount and complexity. Succinct labels assigned to each molecule's kinetics encapsulate the signal's most notable features. Besides this modeling approach, an innovative enrichment strategy is introduced, that is independent of prior data partitioning and capable of segregating the temporal response into its thermodynamically relevant components. This approach allows for a continuous assessment of each molecule's contribution to these components, obviating the need for exclusive allocation. The application of various analytical approaches to heat acclimation experiments in Chlamydomonas highlights the relevance and potential of time series experiments and specifically tailored analysis techniques. The integration of different system levels has led to the identification of regulatory peculiarities, such as an increased correlation between transcripts and corresponding proteins during acclimation responses. These and other insights may herald new avenues of research that could ultimately enhance plant robustness in the face of increasing environmental perturbations.
The growing popularity of time series experiments necessitates dedicated analytical approaches that empower researchers and analysts to decipher patterns, discern trends, and unravel the underlying structures within the data, facilitating predictions and the derivation of meaningful conclusions that could potentially build bridges between the interweaved systems levels.
Cyanobacteria oxygenated Earth's atmosphere ~2.4 billion years ago, during the Great Oxygenation Event (GOE), through oxygenic photosynthesis. Their high iron requirement was presumably met by high levels of Fe(II) in the anoxic Archean environment. We found that many deeply branching Cyanobacteria, including two Gloeobacter and four Pseudanabaena spp., cannot synthesize the Fe(II) specific transporter, FeoB. Phylogenetic and relaxed molecular clock analyses find evidence that FeoB and the Fe(III) transporters, cFTR1 and FutB, were present in Proterozoic, but not earlier Archaean lineages of Cyanobacteria. Furthermore Pseudanabaena sp. PCC7367, an early diverging marine, benthic strain grown under simulated Archean conditions, constitutively expressed cftr1, even after the addition of Fe(II). Our genetic profiling suggests that, prior to the GOE, ancestral Cyanobacteria may have utilized alternative metal iron transporters such as ZIP, NRAMP, or FicI, and possibly also scavenged exogenous siderophore bound Fe(III), as they only acquired the necessary Fe(II) and Fe(III) transporters during the Proterozoic. Given that Cyanobacteria arose 3.3–3.6 billion years ago, it is possible that limitations in iron uptake may have contributed to the delay in their expansion during the Archean, and hence the oxygenation of the early Earth.
In selective laser melting (SLM), a powdered material is locally melted by a laser and, after cooling, forms a coherent solid structure that enables the production of complex geometries with various materials. The process involves extreme heating and cooling rates and, thus, large temperature gradients, which lead to anisotropic material properties on the macroscopic scale and, in the worst case, reduced mechanical properties. In order to reliably predict the final mechanical component properties, simulations can be performed at different time and length scales. Enormous computational resources are often required to perform such simulations. In order to transform these simulations into suitable surrogate models, the generated data must be compressed and evaluated in a suitable way. This paper shows first preliminary work and a possible new data description of such simulations.
In diesem Beitrag stellt sich die Nachwuchswissenschaftlerin Dr.-Ing. Dorina Strieth vom Lehrgebiet Bioverfahrenstechnik der TU Kaiserslautern vor. Neben aktuellen Forschungsarbeiten und Lehraktivität berichtet sie über die Notwendigkeit des Wissenstransfers in die Zivilgesellschaft. Fachlich berichtet sie von aktuellen Ergebnissen der intelligenten Nutzung phototropher Biofilme sowie dem Potenzial zur biotechnologischen Herstellung nachhaltiger Baumaterialien.
Tracking waterborne microplastic (MP) in urban areas is a challenging task because of the various sources and transport pathways involved. Since MP occurs in low concentrations in most wastewater and stormwater streams, large sample volumes need to be captured, prepared, and carefully analyzed. The recent research in urban areas focused mainly on MP emissions at wastewater treatment plants (WWTPs), as obvious entry points into receiving waters. However, important transport pathways under wet-weather conditions are yet not been investigated thoroughly. In addition, the lack of comprehensive and comparable sampling strategies complicated the attempts for a deeper understanding of occurrence and sources. The goal of this paper is to (i) introduce and describe sampling strategies for MP at different locations in a municipal catchment area under dry and wet-weather conditions, (ii) quantify MP emissions from the entire catchment and two other smaller ones within the bigger catchment, and (iii) compare the emissions under dry and wet-weather conditions. WWTP has a high removal rate of MP (>96%), with an estimated emission rate of 189 kg/a or 0.94 g/[population equivalents (PEQ · a)], and polyethylene (PE) as the most abundant MP. The specific dry-weather emissions at a subcatchment were ≈30 g/(PEQ · a) higher than in the influent of WWTP with 23 g/(PEQ · a). Specific wet-weather emissions from large sub-catchment with higher traffic and population densities were 1952 g/(ha · a) higher than the emissions from smaller catchment (796 g/[ha · a]) with less population and traffic. The results suggest that wet-weather transport pathways are likely responsible for 2–4 times more MP emissions into receiving waters compared to dry-weather ones due to tire abrasion entered from streets through gullies. However, more investigations of wet-weather MP need to be carried out considering additional catchment attributes and storm event characteristics.