Technische Universität Dortmund

Taking actual engineering as a case study, this article provides a novel scheme of applying the inductive power filtering method (IPFM) to resolve the harmonic resonance issues in large-scale photovoltaic (PV) plant. By using IPFM's special structure and dual zero-impedance design, the impedance network of large-scale PV plant is reshaped while the performance of power filters is improved, so that the harmonic resonance due to the interaction between the inverters and the power grid is suppressed. First, the topology and components of the IPFM-based large-scale PV plant are introduced. The three-phase mathematical model in harmonic domain is then established. Based on the deduced transfer matrix, the simplified circuit of the studied large-scale PV plant is obtain-ed. Moreover, the IPFM's resonance damping mechanisms on resonant frequency shift and harmonic amplification mitigation are analyzed. Both simulation and engineering tests verify the feasibility of the proposal.

Enzyme-catalyzed replication of nucleic acid sequences is a prerequisite for the survival and evolution of biological entities. Before the advent of protein synthesis, genetic information was most likely stored in and replicated by RNA. However, experimental systems for sustained RNA-dependent RNA-replica-tion are difficult to realise, in part due to the high thermodynamic stability of duplex products and the low chemical stability of catalytic RNAs. Using a derivative of a group I intron as a model for an RNA replicase, we show that heated air-water interfaces that are exposed to a plausible CO 2-rich atmosphere enable sense and antisense RNA replication as well as template-dependent synthesis and catalysis of a functional ribozyme in a one-pot reaction. Both reactions are driven by autonomous oscillations in salt concentrations and pH, resulting from precipitation of acidified dew droplets, which transiently destabilise RNA duplexes. Our results suggest that an abundant Hadean microenvironment may have promoted both replication and synthesis of functional RNAs.

In the fields of business process modeling, logistics, and information model development, Reference Models (RMs) have shown to enhance standardization, support the common understanding of terminology and procedures, reduce the modeling efforts and cost through the paradigm “Design by Reuse”, and enable knowledge transfer. Utilizing RMs in Building Performance Simulation (BPS) shows potential to achieve similar benefits. However, there is no universally agreed understanding of RMs. In a previous scientific publication, we provided a comprehensive overview of the diversely interpreted definitions, benefits, and attributes of RMs and related terms. Additionally, to transfer the approach of RMs to BPS, a definition for RMs applicable to BPS has been provided, and the identified RM qualities were matched with BPS’s challenges. However, a sound evaluation of the success of transferring RMs to BPS is lacking. Therefore, this scientific contribution firstly includes the analysis conducted in the previous scientific contribution constituting a common understanding about RMs and their elements for BPS. Secondly, by conducting expert interviews, the applicability and validity of the developed concept of RMs for BPS are surveyed. In total, ten experts (seven BPS experts and three RM experts) evaluated the quality of creating transparency about the understanding of RMs and the level of success of their transfer toward BPS. The experts consistently see a great benefit of RMs in BPS, but for BPS experts the transfer and possible application of RMs in BPS is not sufficiently clear. Accordingly, the key output of the conducted survey is that a clearer and more detailed application example, e.g., describing at a more easy-to-understand level of detail an exemplary class of the provided example of an RM, is required for a more profound transfer of RMs to BPS.

The main goal of this study is to develop an experimental toolbox to estimate the self-diffusion coefficient of active ingredients (AI) in single-phase amorphous solid dispersions (ASD) close to the glass transition of the mixture using dielectric spectroscopy (DS) and oscillatory rheology. The proposed methodology is tested for a model system containing the insecticide imidacloprid (IMI) and the copolymer copovidone (PVP/VA) prepared via hot-melt extrusion. For this purpose, reorientational and the viscoelastic structural (α-)relaxation time constants of hot-melt-extruded ASDs were obtained via DS and shear rheology, respectively. These were then utilized to extract the viscosity as well as the fragility index of the dispersions as input parameters to the fractional Stokes-Einstein (F-SE) relation. Furthermore, a modified version of Almond-West (AW) formalism, originally developed to describe charge diffusion in ionic conductors, was exercised on the present model system for the estimation of the AI diffusion coefficients based on shear modulus relaxation times. Our results revealed that, at the calorimetric glass-transition temperature (Tg), the self-diffusion coefficients of the AI in the compositional range from infinite dilution up to 60 wt % IMI content lied in the narrow range of 10-18-10-20 m2 s-1, while the viscosity values of the dispersions at Tg varied between 108 Pa s and 1010 Pa s. In addition, the phase diagram of the IMI-PVP/VA system was determined using the melting point depression method via differential scanning calorimetry (DSC), while mid-infrared (IR) spectroscopy was employed to investigate the intermolecular interactions within the solid dispersions. In this respect, the findings of a modest variation in melting point at different compositions stayed in agreement with the observations of weak hydrogen bonding interactions between the AI and the polymer. Moreover, IR spectroscopy showed the intermolecular IMI-IMI hydrogen bonding to have been considerably suppressed, as a result of the spatial separation of the AI molecules within the ASDs. In summary, this study provides experimental approaches to study diffusivity in ASDs using DS and oscillatory rheology, in addition to contributing to an enhanced understanding of the interactions and phase behavior in these systems.

Proteins can specifically bind to curved membranes through curvature-induced hydrophobic lipid packing defects. The chemical diversity among such curvature "sensors" challenges our understanding of how they differ from general membrane "binders" that bind without curvature selectivity. Here, we combine an evolutionary algorithm with coarse-grained molecular dynamics simulations (Evo-MD) to resolve the peptide sequences that optimally recognize the curvature of lipid membranes. We subsequently demonstrate how a synergy between Evo-MD and a neural network (NN) can enhance the identification and discovery of curvature sensing peptides and proteins. To this aim, we benchmark a physics-trained NN model against experimental data and show that we can correctly identify known sensors and binders. We illustrate that sensing and binding are phenomena that lie on the same thermodynamic continuum, with only subtle but explainable differences in membrane binding free energy, consistent with the serendipitous discovery of sensors.

Much of Earth’s carbon may have been stripped away from the silicate mantle by dense metallic-iron during core formation. However, at deep magma ocean conditions carbon becomes less siderophile and thus large amounts of it may be stranded instead in the deep mantle. Here, we describe the structure and compaction mechanisms of carbonate glass to deep mantle pressures. Our results, based on non-resonant inelastic X-ray scattering, X-ray diffraction and ab initio calculations, demonstrate a pressure-induced change in hybridization of carbon from sp² to sp³ starting at 40 GPa, due to the conversion of [3]CO3²⁻ groups into [4]CO4⁴⁻ units, which is completed at ~112 GPa. The pressure-induced change of carbon coordination number from three to four increases possibilities for carbon-oxygen interactions with lower mantle silicate melts. sp³ hybridized carbon provides a mechanism for changing the presumed siderophile nature of deep carbon, becoming a possible source for carbon-rich emissions registered at the surface in intra-plate and near-ridge hot spots.

Teacher students’ professional educational knowledge is of great importance in academic teacher education. In response to the need to continuously optimize and improve teacher education, we developed a standards-based test instrument designed along the Standards of Teacher Education of the German education administration. The so-called ESBW (Essen Test for the Assessment of Standards-Based Educational Knowledge) is intended to assess educational knowledge as it is defined in these standards. This Brief Report aims to investigate whether the ESBW, as an exclusively standards-based developed test, can empirically be distinguished from a similar, but non-originally standards-based developed test, here the BilWiss 2.0 test, which also partially covers the standards. Competing structural equation models based on a study with 216 teacher students revealed that the ESBW short scale can be empirically distinguished from the BilWiss 2.0 short version, indicating that both instruments partly measure different aspects of educational knowledge. In addition, the examination of measurement invariance revealed that the ESBW performed similarly well for both beginning and advanced teacher students. Thus, our results further underline the usefulness of the ESBW for the assessment and evaluation of the German Standards of Teacher Education.

We develop a model-based boosting approach for multivariate distributional regression within the framework of generalized additive models for location, scale, and shape. Our approach enables the simultaneous modeling of all distribution parameters of an arbitrary parametric distribution of a multivariate response conditional on explanatory variables, while being applicable to potentially high-dimensional data. Moreover, the boosting algorithm incorporates data-driven variable selection, taking various different types of effects into account. As a special merit of our approach, it allows for modeling the association between multiple continuous or discrete outcomes through the relevant covariates. After a detailed simulation study investigating estimation and prediction performance, we demonstrate the full flexibility of our approach in three diverse biomedical applications. The first is based on high-dimensional genomic cohort data from the UK Biobank, considering a bivariate binary response (chronic ischemic heart disease and high cholesterol). Here, we are able to identify genetic variants that are informative for the association between cholesterol and heart disease. The second application considers the demand for health care in Australia with the number of consultations and the number of prescribed medications as a bivariate count response. The third application analyses two dimensions of childhood undernutrition in Nigeria as a bivariate response and we find that the correlation between the two undernutrition scores is considerably different depending on the child's age and the region the child lives in.

We present a protocol to evaluate the utility of detergents for purification and delipidation of E. coli membrane proteins. We determine the critical aggregation concentration of detergents. Furthermore, we compare the ability of detergents to extract membrane proteins and to maintain protein-lipid interactions during purification. The protocol describes steps for isolating and delipidating membrane proteins from E. coli membranes by extraction and affinity purification using detergents. The protocol does not enable an absolute quantification of purification outcomes. For complete details on the use and execution of this protocol, please refer to Urner et al.1.

Mixtures of 60% SN (succinonitrile) and 40% GN (glutaronitrile) doped with LiTFSI or LiPF6 at different concentrations are investigated using dielectric spectroscopy. Room temperature conductivities up to 10-3 S cm-1 are measured along with an overall conductivity enhancement of almost five decades compared to pure SN. Additionally, the dynamics of the methylene (CD2) groups of SN and that of the Li+ ions within the mixture are studied in a wide temperature range using 2H and 7Li NMR relaxometry, respectively. Static-field-gradient proton NMR combined with viscosity measurements probe the molecular diffusion. GN addition and Li doping both enhance the electrical conductivity significantly, while leaving the reorientational motion within the matrix essentially unchanged. The times scales and thus the effective energy barriers characterizing the Li ion motion as well as the molecular reorientations are very similar in the liquid and in the plastic phases, findings that argue in favor of the presence of a paddle-wheel mechanism.

Oxindoles and iso-oxindoles are natural product-derived scaffolds that provide inspiration for the design and synthesis of novel biologically relevant compound classes. Notably, the spirocyclic connection of oxindoles with iso-oxindoles has not been explored by nature but promises to provide structurally related bioactive compounds endowed with novel bioactivity. Therefore, methods for their efficient synthesis and the conclusive discovery of their cellular targets are highly desirable. We describe a selective Rh(III)-catalyzed scaffold-divergent synthesis of spirooxindole-isooxindoles and spirooxindole-oxindoles from differently protected diazooxindoles and N-pivaloyloxy aryl amides which includes a functional group-controlled Lossen rearrangement as key step. Unbiased morphological profiling of a corresponding compound collection in the Cell Painting assay efficiently identified the mitotic kinesin Eg5 as the cellular target of the spirooxindoles, which defines a unique Eg5 inhibitor chemotype.

This paper investigates digital firm birth activity in municipalities in the urban hinterland of core cities in Germany. It conducts panel fixed-effect regressions for monocentric and polycentric urban labour market regions covering the years 1995–2017. The digital industry’s regional distribution is shaped significantly by the closest core cities: municipalities in monocentric urban regions (MURs) profit from urban population growth and universities’ general knowledge. Municipalities in polycentric urban regions (PURs), however, are affected by industry-specific externalities, that is, an above-average growth in the share of firm birth of their closest urban cores. Overall, agglomeration externalities experience spatial decay relative to the core size with all regions benefiting from their own industry-enhancing urbanization externalities as captured by population growth and universities.

5-methylcytosine (mC) and its TET-oxidized derivatives exist in CpG dyads of mammalian DNA and regulate cell fate, but how their individual combinations in the two strands of a CpG act as distinct regulatory signals is poorly understood. Readers that selectively recognize such novel 'CpG duplex marks' could be versatile tools for studying their biological functions, but their design represents an unprecedented selectivity challenge. By mutational studies, NMR relaxation, and MD simulations, we here show that the selectivity of the first designer reader for an oxidized CpG duplex mark hinges on precisely tempered conformational plasticity of the scaffold adopted during directed evolution. Our observations reveal the critical aspect of defined motional features in this novel reader for affinity and specificity in the DNA/protein interaction, providing unexpected prospects for further design progress in this novel area of DNA recognition.

Ensembles are among the state-of-the-art in many machine learning applications. With the ongoing integration of ML models into everyday life, e.g., in the form of the Internet of Things, the deployment and continuous application of models become more and more an important issue. Therefore, small models that offer good predictive performance and use small amounts of memory are required. Ensemble pruning is a standard technique for removing unnecessary classifiers from a large ensemble that reduces the overall resource consumption and sometimes improves the performance of the original ensemble. Similarly, leaf-refinement is a technique that improves the performance of a tree ensemble by jointly re-learning the probability estimates in the leaf nodes of the trees, thereby allowing for smaller ensembles while preserving their predictive performance. In this paper, we develop a new method that combines both approaches into a single algorithm. To do so, we introduce L1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$L_1$$\end{document} regularization into the leaf-refinement objective, which allows us to jointly prune and refine trees at the same time. In an extensive experimental evaluation, we show that our approach not only offers statistically significantly better performance than the state-of-the-art but also offers a better accuracy-memory trade-off. We conclude our experimental evaluation with a case study showing the effectiveness of our method in a real-world setting.

The causes underlying comorbid learning difficulties in reading (RD) and math (MD) are still a matter of debate. Based on current research, two models for the relation of the cognitive profile of isolated and combined learning difficulties (RDMD) are discussed. Regarding the “multi-deficit model”, the profile of RDMD is characterized by the sum of domain-specific core deficits of RD and MD (additivity) as well as shared domain-general risk factors of RD and MD resulting in less severe deficits than expected under additivity (under-additivity). The “three independent disorders model” explains RDMD as a distinct learning disorder, showing a separate cognitive profile with distinct and/or more severe deficits, compared to the sum of RD’s and MD’s profiles (over-additivity). To evaluate these approaches, a meta-analysis including 74 studies, examining children aged 6–12, was conducted. Separate group comparisons for the three subcomponents in the cognitive profiles—reading, math, executive functions (EF)—were considered. Linear hypothesis testing revealed different results regarding the three subcomponents of the cognitive profiles of children with isolated vs. combined learning difficulties: Whereas RDMDs’ deficits in reading and math represented the sum of the deficits in the isolated groups (additivity), there was some evidence that RDMDs’ deficits in EF skills corresponded to under-additivity. Furthermore, group differences in math skills were more pronounced in symbolic than in non-symbolic math tasks, whereas in reading, group differences were larger in phonological processing and reading than in rapid automatized naming and language skills. Results are discussed in terms of interventionoptions for RDMD.

Synthesis platforms are of particular interest to DNA-encoded library (DEL) technologies to facilitate chemistry development, building block validation, and high-throughput library synthesis. A liquid–liquid two-phase flow reactor was designed that enables parallel conduction of reactions on DNA-coupled substrates. The dispersed phase in capillary slug flow contained the DNA reaction mixture and allowed for spatially separated batch experiments in a microchannel. A coiled flow inverter (CFI) tubular reactor with a 3D-printed internal structure on which a capillary is coiled was used for improved mixing and compact setup. An inert continuous phase was introduced, which generated slug flow and prevented backmixing of the individual reactants. In order to enable parallelized reactions, slugs containing a variety of different carboxylic acids were successfully generated to act as individual reaction compartments representing single batch experiments. As a widely used exemplary DEL reaction, the amide coupling reaction was successfully transferred to the tailored flow reaction system and DNA was recovered.

Background and purpose: As part of the commissioning and quality assurance in proton beam therapy, lateral dose profiles and output factors have to be acquired. Such measurements can be performed with point detectors and are especially challenging in small fields or steep lateral penumbra regions as the detector's volume effect may lead to perturbations. To address this issue, this work aims to quantify and correct for such perturbations of six point detectors in small proton fields created via three different delivery techniques. Methods: Lateral dose profile and output measurements of three proton beam delivery techniques (pencil beam scanning, pencil beam scanning combined with collimators, passive scattering with collimators) were performed using high-resolution EBT3 films, a PinPoint 3D 31022 ionization chamber, a microSilicon diode 60023 and a microDiamond detector 60019 (all PTW Freiburg, Germany). Detector specific lateral dose response functions K(x,y) acting as the convolution kernel transforming the undisturbed dose distribution D(x,y) into the measured signal profiles M(x,y) were applied to quantify perturbations of the six investigated detectors in the proton fields and correct the measurements. A signal theoretical analysis in Fourier space of the dose distributions and detector's K(x,y) was performed to aid the understanding of the measurement process with regard to the combination of detector choice and delivery technique. Results: Quantification of the lateral penumbra broadening and signal reduction at the fields center revealed that measurements in the pencil beam scanning fields are only compromised slightly even by large volume ionization chambers with maximum differences in the lateral penumbra of 0.25 mm and 4% signal reduction at the field center. In contrast, radiation techniques with collimation are not accurately represented by the investigated detectors as indicated by a penumbra broadening up to 1.6 mm for passive scattering with collimators and 2.2 mm for pencil beam scanning with collimators. For a 3 mm diameter collimator field, a signal reduction at field center between 7.6% and 60.7% was asserted. Lateral dose profile measurements have been corrected via deconvolution with the corresponding K(x,y) to obtain the undisturbed D(x,y). Corrected output ratios of the passively scattered collimated fields obtained for the microDiamond, microSilicon and PinPoint 3D show agreement better than 0.9% (one standard deviation) for the smallest field size of 3 mm. Conclusions: Point detector perturbations in small proton fields created with three delivery techniques were quantified and found to be especially pronounced for collimated small proton fields with steep dose gradients. Among all investigated detectors, the microSilicon diode showed the smallest perturbations. The correction strategies based on detector's K(x,y) were found suitable for obtaining unperturbed lateral dose profiles and output factors. Approximation of K(x,y) by considering only the geometrical averaging effect has been shown to provide reasonable prediction of the detector's volume effect. The findings of this work may be used to guide the choice of point detectors in various proton fields and to contribute towards the development of a code of practice for small field proton dosimetry. This article is protected by copyright. All rights reserved.