Open Access

The accurate treatment of outflow boundary conditions remains a critical challenge in computational fluid dynamics when predicting aerodynamic forces and/or acoustic emissions. This is particularly evident when employing the lattice Boltzmann method (LBM) as the numerical solution technique, which often suffers from inaccuracies induced by artificial reflections from outflow boundaries. This paper investigates the use of neural networks (NN) to mitigate these adverse boundary effects and enable truncated domain requirements. Two distinct NN-based approaches are proposed: (1) direct reconstruction of unknown particle distribution functions at the outflow boundary; and (2) enhancement of established characteristic boundary conditions (CBC) by dynamically tuning their parameters. The direct reconstruction model was trained on data generated from a 2D flow over a cylindrical obstruction. The drag, lift, and Strouhal number were used to test the new boundary condition. We analyzed results for various Reynolds numbers and restricted domain sizes where it demonstrated significantly improved predictions when compared with the traditional Zou & He boundary condition. To examine the robustness of the NN-based reconstruction, the same condition was applied to the simulation of a NACA0012 airfoil, again providing accurate aerodynamic performance predictions. The neural-enhanced CBC were evaluated on a 2D convected vortex benchmark and showed superior performance in minimizing density errors compared to CBCs with fixed parameters. These findings highlight the potential of NN-integrated boundary conditions to improve accuracy and reduce computational expense of aerodynamic and acoustic emissions simulations with the LBM.

Polyphenols, a diverse group of phytochemicals, are an indispensable component of the antioxidant defense system, given their capacity to neutralize free radicals and modulate redox reactions. This review examines the chemical diversity and antioxidant potential of polyphenols derived from viticultural byproducts, including grape skins, seeds, pomace, and stems. These biomass sources provide a sustainable reservoir of bioactive compounds with potential applications in the development of functional and biobased materials. This review also addresses the methodological challenges inherent to this field, such as the variability of extraction procedures and test conditions. This study critically examines the influence of the structural characteristics of polyphenols, including the number, nature, and distribution of hydroxyl groups as well as molecular size, on antioxidant activity. Additionally, innovative extraction techniques that enhance yield and bioactivity are presented and evaluated. Besides conventional monomeric and oligomeric polyphenolic compounds, lignins, a class of high-molecular-weight polyphenols of industrial importance and stability in oxidative environments, are addressed. The results underscore the necessity for standardized multiassay approaches to precisely assess antioxidant capacity and facilitate targeted polyphenol application in diverse fields. Future research should address the intricate interplay between biomass composition, extraction parameters, and polyphenol functionality to tailor their utilization.

Hepatic insulin resistance is an important pathophysiology in type 2 diabetes, and the mechanisms by which high-caloric diets induce insulin resistance are unclear. Among vertebrate animals, mammals have retained a unique molecular change that allows an intracellular arrestin domain-containing protein called Thioredoxin-Interacting Protein (TXNIP) to bind covalently to thioredoxin, allowing TXNIP to "sense" oxidative stress(1). Here, we show that a single cysteine in TXNIP mediates the development of hepatic insulin resistance in the setting of a high-fat diet (HFD). Mice with an exchange of TXNIP Cysteine 247 for Serine (C247S) showed improved whole-body and hepatic insulin sensitivity compared to wild-type (WT) controls following an 8-week HFD. HFD-fed TXNIP C247S mouse livers also showed improved insulin signaling. The Transmembrane 7 superfamily member 2 (Tm7sf2) gene encodes for a sterol reductase involved in the process of cholesterol biosynthesis (2). We identified TM7SF2 as a potential mediator of enhanced insulin signaling in HFD-fed TXNIP C247S mouse livers. TM7SF2 increased Akt phosphorylation and suppressed gluconeogenic markers PCK1 and G6Pc specifically under oxidative-stress-induced conditions in HepG2 cells. We also present data suggesting that a heterozygous variant of TXNIP C247 is well-tolerated in humans. Thus, mammals have a single redox-sensitive amino acid in TXNIP that mediates insulin resistance in the setting of a HFD. Our results reveal an evolutionarily conserved mechanism for hepatic insulin resistance in obesity. Hepatic insulin resistance is an important pathophysiology in type 2 diabetes, and the mechanisms by which high-caloric diets induce insulin resistance are unclear. Among vertebrate animals, mammals have retained a unique molecular change that allows an intracellular arrestin domain-containing protein called Thioredoxin-Interacting Protein (TXNIP) to bind covalently to thioredoxin, allowing TXNIP to "sense" oxidative stress. Here, we show that a single cysteine in TXNIP mediates the development of hepatic insulin resistance in the setting of a high-fat diet (HFD). Mice with an exchange of TXNIP Cysteine 247 for Serine (C247S) showed improved whole-body and hepatic insulin sensitivity compared with WT controls following an 8-week HFD. HFD-fed TXNIP C247S mouse livers also showed improved insulin signaling. The Transmembrane 7 Superfamily Member 2 (Tm7sf2) gene encodes for a sterol reductase involved in the process of cholesterol biosynthesis. We identified TM7SF2 as a potential mediator of enhanced insulin signaling in HFD-fed TXNIP C247S mouse livers. TM7SF2 increased Akt phosphorylation and suppressed gluconeogenic markers PCK1 and G6Pc specifically under oxidative stress-induced conditions in HepG2 cells. We also present data suggesting that a heterozygous variant of TXNIP C247 is well tolerated in humans. Thus, mammals have a single redox-sensitive amino acid in TXNIP that mediates insulin resistance in the setting of an HFD. Our results reveal an evolutionarily conserved mechanism for hepatic insulin resistance in obesity.

In der vorliegenden Arbeit werden die nematischen Flüssigkristallgemische (E7 und E8) zum Zwecke der Gassensorik mit einer reaktiven, optisch aktiven Substanz dotiert. Die Dotierung verursacht die Ausbildung einer chiral-nematischen Phase, die einen eindimensionalen photonischen Kristall mit Reflexionsmaxima im sichtbaren Bereich des elektromagnetischen Spektrums erzeugt. Infolge einer chemischen Reaktion des Dotiermittels mit dem einem Analyten, ändert sich mit seiner chemischen Zusammensetzung auch dessen helical twisting power (HTP). Diese Änderung verursacht eine Verschiebung des reflektierten Wellenlängenbereichs, was als Änderung der farblichen Erscheinung mit dem bloßen Auge wahrgenommen werden kann. In dieser Arbeit wird das koaxiale Elektrospinnen verwendet, um Flüssigkristalle in Polymerfasern von wenigen Mikrometern Durchmesser einzukapseln. Der Vergleich zwischen eingekapseltem und nicht eingekapseltem dotierten Flüssigkristall wird mit einer dafür entwickelten Reaktionskammer UV/VIS-spektroskopisch durchgeführt. Die ablaufenden Reaktionen werden mittels FTIR-Spektroskopie untersucht. Die Fasern und die verwendeten Flüssigkristalle werden lichtmikroskopisch charakterisiert. Es werden zusätzlich Möglichkeiten untersucht die Wasserbeständigkeit der hergestellten Fasern zu verbessern, um ihre Eignung für künftige technische Anwendungen zu steigern. Hierzu wird das triaxiale Elektrospinnen verwendet, um die Fasern mit einer zusätzlichen wasserbeständigen Polymerhülle zu überziehen. Es wird zudem die Möglichkeit untersucht koaxial gesponnene Fasern nachträglich zu vernetzen, um so eine Wasserfestigkeit zu erzielen.

The electrolytic in situ generation of oxidants is an increasingly widespread technique for producing sanitized and thus safe process water in ultrapure water distribution systems. In particular, the anodic production of ozone on functionalized electrodes is a commercially available option for providing pharmaceutical-grade water. The present work therefore investigates the use of a newly developed electrolyzer with a polymer electrolyte membrane (PEM) and lead dioxide (PbO2) electrodes for drinking and ultrapure water treatment. The selective analysis of electrolytically generated oxidizing agents or reactive oxygen species such as ozone (O3), hydrogen peroxide (H2O2) and hydroxyl radicals (·OH) is often impeded by cross-sensitivities of commonly used photometric assays. To account for these imperfections, a step-by-step procedure to consolidate different analytical methods was developed. Depending on the applied current density, different electrolytically generated species can be detected selectively and enable the observation that the electrolytic generation of ozone only increases significantly for current densities above 0.5 A cm-2. In addition, the evolution of H2O2 only occurs in significant amounts in the presence of an organic impurity. The resulting, rapid decomposition of ozone via the peroxone process requires several equivalents of H2O2, depending on the present amount of dissolved O3. In order to provide a sensitive in- or on-line detection for ozone in ultrapure water, electrode materials based on Pt-functionalized ionomers were developed using a modified impregnation-reduction process. The metal loading on the sensor material was determined satisfactorily using a non-destructive approach by means of computed tomography (CT). Different synthesis conditions led to different sensor properties in terms of sensitivity and applicable concentration range. After evaluation of different models by an objective information criterion, the potentiometric sensor behavior is best described by a Langmuir pseudo-isotherm. On average, 2.9 μg L-1 of dissolved ozone was found as the detection limit for all sensor materials produced, which is comparable to complex reference analysis. Extending the application range for PEM electrolysis to the drinking water sector was evaluated by exposing the analytic feed to different water hardness levels. The electroosmosis of water is a direct function of the current density and can be estimated at 95 ± 2 mmol A-1 h-1. The transport rates of sodium, potassium, calcium and magnesium ions were modeled as a function of the current density and water hardness and were directly related to the ion mobility, independent of the water quality. Permeation leads to higher pH values of the catholyte within a few minutes and consequently to insoluble hydroxides and carbonates of the formerly dissolved hardeners. The introduction of an auxiliary cathode in the anode compartment was able to reduce tap water cation permeation indiscriminately by 18 ± 4 %. The results show that the selected methods are suitable and directly applicable for the sensitive and selective detection of in situ produced disinfectants, in particular electrolytically generated ozone in the aqueous phase. An initial transfer of this PEM electrolyzer into the tap and drinking water environment is showcased the example of temporarily stagnant water with a constructive solution for suppressing unwanted ion crossover.

The Garrulus Field-D dataset represents a 0.3-hectare post-harvest area located in the Arnsberg Forest, Germany. Data was acquired using an Unmanned Aerial Vehicle (UAV), and the area was reconstructed into a geo-referenced RGB orthomosaic with a spatial resolution of approximately 10 cm per pixel.

In mobile network research, the integration of real-world components such as User Equipment (UE) with open-source network infrastructure is essential yet challenging. To address these issues, we introduce open5Gcube, a modular framework designed to integrate popular open-source mobile network projects into a unified management environment. Our publicly available framework allows researchers to flexibly combine different open-source implementations, including different versions, and simplifies experimental setups through containerization and lightweight orchestration. We demonstrate the practical usability of open5Gcube by evaluating its compatibility with various commercial off-the-shelf (COTS) smartphones and modems across multiple mobile generations (2G, 4G, and 5G). The results underline the versatility and reproducibility of our approach, significantly advancing the accessibility of rigorous experimentation in mobile network laboratories.

The first Data Competence College was hosted from March 27th to 28th, 2025 at the IT center of RWTH Aachen. Based on the concept of the Wissenschaftskolleg in Berlin or the Institute of Advanced Studies in Princeton, we invited two individuals with high data competence from different scientific fields (“Data Experts”) to participate as part of the data competence college: Prof. Sebastian Houben (Hochschule Bonn-Rhein-Sieg, specialist in AI and autonomous systems) Dr. Moritz Wolter (University of Bonn, expert in high performance computing and machine learning) For two days we aimed to create a space where not only local scientists, and especially early career researchers, learn from the data experts and each other regarding research data and methods but also data experts could inspire each other. The schedule included keynote presentations by all data experts, poster and group presentations by the participants, 1:1 sessions between data experts and early career researchers, as well as a method- and data-related workshop. We aimed foremost to create an environment in which everyone feels safe to give input, share their knowledge and learn from the other participants and experts.

Background: Soluble CD21 (sCD21) is the product of metalloprotease-mediated proteolysis of CD21, a mechanism in which the entire extracellular domain of CD21 is shed from the cell surface. Through its retained ligand-binding ability and presence in human serum, sCD21 joins the growing list of surface proteins shed from the leukocyte cell surface which allows modulation of the immune response. Summary: sCD21 plays a multifaceted role in the body, including the promotion of inflammatory responses through receptor-ligand interactions with monocyte CD23, acting as a decoy receptor during Epstein-Barr virus infection preventing lymphoproliferation, and suppression of IgG and IgE responses by competitively inhibiting cell surface CD21. Clinical studies have shown that in comparison with healthy individuals, levels of sCD21 in serum are significantly altered in various diseases, highlighted by diverse viral infections, B-cell leukemias, and autoimmune disorders. Key Messages: Although findings of prevalence and functionality suggest sCD21 to be a key modulator of cellular and humoral immunity, questions remain about its origins and the regulation of its responses. Here, we aim to clarify and connect the advances in understanding sCD21 over time with emphasis on its generation by surface cleavage, binding partners, and functional roles. We also provide an outlook on its clinical significance and usage as a diagnostic target and therapeutic biomarker to monitor treatment efficacy in the context of chronic autoimmune disorders.

Der vorliegende Beitrag unternimmt den Versuch, Wissenschaftskommunikation auch als medienästhetische Praxis zu lesen, indem die Entwicklung eines spielerischen Formats in einem geisteswissenschaftlichen Forschungslabor untersucht wird. Das Labor wird dabei als ein Hybrid zwischen räumlichem Format und akademischer Formation verstanden, dessen Gestaltung, Ausstattung und spezifische Praktiken miteinander verflochten sind. Im Fokus steht daher die dynamische Interaktion zwischen Raum und Akteur:innen, die als interdependentes System begriffen wird, das fünf Praktiken – Dokumentieren, Forschen, Spielen, Verhandeln und Produzieren – umfasst. Verdeutlicht werden diese Praktiken am Beispiel der Entwicklung eines Brettspiels als Format der Wissenschaftskommunikation, was mittels ethnografischer Methoden begleitet und dokumentiert wird. Anhand dieses Fallbeispiels wird schließlich das Innovationspotential des Spiels als (Forschungs-)Format, (Forschungs-)Methode und (Forschungs-)Gegenstand reflektiert. Dieser Rahmen bildet, so die Perspektive, schließlich eine spezifische ‚Labor-Atmosphäre‘ heraus. Mithin lässt sich ein solches ‚Spiel-Labor‘ als experimenteller Raum charakterisieren, der konventionelle Methoden mit ästhetischen Explorationen konfrontiert.

Quadruped robots excel in traversing complex, unstructured environments where wheeled robots often fail. However, enabling efficient and adaptable locomotion remains challenging due to the quadrupeds' nonlinear dynamics, high degrees of freedom, and the computational demands of real-time control. Optimization-based controllers, such as Nonlinear Model Predictive Control (NMPC), have shown strong performance, but their reliance on accurate state estimation and high computational overhead makes deployment in real-world settings challenging. In this work, we present a Multi-Task Learning (MTL) framework in which expert NMPC demonstrations are used to train a single neural network to predict actions for multiple locomotion behaviors directly from raw proprioceptive sensor inputs. We evaluate our approach extensively on the quadruped robot Go1, both in simulation and on real hardware, demonstrating that it accurately reproduces expert behavior, allows smooth gait switching, and simplifies the control pipeline for real-time deployment. Our MTL architecture enables learning diverse gaits within a unified policy, achieving high $R^{2}$ scores for predicted joint targets across all tasks.

Hybrid energy storage systems (HESS), consisting of a battery, hydrogen storage, electrolyzer and fuel cell, have received increasing attention from the scientific community in recent years as they can help increase the use of renewable energy. In this paper, a novel metamodel-based sizing optimization workflow is developed. The workflow is demonstrated on residential building scenarios with real solar power data. Sobol sequences are utilized to vary the component size in simulation, while radial basis functions are employed to approximate simulation results, which are then used for the optimization. The utilization of waste heat from Power-to-Gas and Gas-to-Power processes is evaluated using thermal equations by extending an existing coupled electrochemical and thermodynamic HESS model built in the multiphysical energy system simulator MEgy. The results show a functional workflow that optimizes sizing for a given scenario and outputs the component’s daily energy balance and state of charge.

This thesis investigates how consumers perceive the value of products in the context of online shopping, particularly on Amazon, and how this perception influences their ability to detect fake reviews. With the shift from traditional to digital retail, challenges arise in how consumers assess product value without direct interaction. Given the rise of deceptive online practices like fake reviews, understanding human detection of such misinformation is crucial. The study integrates two theoretical perspectives: the human detection of fake reviews and the concept of perceived customer value. While most research focuses on algorithmic detection, this study emphasizes consumer decision-making and review evaluation. It builds on Holbrook’s customer value typology and tests Leroi-Werelds’ 2019 update, which introduces 24 value types (positive and negative) relevant to modern digital consumption. A quantitative approach was employed using a survey that examined participants' responses to seven Amazon products of varying value levels (e.g. laptop, wireless headphones, robot vacuums). The Consumer Value Index was operationalized with 21 value types tailored to product assessment and the data was analyzed using structural equation modeling. The results support that Leroi-Werelds' typology effectively captures perceived customer value. This contributes to better understanding of consumer behavior and can help design more effective e-commerce strategies and safeguards against fake reviews.

The remarkable success of Deep Learning approaches is often based and demonstrated on large public datasets. However, when applying such approaches to internal, private datasets, one frequently faces challenges arising from structural differences in the datasets, domain shift, and the lack of labels. In this work, we introduce Tabular Data Adapters (TDA), a novel method for generating soft labels for unlabeled tabular data in outlier detection tasks. By identifying statistically similar public datasets and transforming private data (based on a shared autoencoder) into a format compatible with state-of-the-art public models, our approach enables the generation of weak labels. It thereby can help to mitigate the cold start problem of labeling by basing on existing outlier detection models for public datasets. In experiments on 50 tabular datasets across different domains, we demonstrate that our method is able to provide more accurate annotations than baseline approaches while reducing computational time. Our approach offers a scalable, efficient, and cost-effective solution, to bridge the gap between public research models and real-world industrial applications.

Background The increased application of oxidative water treatment is associated with the presence of halogenated disinfection by-products (DBPs) in aqueous systems. Due to their hazard potential, permanent measurements of selected analytes are required to monitor their compliance with regulatory limits and guidelines. However, the simultaneous acquisition of alarming inorganic oxyhalide species by conventional ion chromatography (IC) is often impeded by co-elution of interfering analyte or matrix components, especially when DBPs of multiple halogens are present in solution. This necessitates a complementary, orthogonal detection setup that allows for an element-specific analysis.

Germany aims to achieve a national climate-neutral energy system by 2045. The residential sector still accounts for 29% of end energy consumption, with 74% attributed to the direct use of fossil fuels for heating and hot water. In order to reduce fossil energy use in the household sector, great efforts are being made to design new energy concepts that expand the use of renewable energies to supply electricity and heat. One possibility is to convert parts of the natural gas grid to a hydrogen-based gas grid to deliver and store energy for urban quarters of buildings, especially with older building stock where electrification of heat via heat pumps is difficult due to technical, acoustical, and economic reasons. A comprehensive dataset was generated by a bottom-up analysis with open governmental and statistical data to determine regional building types regarding energy demand, solar potential, and existing grid infrastructure. The buildings’ connections to the electricity, gas, and district heating networks are considered. From this, a representative sample dataset was chosen as input for a newly developed energy system model based on energy flow simulation. The model simulates the interaction of hydrogen generation (HG) (from excess solar energy by electrolysis), storage in a metal-hydride storage (MHS) tank, and hydrogen use in a connected fuel cell (FC), forming a local PVPtGtHP (Photovoltaic Power-to-Gas-to-Heat-and-Power) network. Next to the seasonal hydrogen storage path (HSP), a battery will complete the system to form a hybrid energy storage system (HESS). Paired with seasonal time series for PV power, electricity and heat demand, and a model for connection to grid infrastructure, the simulation of different hydrogen applications and MHS placements aims to analyze operating times and energy share of the systems’ equipment and existing infrastructure. The method to obtain the data set together with the simulation model presented can be used by energy planners for cities, communities, and building developers to analyze the potentials of a quarter or region and plan a transition towards a more energy-efficient and sustainable energy system.

Russia's Maternity Capital program, launched in 2007, is a key policy with an obvious pro-natalist focus aimed at countering the country's long-term depopulation trend by encouraging families to have more children. Initially, providing financial incentives for second and subsequent births. The program was moderately effective in influencing reproductive decisions within this target group. In 2020, the government revised the Maternity Capital program to include first-born children. This redesign shifted the program's emphasis from promoting higher-order births, which are more likely to raise fertility, to broader poverty reduction among families with children, weakening its demographic effectiveness. This study provides a literature review and demographic analysis to evaluate the program's effectiveness in achieving its stated goals. The data sources include state legislation, strategic demographic and family policy documents, official government reports, peer-reviewed research, and expert opinions. The findings show that while Maternity Capital investments have supported families in improving housing and educational access, the program's limited flexibility, due to its paternalistic design restricting fund usage, may constrain its ability to meet diverse family needs. The study observed short-term increases in fertility. Still, the program alone has proven insufficient to reverse long-term demographic decline. This paper identifies gaps between the policy's intentions and the socioeconomic realities faced by families, offering recommendations to improve the Maternity Capital program to achieve more favourable demographic outcomes and address social protection needs and providing insights for policymakers and researchers from the standpoint of its impacts on family social protection and child well-being. It argues that financial support must be substantial, well-targeted, particularly toward families considering a second or subsequent child, and paired with complementary pro-natalist measures rather than replaced by them.