TY - JOUR A1 - Londong, Jörg A1 - Barth, Marcus A1 - Söbke, Heinrich T1 - Reducing antimicrobial resistances by source separation of domestic wastewater JF - Frontiers in Environmental Health N2 - Antimicrobial resistances (AMR) are ranked among the top ten threats to public health and societal development worldwide. Toilet wastewater contained in domestic wastewater is a significant source of AMR entering the aquatic environment. The current commonly implemented combined sewer systems at times cause overflows during rain events, resulting in the discharge of untreated wastewater into the aquatic environment, thus promoting AMR. In this short research article, we describe an approach to transform combined sewer systems into source separation-modified combined sewer systems that separately treat toilet wastewater. We employ simulations for demonstrating that source separation-modified combined sewer systems reduce the emission of AMR- causing substances by up to 11.5 logarithm levels. Thus, source separation- modified combined sewer systems are amongst the most effective means of combating AMR. KEYWORDS KW - Allgemeinheit KW - Öffentlichkeit KW - Gesundheit KW - Kanal KW - Abwasser KW - Antibiotikum KW - Resistenz KW - OA-Publikationsfonds2023 Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20230403-49483 UR - https://www.frontiersin.org/articles/10.3389/fenvh.2023.1151898/full VL - 2023 IS - Volume 2, article 1151898 SP - 1 EP - 5 PB - Frontiers Media CY - Lausanne ER - TY - JOUR A1 - Grimme, Sophie Alice A1 - Kollakidou, Avgi A1 - Sønderskov Zarp, Christian A1 - Hornecker, Eva A1 - Krüger, Norbert A1 - Graf, Phillip A1 - Marchetti, Emanuela T1 - Floor Cleaners as Helper Pets: Projecting Assistive Robots’ Agency on Zoomorphic Affordances JF - SN Computer Science N2 - Care of ageing adults has become a dominant field of application for assistive robot technologies, promising support for ageing adults residing in care homes and staff, in dealing with practical routine tasks and providing social and emotional relieve. A time consuming and human intensive necessity is the maintenance of high hygiene quality in care homes. Robotic vacuum cleaners have been proven effective for doing the job elsewhere, but—in the context of care homes—are counterproductive for residents’ well-being and do not get accepted. This is because people with dementia manifest their agency in more implicit and emotional ways, while making sense of the world around them. Starting from these premises, we explored how a zoomorphic designed vacuum cleaner could better accommodate the sensemaking of people with dementia. Our design reconceptualises robotic vacuum cleaners as a cat-like robot, referring to a playful behaviour and appearance to communicate a non-threatening and familiar role model. Data from an observational study shows that residents responded positively to our prototype, as most of them engaged playfully with it as if it was a pet or a cat-like toy, for example luring it with gestures. Some residents simply ignored the robot, indicating that it was not perceived as frightening or annoying. The level of activity influenced reactions; residents ignored our prototype if busy with other occupations, which proves that it did not cause significant disturbance. We further report results from focus group sessions with formal and informal caregivers who discussed a video prototype of our robot. Caregivers encouraged us to enhance the animal like characteristics (in behaviour and materiality) even further to result in richer interactions and provoke haptic pleasure but also pointed out that residents should not mistake the robot for a real cat. KW - Alter KW - Pflege KW - Serviceroboter KW - Mensch-Maschine-Kommunikation KW - Care home KW - Dementia KW - Assistive robot Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20230524-63796 UR - https://link.springer.com/article/10.1007/s42979-023-01769-2 VL - 2023 IS - volume 4, article 372 SP - 1 EP - 14 PB - Springer CY - Singapur ER - TY - JOUR A1 - Lizarazu, Jorge A1 - Harirchian, Ehsan A1 - Shaik, Umar Arif A1 - Shareef, Mohammed A1 - Antoni-Zdziobek, Annie A1 - Lahmer, Tom T1 - Application of machine learning-based algorithms to predict the stress-strain curves of additively manufactured mild steel out of its microstructural characteristics JF - Results in Engineering N2 - The study presents a Machine Learning (ML)-based framework designed to forecast the stress-strain relationship of arc-direct energy deposited mild steel. Based on microstructural characteristics previously extracted using microscopy and X-ray diffraction, approximately 1000 new parameter sets are generated by applying the Latin Hypercube Sampling Method (LHSM). For each parameter set, a Representative Volume Element (RVE) is synthetically created via Voronoi Tessellation. Input raw data for ML-based algorithms comprises these parameter sets or RVE-images, while output raw data includes their corresponding stress-strain relationships calculated after a Finite Element (FE) procedure. Input data undergoes preprocessing involving standardization, feature selection, and image resizing. Similarly, the stress-strain curves, initially unsuitable for training traditional ML algorithms, are preprocessed using cubic splines and occasionally Principal Component Analysis (PCA). The later part of the study focuses on employing multiple ML algorithms, utilizing two main models. The first model predicts stress-strain curves based on microstructural parameters, while the second model does so solely from RVE images. The most accurate prediction yields a Root Mean Squared Error of around 5 MPa, approximately 1% of the yield stress. This outcome suggests that ML models offer precise and efficient methods for characterizing dual-phase steels, establishing a framework for accurate results in material analysis. KW - Maschinelles Lernen KW - Baustahl KW - Spannungs-Dehnungs-Beziehung KW - Arc-direct energy deposition KW - Mild steel KW - Dual phase steel KW - Stress-strain curve KW - OA-Publikationsfonds2023 Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20231207-65028 UR - https://www.sciencedirect.com/science/article/pii/S2590123023007144 VL - 2023 IS - Volume 20 (2023) SP - 1 EP - 12 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Pollack, Moritz A1 - Lück, Andrea A1 - Wolf, Mario A1 - Kraft, Eckhard A1 - Völker, Conrad T1 - Energy and Business Synergy: Leveraging Biogenic Resources from Agriculture, Waste, and Wastewater in German Rural Areas JF - Sustainability N2 - The imperative to transform current energy provisions is widely acknowledged. However, scant attention has hitherto been directed toward rural municipalities and their innate resources, notably biogenic resources. In this paper, a methodological framework is developed to interconnect resources from waste, wastewater, and agricultural domains for energy utilization. This entails cataloging existing resources, delineating their potential via quantitative assessments utilizing diverse technologies, and encapsulating them in a conceptual model. The formulated models underwent iterative evaluation with engagement from diverse stakeholders. Consequently, 3 main concepts, complemented by 72 sub-concepts, were delineated, all fostering positive contributions to climate protection and providing heat supply in the rural study area. The outcomes’ replicability is underscored by the study area’s generic structure and the employed methodology. Through these inquiries, a framework for the requisite energy transition, with a pronounced emphasis on the coupling of waste, wastewater, and agriculture sectors in rural environments, is robustly analyzed. KW - Energiewende KW - Energieplanung KW - Abfallwirtschaft KW - organischer Abfall KW - Biomasse KW - Regionalentwicklung KW - biogene Abfallstoffe KW - Abwassermanagement KW - OA-Publikationsfonds2023 Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20231222-65172 UR - https://www.mdpi.com/2071-1050/15/24/16573 VL - 2023 IS - volume 15, issue 24, article 16573 SP - 1 EP - 25 PB - MDPI CY - Basel ER - TY - JOUR A1 - Patzelt, Max A1 - Erfurt, Doreen A1 - Ludwig, Horst-Michael T1 - Quantification of cracks in concrete thin sections considering current methods of image analysis JF - Journal of Microscopy N2 - Image analysis is used in this work to quantify cracks in concrete thin sections via modern image processing. Thin sections were impregnated with a yellow epoxy resin, to increase the contrast between voids and other phases of the concrete. By the means of different steps of pre-processing, machine learning and python scripts, cracks can be quantified in an area of up to 40 cm2. As a result, the crack area, lengths and widths were estimated automatically within a single workflow. Crack patterns caused by freeze-thaw damages were investigated. To compare the inner degradation of the investigated thin sections, the crack density was used. Cracks in the thin sections were measured manually in two different ways for validation of the automatic determined results. On the one hand, the presented work shows that the width of cracks can be determined pixelwise, thus providing the plot of a width distribution. On the other hand, the automatically measured crack length differs in comparison to the manually measured ones. KW - Beton KW - Rissbildung KW - Bildanalyse KW - Maschinelles Lernen KW - Mikroskopie KW - concrete KW - crack KW - degradation KW - transmitted light microscopy Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20220811-46754 UR - https://onlinelibrary.wiley.com/doi/full/10.1111/jmi.13091 VL - 2022 IS - Volume 286, Issue 2 SP - 154 EP - 159 ER - TY - JOUR A1 - Chakraborty, Ayan A1 - Anitescu, Cosmin A1 - Zhuang, Xiaoying A1 - Rabczuk, Timon T1 - Domain adaptation based transfer learning approach for solving PDEs on complex geometries JF - Engineering with Computers N2 - In machine learning, if the training data is independently and identically distributed as the test data then a trained model can make an accurate predictions for new samples of data. Conventional machine learning has a strong dependence on massive amounts of training data which are domain specific to understand their latent patterns. In contrast, Domain adaptation and Transfer learning methods are sub-fields within machine learning that are concerned with solving the inescapable problem of insufficient training data by relaxing the domain dependence hypothesis. In this contribution, this issue has been addressed and by making a novel combination of both the methods we develop a computationally efficient and practical algorithm to solve boundary value problems based on nonlinear partial differential equations. We adopt a meshfree analysis framework to integrate the prevailing geometric modelling techniques based on NURBS and present an enhanced deep collocation approach that also plays an important role in the accuracy of solutions. We start with a brief introduction on how these methods expand upon this framework. We observe an excellent agreement between these methods and have shown that how fine-tuning a pre-trained network to a specialized domain may lead to an outstanding performance compare to the existing ones. As proof of concept, we illustrate the performance of our proposed model on several benchmark problems. KW - Maschinelles Lernen KW - NURBS KW - Transfer learning KW - Domain Adaptation KW - NURBS geometry KW - Navier–Stokes equations Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20220811-46776 UR - https://link.springer.com/article/10.1007/s00366-022-01661-2 VL - 2022 SP - 1 EP - 20 ER - TY - JOUR A1 - Guo, Hongwei A1 - Zhuang, Xiaoying A1 - Chen, Pengwan A1 - Alajlan, Naif A1 - Rabczuk, Timon T1 - Analysis of three-dimensional potential problems in non-homogeneous media with physics-informed deep collocation method using material transfer learning and sensitivity analysis JF - Engineering with Computers N2 - In this work, we present a deep collocation method (DCM) for three-dimensional potential problems in non-homogeneous media. This approach utilizes a physics-informed neural network with material transfer learning reducing the solution of the non-homogeneous partial differential equations to an optimization problem. We tested different configurations of the physics-informed neural network including smooth activation functions, sampling methods for collocation points generation and combined optimizers. A material transfer learning technique is utilized for non-homogeneous media with different material gradations and parameters, which enhance the generality and robustness of the proposed method. In order to identify the most influential parameters of the network configuration, we carried out a global sensitivity analysis. Finally, we provide a convergence proof of our DCM. The approach is validated through several benchmark problems, also testing different material variations. KW - Deep learning KW - Kollokationsmethode KW - Collocation method KW - Potential problem KW - Activation function KW - Transfer learning Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20220811-46764 UR - https://link.springer.com/article/10.1007/s00366-022-01633-6 VL - 2022 SP - 1 EP - 22 ER - TY - JOUR A1 - Alalade, Muyiwa A1 - Reichert, Ina A1 - Köhn, Daniel A1 - Wuttke, Frank A1 - Lahmer, Tom ED - Qu, Chunxu ED - Gao, Chunxu ED - Zhang, Rui ED - Jia, Ziguang ED - Li, Jiaxiang T1 - A Cyclic Multi-Stage Implementation of the Full-Waveform Inversion for the Identification of Anomalies in Dams JF - Infrastructures N2 - For the safe and efficient operation of dams, frequent monitoring and maintenance are required. These are usually expensive, time consuming, and cumbersome. To alleviate these issues, we propose applying a wave-based scheme for the location and quantification of damages in dams. To obtain high-resolution “interpretable” images of the damaged regions, we drew inspiration from non-linear full-multigrid methods for inverse problems and applied a new cyclic multi-stage full-waveform inversion (FWI) scheme. Our approach is less susceptible to the stability issues faced by the standard FWI scheme when dealing with ill-posed problems. In this paper, we first selected an optimal acquisition setup and then applied synthetic data to demonstrate the capability of our approach in identifying a series of anomalies in dams by a mixture of reflection and transmission tomography. The results had sufficient robustness, showing the prospects of application in the field of non-destructive testing of dams. KW - Damm KW - Defekt KW - inverse analysis KW - damage identification KW - full-waveform inversion KW - dams KW - wave propagation KW - OA-Publikationsfonds2022 Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20221201-48396 UR - https://www.mdpi.com/2412-3811/7/12/161 VL - 2022 IS - Volume 7, issue 12, article 161 PB - MDPI CY - Basel ER - TY - JOUR A1 - Mehling, Simon A1 - Schnabel, Tobias A1 - Londong, Jörg T1 - Investigation on Energetic Efficiency of Reactor Systems for Oxidation of Micro-Pollutants by Immobilized Active Titanium Dioxide Photocatalysis JF - Water N2 - In this work, the degradation performance for the photocatalytic oxidation of eight micropollutants (amisulpride, benzotriazole, candesartan, carbamazepine, diclofenac, gabapentin, methlybenzotriazole, and metoprolol) within real secondary effluent was investigated using three different reactor designs. For all reactor types, the influence of irradiation power on its reaction rate and energetic efficiency was investigated. Flat cell and batch reactor showed almost similar substance specific degradation behavior. Within the immersion rotary body reactor, benzotriazole and methylbenzotriazole showed a significantly lower degradation affinity. The flat cell reactor achieved the highest mean degradation rate, with half time values ranging from 5 to 64 min with a mean of 18 min, due to its high catalysts surface to hydraulic volume ratio. The EE/O values were calculated for all micro-pollutants as well as the mean degradation rate constant of each experimental step. The lowest substance specific energy per order (EE/O) values of 5 kWh/m3 were measured for benzotriazole within the batch reactor. The batch reactor also reached the lowest mean values (11.8–15.9 kWh/m3) followed by the flat cell reactor (21.0–37.0 kWh/m3) and immersion rotary body reactor (23.9–41.0 kWh/m3). Catalyst arrangement and irradiation power were identified as major influences on the energetic performance of the reactors. Low radiation intensities as well as the use of submerged catalyst arrangement allowed a reduction in energy demand by a factor of 3–4. A treatment according to existing treatment goals of wastewater treatment plants (80% total degradation) was achieved using the batch reactor with a calculated energy demand of 7000 Wh/m3. KW - Fotokatalyse KW - Abwasserreinigung KW - photocatalysis KW - micro-pollutant treatment KW - titanium dioxid KW - reactor design KW - energy per order KW - OA-Publikationsfonds2022 Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20220912-47130 UR - https://www.mdpi.com/2073-4441/14/17/2681 VL - 2022 IS - Volume 14, issue 7, article 2681 SP - 1 EP - 15 PB - MDPI CY - Basel ER - TY - JOUR A1 - Chowdhury, Sharmistha A1 - Zabel, Volkmar T1 - Influence of loading sequence on wind induced fatigue assessment of bolts in TV-tower connection block JF - Results in Engineering N2 - Bolted connections are widely employed in structures like transmission poles, wind turbines, and television (TV) towers. The behaviour of bolted connections is often complex and plays a significant role in the overall dynamic characteristics of the structure. The goal of this work is to conduct a fatigue lifecycle assessment of such a bolted connection block of a 193 m tall TV tower, for which 205 days of real measurement data have been obtained from the installed monitoring devices. Based on the recorded data, the best-fit stochastic wind distribution for 50 years, the decisive wind action, and the locations to carry out the fatigue analysis have been decided. A 3D beam model of the entire tower is developed to extract the nodal forces corresponding to the connection block location under various mean wind speeds, which is later coupled with a detailed complex finite element model of the connection block, with over three million degrees of freedom, for acquiring stress histories on some pre-selected bolts. The random stress histories are analysed using the rainflow counting algorithm (RCA) and the damage is estimated using Palmgren-Miner's damage accumulation law. A modification is proposed to integrate the loading sequence effect into the RCA, which otherwise is ignored, and the differences between the two RCAs are investigated in terms of the accumulated damage. KW - Schadensakkumulation KW - Lebenszyklus KW - Fatigue life KW - Damage accumulation KW - Wind load KW - Rainflow counting algorithm KW - Loading sequence KW - Windlast KW - OA-Publikationsfonds2022 Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20221028-47303 UR - https://www.sciencedirect.com/science/article/pii/S2590123022002730?via%3Dihub VL - 2022 IS - Volume 16, article 100603 SP - 1 EP - 18 PB - Elsevier CY - Amsterdam ER -