@article{LondongBarthSoebke,
  author    = {Londong, J{\"o}rg and Barth, Marcus and S{\"o}bke, Heinrich},
  title     = {Reducing antimicrobial resistances by source separation of domestic wastewater},
  series = {Frontiers in Environmental Health},
  volume    = {2023},
  journal   = {Frontiers in Environmental Health},
  number    = {Volume 2, article 1151898},
  publisher = {Frontiers Media},
  address   = {Lausanne},
  doi       = {10.3389/fenvh.2023.1151898},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20230403-49483},
  pages     = {1 -- 5},
  abstract  = {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},
  subject      = {Allgemeinheit},
  language  = {en}
}
@article{LizarazuHarirchianShaiketal.,
  author    = {Lizarazu, Jorge and Harirchian, Ehsan and Shaik, Umar Arif and Shareef, Mohammed and Antoni-Zdziobek, Annie and Lahmer, Tom},
  title     = {Application of machine learning-based algorithms to predict the stress-strain curves of additively manufactured mild steel out of its microstructural characteristics},
  series = {Results in Engineering},
  volume    = {2023},
  journal   = {Results in Engineering},
  number    = {Volume 20 (2023)},
  publisher = {Elsevier},
  address   = {Amsterdam},
  doi       = {10.1016/j.rineng.2023.101587},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20231207-65028},
  pages     = {1 -- 12},
  abstract  = {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.},
  subject      = {Maschinelles Lernen},
  language  = {en}
}
@article{PollackLueckWolfetal.,
  author    = {Pollack, Moritz and L{\"u}ck, Andrea and Wolf, Mario and Kraft, Eckhard and V{\"o}lker, Conrad},
  title     = {Energy and Business Synergy: Leveraging Biogenic Resources from Agriculture, Waste, and Wastewater in German Rural Areas},
  series = {Sustainability},
  volume    = {2023},
  journal   = {Sustainability},
  number    = {volume 15, issue 24, article 16573},
  publisher = {MDPI},
  address   = {Basel},
  doi       = {10.3390/su152416573},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20231222-65172},
  pages     = {1 -- 25},
  abstract  = {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.},
  subject      = {Energiewende},
  language  = {en}
}