Amplitude envelopes as a means to quantify vowel length contrasts

publishe

Distribution System Monitoring for Smart Power Grids with Distributed Generation Using Artificial Neural Networks

The increasing number of distributed generators connected to distribution grids requires a reliable monitoring of such grids. Economic considerations prevent a full observation of distribution grids with direct measurements. First approaches using a limited number of measurements to monitor such grids exist, some of which use artificial neural networks (ANN). The current ANN-based approaches, however, are limited to static topologies, only estimate voltage magnitudes, do not work properly when confronted with a high amount of distributed generation and often yield inaccurate results. These strong limitations have prevented a true applicability of ANN for distribution grid monitoring. The objective of this paper is to overcome the limitations of existing approaches. We do that by presenting an ANN-based scheme, which advances the state-of-the-art in several ways: Our scheme can cope with a very low number of measurements, far less than is traditionally required by the state-of-the-art weighted least squares state estimation (WLS SE). It can estimate both voltage magnitudes and line loadings with high precision and includes different switching states as inputs. Our contribution consists of a method to generate useful training data by using a scenario generator and a number of hyperparameters that define the ANN architecture. Both can be used for different grids even with a high amount of distributed generation. Simulations are performed with an elaborate evaluation approach on a real distribution grid and a CIGRE benchmark grid both with a high amount of distributed generation from photovoltaics and wind energy converters. They demonstrate that the proposed ANN scheme clearly outperforms state-of-the-art ANN schemes and WLS SE under normal operating conditions and different situations such as gross measurement errors when comparing voltage magnitude and line magnitude estimation errors.Comment: 12 pages, 10 figures, 5 tables, preprin

pandapower - an Open Source Python Tool for Convenient Modeling, Analysis and Optimization of Electric Power Systems

pandapower is a Python based, BSD-licensed power system analysis tool aimed at automation of static and quasi-static analysis and optimization of balanced power systems. It provides power flow, optimal power flow, state estimation, topological graph searches and short circuit calculations according to IEC 60909. pandapower includes a Newton-Raphson power flow solver formerly based on PYPOWER, which has been accelerated with just-in-time compilation. Additional enhancements to the solver include the capability to model constant current loads, grids with multiple reference nodes and a connectivity check. The pandapower network model is based on electric elements, such as lines, two and three-winding transformers or ideal switches. All elements can be defined with nameplate parameters and are internally processed with equivalent circuit models, which have been validated against industry standard software tools. The tabular data structure used to define networks is based on the Python library pandas, which allows comfortable handling of input and output parameters. The implementation in Python makes pandapower easy to use and allows comfortable extension with third-party libraries. pandapower has been successfully applied in several grid studies as well as for educational purposes. A comprehensive, publicly available case-study demonstrates a possible application of pandapower in an automated time series calculation

Surface defects reduce Carbon Nanotube toxicity in vitro

The cytotoxicity of two different types of Multi-walled Carbon Nanotubes (MWCNTs)in A549 lung epithelial cells and HepG2 hepatocytes was investigated. One MWCNT still contained iron that was used as a catalyst during production, while the other one had all iron removed in a post-production heat treatment resulting in significantly fewer surface defects. The WST-8 assay was applied to test cell viability. To check the integrity of the cell membrane, we performed the lactate dehydrogenases assay (LDH)and measured the cellular production of reactive oxygen species (ROS). Finally, to examine cell proliferation, we conducted a cell cycle analysis. The results showed a dose- and time-dependent decrease in cell viability for both MWCNTs in both cell types. Moreover, a dose- and time-dependent increase in LDH leakage was detected, thereby indicating a decreased membrane integrity. The production of ROS was significantly increased in the case of the heat-treated MWCNTs. The heat-treated MWCNTs showed significantly stronger adverse effects when compared to the non-treated MWCNTs. Additionally, the heat-treated MWCNTs induced a dose-dependent cell cycle arrest in A549 cells. Both MWCNTs induced a significant cytotoxicity, whereby the heat treatment, leading to a decrease in surface defects, further increased the indicated adverse effects. © 2019 The Author

Evaluating Ecological Sustainability For The Planning and Operations Of Storage Technologies

With an expected future increase of costs for carbon emissions the logistics industry is targeting to design sustainable warehouses to reduce their carbon footprints. To do so, it is required that every aspect of a warehouse from its general design to the transport processes and technologies must be assessed in terms of its carbon footprint. In this article the carbon footprint, which can be traced back to the storage technology employed within a storage area is analysed. The approach includes surface, material, and technology-related data to calculate the carbon footprint of a logistics concept. Firstly, different dimensions of storage technology carbon footprints are identified. A comprehen-sive model is provided to calculate the carbon footprint of alternative storage technologies in a warehouse. The model is applied in a case study with actual data from a warehouse planning project in the German production industry comparing three alternative storage technologies for a small part storage solution. The author's find highest carbon footprint in the application of an autonomous guided vehicle shelving system compared to automatic storage and retrieval system and manual storage solution using Kanban racks

The Role of ICG in Robot-Assisted Liver Resections

Introduction: Robotic-assisted liver surgery (RALS) with its known limitations is gaining more importance. The fluorescent dye, indocyanine green (ICG), is a way to overcome some of these limitations. It accumulates in or around hepatic masses. The integrated near-infrared cameras help to visualize this accumulation. We aimed to compare the influence of ICG staining on the surgical and oncological outcomes in patients undergoing RALS. Material and Methods: Patients who underwent RALS between 2014 and 2021 at the Department of General Surgery at the University Hospital Schleswig-Holstein, Campus Kiel, were included. In 2019, ICG-supported RALS was introduced. Results: Fifty-four patients were included, with twenty-eight patients (50.9%) receiving preoperative ICG. Hepatocellular carcinoma (32.1%) was the main entity resected, followed by the metastasis of colorectal cancers (17%) and focal nodular hyperplasia (15.1%). ICG staining worked for different tumor entities, but diffuse staining was noted in patients with liver cirrhosis. However, ICG-supported RALS lasted shorter (142.7 ± 61.8 min vs. 246.4 ± 98.6 min, p < 0.001), tumors resected in the ICG cohort were significantly smaller (27.1 ± 25.0 mm vs. 47.6 ± 35.2 mm, p = 0.021) and more R0 resections were achieved by ICG-supported RALS (96.3% vs. 80.8%, p = 0.075). Conclusions: ICG-supported RALS achieve surgically and oncologically safe results, while overcoming the limitations of RALS