Open Access

The use of nematic liquid crystal (LC) mixtures for microwave frequency applicationspresents a fundamental drawback: many of these mixtures have not been properly characterizedat these frequencies, and researchers do not have an a priori clear idea of which behavior they canexpect. This work is focused on developing a new procedure for the extraction of the main parametersof a nematic liquid crystal: dielectric permittivity and loss tangent at 11 GHz under differentpolarization voltages; splay elastic constantK11, which allows calculation of the threshold voltage(Vth); and rotational viscosityγ11, which allows calculating the response time of any arbitrary device.These properties will be calculated by using a resonator-based method, which is implementedwith a new topology of substrate integrated transmission line. The LC molecules should be rotated(polarized) by applying an electric field in order to extract the characteristic parameters; thus,the transmission line needs to have two conductors and low electric losses in order to preserve theintegrity of the measurements. This method was applied to a well-known liquid crystal mixture(GT3-23002 from MERCK) obtaining the permittivity and loss tangent versus bias voltage curves,the splay elastic constant, and the rotational viscosity of the mixture. The results validate the viabilityof the proposed method.

Hydrogen is nowadays in focus as an energy carrier that is locally emission free. Especiallyin combination with fuel-cells, hydrogen offers the possibility of a CO2neutral mobility, providedthat the hydrogen is produced with renewable energy. Structural parts of automotive componentsare often made of steel, but unfortunately they may show degradation of the mechanical propertieswhen in contact with hydrogen. Under certain service conditions, hydrogen uptake into the appliedmaterial can occur. To ensure a safe operation of automotive components, it is therefore necessary toinvestigate the time, temperature and pressure dependent hydrogen uptake of certain steels, e.g., todeduct suitable testing concepts that also consider a long term service application. To investigate thematerial dependent hydrogen uptake, a tubular autoclave was set-up. The underlying paper describesthe set-up of this autoclave that can be pressurised up to 20 MPa at room temperature and can beheated up to a temperature of 250◦C, due to an externally applied heating sleeve. The second focusof the paper is the investigation of the pressure dependent hydrogen solubility of the martensiticstainless steel 1.4418. The autoclave offers a very fast insertion and exertion of samples and thereforehas significant advantages compared to commonly larger autoclaves. Results of hydrogen chargingexperiments are presented, that were conducted on the Nickel-martensitic stainless steel 1.4418.Cylindrical samples 3 mm in diameter and 10 mm in length were hydrogen charged within theautoclave and subsequently measured using thermal desorption spectroscopy (TDS). The resultsshow how hydrogen sorption curves can be effectively collected to investigate its dependence ontime, temperature and hydrogen pressure, thus enabling, e.g., the deduction of hydrogen diffusioncoefficients and hydrogen pre-charging concepts for material testing.

Ghana suffers from frequent power outages, which can be compensated by off-grid energysolutions. Photovoltaic-hybrid systems become more and more important for rural electrificationdue to their potential to offer a clean and cost-effective energy supply. However, uncertainties relatedto the prediction of electrical loads and solar irradiance result in inefficient system control and canlead to an unstable electricity supply, which is vital for the high reliability required for applicationswithin the health sector. Model predictive control (MPC) algorithms present a viable option to tacklethose uncertainties compared to rule-based methods, but strongly rely on the quality of the forecasts.This study tests and evaluates (a) a seasonal autoregressive integrated moving average (SARIMA)algorithm, (b) an incremental linear regression (ILR) algorithm, (c) a long short-term memory (LSTM)model, and (d) a customized statistical approach for electrical load forecasting on real load data of aGhanaian health facility, considering initially limited knowledge of load and pattern changes throughthe implementation of incremental learning. The correlation of the electrical load with exogenousvariables was determined to map out possible enhancements within the algorithms. Results showthat all algorithms show high accuracies with a median normalized root mean square error (nRMSE)<0.1 and differing robustness towards load-shifting events, gradients, and noise. While the SARIMAalgorithm and the linear regression model show extreme error outliers of nRMSE >1, methods viathe LSTM model and the customized statistical approaches perform better with a median nRMSE of0.061 and stable error distribution with a maximum nRMSE of <0.255. The conclusion of this study isa favoring towards the LSTM model and the statistical approach, with regard to MPC applicationswithin photovoltaic-hybrid system solutions in the Ghanaian health sector.

This article explores the relationship between digital transformation and disaster risk.Vulnerability studies aim at differentiating impacts and losses by using fine-grained information fromdemographic, social, and personal characteristics of humans. With ongoing digital development,these characteristics will transform and result in new traits, which need to be identified andintegrated. Digital transformations will produce new social groups, partly human, semi-human,or non-human—some of which already exist, and some which can be foreseen by extrapolating fromrecent developments in the field of brain wearables, robotics, and software engineering. Thoughinvolved in the process of digital transformation, many researchers and practitioners in the field ofDisaster Risk Reduction or Climate Change Adaptation are not yet aware of the repercussions fordisaster and vulnerability assessments. Emerging vulnerabilities are due to a growing dependency ondigital services and tools in the case of a severe emergency or crisis. This article depicts the differentimplications for future theoretical frameworks when identifying novel semi-human groups and theirvulnerabilities to disaster risks. Findings include assumed changes within common indicators of socialvulnerability, new indicators, a typology of humans, and human interrelations with digital extensionsand two different perspectives on these groups and their dependencies with critical infrastructure.

The Enhancement of standard dense phase carbon dioxide (DPCD) pasteurization by additional mechanical effects wasassessed in this work. These effects were induced during pasteurization by the sudden depressurization in a narrow mini-tube. The high flow velocities, moderate pressures (40–80 bar) and low temperatures (25–45°C) lead to intense degasifica-tion and shear stress. The inactivation of the test microorganismEscherichia coliDH5a(E. coliDH5a) was determinedbefore and after depressurization in the minitube, representing entirely chemical DPCD via dissolved CO2and total inacti-vation comprising the effects of dissolved CO2and mechanical effects, respectively. Compared to conventional DPCDpasteurization, which is mostly attributed to chemical effects, the additional mechanical effects increased the inactivationefficiency considerably.