Predesign Considerations for the DC Link Voltage Level of the CENTRELINE Fuselage Fan Drive Unit

Electric propulsion (EP) systems offer considerably more degrees of freedom (DOFs) within the design process of aircraft compared to conventional aircraft engines. This requires large, computationally expensive design space explorations (DSE) with coupled models of the single components to incorporate interdependencies during optimization. The purpose of this paper is to exemplarily study these interdependencies of system key performance parameters (KPIs), e.g., system mass and efficiency, for a varying DC link voltage level of the power transmission system considering the example of the propulsion system of the CENTRELINE project, including an electric motor, a DC/AC inverter, and the DC power transmission cables. Each component is described by a physically derived, analytical model linking specific subdomains, e.g., electromagnetics, structural mechanics and thermal analysis, which are used for a coupled system model. This approach strongly enhances model accuracy and simultaneously keeps the computational effort at a low level. The results of the DSE reveal that the system KPIs improve for higher DC link voltage despite slightly inferior performance of motor and inverter as the mass of the DC power transmission cable has a major share for a an aircraft of the size as in the CENTRELINE project. Modeling of further components and implementation of optimization strategies will be part of future work

Shrinking droplets in electrospray ionization and their influence on chemical equilibria

We investigated how chemical equilibria are affected by the electrospray process, using simultaneous in situ measurements by laser-induced fluorescence (LIF) and phase Doppler anemometry (PDA). The motivation for this study was the increasing number of publications in which electrospray ionization mass spectrometry is used for binding constant determination. The PDA was used to monitor droplet size and velocity, whereas LIF was used to monitor fluorescent analytes within the electrospray droplets. Using acetonitrile as solvent, we found an average initial droplet diameter of 10 µm in the electrospray. The PDA allowed us to follow the evolution of these droplets down to a size of 1 µm. Rhodamine B-sulfonylchloride was used as a fluorescent analyte within the electrospray. By spatially resolved LIF it was possible to probe the dimerization equilibrium of this dye. Measurements at different spray positions showed no influence of the decreasing droplet size on the monomer-dimer equilibrium. However, with the fluorescent dye pair DCM and oxazine 1 it was shown that a concentration increase does occur within electrosprayed droplets, using fluorescence resonance energy transfer as a probe for the average pair distanc

Leveraging driver vehicle and environment interaction: Machine learning using driver monitoring cameras to detect drunk driving

Excessive alcohol consumption causes disability and death. Digital interventions are promising means to promote behavioral change and thus prevent alcohol-related harm, especially in critical moments such as driving. This requires real-time information on a person's blood alcohol concentration (BAC). Here, we develop an in-vehicle machine learning system to predict critical BAC levels. Our system leverages driver monitoring cameras mandated in numerous countries worldwide. We evaluate our system with n=30 participants in an interventional simulator study. Our system reliably detects driving under any alcohol influence (area under the receiver operating characteristic curve [AUROC] 0.88) and driving above the WHO recommended limit of 0.05g/dL BAC (AUROC 0.79). Model inspection reveals reliance on pathophysiological effects associated with alcohol consumption. To our knowledge, we are the first to rigorously evaluate the use of driver monitoring cameras for detecting drunk driving. Our results highlight the potential of driver monitoring cameras and enable next-generation drunk driver interaction preventing alcohol-related harm

New Polymers for Needleless Electrospinning from Low-Toxic Solvents.

Wortmann M, Frese N, Sabantina L, et al. New Polymers for Needleless Electrospinning from Low-Toxic Solvents. Nanomaterials (Basel, Switzerland). 2019;9(1): 52

Stabilization and Incipient Carbonization of Electrospun Polyacrylonitrile Nanofibers Fixated on Aluminum Substrates

Storck JL, Grothe T, Tuvshinbayar K, et al. Stabilization and Incipient Carbonization of Electrospun Polyacrylonitrile Nanofibers Fixated on Aluminum Substrates. Fibers. 2020;8(9): 55.Polyacrylonitrile (PAN) nanofibers, prepared by electrospinning, are often used as a precursor for carbon nanofibers. The thermal carbonization process necessitates a preceding oxidative stabilization, which is usually performed thermally, i.e., by carefully heating the electrospun nanofibers in an oven. One of the typical problems occurring during this process is a strong deformation of the fiber morphologies—the fibers become thicker and shorter, and show partly undesired conglutinations. This problem can be solved by stretching the nanofiber mat during thermal treatment, which, on the other hand, can lead to breakage of the nanofiber mat. In a previous study, we have shown that the electrospinning of PAN on aluminum foils and the subsequent stabilization of this substrate is a simple method for retaining the fiber morphology without breaking the nanofiber mat. Here, we report on the impact of different aluminum foils on the physical and chemical properties of stabilized PAN nanofibers mats, and on the following incipient carbonization process at a temperature of max. 600 °C, i.e., below the melting temperature of aluminum

Effectiveness and User Perception of an In-Vehicle Voice Warning for Hypoglycemia: Development and Feasibility Trial

Background: Hypoglycemia is a frequent and acute complication in type 1 diabetes mellitus (T1DM) and is associated with a higher risk of car mishaps. Currently, hypoglycemia can be detected and signaled through flash glucose monitoring or continuous glucose monitoring devices, which require manual and visual interaction, thereby removing the focus of attention from the driving task. Hypoglycemia causes a decrease in attention, thereby challenging the safety of using such devices behind the wheel. Here, we present an investigation of a hands-free technology—a voice warning that can potentially be delivered via an in-vehicle voice assistant. Objective: This study aims to investigate the feasibility of an in-vehicle voice warning for hypoglycemia, evaluating both its effectiveness and user perception. Methods: We designed a voice warning and evaluated it in 3 studies. In all studies, participants received a voice warning while driving. Study 0 (n=10) assessed the feasibility of using a voice warning with healthy participants driving in a simulator. Study 1 (n=18) assessed the voice warning in participants with T1DM. Study 2 (n=20) assessed the voice warning in participants with T1DM undergoing hypoglycemia while driving in a real car. We measured participants’ self-reported perception of the voice warning (with a user experience scale in study 0 and with acceptance, alliance, and trust scales in studies 1 and 2) and compliance behavior (whether they stopped the car and reaction time). In addition, we assessed technology affinity and collected the participants’ verbal feedback. Results: Technology affinity was similar across studies and approximately 70% of the maximal value. Perception measure of the voice warning was approximately 62% to 78% in the simulated driving and 34% to 56% in real-world driving. Perception correlated with technology affinity on specific constructs (eg, Affinity for Technology Interaction score and intention to use, optimism and performance expectancy, behavioral intention, Session Alliance Inventory score, innovativeness and hedonic motivation, and negative correlations between discomfort and behavioral intention and discomfort and competence trust; all P<.05). Compliance was 100% in all studies, whereas reaction time was higher in study 1 (mean 23, SD 5.2 seconds) than in study 0 (mean 12.6, SD 5.7 seconds) and study 2 (mean 14.6, SD 4.3 seconds). Finally, verbal feedback showed that the participants preferred the voice warning to be less verbose and interactive. Conclusions: This is the first study to investigate the feasibility of an in-vehicle voice warning for hypoglycemia. Drivers find such an implementation useful and effective in a simulated environment, but improvements are needed in the real-world driving context. This study is a kickoff for the use of in-vehicle voice assistants for digital health interventions

Platform Architecture for the Diagram Assessment Domain

Using e-learning and e-assessment environments in higher education bears considerable potential for both students and teachers. In this contribution we present an architecture for a comprehensive e-assessment platform for the modeling domain. The platform – currently developed in the KEA-Mod project – features a micro-service architecture and is based on different inter-operable components. Based on this idea, the KEA-Mod platform will provide e-assessment capabilities for various graph-based modeling languages such as Unified Modeling Language (UML), EntityRelationship diagrams (ERD), Petri Nets, Event-driven Process Chains (EPC) and the Business Process Model and Notation (BPMN) and their respective diagram types

Совершенствование кадровой политики предприятия

Объектом исследования является ПАО Сбербанк России. Цель работы– анализ существующей кадровой политики предприятия и выработка рекомендаций по ее усовершенствованию. В процессе исследования проводились исследования кадрового состава, эффективности использования кадров предприятия, адекватности кадровой политики целям и задачам предприятия. В результате исследования выделены проблемы кадровой политики ПАО Сбербанк России, предложены рекомендации по ее совершенствованию, оценена экономическая эффективность потенциальных изменений.The object of this study is to PAO Sberbank of Russia. The purpose work- analysis of the existing personnel policy of the company and make recommendations for its improvement. The study conducted research personnel, effective use of the human resources, the adequacy of personnel policy goals and objectives of the enterprise. The study highlighted the problem of personnel policy PAT Sberbank of Russia, offered recommendations for its improvement, estimated cost-effectiveness of potential changes

Machine learning for non‐invasive sensing of hypoglycaemia while driving in people with diabetes

Aim: To develop and evaluate the concept of a non-invasive machine learning (ML) approach for detecting hypoglycaemia based exclusively on combined driving (CAN) and eye tracking (ET) data. Materials and Methods: We first developed and tested our ML approach in pronounced hypoglycaemia, and then we applied it to mild hypoglycaemia to evaluate its early warning potential. For this, we conducted two consecutive, interventional studies in individuals with type 1 diabetes. In study 1 (n = 18), we collected CAN and ET data in a driving simulator during euglycaemia and pronounced ypoglycaemia (blood glucose [BG] 2.0-2.5 mmol L-1). In study 2 (n = 9), we collected CAN and ET data in the same simulator but in euglycaemia and mild hypoglycaemia (BG 3.0-3.5 mmol L-1). Results: Here, we show that our ML approach detects pronounced and mild hypoglycaemia with high accuracy (area under the receiver operating characteristics curve 0.88 ± 0.10 and 0.83 ± 0.11, respectively). Conclusions: Our findings suggest that an ML approach based on CAN and ET data, exclusively, enables detection of hypoglycaemia while driving. This provides a promising concept for alternative and non-invasive detection of hypoglycaemia