Open Access

Gestationsdiabetes mellitus (GDM) ist eine der häufigsten Komplikationen in der Schwangerschaft mit einer weltweit steigenden Prävalenz. Betroffene Schwangere und haben gegenüber gesunden Schwangeren ein höheres Risiko an perinataler Morbidität und Mortalität und eine höhere Rate an operativen Entbindungen. Für die Kinder von Frauen mit GDM impliziert dieser schon in der Schwangerschaft erhebliche neonatale und im Folgenden deutlich höhere Langzeitrisiken für (multi-) Morbidität. Im Hinblick auf eine Risikoreduktion für betroffene Frauen und Kinder sind Präventionsmaßnahmen durch Lebensstilveränderungen der gesunden Ernährung und Anpassung bzw. Erhöhung der mütterlichen körperlichen Aktivität innerhalb der Schwangerschaft, welche sich im besten Falle nachhaltig über die Schwangerschaft hinaus etabliert, essentiell. Im besonderen Fokus steht der Einbezug der fachlich involvierten Berufsgruppe der Hebammen, welche im geburtshilflichen Aufgabenfeld der (Sekundär-) Prävention etabliert sind und somit betroffenen Schwangeren professionelle Hilfestellungen anbieten können. Die folgende Ausarbeitung fokussiert die Gesundheitsförderung von Frauen mit GDM und deren Ungeborenen in der Schwangerschaft und darüber hinaus. Im Mittelpunkt steht dabei die ethische Reflexion eines von der Hochschule Esslingen beantragten For-schungsprojektes in Vorbereitung des erforderlichen ethischen Clearings durch die Ethikkommission der Deutschen Gesellschaft für Pflegewissenschaft (DGP).

Highly arousing, affective stimuli have adverse effects on cognition and performance. Perception of affective stimuli is, however, highly subjective and may impact on the interaction of emotion and cognition. Here, we tested the impact of high- versus low-threatening stimuli on response inhibition as a function of perceived threat intensity. Response inhibition was probed using a stop-signal paradigm in 62 healthy adults. We used stop-signals that had previously been paired with an unpleasant electrodermal stimulation (i.e., high-threat stimuli) or that had never been paired with electrodermal stimulation (i.e., low-threat stimuli). High-threat stimuli did not affect stopping performance in general. Only participants who perceived the high-threat stimuli as highly painful showed impaired response inhibition on high-threat trials relative to low-threat trials. Participants who perceived the high-threat as mildly painful, however, showed improved response inhibition on high-threat trials. This effect was not moderated by the current anxious state. This suggests that the impact of negative affective stimuli on cognition critically depends on subjective threat perception. Ratings of affective stimuli should be included in studies probing the emotion–cognition interaction because subjective perception might strongly impact on that interaction.

Our current mobility paradigm increasingly faces economic, ecological, and social limits in urban areas. The aim of this paper is to analyse if a fleet of shared autonomous electric vehicles (AEVs) can meet these challenges while satisfying the current requirements of privately-owned internal combustion engine vehicles (ICEVs). Therefore, analytical models have been developed to simulate and investigate the impacts of mobility behaviour in Berlin and Stuttgart (Germany). The collected data were used to calculate the fleet size, the energy consumption, the emission of particulate matter, nitrogen oxides, and the carbon footprint of different shared AEVs in comparison with privately owned ICEVs. The approach shows that the system of a shared AEV fleet could lower externalities (accident avoidance, traffic jams, free spaces, parking costs and lifetime losses) in cities and generate cost benefits for customers.

This master thesis shows a holistic approach for the optimization of the energy management task for a plug-in hybrid electric vehicle. The ‘Equivalent Consumption Minimization Strategy’ (‘ECMS’) as a local optimal approach is implemented into an embedded controller and applied to a system simulation model in ‘GT-SUITE’, which integrates a hybrid drivetrain and the associated control structure with a thermal management model. Two modifications and one extension to the basic ‘equivalent consumption’ cost function are proposed for the favor of an unambiguous interpretation of the penalty factor term, an enhanced applicability of the ‘ECMS’ close to the battery state of charge limit and an effective applicability of the ‘EMCS’ to the thermal management task. All proposed modifications and extensions prove their applicability in the virtual test environment and recommend themselves for the utilization in further application areas, like the integration of exhaust aftertreatment system, the holistic evaluation of a fuel cell drivetrain or the holistic evaluation of a hybrid ship propulsion system.

Purpose: Hazardous alcohol consumption among university students represents a prime health risk for this population. Due to a lack of appropriate German-language (online) prevention programs, an existing U.S.-American program was transferred to the German higher education area and then evaluated. Method: For this purpose, both qualitative focus groups and a quantitative evaluation were undertaken. The guided focus groups allowed a further adaptation of the prevention program: An improved frequency–quantity questionnaire for the drinking behavior of German-speaking students was developed, and thresholds for hazardous consumption were included. Results: The additional quantitative verification of the adaptation found that 65.9% of the surveyed students found the adapted program suitable for the higher education context and 62.6% stated that every university should provide such a program. Discussion: The adaptation process described here made it possible to develop an online prevention program tailored to the lifeworld of students in the German-speaking region.

Over the past few years, deep neural networks have shown excellent results in multiple tasks, however, there is still an increasing need to address the problem of interpretability to improve model transparency, performance, and safety. Achieving eXplainable Artificial Intelligence (XAI) by combining neural networks with continuous logic and multi-criteria decision-making tools is one of the most promising ways to approach this problem: by this combination, the black-box nature of neural models can be reduced. The continuous logic-based neural model uses so-called Squashing activation functions, a parametric family of functions that satisfy natural invariance requirements and contain rectified linear units as a particular case. This work demonstrates the first benchmark tests that measure the performance of Squashing functions in neural networks. Three experiments were carried out to examine their usability and a comparison with the most popular activation functions was made for five different network types. The performance was determined by measuring the accuracy, loss, and time per epoch. These experiments and the conducted benchmarks have proven that the use of Squashing functions is possible and similar in performance to conventional activation functions. Moreover, a further experiment was conducted by implementing nilpotent logical gates to demonstrate how simple classification tasks can be solved successfully and with high performance. The results indicate that due to the embedded nilpotent logical operators and the differentiability of the Squashing function, it is possible to solve classification problems, where other commonly used activation functions fail.

Slips and stumbles are main causes of falls and result in serious injuries. Balance training is widely applied for preventing falls across the lifespan. Subdivided into two main intervention types, biomechanical characteristics differ amongst balance interventions tailored to counteract falls: conventional balance training (CBT) referring to a balance task with a static ledger pivoting around the ankle joint versus reactive balance training (RBT) using externally applied perturbations to deteriorate body equilibrium. This study aimed to evaluate the efficacy of reactive, slip-simulating RBT compared to CBT in regard to fall prevention and to detect neuromuscular and kinematic dependencies. In a randomized controlled trial, 38 participants were randomly allocated either to CBT or RBT. To simulate stumbling scenarios, postural responses were assessed to posterior translations in gait and stance perturbation before and after 4 weeks of training. Surface electromyography during short- (SLR), medium- (MLR), and long-latency response of shank and thigh muscles as well as ankle, knee, and hip joint kinematics (amplitudes and velocities) were recorded. Both training modalities revealed reduced angular velocity in the ankle joint (P < 0.05) accompanied by increased shank muscle activity in SLR (P < 0.05) during marching in place perturbation. During stance perturbation and marching in place perturbation, hip angular velocity was decreased after RBT (P from TTEST, Pt < 0.05) accompanied by enhanced thigh muscle activity (SLR, MLR) after both trainings (P < 0.05). Effect sizes were larger for the RBT-group during stance perturbation. Thus, both interventions revealed modified stabilization strategies for reactive balance recovery after surface translations. Characterized by enhanced reflex activity in the leg muscles antagonizing the surface translations, balance training is associated with improved neuromuscular timing and accuracy being relevant for postural control. This may result in more efficient segmental stabilization during fall risk situations, independent of the intervention modality. More pronounced modulations and higher effect sizes after RBT in stance perturbation point toward specificity of training adaptations, with an emphasis on the proximal body segment for RBT. Outcomes underline the benefits of balance training with a clear distinction between RBT and CBT being relevant for training application over the lifespan.

This paper provides an analysis of the trend in autonomous driving traffic and the development of infrastructural support, whereas the requirements on the infrastructural support will be analyzed. Then selected traffic scenes will be implemented in an autonomous driving simulator tool in order to figure out the required parameters to assist the autonomous vehicle from the infrastructure.