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Knowledge Management in the Manufacturing Sector: Current Realities, Perceived Developments, and Practices
No full textThe publication addressed knowledge management as an increasingly critical success factor in Germany’s manufacturing industry due to demographic change, the associated foreseeable loss and already existing shortage of experienced specialists, and the increasing need for greater flexibility in production due to constantly changing conditions. This publication examines how companies currently deal with the retention and transfer of knowledge. A survey of six medium-sized industrial companies revealed the gap between knowledge management being considered highly relevant but often only implemented incompletely in practice. Best practices in the companies are presented and an outlook on future activities is provided – including planned workshops on current challenges in knowledge management, potential solutions, and the role of large language models (LLMs) in structuring and automating industrial knowledge management
Situation Awareness for Driver-Centric Driving Style Adaptation
No full textThere is evidence that the driving style of an autonomous vehicle is important to increase the acceptance and trust of the passengers. The driving situation has been found to have a significant influence on human driving behavior. However, current driving style models only partially incorporate driving environment information, limiting the alignment between an agent and the given situation. Therefore, we propose a situation-aware driving style model based on different visual feature encoders pretrained on fleet data, as well as driving behavior predictors, which are adapted to the driving style of a specific driver. Our experiments show that the proposed method outperforms all evaluated baselines significantly and forms plausible situation clusters. Furthermore, we found that feature encoders pretrained on our dataset lead to more precise driving behavior modeling. In contrast, feature encoders pretrained supervised and unsupervised on different data sources lead to more specific situation clusters, which can be utilized to constrain and control the driving style adaptation for specific situations. Moreover, in a real-world setting, where driving style adaptation is happening iteratively, we found the MLP-based behavior predictors achieve good performance initially but suffer from catastrophic forgetting. In contrast, behavior predictors based on situation-dependent statistics can learn iteratively from continuous data streams by design. Overall, our experiments show that important information for driving behavior prediction is contained within the visual feature encoder. The dataset is publicly available at https://huggingface.co/datasets/jHaselberger/SADC-Situation-Awareness-for-Driver-Centric-Driving-Style-Adaptation
Design of twist-modified lab-scale wind turbine rotors for enhanced wake recovery
No full textAbstract
Enhancing power production in wind farms by improved wake recovery has emerged as a major research focus in recent years. By implementing flow control strategies on turbine rotors, researchers aim to mitigate wake effects and optimize energy output across entire wind farms. The wake-diffusion rotor concept proposed by Equinor deviates from traditional rotor designs by modifying the blades’ radial twist angle distribution. The loading on the inner portion of the rotor blades is intentionally decreased to create additional flow entrainment and shear gradients in the center of the wake.
To investigate this, performance and wake flow experiments are conducted on three rotor blade sets, two featuring moderate and radical twist angle modifications, respectively, in a lab-scale experimental campaign. In both cases the inner half of the blades’ radius is de-loaded. The three rotors are mounted to a underwater test turbine equipped with both torque and thrust sensors for performance measurements. The three-dimensional flow field in the wake is captured using a Lagrangian Particle Tracking Velocimetry (LPTV) at several downstream distances of the rotors.
Results from initial power and thrust measurements show only minor differences in the three rotors’ power output at their design tip speed ratio. A comparison of the mean components in the wake indicates an improved wake recovery for the two modified rotors. In the near wake the wake diffusion rotors show locally higher mean velocities in the wake center, where additional wake diffusion is initiated. These initial results indicate a promising potential for the concept, while measurements under various inflows and at larger downstream distances are needed to quantify the full potential
Chapter 8: Einfluss von Technologie auf die Authentizität und Zufriedenheit mit Naturerlebnissen am Beispiel des Wandertourismus in den Allgäuer Alpen
No full textDer Beitrag stellt Ergebnisse einer Untersuchung zum Einfluss von Technologie auf die Wahrnehmung von Authentizität und Zufriedenheit bei Wanderungen in den Allgäuer Alpen vor. Auf Basis ausgewählter Konstrukte von Authentizität und Daten zur Mediennutzung von Wandernden wird eine explorative empirische Untersuchung durchgeführt. Diese basiert auf einer qualitativen Vorstudie mit Experteninterviews und einer quantitativen Befragung von 99 Wandernden im Allgäu. Dabei wurden Aspekte wie Nutzungshäufigkeit, bevorzugte Anwendungen und der Einfluss auf die Wahrnehmung der Umgebung analysiert. Die Ergebnisse zeigen, dass Naturerlebnisse im digitalen Zeitalter von Technologie geprägt sind, jedoch nicht zwangsläufig an Authentizität verlieren. Während Wandernde ohne oder mit geringer Techniknutzung die Natur als authentischer erleben, hat die Techniknutzung keinen signifikanten Einfluss auf die Zufriedenheit
Modeling and Simulation of High-Fidelity FMCW RADAR Sensor for Automated Trains
No full textThis paper focuses on developing a high-fidelity model of a frequency-modulated continuous wave (FMCW) radio detection and ranging (RADAR) sensor for automated train systems. The model uses ray tracing for virtual environmental perception in railway scenarios. It includes a multiple input multiple output (MIMO) antenna array and a complete signal processing toolchain of real RADAR sensors. The model outputs raw data, including range maps (RMs), range-Doppler maps (RDMs), and detection lists, including distance, relative radial velocity, and signal-to-noise ratio (SNR), radar-cross section (RCS), azimuth, and elevation angles. Results show a strong correlation with real measurements with a mean absolute percentage error (MAPE) below 4.8% for all the parameters defined at the detection level. To the author's knowledge, these error levels are among the lowest reported for RADAR sensor model validation. This finding allows for a cost-effective perception of virtual environments, facilitating simulation-based testing of automated railway systems
Digital competencies for public health education in Germany: a Delphi study
No full textAbstract
Despite its growing relevance, digital public health (DiPH) remains underrepresented in many academic public health curricula. Internationally, there is an urgent need to define a core set of competencies to guide the integration of DiPH into public health education and prepare the future public health workforce for the markets. This presentation will present the results of a study that addressed that gap by identifying expert consensus on key DiPH competencies, thereby informing curriculum development nationally and offering transferable insights for European and global contexts. A multi-stage online Delphi study was conducted among academic and practice-based public health experts in Germany. Of 41 experts recruited, 27 completed the survey, which involved rating and refining proposed DiPH competencies based on their perceived importance to a core curriculum. The process is built on our prior work and the ASPHER Core Curriculum for Public Health. Experts identified 90 essential competencies clustered into 11 interdisciplinary domains, including health economics, epidemiology, IT & technology, ethics, health promotion, methods in healthcare research, and digital intervention application. The included competencies reflect the breadth of DiPH: From digital health literacy and regulatory awareness to technical skills like data analysis and the application of digital tools for population health. The results reinforce that DiPH spans the full spectrum of public health disciplines and must be integrated accordingly. Our findings support the development of internationally relevant, future-oriented curricula that equip students for evolving digital landscapes. Embedding these competencies into academic training is an innovative public health action with significant potential to enhance workforce readiness, reduce the digital divide, and promote health equity in the digital age
Characterising acute and chronic care needs: insights from the Global Burden of Disease Study 2019
No full textChronic care manages long-term, progressive conditions, while acute care addresses short-term conditions. Chronic conditions increasingly strain health systems, which are often unprepared for these demands. This study examines the burden of conditions requiring acute versus chronic care, including sequelae. Conditions and sequelae from the Global Burden of Diseases Study 2019 were classified into acute or chronic care categories. Data were analysed by age, sex, and socio-demographic index, presenting total numbers and contributions to burden metrics such as Disability-Adjusted Life Years (DALYs), Years Lived with Disability (YLD), and Years of Life Lost (YLL). Approximately 68% of DALYs were attributed to chronic care, while 27% were due to acute care. Chronic care needs increased with age, representing 86% of YLDs and 71% of YLLs, and accounting for 93% of YLDs from sequelae. These findings highlight that chronic care needs far exceed acute care needs globally, necessitating health systems to adapt accordingly
Interventions to enhance medication therapy safety in older patients with cognitive impairment—protocol of a systematic review with public involvement
No full textIntroduction
Cognitive impairment is considered a challenge in medication management for both the affected patient as well as their caregiver. Numerous studies have investigated interventions aiming to improve medication therapy safety in this population; however, there is insufficient knowledge on interventions which support patients and caregivers effectively. The aim of this systematic review is to (1) identify interventions to improve medication therapy safety in older patients with cognitive impairment, and (2) to evaluate their effectiveness.
Methods and analysis
We will conduct a systematic review of literature with participatory elements of public involvement in every step of the process. Five literature databases (PubMed, CENTRAL, Embase, PsycINFO and CINAHL) will be screened to identify interventions to improve medication therapy safety in older (≥65 years of age) adults with cognitive impairment. To support methodology and evidence synthesis, we will conduct expert panel discussions as well as focus group discussions of caregivers and healthcare professionals. Study selection, data extraction and bias assessment will be conducted independently by two reviewers. For data synthesis, studies will be organised by setting (eg, community setting, hospital setting, nursing home setting)
Agile Frameworks as solution or driver of workplace interruptions in transnational projects
No full textInterruptions occur disproportionately frequently in the service sector, regardless of type of work or level of qualification. In transnational contexts, interruptions increase and become more difficult to manage. This text uses two case studies from agile software development to examine how project management approaches, such as Scrum, can reduce interruptions. The results highlight the central importance of the principle of self-organization
Prediction of CNC Manufacturing Time Under Real-World Conditions Using Graph Convolutional Networks
No full textIn this work, we share our learnings on predicting CNC manufacturing time given CAD models from a real-world dataset using machine learning models. To minimize prediction cost, we focus on extracting relevant information solely from the CAD model, eliminating the need for manual feature labeling. Our experiments reveal that a combination of hand-crafted features with those automatically extracted by the graph convolutional network UV-Net yields the most accurate predictions. Notably, our model exhibits robust performance when applied to a newer, higher-quality dataset, achieving a significant improvement of 32% in mean absolute error compared to a rule-based approach despite the challenges posed by temporal and data quality shifts