GfK (Germany)

Abstract To reduce CO2 emissions, an oil and gas company has electrified two platforms in the Norwegian North Sea. The two platforms had several large gas turbines (GT's) driving 50MW of compression and 60MVA of power generation. The project has connected electric power from shore and discontinued the GT's. A subsea power cable delivers 150MW of power from the 50Hz Norwegian grid (powered by hydro electricity) to the two platforms; a static frequency converter (SFC) is required to feed the existing offshore platform 60Hz loads. This SFC will operate in grid forming mode to provide all the power, protection, and fault clearing functionality for the 60Hz grid. The SFC is 4 quadrant, and the active front end (AFE) will provide grid support for the power from shore cable to improve voltage and frequency disturbance events. The AFE will also export VARs to correct the power factor of the cable feeding power from shore. The compression load to be driven by AFE Variable Speed Drive (VFD)s and will also support the grid with disturbance ride through and power factor correction. Simulation results of the disturbance events will be presented as well as the CO2 reduction calculation. The system was successfully started in 2024 without any shutdown of the platform, a "hot" switch over was achieved.

Objective: Calculation of vascular age can help patients understand the importance of adherence to healthy lifestyle and medications. There are different possibilities to calculate vascular age including methods based on cardiovascular risk scores, but different methods can provide different vascular age results. Our aim was to evaluate vascular age based on the Systematic COronary Risk Evaluation (SCORE), Systematic COronary Risk Evaluation 2 (SCORE 2) and Framingham Risk Score (FRS). Design and method: Subjects between the age of 40–65 years, who took part in a screening program in three general practitioner practices were involved. Vascular ages were defined based on SCORE, SCORE2 and FRS according to literature data. Results: 154 patients were involved to the study, among them 107 had hypertension (HT) and 13 had diabetes (DM). The chronological age of the total population was 54.6 (58.4 – 63.7) years, while SCORE, SCORE2 and FRS vascular ages were 55.0 (44.0 – 60.0), 57.5 (51.0 – 63.0) and 64.0 (54.0 – 76.8) years, respectively. In HT patients chronological age, SCORE, SCORE2 and FRS vascular ages were 54.1 (48.0 – 61.0), 55.0 (45.0 – 60.0), 58.0 (52.0 – 64.0) and 68.0 (59.0 – 80.0) years, respectively. In DM patients chronological age, SCORE, SCORE2 and FRS vascular ages were 58.6 (48.3 – 62.5), 58.0 (45.0 – 62.5), 60.0 (51.0 – 66.0) and 80.0 (76.0 – 80.0) years, respectively (p<0.05). Based on SCORE, SCORE2 and FRS 20.1%, 35.1% and 76.0% of the subjects had increased vascular age compared with chronological age, respectively (p< 0.05). 27 patients (17.5%) had elevated vascular age with SCORE and SCORE2, furthermore, 54 patients (35.1%) had elevated vascular age with SCORE2 and FRS. In addition, 27 patients had elevated vascular age with all the three methods. Conclusions: The differences found between the calculated vascular ages and the proportion of subjects with elevated vascular age warrants further detailed comparison of different vascular age calculation methods.

In our pattern from EuroPLoP '23 we've introduced the Communitydriven Learning pattern.Since then, our experience with this pattern has evolved as we have been able to further iterate on it in different contexts.We've gained some new insights into how to better introduce and maintain the pattern into a company as well as how it can help during company transition times such as mergers and acquisitions.Also, we've gained insight into another example for this pattern in a different software company, doing its own flavour of Community-driven Learning.During the 2023 conference the main question we were asked was how we are able to introduce and establish a Community-driven Learning pattern within a company.So, given the new insights we've gathered, we believe it is quite valuable to shed more light on some intricacies and "secret ingredients" that make these pattern work in two Case Studies -GfK -an NIQ Company [5][29] and EPAM Systems [23][28].

Because we live in a manufactured world, empowering the manufacturing workforce is crucial. People-centric Production (PCP) in Industry 5.0 places people at the center of the industrial transformation, offering benefits such as bottom-up innovation potential, enhanced shop floor employee engagement, as well as increased acceptance, pace and value-added of digitalization efforts. However, there is a lack of understanding how to envision, implement and assess PCP. This conceptual paper presents an assessment tool for guiding the implementation of PCP in manufacturing, identifying three design criteria - work, learning, and culture - and a total of 13 action areas for PCP. Foundational elements such as health and safety, ergonomics, and remuneration are also highlighted. The evaluation approach based on the EU Common Assessment Framework and Industry 4.0 maturity assessments is proposed to enable continuous monitoring and improvement. The applicability of the tool was evaluated within an industrial case study, with future research focusing on longitudinal effects and the applicability of the tool in small enterprises. Implementation challenges related to the interdisciplinary nature of PCP are identified, highlighting the need for multi-stakeholder collaboration structures.

Bike Sharing Systems (BSSs) have gained popularity in the last two decades, becoming an integrated part of our mobility ecosystem. One of the major issues BSS operators struggle to deal with is forecasting the mobility demand, namely how many bikes are needed, when, and where. To deal with this challenge, this paper introduces a novel deep learning architecture that combines Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and contextual data to predict hourly demand in large-scale bike-sharing systems one day ahead. Specifically, the CNN layer captures spatio-temporal trends and is used to model long-term demand patterns, while the LSTM layer focuses on short-term mobility strategies. A separate Neural Network is used to include contextual data, such as weather data or special events. Predictions are provided for the next 24 hours at a frequency of one hour. The modular structure of the model ensures that the framework can be used in different situations and that individual components can be replaced. Similarly, additional contextual information can be included with limited effort. Results show that such a modular framework, where each module captures different dynamics (weather effects, long-term mobility patterns, short-term mobility patterns) outperforms the baseline models and also reduces their computational times.

Pulse wave velocity (PWV), the most accepted biomarker of arterial stiffening can be measured by different methods and in the past decade, its 24 h monitoring has also become available. The aim of our study was to compare office and ambulatory PWVs and in a proportion of patients to compare the changes of PWVs after the initiation of lifestyle modifications or antihypertensive medication. Office carotid-femoral PWV was measured with the tonometric PulsePen device (PP PWV), first hour and 24 h ambulatory oscillometric PWVs were evaluated with Mobil-O-Graph (MOB first hour PWV and MOB 24 h PWV, respectively). In new hypertensive patients, the measurements were repeated 3 months after the initiation of antihypertensive medication. In white-coat hypertensive patients after lifestyle modifications the measurements were repeated at 12 months. One hundred and five participants were involved with 22 new hypertensive and 22 white-coat hypertensive (WhHT) patients. PP PWV [8.7 (7.3-9.9) m/s] differed from MOB first hour PWV [7.3 (6.5-8.8) m/s] and MOB 24 h PWV [7.4 (6.4-8.8) m/s] as well (P < 0.05). PP PWV significantly decreased both in hypertensive [by 0.9 (0.4-1.5) m/s, P < 0.05] and WhHT patients [by 0.3 (-0.1 to 1) m/s, P < 0.05]. MOB first hour PWV did not change neither in hypertensive patients, nor in WhHT patients. MOB 24 h PWV decreased only in hypertensive patients [by 0.2 (0-0.6) m/s], which was less pronounced compared with PP PWV (P < 0.05). The significant differences observed both in the cross-sectional and in the prospective parts of our study suggests that the two methods are not interchangeable.

Abstract The histories of East and West Germany traditionally emphasize the Cold War rivalries between the communist and capitalist nations. Yet, even as the countries diverged in their political directions, they had to create new ways of working together economically. This book examines the material culture of increasing economic contacts in divided Germany from the 1940s until the 1990s. Trade events, such as fairs and product shows, became one of the few venues for sustained links and knowledge between the two countries after the building of the Berlin Wall. The book uses industrial design, epitomized by the furniture industry, to show how a network of politicians, entrepreneurs, and cultural brokers attempted to nationally re-inscribe their production cultures, define a postwar German identity, and regain economic stability and political influence in postwar Europe. What started as a competition for ideological superiority between East and West Germany quickly turned into a shared, politically legitimizing quest for an untainted post-fascist modernity. This work follows products from the drawing board into the homes of ordinary Germans to offer insights into how converging visions of German industrial modernity created shared expectations about economic progress and living standards. The book reveals how intra-German and European trade policies drove the creation of products and generated a certain convergence of East and West German taste by the 1980s.

Self-tracking technologies (STTs) in the form of smart devices and mobile applications enable consumers to monitor, analyze, and interpret personal performance data on health and physical or financial well-being. As a result of self-tracking, consumers are not only expected to check their personal performance more actively but also to implement service professionals’ advice to improve their well-being more accurately. Despite the growing popularity of STTs, empirical evidence on the extent to which STT use enhances advice compliance remains scant. A field experiment with 538 participants in a health-care setting suggests that STT use does not increase advice compliance per se. Rather, the effectiveness of STTs depends on consumers’ self-efficacy. For consumers low in self-efficacy, STT use can even undermine advice compliance. A lab experiment with 831 participants replicates and generalizes the findings to a nonmedical professional service (i.e., fitness training). As assessments of self-efficacy might be difficult in practice, service providers in health care can use consumers’ body mass index as an easy-to-measure proxy to predict STT effectiveness. Finally, the lab experiment also identifies perceived empowerment and personalization as psychological mechanisms mediating the influence of STT use on advice compliance.

The paper gives a brief overview of the four shared tasks that are to be organized at the PAN 2020 lab on digital text forensics and stylometry, hosted at CLEF conference. The tasks include author profiling, celebrity profiling, cross-domain author verification, and style change detection, seeking to advance the state of the art and to evaluate it on new benchmark datasets.

Data and artificial intelligence (AI) are revolutionising the way customers interfacewith organisations and vice versa. However, there is limited knowledge regarding howdata and AI are used as material during the design process. It is to this practice-research gap that this paper responds, by providing practical insight into a projectbetween a Dutch airline, named AirlineX for the purpose of this paper, and auniversity-based design team. The project let to the development of a principle forpersonality-driven design that deeply personalises digital touchpoints within AirlineX’s operations. This principle is informed by ‘big five’ personality theory from the field of psychology. Further, a framework for incorporating AI and data asmeaningful subject matter into the design process is presented. This frameworkassists organisations to develop dialogue with customers beyond the purchase pointthrough personal data, representing a democratisation of the traditional business tocustomer (B2C) perspective. The paper concludes with directions for future researchthat point toward the growing need for ethical discourse regarding technology, designand society.