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Towards Privacy-friendly Telepresence Robots for Schoolchildren with Long-term Illnesses – User Needs of Relevant User Groups (2024)

Büttner, Sebastian Thomas ; Neumann, Paul ; Hernández Acosta, Luca ; Reinhardt, Delphine ; Prilla, Michael

Abstract Remote participation in school is possible today with the help of telepresence robots. Such technologies can offer great opportunities for children with long-term illnesses to continue attending school. Consequently, telepresence robots are already used in some schools when children are absent for long periods. However, despite their positive impact, such robots also create challenges for the privacy of people involved in such a setting. Therefore, in this paper, we discuss the user needs of such robots in this specific and privacy-sensitive application field. We held three workshops with different user groups with and without experience with the robots. Among them were formerly and currently ill children, parents, teachers, head teachers, media educators, and supporting personnel. We discussed their experiences (if any), ideas, expectations, and concerns with a focus on privacy aspects to find out about the user needs of different user groups. Our results reveal various interrelationships and conflicts between the individual actors. They serve as a basis to discuss the implications for the design of future telepresence robots for schoolchildren.

Why does the robot only select men? How women and men perceive autonomous social robots that have a gender bias (2024)

Büttner, Sebastian Thomas ; Goudarzi, Maral ; Prilla, Michael

Abstract Future social robots will act autonomously in the world. Autonomous behavior is usually realized by using AI models built with real-world data, which often reflect existing inequalities and prejudices in society. Even if designers do not intend it, there are risks that robots will be developed that discriminate against certain users, e. g. based on gender. In this work, we investigate the implications of a gender-biased robot that disadvantages women, which unfortunately is a bias in AI that is often reported. Our experiment shows that both men and women perceive the gender-biased robot to be unfair. However, our work indicates that women are more aware that a gender bias causes this unfairness. We also show that gender bias results in the robot being perceived differently. While the gender bias resulted in lower likability and intelligence ratings by women, men seem to lose trust in the robot if it behaves unfairly.

"I Don't Want Parents to Watch My Lessons" – Privacy Trade-offs in the Use of Telepresence Robots in Schools for Children with Long-term Illnesses (2024)

Neumann, Paul ; Büttner, Sebastian Thomas ; Hernández Acosta, Luca ; Reinhardt, Delphine ; Prilla, Michael

Abstract Telepresence robots offer great opportunities for children with long-term illnesses to continue attending school. Consequently, they are already used if children are absent for long periods. When designing such systems, the privacy of various stakeholders must be considered. However, conflicts often arise because the privacy requirements of different user groups cannot be fulfilled simultaneously. In this paper, we analyze the corresponding trade-offs that have to be made when designing telepresence robots under conflicting privacy requirements. We analyzed previous literature and held three workshops with different user groups (children, parents, teachers, head teachers, media educators, and supporting personnel) with and without experience with telepresence robots in schools. Based on the literature and the workshop results, we present four major privacy trade-offs we identified and discuss design approaches for them. With this work, we contribute to the design research on telepresence robots in schools by revealing the major privacy-related conflicts and potential design approaches to overcome the conflicts.

The Impact of Smart-glass-based Video Tutorials on Knowledge Transfer in Practice (2024)

Gümüs, Esra ; Gutzmann, Jan Christoph ; Büttner, Sebastian Thomas ; Prilla, Michael

Abstract Video tutorials are an effective method of knowledge transfer and learning. However, they are often time-consuming to create and difficult to access during work. This paper introduces an approach that simplifies the creation of video tutorials in the workplace and promotes their use during work. By utilizing smart glasses, practitioners can record video tutorials during their daily work processes and utilize them for knowledge transfer to other employees without much additional effort. This offers the advantage of directly and easily sharing expertise in the workplace without being constrained by time or location. Given the intended simplicity of this approach, the question arises whether it creates video tutorials that help people in their learning. The paper presents a study that compares the effectiveness of knowledge transfer using these video tutorials against traditional personal training methods in the workplace. With 18 participants from the nursing and production sectors, we observed the training and learning outcomes of using video tutorials on smart glasses over multiple sessions, comparing them with personal training, which is considered the standard for practical onboarding. The study results indicate that learning with video tutorials does not significantly differ in terms of learning outcomes from traditional personal training methods. Overall, this study highlights the potential of video tutorials with smart glasses for knowledge transfer in workplaces, while also identifying challenges and opportunities for optimizing onboarding processes for employees.

Interaction of Robot Speed and Distance in Human-Robot Collaboration: Impact on Human Trust, Safety, and Comfort (2024)

Büttner, Sebastian Thomas ; Alhaji, Basel ; Katariya, Kaushikbhai ; Prilla, Michael

Abstract Robots are gradually being freed from their safety fences due to the advances in safety features integrated with most new robots. These robots enable a new form of human-robot interaction in which contact is possible. There are two robot-related factors that play a decisive role in the interaction dynamics and human perception in such a case - the motion speed and distance the robot keeps away from the human. Literature indicates that these factors influence whether humans perceive trust, safety, and comfort, which are essential components in the acceptance of robots by their end users. However, although speed and distance were intensively investigated in isolation, little is known about their combined effect. To this end, we conducted an experiment investigating the impact of the industrial robot arm’s approaching speed and stopping distance on the users’ trust, safety, and comfort while they were performing a collaborative task. Our results provide interesting insights into the implications of speed and trust. While we were able to replicate former studies in terms of implications of speed and distance, our data does not show any interaction effect between the two variables. However, our participants’ observations indicated that distance impacts the dependent variables more severely than speed.

Trust dynamics in human interaction with an industrial robot (2024)

Alhaji, Basel ; Büttner, Sebastian Thomas ; Kumar, Shushanth Sanjay ; Prilla, Michael

ABSTRACT Trust is important for collaboration. In hybrid teams of humans and robots, trust enables smooth collaboration and reduces risks. Just as collaboration between humans and robots differs from interpersonal collaboration, so does the nature of trust in human-robot interaction (HRI). Therefore, further investigations on trust formation and dissolution in HRI, factors affecting it, and means for keeping trust on an appropriate level are needed. However, our knowledge of interpersonal trust and trust in autonomous agents cannot be transferred directly to HRI. In this paper, we present a study with 32 participants on trust formation and dissolution as well as forecasting to influence trust in an industry robot. Results show differences in dynamics and factors of trust formation and dissolution. Additionally, we find that the effect of forecasting on trust depends on task success. These findings support the design of trustful human-robot interaction and corresponding robotic team members.

How Sensory Processing Sensitivity Shapes Employee Reactions to Core Job Characteristics (2023)

Vieregge, Jaqueline ; Sutter, Christine ; Sülzenbrück, Sandra

Abstract Sensory processing sensitivity (SPS) is a personality trait characterized by a high sensitivity to sensory stimuli (Aron & Aron, 1997). On the basis of environmental sensitivity theory (Pluess & Boniwell, 2015) as well as the job characteristics model (Hackman & Oldham, 1976), we investigated the moderating impact of SPS (HSP Scale; Aron & Aron, 1997; Konrad & Herzberg, 2019) on the relationship between job characteristics (Work Design Questionnaire; Morgeson & Humphrey, 2006; Stegmann et al., 2010) and organizational citizenship behavior (OCB Scale; Podsakoff et al., 1990). The results of our two-wave survey study with 199 employees from a broad range of industries and students indicate that SPS strengthens the relationship between feedback as well as task significance and OCB, but SPS weakens the relationship between autonomy (work methods) as well as task variety and OCB.

Expressive writing as a practice against work stress: An experimental study (2024)

Lukenda, Kimberly ; Sülzenbrück, Sandra ; Sutter, Christine

Abstract This study examines the effectiveness of expressive writing in reducing work stress. Expressive writing involves structured written exercises of self-disclosure for cognitive and affective processing of stressful experiences over several writing sessions. Using a 3x3 mixed design, we examined the effects of the intervention on work stress as well as work-related motivation and attitudes in 62 German participants. We found a sex-specific effect in the significant reduction of exhaustion in men in the experimental group. In contrast, women in the control group showed significantly higher levels of exhaustion. This effect was not found for women in the experimental group. Despite the limitations of our research in terms of sample differences in baseline levels, our research identifies an alleviating effect of expressive writing on emotional exhaustion as the core facet of burnout. Future research should specifically select individuals with higher levels of stress to address the limitations mentioned.

Investigating the Influence of Mindfulness Intervention on Knowledge Sharing in High‑Responsibility Teams (2025)

Thiemann, Markus ; Sutter, Christine ; Sülzenbrück, Sandra

Abstract In this experimental study, we investigated the impact of a mindfulness intervention on knowledge sharing and knowledge collecting (de Vries et al. 2016) during a 2-day command post exercise of German senior police officers. The setting of the experiment took place under the terms of a command post exercise (Stabs-Rahmenübung), held once a year at the Federal Academy for Civil Defense and Civil Protection (Bundesakademie für Bevölkerungs- und Zivilschutz—BABZ). The BABZ provides premises and technical facilities for advanced training, and the police officers spend a week focusing exclusively on learning and training for police staff work (polizeiliche Stabsarbeit). Mayring (2010) identified three aspects (cognition, emotion, action) influencing communication’s semiotic aspect. These three aspects were addressed during the mindfulness intervention used in the field study, with participants being asked to provide a short statement about their current state of cognition, emotion, and physical experience. The intervention was conducted before and after each of the two daily command post exercises for the experimental group (N = 46), while no intervention occurred for the control group (N = 58). Knowledge sharing and knowledge collecting were assessed with the Knowledge Donating and Knowledge Collecting Items (de Vries et al. 2016). While planned contrast analyses revealed no effect on knowledge donating, our data suggest a tendency for a positive influence of the mindfulness intervention on knowledge collecting. These differences between knowledge sharing and collecting might occur because knowledge collecting is more implicit and part of the process of building a shared mental model (Cannon-Bowers et al. 1993). The impact of knowledge collecting will be discussed in light of the self-determination theory (Ryan and Deci 2000) and the impact of individual and collective mindfulness in high-reliability organizations (Weick and Sutcliffe 2001), respectively high-responsibility teams (Hagemann et al. 2012).

NGS Detects Extensive Genomic Alterations in Survivors of Irradiated Normal Human Fibroblast Cells (2024)

Soni, Aashish ; Beißer, Daniela ; Mladenov, Emil ; Höller, Matthias ; Wohlers, Inken ; Nikolov, Vladimir ; Magin, Simon ; Mussfeldt, Tamara ; Klein-Hitpass, Ludger ; Cornforth, Michael N. ; Loucas, Bradford D. ; Rahmann, Sven ; Iliakis, George

Abstract It is thought that cells surviving ionizing radiation exposure repair DNA double-strand breaks (DSBs) and restore their genomes. However, the recent biochemical and genetic characterization of DSB repair pathways reveals that only homologous recombination (HR) can function in an error-free manner and that the non-homologous end joining (NHEJ) pathways canonical NHEJ (c-NHEJ), alternative end joining (alt-EJ), and single-strand annealing (SSA) are error-prone, and potentially leave behind genomic scars and altered genomes. The strong cell cycle restriction of HR to S/ G2 phases and the unparalleled efficiency of c-NHEJ throughout the cell cycle, raise the intriguing question as to how far a surviving cell reaches after repairing the genome back to its pre-irradiation state. Indeed, there is evidence that the genomes of cells surviving radiation treatment harbor extensive genomic alterations. To directly investigate this possibility, we adopted next-generation sequencing (NGS) technologies and tested a normal human fibroblast cell line, 82-6 hTert, after exposure up to 6 Gy. Cells were irradiated and surviving colonies expanded and the cells frozen. Sequencing analysis using the Illumina sequencing platform and comparison with the unirradiated genome detected frequent genomic alterations in the six investigated radiation survivor clones, including translocations and large deletions. Translocations detected by this analysis and predicted to generate visible cytogenetic alterations were frequently (three out of five) confirmed using mFISH cytogenetic analysis. PCR analysis of selected deletions also confirmed seven of the ten examined. We conclude that cells surviving radiation exposure tolerate and pass to their progeny a wide spectrum of genomic alterations. This recognition needs to be integrated into the interpretation of biological results at all endpoints, as well as in the formulation of mathematical models of radiation action. NGS analysis of irradiated genomes promises to enhance molecular cytogenetics by increasing the spectrum of detectable genomic alterations and advance our understanding of key molecular radiobiological effects and the logic underpinning DSB repair. However, further developments in the technology will be required to harness its full potential.

Demonstrating Surface and Plasma Chemistry with a Nonthermal Atmospheric Plasma (2025)

Geyer, Marvin ; Böhm, Christine ; Zellmer, Florian ; Meier, Julian ; Ostermann, Rainer

Abstract A low-cost plasma nozzle/setup was developed to allow demonstrations, and it invites hands-on experimentation with nonthermal plasmas of air and other gases. Several high-tech plasma applications, such as surface cleaning and activation, as well as mild but effective sterilization, will be explained and adapted to be eagerly explored by undergraduate and senior high school students. The results were surprisingly similar to those obtained with a commercial plasma treatment system. While the focus is on the experimental introduction to plasma physics and chemistry, it will be highlighted how a multidisciplinary approach enables the study and discussion of important concepts ranging from surface energies and contact angles to environmental or microbiological control.

Electrospinning as a Fascinating Platform for Teaching Applied Polymer Science with Safe and Sustainable Experiments (2024)

Noll, Jessica ; Leven, Felix ; Limberg, Johannes ; Weidmann, Christoph ; Ostermann, Rainer

Abstract Electrospinning has been widely used as a versatile technique to generate nanofibers of various materials. It is also helpful in teaching topics ranging from macromolecular chemistry to physics, safety, and sustainability at various levels of difficulty and student involvement. Simple and safe hands-on experiments/manual assays can be realized for less than 30 euros to demonstrate polymer viscosity and nanofiber alignment and solubility. Students can further study (super)hydrophobicity and even upcycle packaging waste into useful filter materials but also improve the electrospinning setup from a manual assay to an inexpensive Arduino-based 3D printed research platform. Alternatively, the latter can be used for teacher demonstrations of more challenging experiments that can also be easily done using a commercial syringe-pump.

MARGE (Germany) (2024)

Simon, Jelena ; Ettl, Kerstin

Abstract This case describes the difficulties of business succession from a successor’s perspective. The case is based on a real metalworking family business in an old industrial region in Germany. The key issues are challenges that, Michelle and Adrian, two second-generation successors within one company, face in teambuilding and finding their own place within the corporation and their team. The case is divided into two parts: Part 1 is a newspaper article about the succession process that points out stereotypes and writing styles in reporting on entrepreneurship/succession in the media. Part 2 is the succession story written from Michelle’s perspective and highlights challenges that female successors face in traditional male-dominated areas, for instance, when it comes to questions of balancing work and family needs. The case addresses issues relating to entrepreneurship and business succession classes, with sociological and psychological overtones.

The Effects of Formal and Informal Institutions on Women’s Entrepreneurship in Georgia (2024)

Griessbach, Lela ; Ettl, Kerstin

Abstract The aim of this chapter is to address the impact of formal and informal institutions on women’s entrepreneurship and to extend our understanding of issues influencing women-owned businesses and women’s entrepreneurship in the context of a transition economy, such as Georgia. Our work specifically addresses formal institutions, such as governmental initiatives, financial institutions, and business-relevant education. It also addresses informal institutions, such as women’s role in society, the use of informal networks of connections and acquaintances, and gift-giving practices. Drawing on results from an online focus group discussion with women entrepreneurs in Georgia, we provide initial insights into the critical constraints and supporting factors that local women entrepreneurs experience. Based on the interrelation of institutions and women’s entrepreneurship, we conclude that changes in formal institutions do not lead directly to changing informal institutions. Due to the lack of trust in formal institutions, women entrepreneurs are more likely to look for business development solutions outside the official frames, including illegal or unethical means of survival. Nevertheless, increased opportunities – induced by specific entrepreneurship policies and programs – encourage women to enter entrepreneurship. In addition to this, entrepreneurship enables women in Georgia to overcome gender discrimination issues, which is more prevalent in employment relationships in the local labor market than in entrepreneurial activities.

The Integration of Refugees in Small and Medium-Sized Enterprises (SMEs): Case Studies from Practice (2024)

Köhler, Theresa ; Ettl, Kerstin

Abstract With the increasing number of refugees seeking protection and a new home in Germany, our society faces the task of optimally integrating those refugees. This includes integration into the labor market. This chapter deals with the integration of refugees into small and medium-sized enterprises (SMEs). We explore why SMEs choose to integrate refugees into their business, what opportunities they see in it, what challenges integration brings, and what management considers as success factors of a successful integration. The results show that a successful integration is based on a combination of the individual commitment of the refugees, the acceptance and support of the management and workforce in the company as well as clear, reliable institutional framework conditions.

Journalism and Advertising: On the Separation of Editorial Content and Commercial Communication (2024)

Weinacht, Stefan

Abstract The principle of separation between editorial content and commercial communication protects both the democratic and the commercial function of mass media. This article compiles all available statutory and professional regulations in Germany as an example of the various aspects of the principle of separation, such as the labeling obligation, the prohibition of paid content and tying transactions, as well as the handling of numerous forms of presentation of editorial advertising. Subsequently, the state of research is reported for the individual aspects of the principle of separation, in particular with regard to description and effect. Finally, proposed solutions for current application and desiderata are compiled.

Media Brand Management (2024)

Tropp, Jörg ; Weinacht, Stefan

Abstract The management of media brands faces challenges. In order to be able to point out possible solutions, this article first explains the concept and the nature of “media brands.” Subsequently, various theoretical approaches to the explanation of media brands and their management are presented. Regardless of theoretical preferences, it is important to keep in mind the brand-strategic complexity of media management that is subsequently described. Due to their specificity, special attention is paid to the basic strategic positioning options and to the communication management of media brands. In this way, the special features of media brand management become clear in comparison with other products and services.

We don’t need another (s)hero?: Role models, contradictions, discourses, and ideal feminine identity in entrepreneurial podcast dialogues (2024)

Voß, Julia ; Ettl, Kerstin

What’s driving them? A qualitative approach to explore women entrepreneurs’ motivations in STEM (2024)

Voß, Julia ; Butz, Benjamin ; Ettl, Kerstin

Abstract Purpose Entrepreneurship and the disciplines of science, technology, engineering and mathematics (STEM) are considered important drivers of innovation. At the same time, the representation of women entrepreneurs in STEM remains low. Despite this disparity, a number of women still choose to start ventures and persist in pursuing their innovations in STEM. This study aims to examine the motivational factors that drive women entrepreneurs to approach and consistently pursue their innovations and ventures in STEM. Design/methodology/approach Drawing on the concept of the heterogeneity of motivational factors (Graham and Bonner, 2022) and Social Cognitive Theory (Bandura, 1986, 2001; Wood and Bandura, 1989), 24 semi-structured interviews were conducted with women entrepreneurs in STEM. This approach allowed for an in-depth exploration of the heterogeneous motivational factors influencing women entrepreneurs in STEM. Findings The motivations of women entrepreneurs in STEM are multifaceted, interrelated and dynamic. They encompass personal and cognitive, behavioral and environmental factors and partly change over time. This study reveals two levels of heterogeneity: the heterogeneity of women entrepreneurs’ entrepreneurial motivations, and the within-context heterogeneity of women entrepreneurs in STEM themselves. Originality/value This study addresses the need for a deeper understanding of women entrepreneurs in STEM. By focusing on nuanced aspects of entrepreneurial motivations that are often overlooked in the existing literature, this research provides valuable insights and discusses implications for theory, policy and education.

When failure is not an option: a police firearms training concept for improving decision-making in shoot/don’t shoot scenarios (2024)

Olma, Joshua ; Sutter, Christine ; Sülzenbrück, Sandra

Abstract Sometimes, policing requires a quick and correct assessment of potentially hazardous situations. The training of tactical gaze control and visual attention, and its positive impact on efficient shoot/don’t shoot decisions in police cadets’ use of firearms has recently been demonstrated. On this basis, we designed an individual videobased police firearms training that was grounded on the Four-Component Instructional Design Model (4C/ID). We shifted toward an individual blended learning approach where we applied an intervention training focused on situational awareness, tactical gaze control, and visual attention. In a preregistered lab experiment, N = 45 senior police officers were randomly allocated to the intervention training or an active control training that resembled a traditional police firearms training. Both groups watched a self-produced educational video before proceeding to the practical training in our indoor firing range. In a pre- and post-test, they engaged in realistic shoot/don’t shoot video scenarios. Both groups did very well regarding decision-making, the optimal muzzle position, and the tactical conduct to keep both eyes open before shooting. Although both groups performed on a comparable level in the pre-test’s shoot scenarios, the intervention group significantly improved their response times and time until the first hit. Overall, we were able to provide an adapted, didactically based police firearms training that supplements current standards. We demonstrated that experts are still susceptible to innovative training concepts and therefore substantiate the recommendation to devote more attention to approaches that emphasize the importance of situational awareness, tactical gaze control, and visual attention in police firearms training.

Digital Assessment Methods in Mechanical Engineering: Case Study of a Computer-Aided Design Course within a Blended Learning Environment (2024)

Wendland, Michael

Abstract This paper presents an integrated approach combining in-person teaching with digital assessments in a mechanical engineering course, leveraging a blended learning environment to enhance both physical and remote instructions. The primary focus is on addressing challenges associated with digital exams, such as hardware and software limitations, connectivity issues, and the risk of cheating. To mitigate these barriers for digital exams, the paper proposes creating unique exam tasks based on student IDs and employing semi-automatic grading of computer drawings using Python scripts, which streamline the assessment process while ensuring accuracy and fairness. Traditional assessment methods typically involve written exams, computer-based tests, practical labs, oral exams, and project-based assessments. These methods require students to attend exams in person, with activities strictly monitored to prevent cheating. However, this approach highlights the need to transition from traditional methods to digital assessments, focusing on knowledge application rather than simply recalling the information. In order to show the possibility of such a transition, a Computer-Aided Design (CAD) course is employed as a case study, where theoretical knowledge is assessed through digital quizzes and practical skills via design challenges and final exams. By creating unique tasks based on student IDs, the course ensures exam integrity and fairness and still allows students to work on the assigned problem on their own computer device and on their own time schedule. Additionally, a semi-automatic system compares the volumetric properties of student-generated 3D models with reference solutions using Python scripts. This approach significantly reduces manual grading workload while maintaining high assessment standards. The course structure aligns learning activities with desired outcomes through the Constructive Alignment of Biggs et. al. Weekly quizzes handled via Moodle automatically grade the theoretical knowledge of the students, while biweekly tutorials and practical sessions support the transition from theory to practical application. Design challenges, graded and contributing to the final exam score, motivate students and provide continuous feedback and assessment. This dynamic learning environment not only engages students but also enhances the retention of theoretical knowledge and its practical application through digital tools. In conclusion, this paper showcases the successful integration of digital assessment methodologies in mechanical engineering education. By addressing and overcoming challenges early, and aligning learning activities with outcomes, the blended learning approach enhances the educational experience. The strategic use of unique exam tasks and semi-automatic grading systems not only ensures fair and accurate assessments but also prepares students for the demands of the digital age in their professional careers.

Exploring the Effects of Cybersecurity Awareness and Decision-Making Under Risk (2025)

Hörnemann, Jan ; Braun, Oskar ; Theis, Daniel ; Pohlmann, Norbert ; Urban, Tobias ; Große-Kampmann, Matteo

Abstract This paper challenges the conventional assumption in cybersecurity that users act as rational actors. Despite numerous technical solutions, awareness campaigns, and organizational strategies aimed at bolstering cybersecurity, these often overlook the prevalence of non-rational user behavior. Our study, involving a survey of 208 participants, empirically demonstrates this aspect. We found that a significant portion of users (55.3%) would accept a substantial risk (35%) to click on a potentially malicious link or attachment. This propensity increases to 61% when users are led to believe there is a 65% chance of facing no adverse consequences. To address this irrationality, we explored the efficacy of nudging mechanisms within email systems. Our qualitative user study revealed that incorporating a simple colored nudge in the email intably enhance the ability of users to discern malicious emails, improving decision-making accuracy by an average of 10%.

Evolutionary processes of cluster policies in post-industrial regions: implementing the smart specialisation strategy in the Ruhr Metropolis (2024)

Beck, Rasmus C. ; Domanski, Dmitri ; Frohne, Julia

Focusing on the implementation of the Smart Specialisation Strategy (S3), the chapter examines the development of cluster policies in the Ruhr Metropolis as a post-industrial region. The chapter traces the historical development of the Ruhr Area from its industrial peak in the 20th century to its slow transformation into a post-industrial landscape characterised by high urban density, new knowledge-based clusters and a persistent structural lack of effective regional cooperation. The analysis shows the conceptual shift from traditional cluster policies to the S3 approach, introduced by the European Union in 2014. The Smart Specialisation Strategy calls for a focus on comparative regional strengths and the involvement of a wide range of stakeholders in the identification of clusters for sustainable economic growth. The chapter also discusses the challenges and milestones in developing a coherent and effective Smart Specialisation Strategy, emphasising the need for inter-municipal cooperation and a new multi-level approach to regional governance. Using the case of the Ruhr Metropolis, the chapter highlights the opportunities and constraints of S3 policies to revitalise post-industrial regions by promoting innovation and adapting to global economic trends in cluster development, thus showing a way forward for other regions with similar structural challenges.

Corporate debt ratios and managerial personality traits: A content analysis of chief executive officers’ speeches at annual general meetings (2025)

Rojahn, Joachim ; Sülzenbrück, Sandra ; Lübke, Karsten

This study contributes to the literature by analysing the joint association of managerial overconfidence, certainty, narcissism, and the Big Five personality traits with debt ratios in the institutional setting of the German two-tier system. Moreover, it provides insights into how corporate governance quality moderates the effects of personality. The analysis relied on the chief executive officers’ (CEOs’) speeches at annual general meetings (AGMs) that were voluntarily disseminated, a novel data source. Managers’ personality traits were measured using software-aided content analysis, and their impact on the debt ratio was analysed using panel regressions. Consistent with previous studies, the debt ratios of German issuers are significantly and positively related to the proxies of managerial certainty and narcissism. However, their model inclusion contributes only marginally to explanatory power. Conversely, the coefficients of the proxies for the Big Five personality traits remained statistically non-significant. Moreover, a significantly negative relationship between debt ratios and the interaction term between a proxy for corporate governance quality and managerial certainty is observed that corresponds to the risk-mitigating impact of corporate governance.

Blended police firearms training improves performance in shoot/don't shoot scenarios: a systematic replication with police cadets (2024)

Olma, Joshua ; Sülzenbrück, Sandra ; Sutter, Christine

Senior police officers' tactical gaze control and visual attention improve with an individual video-based police firearms training. To validate the efficacy of said intervention training, a previous experiment was systematically replicated with a sample of N = 52 second-year police cadets. Participants were randomly assigned to the intervention training that focused on situational awareness, tactical gaze control, and visual attention, or an active control training that addressed traditional marksmanship skills. In a pre- and post-test, they had to engage in dynamic shoot/don't shoot video scenarios in an indoor firing range. Overall, the previous findings were replicated: Baseline levels of performance were elevated, yet the intervention group significantly improved their response time and time until the first hit. False positive decision-making cannot be reported at all; false negatives were marginal in the pre-test and eliminated after training. Further, the outcomes of the previous sample of senior officers and the present sample of cadets are compared and lead to the conclusion that the presented approach is a valuable extension of current training standards for both senior police officers and police cadets.

Improvement by imagining actions: Bimanual transfer effects after action imagery practice in a sequential reaction time task (2025)

Dahm, Stephan ; Rieger, Martina ; Sülzenbrück, Sandra

Action-imagery-practice refers to the repetitive use of action imagery to improve subsequent performance leading to partially different representation types than action-execution-practice (AEP). This study explored the representation types in kinesthetic action-imagery-practice (K-AIP) and visual action-imagery-practice (V-AIP) in a serial reaction time task using the crossed hand transfer paradigm. 169 participants (age M ± SD = 25.2 ± 3.9) were randomly assigned to AEP, K-AIP, V-AIP, or control-practice (CP), practicing with uncrossed hands on ten consecutive days. Tests involved the same sequence, a mirror sequence, a shifted sequence, and a shifted mirror sequence, each with uncrossed and crossed hands. With crossed hands, sequence-specific transfer effects indicated only little evidence for effector-independent representations in late stages of learning in AEP and V-AIP. Performance in the same sequence with uncrossed hands indicated the acquisition of stimulus-response location associated effector-dependent sequence-specific representations in AEP, K-AIP and V-AIP, but not in CP. These visual-spatial effector-dependent representations were stronger after AEP than after AIP. Overall, no important differences between both AIP groups were observed, and both groups reported similar focus on kinesthesis and vision, suggesting that irrespective of the instructions, rather than focusing on one single modality, AIP always involves a combination of both modalities - vision and kinesthesis - that promote motor learning.

Protective effects of dispositional resilience and mindfulness against emotional exhaustion from workplace social stressors (2024)

Externbrink, Kai ; Thiem, Verena

Experiencing relational devaluation at work through social stressors has been linked to various detrimental outcomes. In the current study, we investigate the role of hardiness and mindfulness as personal resources which help employees to effectively cope with such stressors and thereby prevent burnout. We focus on trait mindfulness as the innate capacity of paying and maintaining attention to present-moment experiences with an open and nonjudgmental attitude. It has been shown to promote concentration and well-being and to facilitate decision making; it is often seen as an important resource for overcoming challenges in everyday work life. Hardiness also constitutes a personality profile of dispositional resilience that describes how people deal with stressful events and includes the core aspects of challenge (conviction that challenges offer opportunities), engagement (actively tackling tasks and challenges) and a sense of control (conviction of influence over one's own life circumstances). People with high hardiness show better health and higher job satisfaction and performance. We build our hypothesis according to the extended version of the Job Demands–Resources model, which states that personal resources protect employees from burnout, because they shape employees’ perceptions of and reactions towards their work environment. In a similar vein, stress theory suggests that personal resources mitigate burnout through lower stress appraisals, greater use of adaptive coping, and flexibility in matching coping to appraisals. We measured social stressors at work with the scale developed by Frese et al and further asked participants to work on the Maslach Burnout Inventory, the Mindful Attention and Awareness Scale and the short version of the Revised Norwegian Dispositional Resilience (Hardiness) Scale. Our cross-sectional study was based on a sample of N = 174 employees from a broad range of organizations and job types. Statistical Analyses revealed significant negative correlations of both personal resources with reported symptoms of burnout and the perception of social stressors as well. However, in line with prior research, they indeed did not attenuate the relationship between social stressors and emotional exhaustion at work. Theoretical and practical implications as well as limitations and avenues for future research are discussed.

STOUT V2.0: SMILES to IUPAC name conversion using transformer models (2024)

Rajan, Kohulan ; Zielesny, Achim ; Steinbeck, Christoph

Naming chemical compounds systematically is a complex task governed by a set of rules established by the International Union of Pure and Applied Chemistry (IUPAC). These rules are universal and widely accepted by chemists worldwide, but their complexity makes it challenging for individuals to consistently apply them accurately. A translation method can be employed to address this challenge. Accurate translation of chemical compounds from SMILES notation into their corresponding IUPAC names is crucial, as it can significantly streamline the laborious process of naming chemical structures. Here, we present STOUT (SMILES-TO-IUPAC-name translator) V2, which addresses this challenge by introducing a transformer-based model that translates string representations of chemical structures into IUPAC names. Trained on a dataset of nearly 1 billion SMILES strings and their corresponding IUPAC names, STOUT V2 demonstrates exceptional accuracy in generating IUPAC names, even for complex chemical structures. The model’s ability to capture intricate patterns and relationships within chemical structures enables it to generate precise and standardised IUPAC names. While established deterministic algorithms remain the gold standard for systematic chemical naming, our work, enabled by access to OpenEye’s Lexichem software through an academic license, demonstrates the potential of neural approaches to complement existing tools in chemical nomenclature.

Improving the Accuracy of PV Yield Calculation by Exploitation of Real Weather Data (2025)

Schneider, Andreas ; Buschmann, Matthias ; Rabanal-Arabach, Jorge ; Chochollek, Julia ; Nierhoff, Thomas

The precision of yield calculation of modern design and simulation software for photovoltaic systems strongly rely, beside the accuracy of the specified module and inverter data, on the quality of the weather data. Since data from weather stations is not available for most locations world-wide this data is calculated by using modern interpolation methods. Beside this, simulation software typically uses historical weather data. In this work the mismatch of yield simulation results based on proprietary data, meaning interpolated or also called synthetical data, and data coming from a weather station in proximity to the installation is evaluated. The simulated data sets are compared to measurement data as obtained by the inverter output and hence give a profound understanding how interpolated data may influence the simulation results. The outcome shows that the quality of the yield simulation, if compared to the measurement data, is increased by a factor of up to four if on-site weather data is used as input for the simulation. The largest source of deviation is irradiation, which varies up to 10% if synthetical and measured irradiation on-site is compared. The second largest sources for simulation mismatches are power calculation and module temperature correction.

Digital Drawing Tools for Assessing Mental Health Conditions – A Scoping Review (2024)

Unger, Sebastian ; Robens, Sibylle ; Anderle, Laura ; Ostermann, Thomas

Introduction: Drawing tasks are an elementary component of psychological assessment in the evaluation of mental health. With the rise of digitalization not only in psychology but healthcare in general, digital drawing tools (dDTs) have also been developed for this purpose. This scoping review aims at summarizing the state of the art of dDTs available to assess mental health conditions in people above preschool age. Methods: PubMed, PsycInfo, PsycArticles, CINAHL, and Psychology and Behavioral Sciences Collection were searched for dDTs from 2000 onwards. The focus was on dDTs, which not only evaluate the final drawing, but also process data. Results: After applying the search and selection strategy, a total of 37 articles, comprising unique dDTs, remained for data extraction. Around 75 % of these articles were published after 2014 and most of them target adults (86.5 %). In addition, dDTs were mainly used in two areas: tremor detection and assessment of cognitive states, utilizing, for example, the Spiral Drawing Test and the Clock Drawing Test. Conclusion: Early detection of mental diseases is an increasingly important field in healthcare. Through the integration of digital and art based solutions, this area could expand into an interdisciplinary science. This review shows that the first steps in this direction have already been taken and that the possibilities for further research, e.g., on the optimized application of dDTs, are still open.

Effect of Electrolyte pH in Additive-Free NiFe Catalyst Electrodeposition for Electro-Catalytic OER Applications (2024)

Cieluch, Maximilian ; Kazamer, Norbert ; Böhm, Leonard ; Sanden, Sebastian ; Zerebecki, Swen ; Wirkert, Florian ; Apfel, Ulf-Peter ; Brodmann, Michael

This study presents the correlation between electrolyte pH, surface morphology, chemical speciation and electro-catalytic oxygen evolution activity of additive-free electrodeposited NiFe catalysts for application in anion exchange membrane water electrolysis. Spherical morphologies were identified at pH 0, shifting towards honey-combed structures at pH 4 with increasing surface area, especially at pH 3. Further, the electrolyte pH was found to influence the NiFe composition and electro-catalytic activity. Enhanced OER activity was noted at pH 2 with overpotentials of 214 mV at 10 mA cm−2 and 267 mV at 100 mA cm−2. The results reveal that the electrolyte pH is a parameter not only influencing the morphology but also tailoring the surface area, Fe oxide and Fe hydroxide composition and consequently the catalytic activity. Further, the outcomes highlight the electrolyte pH as a key process parameter that should be adjusted according to the application, and may substitute the addition of electrolyte-additives, proposing a simpler method for improving catalyst electrodeposition.

Envisioning Secure and Private 6G-Enabled Cognitive Personal Informatics (2024)

Triesch, Anna ; Hörnemann, Jan ; Urban, Tobias ; Große-Kampmann, Matteo

This Paper explores how emergent technologies such as 6G and tactile Internet can potentially enhance cognitive, personal informatics (CPI) in participatory healthcare, promoting patient-centered healthcare models through high-speed, reliable communication networks. It highlights the transition to improved patient engagement and better health outcomes facilitated by these technologies, underscoring the importance of ultra-reliable, low-latency communications (URLLC) and realizing the tactile Internet’s potential in healthcare. This innovation could dramatically transform telemedicine and mobile health (mHealth) by enabling remote healthcare delivery while providing a better understanding of the inner workings of the patient. While generating many advantages, these developments have disadvantages and risks. Therefore, this study addresses the critical security and privacy concerns related to the digital transformation of healthcare. Our work focuses on the challenges of managing and understanding cognitive data within the CPI and the potential threats from analyzing such data. It proposed a comprehensive analysis of potential vulnerabilities and cyber threats, emphasizing the need for robust security frameworks designed with resilience in mind to protect sensitive cognitive data. We present scenarios for reward and punishment systems and their impacts on users. In conclusion, we outline a vision for the future of secure, resilient, and patient-centric digital healthcare systems that leverage 6G and the tactile Internet to enhance the CPI. We offer policy recommendations and strategic directions for stakeholders to create a secure, empowering environment for patients to manage their cognitive health information.

Abrasive Wear Behavior of Batch Hot-Dip Galvanized Coatings (2024)

Pinger, Thomas ; Brand, Marco ; Grothe, Sonja ; Marginean, Gabriela

In recent decades, batch hot-dip galvanized (HDG) steel has proven itself in practical applications due to the good corrosion resistance of its components. Despite the importance of the mechanical-load-bearing capacity of these coatings, the wear behavior has, so far, only been investigated very sporadically and not systematically, so a quantification of the wear behavior and statements on the mechanisms are vague. Therefore, two body wear tests with bonded abrasive grain were carried out. Varying the friction rolls, load, and total number of cycles, the wear behavior was investigated. The mass loss and the layer thickness reduction were measured at different intervals. After the test, the microstructure in the cross- section and the hardness according to Vickers (0.01 HV) were evaluated. The results showed that the wear behavior of HDG coatings against abrasive loads can be characterized with the selected test conditions. Initially, the applied load removed the soft η-phase. As the total number of cycles increases, the η- and ζ-phases deform plastically, resulting in a lower mass reduction compared to that expected from the measured layer thickness. The characteristic structure of a batch HDG coating with hard intermetallic Zn-Fe phases and an outer pure zinc phase has demonstrated effective resistance to abrasion.

Design, Manufacturing, Microstructure, and Surface Properties of Brazed Co-Based Composite Coatings Reinforced with Tungsten Carbide Particles (2024)

Utu, Ion-Dragos ; Anghel (Petculescu), Iasmina-Madalina ; Hulka, Iosif ; Marginean, Gabriela

Brazing is a joining process that involves melting a filler metal and flowing it into the joint between two closely fitting parts. While brazing is primarily used for joining metals, it can also be adapted for certain coating deposition applications. The present study investigates the microstructure and corrosion behavior and sliding wear resistance of WC (Tungsten Carbide)-CoCr-Ni reinforced Co-based composite coatings deposited onto the surface of AISI 904L stainless steel using a vacuum brazing method. The primary objective of this experimental work was to evaluate the influence of WC-based particles added to the microstructure and the properties of the brazed Co composite coating. The focus was on enhancing the sliding wear resistance of the coatings while ensuring that their corrosion resistance in chloride media was not adversely affected. The morphology and microstructure of the composite coatings were investigated using scanning electron microscopy (SEM) and phase identification by X-ray diffraction (XRD). The SEM analysis revealed in the coating the presence of intermetallic compounds and carbides, which increase the hardness of the material. The sliding wear resistance was assessed using the pin-on-disk method, and the corrosion properties were determined using electrochemical measurements. The results obtained showed that as the WC particle ratio in the Co-based composite coating increased, the mechanical properties improved, the alloy became harder, and the tribological properties were improved. The evaluation of the electrochemical tests revealed no significant alterations of the manufactured composite in comparison with the Co-based alloys. In all cases, the corrosion behavior was better compared with that of the stainless-steel substrate.

Effect of the Printing Scenario on the Dimensional Accuracy and the Tensile Strength of Different Colored PLA Specimens Produced by Fused Deposition Modeling (2024)

Cojocaru, Vasile ; Turiac, Raul Rusalin ; Frunzaverde, Dorina ; Trisca, Gelu ; Bacescu, Nicoleta ; Marginean, Gabriela

Dimensional accuracy and mechanical properties of components printed by fused deposition modeling (FDM) are influenced by several process parameters. In this paper, the authors targeted the effect of the printing scenario and the PLA (polylactic acid) color on parts’ quality. Three scenarios were analyzed: individually printing, simultaneously printing of three, respective five specimens of natural (transparent), red, grey, and black PLA. The temperature variations of successive deposited layers were recorded for the black PLA. The dimensional accuracy of tensile specimens was evaluated, tensile tests were performed, and the results were correlated with the mesostructure of the prints. The effect of the independent variables on the measured parameters was analyzed by ANOVA. The experiments revealed differences for the same printing scenario regarding cross-section area (up to 5.71%) and tensile strength (up to 10.45%) determined by the material color. The number of specimens printed simultaneously and the position of the pecimens on the build plate were found to influence too, but less than the color. Thus, increasing from one to five the number of specimens printed at a time altered both the dimensional accuracy (up to 3.93% increase of the cross-section area) and the tensile strength (up to 3.63% reduction).

Random Forest Classification of Cognitive Impairment Using Digital Tree Drawing Test (dTDT) Data (2024)

Unger, Sebastian ; Bayram, Zafer ; Anderle, Laura ; Ostermann, Thomas

Early detection and diagnosis of dementia is a major challenge for medical research and practice. Hence, in the last decade, digital drawing tests became popular, showing sometimes even better performance than their paper-and-pencil versions. Combined with machine learning algorithms, these tests are used to differentiate between healthy people and people with mild cognitive impairment (MCI) or early Alzheimer’s disease (eAD), commonly using data from the Clock Drawing Test (CDT). In this investigation, a Random Forest Classification (RF) algorithm is trained on digital Tree Drawing Test (dTDT) data, containing socio-medical information and process data of 86 healthy people, 97 people with MCI, and 74 people with eAD. The results indicate that the binary classification works well for homogeneous groups, as demonstrated by a sensitivity of 0.85 and a specificity of 0.9 (AUC of 0.94). In contrast, the performance of both binary and multiclass classification degrades for groups with het erogeneous characteristics, which is reflected in a sensitivity of 0.91 and 0.29 and a specificity of 0.44 and 0.36 (AUC of 0.74 and 0.65), respectively. Nevertheless, as the early detection of cognitive impairment becomes increasingly important in healthcare, the results could be useful for models that aim for automatic identification

Comparative Study of Corrosion Performance of LVOF-Sprayed Ni-Based Composite Coatings Produced Using Standard and Reducing Flame Spray Stoichiometry (2024)

Idir, Abdelhek ; Delloro, Francesco ; Younes, Rassim ; Bradai, Mohand Amokrane ; Sadeddine, Abdelhamid ; Marginean, Gabriela

Coating efficiency and quality can be significantly improved by carefully optimizing the coating parameters. Particularly in the flame spray method, the oxygen/fuel ratio, which is classified as oxidizing flame stoichiometry (excess oxygen) and reduces flame stoichiometry (excess acetylene), and spray distance are the most critical factors, as they correlate significantly with coating porosity and corrosion performance. Hence, understanding the effects of these parameters is essential to further minimize the porosity, improving the corrosion performance of thermally sprayed coatings. In this work, a NiWCrBSi alloy coating was deposited via the oxyacetylene flame spray/Flexicord-wire (FS/FC) method. The effect of the flame oxygen/fuel ratio and spray distance on the microstructure properties and corrosion behavior of the coatings was investigated. Afterwards, the microstructure, phases’ compositions, spray distance, and corrosion performance were studied. The equivalent circuit model was proposed, and the corrosion mechanism was discussed. The obtained results highlight that the oxygen-to-fuel ratio is a promising solution for the further application of flame spray/Flexicordwire (FS/FC) cermet coatings in hostile environments. Depending on the flame’s oxygen/fuel ratio, careful selection of the flame stoichiometry provides low porosity and high corrosion performance.

STOUT V2.0: SMILES to IUPAC name conversion using transformer models (2024)

Rajan, Kohulan ; Zielesny, Achim ; Steinbeck, Christoph

Naming chemical compounds systematically is a complex task governed by a set of rules established by the International Union of Pure and Applied Chemistry (IUPAC). These rules are universal and widely accepted by chemists worldwide, but their complexity makes it challenging for individuals to consistently apply them accurately. A translation method can be employed to address this challenge. Accurate translation of chemical compounds from SMILES notation into their corresponding IUPAC names is crucial, as it can significantly streamline the laborious process of naming chemical structures. Here, we present STOUT (SMILES-TO-IUPAC-name translator) V2.0, which addresses this challenge by introducing a transformer-based model that translates string representations of chemical structures into IUPAC names. Trained on a dataset of nearly 1 billion SMILES strings and their corresponding IUPAC names, STOUT V2.0 demonstrates exceptional accuracy in generating IUPAC names, even for complex chemical structures. The model's ability to capture intricate patterns and relationships within chemical structures enables it to generate precise and standardised IUPAC names. Deterministic algorithms for systematically naming chemical structures have been available for many years. Also, this work has only been possible through an academic license for OpenEye’s Lexichem software.

An automated calculation pipeline for differential pair interaction energies with molecular force fields using the Tinker Molecular Modeling Package (2024)

Bänsch, Felix ; Daniel, Mirco ; Lanig, Harald ; Steinbeck, Christoph ; Zielesny, Achim

An automated pipeline for comprehensive calculation of intermolecular interaction energies based on molecular force-fields using the Tinker molecular modelling package is presented. Starting with non-optimized chemically intuitive monomer structures, the pipeline allows the approximation of global minimum energy monomers and dimers, configuration sampling for various monomer–monomer distances, estimation of coordination numbers by molecular dynamics simulations, and the evaluation of differential pair interaction energies. The latter are used to derive Flory–Huggins parameters and isotropic particle–particle repulsions for Dissipative Particle Dynamics (DPD). The computational results for force fields MM3, MMFF94, OPLS-AA and AMOEBA09 are analyzed with Density Functional Theory (DFT) calculations and DPD simulations for a mixture of the non-ionic polyoxyethylene alkyl ether surfactant C10E4 with water to demonstrate the usefulness of the approach.

Manufacturing and investigation of MEAs for PEMWE based on glass fibre reinforced PFSA/ssPS composite membranes and catalyst-coated substrates prepared via catalyst electrodeposition (2024)

Cieluch, Maximilian ; Düerkop, Dennis ; Kazamer, Norbert ; Wirkert, Florian ; Podleschny, Pit ; Rost, Ulrich ; Schmiemann, Achim ; Brodmann, Michael

Since high costs restrict the wide-range implementation of green hydrogen production capacities based on proton exchange membrane water electrolysis (PEMWE), efforts on cost reduced components need to be made. Beside the necessary noble metal catalyst, the membrane material is a main cost driver. In this work, a novel glass fibre reinforced PFSA/ssPS composite membrane is investigated as an alternative to widely used Nafion®. These membranes are processed into membrane-electrode-assemblies (MEAs) in conjunction with catalyst-coated substrates, prepared via electrochemical catalyst deposition. This approach is promising to reduce costs due to less expensive raw materials and due to increasing catalyst utilization by graded catalyst layers. Characterisation of the components and entire MEAs was performed ex-situ as well as in-situ via PEMWE operation.

Non-Neutral Monetary Policy towards Sustainability in the Eurozone - An Analysis of Refinancing Operations and Minimum Reserve Policy (2024)

Walter, Johann

Johann Friedrich Pfeiffer on Adam Smith: An Early Reception of Adam Smith in the German States (2024)

Greitens, Jan ; Schumacher, Reinhard

Different from the usual portrayal that it was not until the turn of the nineteenth century that Adam Smith’s work became influential, the extensive commentary by Johann Friedrich Pfeiffer shows an immediate effect on German economic discourse. Pfeiffer, who lived from 1717 until 1787, was as a late and liberal cameralist. Pfeiffer was a prolific writer and well-known scholar during his lifetime. He lived so close to 1789 that his works were only briefly received, and he was not able to leave a lasting legacy on cameralist thought, which withered after the French Revolution. The significance of Smith was recognized by Pfeiffer, placing Smith above most of his German-speaking contemporaries. Both Pfeiffer and Smith addressed many similar topics and Pfeiffer expresses his agreement with large parts of Smith’s Wealth of Nations. However, Pfeiffer criticizes Smith because of Smith’s general support of free trade and his idealistic concept of a system of natural liberty. Pfeiffer, in contrast, was much more in favor of state interventions, given the lack of knowledge and irrational behavior of humans. While their political points of view differ, there are many theoretical similarities between them.

Investigation of the saving potential of noble catalyst material in polymer electrolyte membrane fuel cells (2021)

Podleschny, Pit Yannick

The energy transition towards renewable energies for the overall energy supply (electricity, heat, mobility, etc.) is already well advanced and the further expansion is planned. The volatility of renewable energies is being addressed by the hydrogen technology. However, there is still a need for optimization of the cost-efficient reconversion of stored energy in the form of hydrogen, e.g. in applications for decarbonization of the power grid or of the mobility sector. For instance, the cost of an automotive low-temperature polymer electrolyte membrane fuel cell (PEMFC) must be lowered by reducing the platinum loading and the lifetime must be further improved to achieve the competitiveness of this technology. The aim of the present thesis was to develop membrane electrode assemblies (MEAs) with ultra-low platinum loading, high performance and increased lifetime for the use in PEMFCs. They are fabricated by an innovative MEA preparation process based on the pulse electrodeposition of platinum (Pt) using carbon nanofibers (CNFs) as a catalyst support with enhanced resistance to carbon oxidation reaction. The design of the MEA preparation process and the development of ultra-low Pt-loaded anodes and cathodes was the starting point of this thesis. It was found that the Pt/CNF catalyst used on the anode side had better characteristics than a commercial Pt/C catalyst, since the same power output of 0.525 W cm-2 was obtained with 10 .....

Effect of Feedstock Powder Intrinsic Characteristics on the Tribological Behavior of Inductively Remelted NiCrBSi Flame-Sprayed Coatings (2023)

Muntean, Roxana ; Valean, Petru-Cristian ; Kazamer, Norbert ; Utu, Ion-Dragos ; Marginean, Gabriela ; Serban, Viorel Aurel

Ni-based alloys are among the materials of choice in developing high-quality coatings for ambient and high temperature applications that require protection against intense wear and corrosion. The current study aims to develop and characterize NiCrBSi coatings with high wear resistance and improved adhesion to the substrate. Starting with nickel-based feedstock powders, thermally sprayed coatings were initially fabricated. Prior to deposition, the powders were characterized in terms of microstructure, particle size, chemical composition, flowability, and density. For comparison, three types of powders with different chemical compositions and characteristics were deposited onto a 1.7227 tempered steel substrate using oxyacetylene flame spraying, and subsequently, the coatings were inductively remelted. Ball-on-disc sliding wear testing was chosen to investigate the tribological properties of both the as-sprayed and induction-remelted coatings. The results reveal that, in the case of as-sprayed coatings, the main wear mechanisms were abrasive, independent of powder chemical composition, and correlated with intense wear losses due to the poor intersplat cohesion typical of flame-sprayed coatings. The remelting treatment improved the performance of the coatings in terms of wear compared to that of the as-sprayed ones, and the density and lower porosity achieved during the induction post-treatment had a significant positive role in this behavior.

Considerations on the Wear Behavior of Vacuum-Remelted ZrO2-Reinforced Self-Fluxing Ni-Based Thermally Sprayed Alloys (2023)

Kazamer, Norbert ; Muntean, Roxana ; Utu, Ion-Dragos ; Marginean, Gabriela

Without proper post-processing (often using flame, furnace, laser remelting, and induction) or reinforcements’ addition, Ni-based flame-sprayed coatings generally manifest moderate adhesion to the substrate, high porosity, unmelted particles, undesirable oxides, or weak wear resistance and mechanical properties. The current research aimed to investigate the addition of ZrO2 as reinforcement to the self-fluxing alloy coatings. Mechanically mixed NiCrBSi-ZrO2 powders were thermally sprayed onto an industrially relevant high-grade steel. After thermal spraying, the samples were differently post-processed with a flame gun and with a vacuum furnace, respectively. Scanning electron microscopy showed a porosity reduction for the vacuum-heat-treated samples compared to that of the flame-post-processed ones. X-ray diffraction measurements showed differences in the main peaks of the patterns for the thermal processed samples compared to the as-sprayed ones, these having a direct influence on the mechanical behavior of the coatings. Although a slight microhardness decrease was observed in the case of vacuum-remelted samples, the overall low porosity and the phase differences helped the coating to perform better during wear-resistance testing, realized using a ball-on-disk arrangement, compared to the as-sprayed reference samples.

The Influence of the Layer Height and the Filament Color on the Dimensional Accuracy and the Tensile Strength of FDM-Printed PLA Specimens (2023)

Frunzaverde, Doina ; Cojocaru, Vasile ; Bacescu, Nicoleta ; Ciubotariu, Costel-Relu ; Miclosina, Calin-Octavian ; Turiac, Raul Rusalin ; Marginean, Gabriela

Among the FDM process variables, one of the less addressed in previous research is the filament color. Moreover, if not explicitly targeted, the filament color is usually not even mentioned. Aiming to point out if, and to what extent, the color of the PLA filaments influences the dimensional precision and the mechanical strength of FDM prints, the authors of the present research carried out experiments on tensile specimens. The variable parameters were the layer height (0.05 mm, 0.10 mm, 0.15 mm, 0.20 mm) and the material color (natural, black, red, grey). The experimental results clearly showed that the filament color is an influential factor for the dimensional accuracy as well as for the tensile strength of the FDM printed PLA parts. Moreover, the two way ANOVA test performed revealed that the strongest effect on the tensile strength was exerted by the PLA color (2 = 97.3%), followed by the layer height (2 = 85.5%) and the interaction between the PLA color and the layer height (2 = 80.0%). Under the same printing conditions, the best dimensional accuracy was ensured by the black PLA (0.17% width deviations, respectively 5.48% height deviations), whilst the grey PLA showed the highest ultimate tensile strength values (between 57.10 MPa and 59.82 MPa).

Impact of cobalt content and grain growth inhibitors in laser-based powder bed fusion of WC-Co (2022)

Schwanekamp, Tobias ; Marginean, Gabriela ; Reuber, Martin ; Ostendorf, Andreas

Processing of tungsten carbide‑cobalt (WC-Co) by laser-based powder bed fusion (PBF-LB) can result in characteristic microstructure defects such as cracks, pores, undesired phases and tungsten carbide (WC) grain growth, due to the heterogeneous energy input and the high thermal gradients. Besides the processing conditions, the material properties are affected by the initial powder characteristics. In this paper, the impact of powder composition on microstructure, phase formation and mechanical properties in PBF-LB of WC-Co is studied. Powders with different cobalt contents from 12 wt.-% to 25 wt.-% are tested under variation of the laser parameters. Furthermore, the impact of vanadium carbide (VC) and chromium (Cr) additives is investigated. Both are known as grain growth inhibitors for conventional sintering processes. The experiments are conducted at a pre-heating temperature of around 800 ◦C to prevent crack formation in the samples. Increasing laser energy input reduces porosity but leads to severe embrittlement for low cobalt content and to abnormal WC grain growth for high cobalt content. It is found that interparticular porosity at low laser energy is more severe for low cobalt content due to poor wetting of the liquid phase. Maximum bending strength of σB > 1200 MPa and Vickers hardness of approx. 1000 HV3 can be measured for samples generated from WC-Co 83/17 powder with medium laser energy input. The addition of V and Cr leads to increased formation of additional phases such as Co3W3C, Co3V and Cr23C6 and to increased lateral and multi-laminar growth of the WC grains. In contrast to conventional sintering, a grain growth inhibiting effect of V and Cr in the laser molten microstructure is not achieved.

Corrosion behavior of 316L additively produced by Directed Energy Deposition-Arc (2023)

Brand, Marco ; Moeini, Ghazal ; Marginean, Gabriela

Among all additive manufacturing processes, Directed Energy Deposition-Arc (DED-Arc) shows significantly shorter production times and is particularly suitable for large-volume components of simple to medium complexity. To exploit the full potential of this process, the microstructural, mechanical and corrosion behavior have to be studied. High stickout distances lead to a large offset, which leads to an instable electric arc and thus defects such as lack of fusion. Since corrosion preferentially occurs at such defects, the main objective of this work is to investigate the influence of the stickout distance on the corrosion behavior and microstructure of stainless steel manufactured by DED-Arc. Within the heterogenous structure of the manufactured samples lack of fusion defects were detected. The quantity of such defects was reduced by applying a shorter stickout distance. The corrosion behavior of the additively manufactured specimens was investigated by means of potentiodynamic polarization measurements. The semi-logarithmic current density potential curves showed a similar course and thus similar corrosion resistance like that of the conventionally forged sample. The polarization curve of the reference material shows numerous current peaks, both in the anodic and cathodic regions. This metastable behavior is induced by the presence of manganese sulfides. On the sample surface a local attack by pitting corrosion was identified.

Influence of the powder feed rate on the properties of HVOF sprayed WC-based cermet coatings (2022)

Eßler, Julian ; Woelk, Dino ; Utu, Dragos ; Marginean, Gabriela

In this study, the characteristics of HVOF sprayed WC/Co-Cr and WC/Cr3C2/Ni coatings were investigated in correlation with the variation of the powder feed rate. For this purpose, the mass flow was adjusted to four different levels. The other process parameters were all kept constant. The morphological and mechanical properties as well as the electrochemical corrosion behaviour were investigated and associated with the achieved microstructure. Both scanning electron microscopy and confocal laser scanning microscopical images of the cross sections demonstrated a good correlation between the selected powder feed rate and the degree of internal porosity produced, which can be attributed to the deposition process. The coatings which fulfilled the requirements of the pre-qualification step were selected for further hardness measurements, tribological tests and electrochemical corrosion measurements in a 3.5 wt% NaCl aqueous solution. It was found that the powder feed rate strongly influenced the characteristics of the HVOF-sprayed cermet coatings. The tendency to crack formation, especially at the interface coating/substrate, was lower for the samples coated with a lower mass flow rate. These studies have shown that the applied powder feed rates had an important influence on the coatings microstructure and implicitly on the sliding wear behavior respectively on the electrochemical corrosion resistance of the investigated cermet coatings.

Impact of COVID-19 Pandemic on the Educational-Instructional Process of the Students from Technical Faculties (2022)

Spunei, Elisabeta ; Frumusanu, Natalita-Mihaela ; Muntean, Roxana ; Marginean, Gabriela

Even though we live in a period when the word digitization is prevalent in many social areas, the COVID-19 pandemic has divided mankind into two main categories: some people have seen this crisis as an opportunity to move the activities online and, furthermore, to accelerate digitization in as many areas as possible, while others have been reluctant, keeping their preferences for face-to-face activities. The current work presents the results of an analysis on 249 students from 11 engineering faculties. The study aims to identify the impact of the COVID-19 pandemic on students’ educational experiences when switching from face-to-face to online education during a public health emergency or COVID 19-related state of alert. The overall conclusion was that, although the pandemic has brought adverse consequences on the health and life quality of many people, the challenges that humankind has been subjected to have led to personal and professional development and have opened up new perspectives for carrying out the everyday activities.

Hot-Corrosion and Particle Erosion Resistance of Co-Based Brazed Alloy Coatings (2022)

Utu, Ion-Dragos ; Hulka, Iosif ; Kazamer, Norbert ; Constantin, Albert Titus ; Marginean, Gabriela

Tape brazing constitutes a cost-effective alternative surface protection technology for complex-shaped surfaces. The study explores the characteristics of high-temperature brazed coatings using a cobalt-based powder deposited on a stainless-steel substrate in order to protect parts subjected to hot temperatures in a wear-exposed environment. Microstructural imaging corroborated with x-ray diffraction analysis showed a complex phased structure consisting of intermetallic Cr-Ni, C-Co-W Laves type, and chromium carbide phases. The surface properties of the coatings, targeting hot corrosion behavior, erosion, wear resistance, and microhardness, were evaluated. The high-temperature corrosion test was performed for 100 h at 750 C in a salt mixture consisting of 25 wt.% NaCl + 75 wt.% Na2SO4. The degree of corrosion attack was closely connected with the exposure temperature, and the degradation of the material corresponding to the mechanisms of low-temperature hot corrosion. The erosion tests were carried out using alumina particles at a 90 impingement angle. The results, correlated with the microhardness measurements, have shown that Co-based coatings exhibited approximately 40% lower material loss compared to that of the steel substrate.

The Influence of the Printing Temperature and the Filament Color on the Dimensional Accuracy, Tensile Strength, and Friction Performance of FFF-Printed PLA Specimens (2022)

Frunzaverde, Doina ; Cojocaru, Vasile ; Ciubotariu, Costel-Relu ; Miclosina, Calin-Octavian ; Ardeljan, Deian Dorel ; Ignat, Emil Florin ; Marginean, Gabriela

The printing variable least addressed in previous research aiming to reveal the effect of the FFF process parameters on the printed PLA part’s quality and properties is the filament color. Moreover, the color of the PLA, as well as its manufacturer, are rarely mentioned when the experimental conditions for the printing of the samples are described, although current existing data reveal that their influence on the final characteristics of the print should not be neglected. In order to point out the importance of this influential parameter, a natural and a black-colored PLA filament, produced by the same manufacturer, were selected. The dimensional accuracy, tensile strength, and friction properties of the samples were analyzed and compared for printing temperatures ranging from 200 C up to 240 C. The experimental results clearly showed different characteristics depending on the polymer color of samples printed under the same conditions. Therefore, the optimization of the FFF process parameters for the 3D-printing of PLA should always start with the proper selection of the type of the PLA material, regarding both its color and the fabricant.

Investigations of Cavitation Erosion and Corrosion Behavior of Flame-Sprayed NiCrBSi/WC-12Co Composite Coatings (2022)

Ciubotariu, Costel-Relu ; Frunzaverde, Doina ; Marginean, Gabriela

Flame-sprayed NiCrBSi/WC-12Co composite coatings were deposited in different ratios on the surface of stainless steel. Oxyacetylene flame remelting treatment was applied to surfaces for refinement of the morphology of the layers and improvement of the coating/substrate adhesion. The performance of the coated specimens to cavitation erosion and electrochemical corrosion was evaluated by an ultrasonic vibratory method and, respectively, by polarization measurements. The microstructure was investigated by means of scanning electron microscopy (SEM) combined with energy dispersive X-ray analysis (EDX). The obtained results demonstrated that the addition of 15 wt.% WC-12Co to the self-fluxing alloy improves the resistance to cavitation erosion (the terminal erosion rate (Vs) decreased with 15% related to that of the NiCrBSi coating) without influencing the good corrosion resistance in NaCl solution. However, a further increase in WC-Co content led to a deterioration of these coating properties (the Vs has doubled related to that of the NiCrBSi coating). Moreover, the corrosion behavior of the latter composite coating was negatively influenced, a fact confirmed by increased values for the corrosion current density (icorr). Based on the achieved experimental results, one may summarize that NiCrBSi/WC-Co composite coatings are able to increase the life cycle of expensive, high-performance components exposed to severe cavitation conditions.

Porous Transport Layers for Proton Exchange Membrane Electrolysis Under Extreme Conditions of Current Density, Temperature and Pressure (2021)

Stiber, Svenja ; Balzer, Harald ; Wierhake, Astrid ; Wirkert, Florian ; Roth, Jeffrey ; Rost, Ulrich ; Brodmann, Michael ; Lee, Jason Keonhag ; Bazylak, Amy ; Waiblinger, Wendelin ; Sau Gago, Aldo ; Friedrich, Kaspar Andreas

Hydrogen produced via water electrolysis powered by renewable electricity or green H2 offers new decarbonization pathways. Proton exchange membrane water electrolysis (PEMWE) is a promising technology although the current density, temperature, and H2 pressure of the PEMWE will have to be increased substantially to curtail the cost of green H2. Here, a porous transport layer for PEMWE is reported, that enables operation at up to 6 A cm−2, 90 °C, and 90 bar H2 output pressure. It consists of a Ti porous sintered layer (PSL) on a low‐cost Ti mesh (PSL/mesh‐PTL) by diffusion bonding. This novel approach does not require a flow field in the bipolar plate. When using the mesh‐PTL without PSL, the cell potential increases significantly due to mass transport losses reaching ca. 2.5 V at 2 A cm−2 and 90 °C.

A novel advanced test system for polymer electrolyte membrane water electrolysis based on hydraulic cell compression (2022)

Rost, Ulrich ; Wirkert, Florian Josef ; Roth, Jeffrey ; Brodmann, Michael ; Stiber, Svenja ; Saul Gago, Aldo ; Friedrich, Kaspar Andreas

In this work, a novel polymer electrolyte membrane water electrolyzer (PEMWE) test cell based on hydraulic single-cell compression is described. In this test cell, the current density distribution is almost homogeneous over the active cell area due to hydraulic cell clamping. As the hydraulic medium entirely surrounds the active cell components, it is also used to control cell temperature resulting in even temperature distribution. The PEMWE single-cell test system based on hydraulic compression offers a 25 cm2 active surface area (5.0 × 5.0 cm) and can be operated up to 80°C and 6.0 A/cm2. Construction details and material selection for the designed test cell are given in this document. Furthermore, findings related to pressure distribution analyzed by utilizing a pressure-sensitive foil, the cell performance indicated by polarization curves, and the reproducibility of results are described. Experimental data indicate the applicability of the presented testing device for relevant PEMWE component testing and material analysis.

Development of a Bifunctional Ti-Based Gas Diffusion Electrode for ORR and OER by One- and Two-Step Pt-Ir Electrodeposition (2022)

Cieluch, Maximilian ; Podleschny, Pit Yannick ; Kazamer, Norbert ; Wirkert, Florian ; Rost, Ulrich ; Brodmann, Michael

The present paper presents one- and two-step approaches for electrochemical Pt and Ir deposition on a porous Ti-substrate to obtain a bifunctional oxygen electrode. Surface pre-treatment of the fiber-based Ti-substrate with oxalic acid provides an alternative to plasma treatment for partially stripping TiO2 from the electrode surface and roughening the topography. Electrochemical catalyst deposition performed directly onto the pretreated Ti-substrates bypasses unnecessary preparation and processing of catalyst support structures. A single Pt constant potential deposition (CPD), directly followed by pulsed electrodeposition (PED), created nanosized noble agglomerates. Subsequently, Ir was deposited via PED onto the Pt sub-structure to obtain a successively deposited PtIr catalyst layer. For the co-deposition of PtIr, a binary PtIr-alloy electrolyte was used applying PED. Micrographically, areal micro- and nano-scaled Pt sub-structure were observed, supplemented by homogenously distributed, nanosized Ir agglomerates for the successive PtIr deposition. In contrast, the PtIr co-deposition led to spherical, nanosized PtIr agglomerates. The electrochemical ORR and OER activity showed increased hydrogen desorption peaks for the Pt-deposited substrate, as well as broadening and flattening of the hydrogen desorption peaks for PtIr deposited substrates. The anodic kinetic parameters for the prepared electrodes were found to be higher than those of a polished Ir-disc.

Electrodeposition of a Ni-Mo alloy Catalyst with Optimized Mo-Content for Hydrogen Evolution Reaction in AEM-Electrolysis (2022)

Böhm, Leonard ; Thielker, Klaus ; Kazamer, Norbert ; Wirkert, Florian Josef ; Marginean, Gabriela ; Rost, Ulrich ; Apfel, Ulf-Peter ; Brodmann, Michael

Various aqueous citrate electrolyte compositions for the Ni-Mo electrodeposition are explored in order to deposit Ni-Mo alloys with Mo-content ranging from 40 wt% to 65 wt% to find an alloy composition with superior catalytic activity towards the hydrogen evolution reaction (HER). The depositions were performed on copper substrates mounted onto a rotating disc electrode (RDE) and were investigated via scanning electron microscopy (SEM), X-ray fluorescence (XRF) and X-ray diffraction (XRD) methods as well as linear sweep voltammetry (LSV) and impedance spectroscopy. Kinetic parameters were calculated via Tafel analysis. Partial deposition current densities and current efficiencies were determined by correlating XRF measurements with gravimetric results. The variation of the electrolyte composition and deposition parameters enabled the deposition of alloys with Mo-content over the range of 40-65 wt%. An increase in Mo-content in deposited alloys was recorded with an increase in rotation speed of the RDE. Current efficiency of the deposition was in the magnitude of <1%, which is characteristic for the deposition of alloys with high Mo-content. The calculated kinetic parameters were used to determine the Mo-content with the highest catalytic activity for use in the HER.

Towards Replacing Titanium with Copper in the Bipolar Plates for Proton Exchange Membrane Water Electrolysis (2022)

Kellenberger, Andrea ; Vaszilcsin, Nicolae ; Duca, Delia ; Dan, Mircea Laurentiu ; Duteanu, Narcis ; Stiber, Svenja ; Morawietz, Tobias ; Biswas, Indro ; Ansar, Syed Asif ; Gazdzicki, Pawel ; Wirkert, Florian Josef ; Roth, Jeffrey ; Rost, Ulrich ; Brodmann, Michael ; Saul Gago, Aldo ; Friedrich, Kaspar Andreas

For proton exchange membrane water electrolysis (PEMWE) to become competitive, the cost of stack components, such as bipolar plates (BPP), needs to be reduced. This can be achieved by using coated low-cost materials, such as copper as alternative to titanium. Herein we report on highly corrosion-resistant copper BPP coated with niobium. All investigated samples showed excellent corrosion resistance properties, with corrosion currents lower than 0.1 µA cm−2 in a simulated PEM electrolyzer environment at two different pH values. The physico-chemical properties of the Nb coatings are thoroughly characterized by scanning electron microscopy (SEM), electrochemical impedance spectroscopy (EIS), X-ray photoelectron spectroscopy (XPS), and atomic force microscopy (AFM). A 30 µm thick Nb coating fully protects the Cu against corrosion due to the formation of a passive oxide layer on its surface, predominantly composed of Nb2O5. The thickness of the passive oxide layer determined by both EIS and XPS is in the range of 10 nm. The results reported here demonstrate the effectiveness of Nb for protecting Cu against corrosion, opening the possibility to use it for the manufacturing of BPP for PEMWE. The latter was confirmed by its successful implementation in a single cell PEMWE based on hydraulic compression technology.

MFsim [Softwarecode, Version 2.6.4.0] (2024)

Zielesny, Achim

From https://github.com/zielesny/MFsim: MFsim - An open Java all-in-one rich-client simulation environment for mesoscopic simulation MFsim is an open Java all-in-one rich-client computing environment for mesoscopic simulation with Jdpd as its default simulation kernel for Molecular Fragment Dissipative Particle Dynamics (DPD). The environment integrates and supports the complete preparation-simulation-evaluation triad of a mesoscopic simulation task. Productive highlights are a SPICES molecular structure editor, a PDB-to-SPICES parser for particle-based peptide/protein representations, a support of polymer definitions, a compartment editor for complex simulation box start configurations, interactive and flexible simulation box views including analytics, simulation movie generation or animated diagrams. As an open project, MFsim enables customized extensions for different fields of research. MFsim uses several open libraries (see MFSimVersionHistory.txt for details and references below) and is published as open source under the GNU General Public License version 3 (see LICENSE). MFsim has been described in the scientific literature and used for DPD studies (see references below).

Jdpd [Softwarecode, Version 1.6.2.0] (2024)

Zielesny, Achim

From https://github.com/zielesny/Jdpd: Jdpd - An open Java Simulation Kernel for Molecular Fragment Dissipative Particle Dynamics (DPD) Jdpd is an open Java simulation kernel for Molecular Fragment Dissipative Particle Dynamics (DPD) with parallelizable force calculation, efficient caching options and fast property calculations. It is characterized by an interface and factory-pattern driven design for simple code changes and may help to avoid problems of polyglot programming. Detailed input/output communication, parallelization and process control as well as internal logging capabilities for debugging purposes are supported. The kernel may be utilized in different simulation environments ranging from flexible scripting solutions up to fully integrated “all-in-one” simulation systems like MFsim. Since Jdpd version 1.6.1.0 Jdpd is available in a (basic) double-precision version and a (derived) single-precision version (= JdpdSP) for all numerical calculations, where the single precision version needs about half the memory of the double precision version. Jdpd uses the Apache Commons Math and Apache Commons RNG libraries and is published as open source under the GNU General Public License version 3. This repository comprises the Java bytecode libraries (including the Apache Commons Math and RNG libraries), the Javadoc HTML documentation and the Netbeans source code packages including Unit tests. Jdpd has been described in the scientific literature (the final manuscript 2018 - van den Broek - Jdpd - Final Manucsript.pdf is added to the repository) and used for DPD studies (see references below). See text file JdpdVersionHistory.txt for a version history with more detailed information.

Thermal Stress at the Surface of Thick Conductive Plates Induced by Sinusoidal Current Pulses (2016)

Löffler, Markus ; Mecking, Klaus ; Lindenblatt, Tobias ; Schneider, Markus

Design of a Flat-Channel Launcher (2016)

Löffler, Markus ; Spahn, Emil

A German townhouse with electric infrared heating panels and a wood pellet stove occupants experience and energy monitoring results (2016)

Löffler, Markus ; Oberladstätter, Oswald

The intention of this report is to serve as a "real life" documentation of using electric infrared heating panels in a remodeled older residential building.

Effect of Network Architecture on Physics-Informed Deep Learning of the Reynolds-Averaged Turbulent Flow Field around Cylinders without Training Data (2024)

Harmening, Jan Hauke ; Peitzmann, Franz-Josef ; el Moctar, Bettar Ould

Unsupervised physics-informed deep learning can be used to solve computational physics problems by training neural networks to satisfy the underlying equations and boundary conditions without labeled data. Parameters such as network architecture and training method determine the training success. However, the best choice is unknown a priori as it is case specific. Here, we investigated network shapes, sizes, and types for unsupervised physics-informed deep learning of the two-dimensional Reynolds averaged flow around cylinders. We trained mixed-variable networks and compared them to traditional models. Several network architectures with different shape factors and sizes were evaluated. The models were trained to solve the Reynolds averaged Navier-Stokes equations incorporating Prandtl’s mixing length turbulence model. No training data were deployed to train the models. The superiority of the mixed-variable approach was confirmed for the investigated high Reynolds number flow. The mixed-variable models were sensitive to the network shape. For the two cylinders, differently deep networks showed superior performance. The best fitting models were able to capture important flow phenomena such as stagnation regions, boundary layers, flow separation, and recirculation. We also encountered difficulties when predicting high Reynolds number flows without training data.

PySSA: end-user protein structure prediction and visual analysis with ColabFold and PyMOL (2024)

Kullik, Hannah ; Urban, Martin ; Schaub, Jonas ; Loidl-Stahlhofen, Angelika ; Zielesny, Achim

Computational methods for the accurate prediction of protein folding based on amino acid sequences have been researched for decades. The field has been significantly advanced in recent years by deep learning-based approaches, like AlphaFold, RoseTTAFold, or ColabFold. Although these can be used by the scientific community in various, mostly free and open ways, they are not yet widely used by bench scientists in relevant fields such as protein biochemistry or molecular biology, who are often not familiar with software tools such as scripting notebooks, command-line interfaces or cloud computing. In addition, visual inspection functionalities like protein structure displays, structure alignments, and specific protein hotspot analyses are required as a second step to interpret and apply the predicted structures in ongoing research studies. PySSA (Python rich client for visual protein Sequence to Structure Analysis) is an open Graphical User Interface (GUI) application combining the protein sequence to structure prediction capabilities of ColabFold with the open-source variant of the molecular structure visualisation and analysis system PyMOL to make both available to the scientific end-user. PySSA enables the creation of managed and shareable projects with defined protein structure prediction and corresponding alignment workflows that can be conveniently performed by scientists without specialised computer skills or programming knowledge on their local computers. Thus, PySSA can help make protein structure prediction more accessible for end-users in protein chemistry and molecular biology as well as be used for educational purposes. It is openly available on GitHub, alongside a custom graphical installer executable for the Windows operating system: https://github.com/urban233/PySSA/wiki/Installation-for-Windows-Operating-System. To demonstrate the capabilities of PySSA, its usage in a protein mutation study on the protein drug Bone Morphogenetic Protein 2 (BMP2) is described: the structure prediction results indicate that the previously reported BMP2-2Hep-7M mutant, which is intended to be less prone to aggregation, does not exhibit significant spatial rearrangements of amino acid residues interacting with the receptor.

An automated Calculation Pipeline for Differential Pair Interaction Energies with Molecular Force Fields using the Tinker Molecular Modeling Package (2024)

Bänsch, Felix ; Daniel, Mirco ; Lanig, Harald ; Steinbeck, Christoph ; Zielesny, Achim

An automated pipeline for comprehensive calculation of intermolecular interaction energies based on molecular force-fields using the Tinker molecular modelling package is presented. Starting with non-optimized chemically intuitive monomer structures, the pipeline allows the approximation of global minimum energy monomers and dimers, configuration sampling for various monomer-monomer distances, estimation of coordination numbers by molecular dynamics simulations, and the evaluation of differential pair interaction energies. The latter are used to derive Flory-Huggins parameters and isotropic particle-particle repulsions for Dissipative Particle Dynamics (DPD). The computational results for force fields MM3, MMFF94, OPLS-AA and AMOEBA09 are analyzed with Density Functional Theory (DFT) calculations and DPD simulations for a mixture of the non-ionic polyoxyethylene alkyl ether surfactant C10E4 with water to demonstrate the usefulness of the approach.

Advancements in Hand-Drawn Chemical Structure Recognition through an Enhanced DECIMER Architecture (2024)

Rajan, Kohulan ; Brinkhaus, Henning Otto ; Zielesny, Achim ; Steinbeck, Christoph

Accurate recognition of hand-drawn chemical structures is crucial for digitising hand-written chemical information found in traditional laboratory notebooks or for facilitating stylus-based structure entry on tablets or smartphones. However, the inherent variability in hand-drawn structures poses challenges for existing Optical Chemical Structure Recognition (OCSR) software. To address this, we present an enhanced Deep lEarning for Chemical ImagE Recognition (DECIMER) architecture that leverages a combination of Convolutional Neural Networks (CNNs) and Transformers to improve the recognition of hand-drawn chemical structures. The model incorporates an EfficientNetV2 CNN encoder that extracts features from hand-drawn images, followed by a Transformer decoder that converts the extracted features into Simplified Molecular Input Line Entry System (SMILES) strings. Our models were trained using synthetic hand-drawn images generated by RanDepict, a tool for depicting chemical structures with different style elements. To evaluate the model's performance, a benchmark was performed using a real-world dataset of hand-drawn chemical structures. The results indicate that our improved DECIMER architecture exhibits a significantly enhanced recognition accuracy compared to other approaches.

Also in Chemistry, Deep Learning Models Love Really Big Data (2024)

Rajan, Kohulan ; Zielesny, Achim ; Steinbeck, Christoph

Inspired by the super-human performance of deep learning models in playing the game of Go after being presented with virtually unlimited training data, we looked into areas in chemistry where similar situations could be achieved. Encountering large amounts of training data in chemistry is still rare, so we turned to two areas where realistic training data can be fabricated in large quantities, namely a) the recognition of machine-readable structures from images of chemical diagrams and b) the conversion of IUPAC(-like) names into structures and vice versa. In this talk, we outline the challenges, technical implementation and results of this study. Optical Chemical Structure Recognition (OCSR): Vast amounts of chemical information remain hidden in the primary literature and have yet to be curated into open-access databases. To automate the process of extracting chemical structures from scientific papers, we developed the DECIMER.ai project. This open-source platform provides an integrated solution for identifying, segmenting, and recognising chemical structure depictions in scientific literature. DECIMER.ai comprises three main components: DECIMER-Segmentation, which utilises a Mask-RCNN model to detect and segment images of chemical structure depictions; DECIMER-Image Classifier EfficientNet-based classification model identifies which images contain chemical structures and DECIMER-Image Transformer which acts as an OCSR engine which combines an encoder-decoder model to convert the segmented chemical structure images into machine-readable formats, like the SMILES string. DECIMER.ai is data-driven, relying solely on the training data to make accurate predictions without hand-coded rules or assumptions. The latest model was trained with 127 million structures and 483 million depictions (4 different per structure) on Google TPU-V4 VMs Name to Structure Conversion: The conversion of structures to IUPAC(-like) or systematic names has been solved algorithmically or rule-based in satisfying ways. This fact, on the other side, provided us with an opportunity to generate a name-structure training pair at a very large scale to train a proof-of-concept transformer network and evaluate its performance. In this work, the largest model was trained using almost one billion SMILES strings. The Lexichem software utility from OpenEye was employed to generate the IUPAC names used in the training process. STOUT V2 was trained on Google TPU-V4 VMs. The model's accuracy was validated through one-to-one string matching, BLEU scores, and Tanimoto similarity calculations. To further verify the model's reliability, every IUPAC name generated by STOUT V2 was analysed for accuracy and retranslated using OPSIN, a widely used open-source software for converting IUPAC names to SMILES. This additional validation step confirmed the high fidelity of STOUT V2's translations.

The DECIMER.ai Project (2024)

Rajan, Kohulan ; Zielesny, Achim ; Steinbeck, Christoph

Over the past few decades, the number of publications describing chemical structures and their metadata has increased significantly. Chemists have published the majority of this information as bitmap images along with other important information as human-readable text in printed literature and have never been retained and preserved in publicly available databases as machine-readable formats. Manually extracting such data from printed literature is error-prone, time-consuming, and tedious. The recognition and translation of images of chemical structures from printed literature into machine-readable format is known as Optical Chemical Structure Recognition (OCSR). In recent years, deep-learning-based OCSR tools have become increasingly popular. While many of these tools claim to be highly accurate, they are either unavailable to the public or proprietary. Meanwhile, the available open-source tools are significantly time-consuming to set up. Furthermore, none of these offers an end-to-end workflow capable of detecting chemical structures, segmenting them, classifying them, and translating them into machine-readable formats. To address this issue, we present the DECIMER.ai project, an open-source platform that provides an integrated solution for identifying, segmenting, and recognizing chemical structure depictions within the scientific literature. DECIMER.ai comprises three main components: DECIMER-Segmentation, which utilizes a Mask-RCNN model to detect and segment images of chemical structure depictions; DECIMER-Image Classifier EfficientNet-based classification model identifies which images contain chemical structures and DECIMER-Image Transformer which acts as an OCSR engine which combines an encoder-decoder model to convert the segmented chemical structure images into machine-readable formats, like the SMILES string. A key strength of DECIMER.ai is that its algorithms are data-driven, relying solely on the training data to make accurate predictions without any hand-coded rules or assumptions. By offering this comprehensive, open-source, and transparent pipeline, DECIMER.ai enables automated extraction and representation of chemical data from unstructured publications, facilitating applications in chemoinformatics and drug discovery.

Advancements in hand-drawn chemical structure recognition through an enhanced DECIMER architecture (2024)

Rajan, Kohulan ; Brinkhaus, Henning Otto ; Steinbeck, Christoph ; Zielesny, Achim

Accurate recognition of hand-drawn chemical structures is crucial for digitising hand-written chemical information in traditional laboratory notebooks or facilitating stylus-based structure entry on tablets or smartphones. However, the inherent variability in hand-drawn structures poses challenges for existing Optical Chemical Structure Recognition (OCSR) software. To address this, we present an enhanced Deep lEarning for Chemical ImagE Recognition (DECIMER) architecture that leverages a combination of Convolutional Neural Networks (CNNs) and Transformers to improve the recognition of hand-drawn chemical structures. The model incorporates an EfficientNetV2 CNN encoder that extracts features from hand-drawn images, followed by a Transformer decoder that converts the extracted features into Simplified Molecular Input Line Entry System (SMILES) strings. Our models were trained using synthetic hand-drawn images generated by RanDepict, a tool for depicting chemical structures with different style elements. A benchmark was performed using a real-world dataset of hand-drawn chemical structures to evaluate the model's performance. The results indicate that our improved DECIMER architecture exhibits a significantly enhanced recognition accuracy compared to other approaches.

An Augmented Multiphase Rail Launcher With a Modular Design: Extended Setup and Muzzle Fed Operation (2024)

Löffler, Markus-Jan ; Zellmer, Florian ; Schneider, Markus ; Kreischer, C.

Augmented Electromagnetic Accelerators - Technical Solutions and New Ideas (2013)

Löffler, Markus Jan ; Roch, Martin ; Hundertmark, Stephan ; Zacharias, Peter

An Augmented Multiphase Rail Launcher With a Modular Design: Theoretical Considerations and Development (2023)

Zellmer, Florian ; Löffler, Jan Markus ; Schneider, Markus ; Kreischer, Christian

Actively Controlling the Muzzle Velocity of a Railgun (2013)

Löffler, Markus Jan ; Siaenen, Thorbjörn ; Schneider, Markus ; Zacharias, Peter

Electromechanical Modeling of Components of a Linear Electromagnetic Accelerator (2013)

Löffler, Markus Jan ; Schneider, Markus ; Rackauskas, Justinas

First Experiment With the Modular Augmented Staged Electromagnetic Launcher (2014)

Löffler, Markus Jan ; Roch, Martin ; Hundertmark, Stephan ; Zacharias, Peter

The Modular Augmented Staged Electromagnetic Launcher Operated in the Energy Storage Mode (2015)

Löffler, Markus Jan ; Roch, Martin ; Hundertmark, Stephan ; Zacharias, Peter

Temperature Versus Magnetic Pressure at the Surface of a Semi-Infinite Plate (2013)

Löffler, Markus Jan ; Schneider, Markus

Unsupervised physics-informed deep learning of the flow around an airfoil using a mixed-variable network (2024)

Harmening, Jan Hauke ; Peitzmann, Franz-Josef

Bifacial modules: design options, characterisation and reliability (2018)

Schneider, Andreas ; van Aken, Bas ; Gerritsen, Eric ; Prakash, Jai ; Fakhfouri, Vahid ; Yong Sheng, Khoo ; Halm, Andreas

Bifacial photovoltaic (PV) modules are able to utilize light from both sides and can therefore significantly increase the electric yield of PV power plants, thus reducing the cost and improving profitability. Bifacial PV technology has a huge potential to reach a major market share, in particular when considering utility scale PV plants. Accordingly, bifacial PV is currently attracting increasing attention from involved engineers, scientists and investors. There is a lack of available, structured information about this topic. A book that focuses exclusively on bifacial PV thus meets an increasing need. Bifacial Photovoltaics: Technology, applications and economics provides an overview of the history, status and future of bifacial PV technology with a focus on crystalline silicon technology, covering the areas of cells, modules, and systems. In addition, topics like energy yield simulations and bankability are addressed. It is a must-read for researchers and manufacturers involved with cutting-edge photovoltaics.

Continuous and Scalable Laser Synthesis of Atom Clusters with Tunable Surface Oxidation for Electrocatalytic Water Splitting (2024)

Kazamer, Norbert ; Tack, Meike ; Usama, Muhammad ; Brodmann, Michael ; Barcikowski, Stephan ; Reichenberger, Sven

A Multiphase Rail Launcher With Circular Geometry and Modular Design: Development, Construction, and First Experiments (2023)

Löffler, Markus-Jan ; Zellmer, Florian ; Schneider, Markus ; Kreischer, C.

Augmented Three-Phase AC-Railgun: Basic Considerations (2023)

Löffler, Markus-Jan ; Zellmer, Florian

MFsim [Softwarecode, Version 2.6.0.0] (2023)

Zielesny, Achim

MFsim - An open Java all-in-one rich-client simulation environment for mesoscopic simulation MFsim is an open Java all-in-one rich-client computing environment for mesoscopic simulation with Jdpd as its default simulation kernel for Molecular Fragment Dissipative Particle Dynamics (DPD). The environment integrates and supports the complete preparation-simulation-evaluation triad of a mesoscopic simulation task. Productive highlights are a SPICES molecular structure editor, a PDB-to-SPICES parser for particle-based peptide/protein representations, a support of polymer definitions, a compartment editor for complex simulation box start configurations, interactive and flexible simulation box views including analytics, simulation movie generation or animated diagrams. As an open project, MFsim enables customized extensions for different fields of research. MFsim uses several open libraries (see MFSimVersionHistory.txt for details and references below) and is published as open source under the GNU General Public License version 3 (see LICENSE). MFsim has been described in the scientific literature and used for DPD studies.

Jdpd [Softwarecode, Version 1.6.1.0] (2023)

Zielesny, Achim

Jdpd - An open Java Simulation Kernel for Molecular Fragment Dissipative Particle Dynamics (DPD) Jdpd is an open Java simulation kernel for Molecular Fragment Dissipative Particle Dynamics (DPD) with parallelizable force calculation, efficient caching options and fast property calculations. It is characterized by an interface and factory-pattern driven design for simple code changes and may help to avoid problems of polyglot programming. Detailed input/output communication, parallelization and process control as well as internal logging capabilities for debugging purposes are supported. The kernel may be utilized in different simulation environments ranging from flexible scripting solutions up to fully integrated “all-in-one” simulation systems like MFsim. Since Jdpd version 1.6.1.0 Jdpd is available in a (basic) double-precision version and a (derived) single-precision version (= JdpdSP) for all numerical calculations, where the single precision version needs about half the memory of the double precision version. Jdpd uses the Apache Commons Math and Apache Commons RNG libraries and is published as open source under the GNU General Public License version 3. This repository comprises the Java bytecode libraries (including the Apache Commons Math and RNG libraries), the Javadoc HTML documentation and the Netbeans source code packages including Unit tests. Jdpd has been described in the scientific literature (the final manuscript 2018 - van den Broek - Jdpd - Final Manucsript.pdf is added to the repository) and used for DPD studies (see references below). See text file JdpdVersionHistory.txt for a version history with more detailed information.

An automated Calculation Pipeline for Differential Pair Interaction Energies with Molecular Force Fields using the Tinker Molecular Modeling Package (2024)

Zielesny, Achim ; Daniel, Mirco ; Lanig, Harald ; Steinbeck, Christoph

An automated pipeline for comprehensive calculation of intermolecular interaction energies based on molecular force-fields using the Tinker molecular modelling package is presented. Starting with non-optimized chemically intuitive monomer structures, the pipeline allows the approximation of global minimum energy monomers and dimers, configuration sampling for various monomer-monomer distances, estimation of coordination numbers by molecular dynamics simulations, and the evaluation of differential pair interaction energies. The latter are used to derive Flory-Huggins parameters and isotropic particle-particle repulsions for Dissipative Particle Dynamics (DPD). The computational results for force fields MM3, MMFF94, OPLSAA and AMOEBA09 are analyzed with Density Functional Theory (DFT) calculations and DPD simulations for a mixture of the non-ionic polyoxyethylene alkyl ether surfactant C10E4 with water to demonstrate the usefulness of the approach.

n-type silicon modules (2023)

Schneider, Andreas ; Muñoz-Cerón, Emilio ; Rabanal-Arabach, Jorge ; Voroshazi, Eszter ; Contreras Lisperguer, Rubén ; Barth, Vincent

The photovoltaic industry is facing an exponential growth in the recent years fostered by a dramatic decrease in installation prices. This cost reduction is achieved by means of several mechanisms. First, because of the optimization of the design and installation process of current PV projects, and second, by the optimization, in terms of performance, in the manufacturing techniques and material combinations within the modules, which also has an impact on both, the installation process, and the levelized cost of electricity (LCOE). One popular trend is to increase the power delivered by photovoltaic modules, either by using larger wafer sizes or by combining more cells within the module unit. This solution means a significant increase in the size of these devices, but it implies an optimization in the design of photovoltaic plants. This results in an installation cost reduction which turns into a decrease in the LCOE. However, this solution does not represent a breakthrough in addressing the real challenge of the technology which affects the module requirements. The innovation efforts must be focused on improving the modules capability to produce energy without enlarging the harvesting area. This challenge can be faced by approaching some of the module characteristics which are summarized in this chapter.

A Simple Approach for Module Temperature and Power Prediction (2023)

Schneider, Andreas ; Chochollek, Julia ; Nierhoff, Thomas

In this work a mathematical approach to calculate solar panel temperature based on measured irradiance, temperature and wind speed is applied. With the calculated module temperature, the electrical solar module characteristics is determined. A program developed in MatLab App Designer allows to import measurement data from a weather station and calculates the module temperature based on the mathematical NOCT and stationary approach with a time step between the measurements of 5 minutes. Three commercially available solar panels with different cell and interconnection technologies are used for the verification of the established models. The results show a strong correlation between the measured and by the stationary model predicted module temperature with a coefficient of determination R2 close to 1 and a root mean square deviation (RMSE) of ≤ 2.5 K for a time period of three months. Based on the predicted temperature, measured irradiance in module plane and specific module information the program models the electrical data as time series in 5-minute steps. Predicted to measured power for a time period of three months shows a linear correlation with an R2 of 0.99 and a mean absolute error (MAE) of 3.5, 2.7 and 4.8 for module ID 1, 2 and 3. The calculated energy (exemplarily for module ID 2) based on the measured, calculated by the NOCT and stationary model for this time period is 118.4 kWh, resp. 116.7 kWh and 117.8 kWh. This is equivalent to an uncertainty of 1.4% for the NOCT and 0.5% for the stationary model.

Advanced Determination of Temperature Coefficients of Photovoltaic Modules by Field Measurements (2023)

Schneider, Andreas ; Chochollek, Julia ; Nierhoff, Thomas

In this work data from outdoor measurements, acquired over the course of up to three years on commercially available solar panels, is used to determine the temperature coefficients and compare these to the information as stated by the producer in the data sheets. A program developed in MatLab App Designer allows to import the electrical and ambient measurement data. Filter algorithms for solar irradiance narrow the irradiance level down to ~1000 W/m2 before linear regression methods are applied to obtain the temperature coefficients. A repeatability investigation proves the accuracy of the determined temperature coefficients which are in good agreement to the supplier specification if the specified values for power are not larger than -0.3%/K. Further optimization is achieved by applying wind filter techniques and days with clear sky condition. With the big (measurement) data on hand it was possible to determine the change of the temperature coefficients for varying irradiance. As stated in literature we see an increase of the temperature coefficient of voltage and a decline for the temperature coefficient of power with increasing irradiance.

An SSI Based System for Incentivized and Self-determined Customer-to-Business Data Sharing in a Local Economy Context (2020)

Wittek, Kevin ; Lazzati, Laura ; Bothe, David ; Sinnaeve, Ann-Julie ; Pohlmann, Norbert

With ongoing developments in the field of smart cities and digitalization in general, data is becoming a driving factor and value stream for new and existing economies alike. However, there exists an increasing centralization and monopolization of data holders and service providers, especially in the form of the big US-based technology companies in the western world and central technology providers with close ties to the government in the Asian regions. Self Sovereign Identity (SSI) provides the technical building blocks to create decentralized data-driven systems, which bring data autonomy back to the users. In this paper we propose a system in which the combination of SSI and token economy based incentivisation strategies makes it possible to unlock the potential value of data-pools without compromising the data autonomy of the users.

Measuring the Impact of the GDPR on Data Sharing in Ad Networks (2020)

Urban, Tobias ; Tatang, Dennis ; Degeling, Martin ; Holz, Thorsten ; Pohlmann, Norbert

The European General Data Protection Regulation (GDPR), which went into effect in May 2018, brought new rules for the processing of personal data that affect many business models, including online advertising. The regulation’s definition of personal data applies to every company that collects data from European Internet users. This includes tracking services that, until then, argued that they were collecting anonymous information and data protection requirements would not apply to their businesses. Previous studies have analyzed the impact of the GDPR on the prevalence of online tracking, with mixed results. In this paper, we go beyond the analysis of the number of third parties and focus on the underlying information sharing networks between online advertising companies in terms of client-side cookie syncing. Using graph analysis, our measurement shows that the number of ID syncing connections decreased by around 40 % around the time the GDPR went into effect, but a long-term analysis shows a slight rebound since then. While we can show a decrease in information sharing between third parties, which is likely related to the legislation, the data also shows that the amount of tracking, as well as the general structure of cooperation, was not affected. Consolidation in the ecosystem led to a more centralized infrastructure that might actually have negative effects on user privacy, as fewer companies perform tracking on more sites.

Beyond the Front Page: Measuring Third Party Dynamics in the Field (2020)

Urban, Tobias ; Degeling, Martin ; Holz, Thorsten ; Pohlmann, Norbert

In the modern Web, service providers often rely heavily on third parties to run their services. For example, they make use of ad networks to finance their services, externally hosted libraries to develop features quickly, and analytics providers to gain insights into visitor behavior. For security and privacy, website owners need to be aware of the content they provide their users. However, in reality, they often do not know which third parties are embedded, for example, when these third parties request additional content as it is common in real-time ad auctions. In this paper, we present a large-scale measurement study to analyze the magnitude of these new challenges. To better reflect the connectedness of third parties, we measured their relations in a model we call third party trees, which reflects an approximation of the loading dependencies of all third parties embedded into a given website. Using this concept, we show that including a single third party can lead to subsequent requests from up to eight additional services. Furthermore, our findings indicate that the third parties embedded on a page load are not always deterministic, as 50 % of the branches in the third party trees change between repeated visits. In addition, we found that 93 % of the analyzed websites embedded third parties that are located in regions that might not be in line with the current legal framework. Our study also replicates previous work that mostly focused on landing pages of websites. We show that this method is only able to measure a lower bound as subsites show a significant increase of privacy-invasive techniques. For example, our results show an increase of used cookies by about 36 % when crawling websites more deeply.

Plenty of Phish in the Sea: Analyzing Potential Pre-Attack Surfaces (2020)

Urban, Tobias ; Große-Kampmann, Matteo ; Tatang, Dennis ; Holz, Thorsten ; Pohlmann, Norbert

Advanced Persistent Threats (APTs) are one of the main challenges in modern computer security. They are planned and performed by well-funded, highly-trained and often state-based actors. The first step of such an attack is the reconnaissance of the target. In this phase, the adversary tries to gather as much intelligence on the victim as possible to prepare further actions. An essential part of this initial data collection phase is the identification of possible gateways to intrude the target. In this paper, we aim to analyze the data that threat actors can use to plan their attacks. To do so, we analyze in a first step 93 APT reports and find that most (80 %) of them begin by sending phishing emails to their victims. Based on this analysis, we measure the extent of data openly available of 30 entities to understand if and how much data they leak that can potentially be used by an adversary to craft sophisticated spear phishing emails. We then use this data to quantify how many employees are potential targets for such attacks. We show that 83 % of the analyzed entities leak several attributes of uses, which can all be used to craft sophisticated phishing emails.

Statistical anomaly detection in ethereum transaction graphs (2020)

Wittek, Kevin ; Wittek, Neslihan ; Ioniță, Andrei ; Pohlmann, Norbert

The set of transactions that occurs on the public ledger of an Ethereum network in a specific time frame can be represented as a directed graph, with vertices representing addresses and an edge indicating the interaction between two addresses. While there exists preliminary research on analyzing an Ethereum network by the means of graph analysis, most existing work is focused on either the public Ethereum Mainnet or on analyzing the different semantic transaction layers using static graph analysis in order to carve out the different network properties (such as interconnectivity, degrees of centrality, etc.) needed to characterize a blockchain network. By analyzing the consortium-run bloxberg Proof-of-Authority (PoA) Ethereum network, we show that we can identify suspicious and potentially malicious behaviour of network participants by employing statistical graph analysis. We thereby show that it is possible to identify the potentially malicious exploitation of an unmetered and weakly secured blockchain network resource. In addition, we show that Temporal Network Analysis is a promising technique to identify the occurrence of anomalies in a PoA Ethereum network.

Our (in)Secure Web: Understanding Update Behavior of Websites and Its Impact on Security (2021)

Demir, Nurullah ; Urban, Tobias ; Wittek, Kevin ; Pohlmann, Norberg

Software updates take an essential role in keeping IT environments secure. If service providers delay or do not install updates, it can cause unwanted security implications for their environments. This paper conducts a large-scale measurement study of the update behavior of websites and their utilized software stacks. Across 18 months, we analyze over 5.6M websites and 246 distinct client- and server-side software distributions. We found that almost all analyzed sites use outdated software. To understand the possible security implications of outdated software, we analyze the potential vulnerabilities that affect the utilized software. We show that software components are getting older and more vulnerable because they are not updated. We find that 95 % of the analyzed websites use at least one product for which a vulnerability existed.

A Crypto-Token Based Charging Incentivization Scheme for Sustainable Light Electric Vehicle Sharing (2021)

Wittek, Kevin ; Finke, Sebastian ; Schelte, Nora ; Pohlmann, Norbert ; Severengiz, Semih

The ecological impact of shared light electric vehicles (LEV) such as kick scooters is still widely discussed. Especially the fact that the vehicles and batteries are collected using diesel vans in order to charge empty batteries with electricity of unclear origin is perceived as unsustainable. A better option could be to let the users charge the vehicles themselves whenever it is necessary. For this, a decentralized,flexible and easy to install network of off-grid solar charging stations could bring renewable electricity where it is needed without sacrificing the convenience of a free float sharing system. Since the charging stations are powered by solar energy the most efficient way to utilize them would be to charge the vehicles when the sun is shining. In order to make users charge the vehicle it is necessary to provide some form of benefit for them doing so. This could be either a discount or free rides. A particularly robust and well-established mechanism is controlling incentives via means of blockchain-based cryptotokens. This paper demonstrates a crypto-token based scheme for incentivizing users to charge sharing vehicles during times of considerable solar irradiation in order to contribute to more sustainable mobility services.

Towards Understanding First-Party Cookie Tracking in the Field (2022)

Demir, Nurullah ; Theis, Daniel ; Urban, Tobias ; Pohlmann, Norbert

Third-party tracking is a common and broadly used technique on the Web. Different defense mechanisms have emerged to counter these practices (e.g. browser vendors that ban all third-party cookies). However, these countermeasures only target third-party trackers and ignore the first party because the narrative is that such monitoring is mostly used to improve the utilized service (e.g. analytical services). In this paper, we present a large-scale measurement study that analyzes tracking performed by the first party but utilized by a third party to circumvent standard tracking preventing techniques. We visit the top 15,000 websites to analyze first-party cookies used to track users and a technique called “DNS CNAME cloaking”, which can be used by a third party to place first-party cookies. Using this data, we show that 76% of sites effectively utilize such tracking techniques. In a long-running analysis, we show that the usage of such cookies increased by more than 50% over 2021.

On the Similarity of Web Measurements Under Different Experimental Setups (2023)

Demir, Nurullah ; Hörnemann, Jan ; Große-Kampmann, Matteo ; Urban, Tobias ; Pohlmann, Norbert ; Holz, Thorsten ; Wressnegger, Christian

Measurement studies are essential for research and industry alike to understand the Web’s inner workings better and help quantify specific phenomena. Performing such studies is demanding due to the dynamic nature and size of the Web. An experiment’s careful design and setup are complex, and many factors might affect the results. However, while several works have independently observed differences in the outcome of an experiment (e.g., the number of observed trackers) based on the measurement setup, it is unclear what causes such deviations. This work investigates the reasons for these differences by visiting 1.7M webpages with five different measurement setups. Based on this, we build ‘dependency trees’ for each page and cross-compare the nodes in the trees. The results show that the measured trees differ considerably, that the cause of differences can be attributed to specific nodes, and that even identical measurement setups can produce different results.

DECIMER.ai: an open platform for automated optical chemical structure identification, segmentation and recognition in scientific publications (2023)

Rajan, Kohulan ; Brinkhaus, Henning Otto ; Agea, M. Isabel ; Zielesny, Achim ; Steinbeck, Christoph

The number of publications describing chemical structures has increased steadily over the last decades. However, the majority of published chemical information is currently not available in machine-readable form in public databases. It remains a challenge to automate the process of information extraction in a way that requires less manual intervention - especially the mining of chemical structure depictions. As an open-source platform that leverages recent advancements in deep learning, computer vision, and natural language processing, DECIMER.ai (Deep lEarning for Chemical IMagE Recognition) strives to automatically segment, classify, and translate chemical structure depictions from the printed literature. The segmentation and classification tools are the only openly available packages of their kind, and the optical chemical structure recognition (OCSR) core application yields outstanding performance on all benchmark datasets. The source code, the trained models and the datasets developed in this work have been published under permissive licences. An instance of the DECIMER web application is available at https://decimer.ai.

A Large-Scale Study of Cookie Banner Interaction Tools and Their Impact on Users' Privacy (2023)

Demir, Nurullah ; Pohlmann, Norbert ; Urban, Tobias ; Wressnegger, Christian

Cookie notices (or cookie banners) are a popular mechanism for websites to provide (European) Internet users a tool to choose which cookies the site may set. Banner implementations range from merely providing information that a site uses cookies over offering the choice to accepting or denying all cookies to allowing fine-grained control of cookie usage. Users frequently get annoyed by the banner’s pervasiveness as they interrupt “natural” browsing on the Web. As a remedy, different browser extensions have been developed to automate the interaction with cookie banners. In this work, we perform a large-scale measurement study comparing the effectiveness of extensions for “cookie banner interaction.” We configured the extensions to express different privacy choices (e.g., accepting all cookies, accepting functional cookies, or rejecting all cookies) to understand their capabilities to execute a user’s preferences. The results show statistically significant differences in which cookies are set, how many of them are set, and which types are set—even for extensions that aim to implement the same cookie choice. Extensions for “cookie banner interaction” can effectively reduce the number of set cookies compared to no interaction with the banners. However, all extensions increase the tracking requests significantly except when rejecting all cookies.

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