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Powder bed-based additive manufacturing processes offer an extended freedom in design and enable the processing of metals, ceramics, and polymers with a high level of relative density. The latter is a prevalent measure of process and component quality, which depends on various input variables. A key point in this context is the condition of powder beds. To enhance comprehension of their particle-level formation and facilitate process optimization, simulations based on the Discrete Element Method are increasingly employed in research. To generate qualitatively as well as quantitatively reliable simulation results, an adaptation of the contact model parameterization is necessary. However, current adaptation methods often require the implementation of models that significantly increase computational effort, therefore limiting their applicability. To counteract this obstacle, a sophisticated formula-based adaptation and evaluation method is presented in this research. Additionally, the developed method enables accelerated parameter determination with limited experimental effort. Thus, it represents an integrative component, which supports further research efforts based on the Discrete Element Method by significantly reducing the parameterization effort. The universal nature of deducting this method also allows its adaptation to similar parameterization problems and its implementation in other fields of research.
Betreiber von Produktionsanlagen stehen oft vor der Frage, welche Norm für die Absicherung der Anlage gegen Cyberangriffe heranzuziehen ist. Aus dem IT-Bereich ist die Normreihe ISO 27000 bekannt. Im Produktionsbereich wird häufig die Normreihe IEC 62443 herangezogen. Dieser Beitrag gibt einen Überblick über beide Normreihen und schlägt einen Ansatz zur gemeinsamen Nutzung beider Standards vor.
Parametric study of piezoresistive structures in continuous fiber reinforced additive manufacturing
(2024)
Recent advancements in fiber reinforced additive manufacturing leverage the piezoresistivity of continuous carbon fibers. This effect enables the fabrication of structural components with inherent piezoresistive properties suitable for load measurement or structural monitoring. These are achieved without necessitating additional manufacturing or assembly procedures. However, there remain unexplored variables within the domain of continuous fiber-reinforced additive manufacturing. Crucially, the roles of fiber curvature radii and sensing fiber bundle counts have yet to be comprehensively addressed. Additionally, the compression-sensitive nature of printed carbon fiber-reinforced specimens remains a largely unexplored research area. To address these gaps, this study presents experimental analyses on tensile and three-point flexural specimens incorporating sensing carbon fiber strands. All specimens were fabricated with three distinct curvature radii. For the tensile specimens, the number of layers was also varied. Sensing fiber bundles were embedded on both tensile and compression sides of the flexural specimens. Mechanical testing revealed a linear-elastic behavior in the specimens. It was observed that carbon fibers supported the majority of the load, leading to brittle fractures. The resistance measurements showed a dependence on both the number of sensing layers and the radius of curvature, and exhibited a slight decreasing trend in the cyclic tests. Compared with the sensors subjected to tensile stress, the sensors embedded on the compression side showed a lower gauge factor.
Background
The eResearch system “Prospective Monitoring and Management App (PIA)” allows researchers to implement questionnaires on any topic and to manage biosamples. Currently, we use PIA in the longitudinal study ZIFCO (Integrated DZIF Infection Cohort within the German National Cohort) in Hannover (Germany) to investigate e.g. associations of risk factors and infectious diseases. Our aim was to assess user acceptance and compliance to determine suitability of PIA for epidemiological research on transient infectious diseases.
Methods
ZIFCO participants used PIA to answer weekly questionnaires on health status and report spontaneous onset of symptoms. In case of symptoms of a respiratory infection, the app requested participants to self-sample a nasal swab for viral analysis. To assess user acceptance, we implemented the System Usability Scale (SUS) and fitted a linear regression model on the resulting score. For investigation of compliance with submitting the weekly health questionnaires, we used a logistic regression model with binomial response.
Results
We analyzed data of 313 participants (median age 52.5 years, 52.4% women). An average SUS of 72.0 reveals good acceptance of PIA. Participants with a higher technology readiness score at the beginning of study participation also reported higher user acceptance. Overall compliance with submitting the weekly health questionnaires showed a median of 55.7%. Being female, of younger age and being enrolled for a longer time decreased the odds to respond. However, women over 60 had a higher chance to respond than women under 60, while men under 40 had the highest chance to respond. Compliance with nasal swab self-sampling was 77.2%.
Discussion
Our findings show that PIA is suitable for the use in epidemiologic studies with regular short questionnaires. Still, we will focus on user engagement and gamification for the further development of PIA to help incentivize regular and long-term participation.
Subclinical mastitis in heifers during early lactation affects udder health, future milk production and, therefore, the risk of premature culling. The aim of this cross-sectional study was to identify pre- and post-partum risk factors associated with a high heifer mastitis rate (HMR), and to find out which period (either pre- or post-partum) contains more risk factors and consequently should be the focus of mastitis control in heifers. A total of 77 herds were included in this study and the potential animal- and farm-related risk factors were recorded during a one-time farm visit. The HMR was provided by the dairy herd improvement test (DHI) as the annual average of the past 11 DHIs. For this study, data were analyzed in two models using generalized linear models. Each model examined the association between possible risk factors and HMR, one including only prepartum risk factors and the other one only post-partum risk factors. One identified pre-partum risk factor was the proportion of udder-healthy cows in the herd. Post-partum risk factors were the type of teat cleaning procedure before milking, teat disinfection, treatment of mastitis in heifers, a body condition score (BCS) of >3.0 in fresh heifers, and the combination of a teat cleaning procedure with a teat disinfectant. The results show the importance of the period shortly after calving for udder health in heifers, as four of the five significant risk factors were identified in this period and three of them were related to the milking process. However, further research with a higher number of herds is needed to minimize individual herd effects.
In this paper, we present a novel approach for real-time rendering of soft eclipse shadows cast by spherical, atmosphereless bodies. While this problem may seem simple at first, it is complicated by several factors. First, the extreme scale differences and huge mutual distances of the involved celestial bodies cause rendering artifacts in practice. Second, the surface of the Sun does not emit light evenly in all directions (an effect which is known as limb darkening). This makes it impossible to model the Sun as a uniform spherical light source. Finally, our intended applications include real-time rendering of solar eclipses in virtual reality, which require very high frame rates. As a solution to these problems, we precompute the amount of shadowing into an eclipse shadow map, which is parametrized so that it is independent of the position and size of the occluder. Hence, a single shadow map can be used for all spherical occluders in the Solar System. We assess the errors introduced by various simplifications and compare multiple approaches in terms of performance and precision. Last but not least, we compare our approaches to the state-of-the-art and to reference images. The implementation has been published under the MIT license.
During machine milking, pathogenic microorganisms can be transmitted from cow to cow through liners. Therefore, in Germany, a spray method for the intermediate disinfection of the milking cluster is often used for prevention. This method of cluster disinfection is easy to perform, requires little time and no extra materials, and the disinfection solution is safe from outside contamination in the spray bottle. Since no data on a systematic efficacy trial are available, the aim of this study was to determine the microbial reduction effect of intermediate disinfection. Therefore, laboratory and field trials were conducted. In both trials, two sprays of 0.85 mL per burst of different disinfectant solutions were sprayed into the contaminated liners. For sampling, a quantitative swabbing method using a modified wet–dry swab (WDS) technique based on DIN 10113-1: 1997-07 was applied. Thus, the effectiveness of disinfectants based on Peracetic Acid, Hydrogen Peroxide and Plasma-Activated Buffered Solution (PABS) was compared. In the laboratory trial, the inner surfaces of liners were contaminated with pure cultures of Escherichia (E.) coli, Staphylococcus (S.) aureus, Streptococcus (Sc.) uberis and Sc. agalactiae. The disinfection of the contaminated liners with the disinfectants resulted in a significant reduction in bacteria with values averaging 1 log for E. coli, 0.7 log for S. aureus, 0.7 log for Sc. uberis and 0.8 log for Sc. agalactiae. The highest reduction was obtained for contamination with E. coli (1.3 log) and Sc. uberis (0.8 log) when PABS was applied and for contamination with S. aureus (1.1 log) and Sc. agalactiae (1 log) when Peracetic Acid Solution (PAS) was used. Treatment with sterile water only led to an average reduction of 0.4 log. In the field trial, after the milking of 575 cows, the liners were disinfected and the total microorganism count from the liner surface was performed. The reduction was measured against an untreated liner within the cluster. Although a reduction in microorganisms was achieved in the field trial, it was not significant. When using PAS, a log reduction of 0.3 was achieved; when using PABS, a log reduction of 0.2 was obtained. The difference between the two disinfection methods was also not significant. Treatment with sterile water only led to a reduction of 0.1 log. The results show that spray disinfection under these circumstances does result in a reduction in the bacteria on the milking liner surface, but for effective disinfection a higher reduction would be preferred.
Mobile crowdsourcing refers to systems where the completion of tasks necessarily requires physical movement of crowdworkers in an on-demand workforce. Evidence suggests that in such systems, tasks often get assigned to crowdworkers who struggle to complete those tasks successfully, resulting in high failure rates and low service quality. A promising solution to ensure higher quality of service is to continuously adapt the assignment and respond to failure-causing events by transferring tasks to better-suited workers who use different routes or vehicles. However, implementing task transfers in mobile crowdsourcing is difficult because workers are autonomous and may reject transfer requests. Moreover, task outcomes are uncertain and need to be predicted. In this paper, we propose different mechanisms to achieve outcome prediction and task coordination in mobile crowdsourcing. First, we analyze different data stream learning approaches for the prediction of task outcomes. Second, based on the suggested prediction model, we propose and evaluate two different approaches for task coordination with different degrees of autonomy: an opportunistic approach for crowdshipping with collaborative, but non-autonomous workers, and a market-based model with autonomous workers for crowdsensing.
We report velocity-dependent internal energy distributions of nitric oxide molecules, NO, scattered off graphene supported on gold to further explore the dynamics of the collision process between NO radicals and graphene. These experiments were performed by directing a molecular beam of NO onto graphene in a surface-velocity map imaging setup, which allowed us to record internal energy distributions of the NO radicals as a function of their velocity. We do not observe bond formation but (1) major contributions from direct inelastic scattering and (2) a smaller trapping–desorption component where some physisorbed NO molecules have residence times on the order of microseconds. This is in agreement with our classical molecular dynamics simulations which also observe a small proportion of two- and multi-bounce collisions events but likewise a small proportion of NO radicals trapped at the surface for the entire length of the molecular dynamics simulations (a few picoseconds). Despite a collision energy of 0.31 eV, which would be sufficient to populate NO(v = 1), we do not detect vibrationally excited nitric oxide.
Although Corynebacterium spp. can be regularly associated with subclinical and clinical mastitis cases in dairy cows, knowledge on their reservoirs in dairy farms is sparse. Therefore, samples were collected at 10 visits with 14 day intervals from bedding material (n = 50), drinking troughs (n = 20), different walking areas (n = 60), cow brushes (n = 8), fly traps (n = 4), the passage to pasture (n = 9) as well as milking liners (n = 80) and milker gloves (n = 20) in one dairy cow farm. Additionally, quarter foremilk samples from all lactating cows (approximately 200) were collected at each visit. All samples underwent microbiological examination and cultured isolates were identified using MALDI-TOF MS. Most Corynebacterium spp. that were cultivated from milk were also isolated from the housing environment and milking-related niches (C. amycolatum, C. confusum, C. stationis, C. variabile, C. xerosis) or from milking-related niches only (C. frankenforstense, C. pilosum, C. suicordis). C. bovis was not cultivated from any environmental niche, while being the dominant species in milk samples. This study demonstrates that many Corynebacterium spp. present in milk samples can also be isolated from the cows’ environment. For C. bovis, the most relevant Corynebacterium species with regard to intramammary infections, it indicates that environmental reservoirs are of little relevance.
This paper reflects the content of the presentation “The Next Generation: Ethernet-APL for Safety Systems” at the NAMUR Annual General Meeting 2022. It deals with the use of the Ethernet Advanced Physical Layer (Ethernet-APL) in combination with the PROFINET/PROFIsafe protocol for safety applications. It describes the virtues of the digital communication between the field and safety system. In parallel the aspect of OT security for this use case is touched as well. The paper proposes a secure architecture, where safety- and non-safety field communications are still separated. At the end a set of requirements for the development of future APL devices is described.
Music streaming platforms offer music listeners an overwhelming choice of music. Therefore, users of streaming platforms need the support of music recommendation systems to find music that suits their personal taste. Currently, a new class of recommender systems based on knowledge graph embeddings promises to improve the quality of recommendations, in particular to provide diverse and novel recommendations. This paper investigates how knowledge graph embeddings can improve music recommendations. First, it is shown how a collaborative knowledge graph can be derived from open music data sources. Based on this knowledge graph, the music recommender system EARS (knowledge graph Embedding-based Artist Recommender System) is presented in detail, with particular emphasis on recommendation diversity and explainability. Finally, a comprehensive evaluation with real-world data is conducted, comparing of different embeddings and investigating the influence of different types of knowledge.
For the introduction of technical nursing care innovations, a usability assessment survey is conducted by nursing staff. The questionnaire is used before and after the introduction of technical products. This poster contribution shows the latest comparison of pre- and post-surveys on selected products.
The aim of this cross-sectional study was to investigate the occurrence of bacteremia in severe mastitis cases of dairy cows. Milk and corresponding blood samples of 77 cases of severe mastitis were bacteriologically examined. All samples (milk and blood) were incubated aerobically and anaerobically to also investigate the role of obligate anaerobic microorganisms in addition to aerobic microorganisms in severe mastitis. Bacteremia occurred if identical bacterial strains were isolated from milk and blood samples of the same case. In addition, pathogen shedding was examined, and the data of animals and weather were collected to determine associated factors for the occurrence of bacteremia in severe mastitis. If Gram-negative bacteria were detected in milk samples, a Limulus test (detection of endotoxins) was also performed for corresponding blood samples without the growth of Gram-negative bacteria. In 74 cases (96.1%), microbial growth was detected in aerobically incubated milk samples. The most-frequently isolated bacteria in milk samples were Escherichia (E.) coli (48.9%), Streptococcus (S.) spp. (18.1%), and Klebsiella (K.) spp. (16%). Obligatory anaerobic microorganisms were not isolated. In 72 cases (93.5%) of the aerobically examined blood samples, microbial growth was detected. The most-frequently isolated pathogens in blood samples were non-aureus Staphylococci (NaS) (40.6%) and Bacillus spp. (12.3%). The Limulus test was positive for 60.5% of cases, which means a detection of endotoxins in most blood samples without the growth of Gram-negative bacteria. Bacteremia was confirmed in 12 cases (15.5%) for K. pneumoniae (5/12), E. coli (4/12), S. dysgalactiae (2/12), and S. uberis (1/12). The mortality rate (deceased or culled) was 66.6% for cases with bacteremia and 34.1% for cases without bacteremia. High pathogen shedding and high humidity were associated with the occurrence of bacteremia in severe mastitis.
Context: Higher education is changing at an accelerating pace due to the widespread use of digital teaching and emerging technologies. In particular, AI assistants such as ChatGPT pose significant challenges for higher education institutions because they bring change to several areas, such as learning assessments or learning experiences.
Objective: Our objective is to discuss the impact of AI assistants in the context of higher education, outline possible changes to the context, and present recommendations for adapting to change.
Method: We review related work and develop a conceptual structure that visualizes the role of AI assistants in higher education.
Results: The conceptual structure distinguishes between humans, learning, organization, and disruptor, which guides our discussion regarding the implications of AI assistant usage in higher education. The discussion is based on evidence from related literature.
Conclusion: AI assistants will change the context of higher education in a disruptive manner, and the tipping point for this transformation has already been reached. It is in our hands to shape this transformation.
The digital transformation with its new technologies and customer expectation has a significant effect on the customer channels in the insurance industry. The objective of this study is the identification of enabling and hindering factors for the adoption of online claim notification services that are an important part of the customer experience in insurance. For this purpose, we conducted a quantitative cross-sectional survey based on the exemplary scenario of car insurance in Germany and analyzed the data via structural equation modeling (SEM). The findings show that, besides classical technology acceptance factors such as perceived usefulness and ease of use, digital mindset and status quo behavior play a role: acceptance of digital innovations, lacking endurance as well as lacking frustration tolerance with the status quo lead to a higher intention for use. Moreover, the results are strongly moderated by the severity of the damage event—an insurance-specific factor that is sparsely considered so far. The latter discovery implies that customers prefer a communication channel choice based on the individual circumstances of the claim.
The paper provides a comprehensive overview of modeling and pricing cyber insurance and includes clear and easily understandable explanations of the underlying mathematical concepts. We distinguish three main types of cyber risks: idiosyncratic, systematic, and systemic cyber risks. While for idiosyncratic and systematic cyber risks, classical actuarial and financial mathematics appear to be well-suited, systemic cyber risks require more sophisticated approaches that capture both network and strategic interactions. In the context of pricing cyber insurance policies, issues of interdependence arise for both systematic and systemic cyber risks; classical actuarial valuation needs to be extended to include more complex methods, such as concepts of risk-neutral valuation and (set-valued) monetary risk measures.
Das Gesundheitsdatennutzungsgesetz – Potenzial für eine bessere Forschung und Gesundheitsversorgung
(2023)
Im Koalitionsvertrag der Ampel-Koalition wird für die laufende Legislaturperiode ein Gesundheitsdatennutzungsgesetz (GDNG) angekündigt. Dieses Gesetz soll „zu einer besseren wissenschaftlichen Nutzung in Einklang mit der DSGVO“ führen. Bekanntermaßen steht unser Gesundheitssystem vor großen Herausforderungen (Demografie, Digitalisierung, Fachkräftemangel, Klimakrise, regionale Unterschiede, etc.) und ist jetzt schon das teuerste in Europa bei mittelmäßiger Leistung. Diese Herausforderungen können effizienter und evidenzgeleitet bewältigt werden, wenn wie im geplanten GDNG angedacht, die Datenressourcen für die Evaluierung und Weiterentwicklung des Gesundheitssystems und der Gesundheitsversorgung optimal genutzt werden. In den folgenden Ausführungen werden aus Sicht von Versorgungsforscher*innen Voraussetzungen und Desiderata für eine optimale Ausgestaltung des Gesetzes formuliert. Das Papier wurde durch das Deutsche Netzwerk Versorgungsforschung (DNVF) und die Arbeitsgruppe Erhebung und Nutzung von Sekundärdaten (AGENS) der Deutschen Gesellschaft für Sozialmedizin und Prävention (DGSMP) und der Deutschen Gesellschaft für Epidemiologie (DGEpi) erstellt und wird von den unterzeichnenden Fachgesellschaften getragen. Das vorliegende Positionspapier und die hier aufgestellten Forderungen sind vor der Veröffentlichung und damit in Unkenntnis des Referentenentwurfs zum GDNG formuliert worden.
Ausgangspunkt des Forschungsvorhabens SchuLae bildet die angestrebte flächendeckende Einführung institutioneller Schutzkonzepte in pädagogischen Kontexten. Im Zuge eines durch Schutzkonzepte in Gang gesetzten Organisationsentwicklungsprozesses sollen Orte des Aufwachsens für Kinder und Jugendliche zu Schutz- und Kompetenzorten werden. Ob Schutzkonzepte tatsächlich zu einem Rückgang von Viktimisierungserfahrungen und einer erhöhten Bereitschaft zur Hilfe(suche) führen, wurde bislang nicht empirisch erfasst. Der vom Bundesministerium für Bildung und Forschung (BMBF) geförderte Verbund „SchuLae – Entwicklung und Wirkung von Schutzkonzepten in Schulen im Längsschnitt“ des Deutschen Jugendinstituts und der Hochschule Hannover untersucht daher die Wirksamkeit schulischer Schutzkonzepte sowohl quantitativ als auch qualitativ im Längsschnitt und fokussiert dabei die Perspektiven von Schüler*innen.