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Complexes like iron (II)-triazoles exhibit spin crossover behavior at ambient temperature and are often considered for possible application. In previous studies, we implemented complexes of this type into polymer nanofibers and first polymer-based optical waveguide sensor systems. In our current study, we synthesized complexes of this type, implemented them into polymers and obtained composites through drop casting and doctor blading. We present that a certain combination of polymer and complex can lead to composites with high potential for optical devices. For this purpose, we used two different complexes [Fe(atrz)3](2 ns)2 and [Fe(atrz)3]Cl1.5(BF4)0.5 with different polymers for each composite. We show through transmission measurements and UV/VIS spectroscopy that the optical properties of these composite materials can reversibly change due to the spin crossover effect.
Compounds that exhibit the spin crossover effect are known to show a change of spin states through external stimuli. This reversible switching of spin states is accompanied by a change of the properties of the compound. Complexes, like iron (II)-triazole complexes, that exhibit this behavior at ambient temperature are often discussed for potential applications. In previous studies we synthesized iron (II)-triazole complexes and implemented them into electrospun nanofibers. We used Mössbauer spectroscopy in first studies to prove a successful implementation with maintaining spin crossover properties. Further studies from us showed that it is possible to use different electrospinning methods to either do a implementation or a deposition of the synthesized solid SCO material into or onto the polymer nanofibers. We now used a solvent in which both, the used iron (II)-triazole complex [Fe(atrz)3](2 ns)2 and three different polymers (Polyacrylonitrile, Polymethylmethacrylate and Polyvinylpyrrolidone), are soluble. This shall lead to a higher homogeneous distribution of the complex along the nanofibers. Mössbauer spectroscopy and other measurements are therefore in use to show a successful implementation without any significant changes to the complex.
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.
Integrated Risk and Opportunity Management (IROM) goes far beyond what is found in organizations today. However, it offers the best opportunity not only to keep pace with the VUCA world, but to actually profit from it. Accordingly, the introduction of opportunity-based thinking in addition to risk-based thinking is part of the design specification for ISO 9000 and ISO 9001. The prerequisite for the successful design of an IROM is the individual definition, control and integration of risk and opportunity management processes, considering eight success factors, the "8 C". Top management benefits directly from the result: better, coordinated decision memos enable faster and more appropriate decisions.
Renewable energy production is one of the strongest rising markets and further extreme growth can be anticipated due to desire of increased sustainability in many parts of the world. With the rising adoption of renewable power production, such facilities are increasingly attractive targets for cyber attacks. At the same time higher requirements on a reliable production are raised. In this paper we propose a concept that improves monitoring of renewable power plants by detecting anomalous behavior. The system does not only detect an anomaly, it also provides reasoning for the anomaly based on a specific mathematical model of the expected behavior by giving detailed information about various influential factors causing the alert. The set of influential factors can be configured into the system before learning normal behaviour. The concept is based on multidimensional analysis and has been implemented and successfully evaluated on actual data from different providers of wind power plants.
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.
Training and evaluating deep learning models on road graphs for traffic prediction using SUMO
(2024)
The escalation of traffic volume in urban areas poses multifaceted challenges including increased accident risks, congestion, and prolonged travel times. Traditional approaches of expanding road infrastructure face limitations such as space constraints and the potential exacerbation of traffic issues.
Intelligent Transport Systems (ITS) present an alternative strategy to alleviate traffic problems by leveraging data-driven solutions. Central to ITS is traffic prediction, a process vital for applications like Traffic Management and Navigation Systems.
Recent advancements in traffic prediction have witnessed a surge of interest, particularly in deep learning methods optimized for graph-based data processing, being considered the most promising avenue presently.
These methods typically rely on real-life datasets containing traffic sensor data such as METR-LA and PeMS. However, the finite nature of real-life data prompts exploration into augmenting training and testing datasets with simulated traffic data.
This thesis explores the potential of utilizing traffic simulations, employing the microscopic traffic simulator SUMO, to train and test deep learning models for traffic prediction. A framework integrating PyTorch and SUMO is proposed for this purpose, aiming to elucidate the feasibility and effectiveness of using simulated traffic data for enhancing predictive models in traffic management systems.
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.
Operators of production plants are increasingly emphasizing secure communication, including real-time communication, such as PROFINET, within their control systems. This trend is further advanced by standards like IEC 62443, which demand the protection of realtime communication in the field. PROFIBUS and PROFINET International (PI) is working on the specification of the security extensions for PROFINET (“PROFINET Security”), which shall fulfill the requirements of secure communication in the field.
This paper discusses the matter in three parts. First, the roles and responsibilities of the plant owner, the system integrator, and the component provider regarding security, and the basics of the IEC 62443 will be described. Second, a conceptual overview of PROFINET Security, as well as a status update about the PI specification work will be given. Third, the article will describe how PROFINET Security can contribute to the defense-in-depth approach, and what the expected operating environment is. We will evaluate how PROFINET Security contributes to fulfilling the IEC 62443-4-2 standard for automation components.
Two of the authors are members of the PI Working Group CB/PG10 Security.
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.
In this paper we describe the selection of a modern build automation tool for an industry research partner of ours, namely an insurance company. Build automation has become increasingly important over the years. Today, build automation became one of the central concepts in topics such as cloud native development based on microservices and DevOps. Since more and more products for build automation have entered the market and existing tools have changed their functional scope, there is nowadays a large number of tools on the market that differ greatly in their functional scope. Based on requirements from our partner company, a build server analysis was conducted. This paper presents our analysis requirements, a detailed look at one of the examined tools and a summarized comparison of two tools.
Antimicrobials are widely used to cure intramammary infections (IMI) in dairy cows during the dry period (DP). Nevertheless, the IMI cure is influenced by many factors and not all quarters benefit from antimicrobial dry cow treatment (DCT). To evaluate the true effect of antibiotic DCT compared to self-cure and the role of causative pathogens on the IMI cure, a retrospective cross-sectional study was performed. The analysis included 2987 quarters infected at dry-off (DO). Information on DCT, causative pathogens, somatic cell count, milk yield, amount of lactation, Body Condition Score, and season and year of DO were combined into categorical variables. A generalized linear mixed model with a random cow, farm and year effect and the binary outcome of bacteriological cure of IMI during the DP was conducted. In the final model, a significant effect (p < 0.05) on DP cure was seen for the DO season and the category of causative pathogens (categories being: Staphylococcus aureus, non-aureus staphylococci, streptococci, coliforms, ‘other Gram-negative bacteria’, ‘other Gram positive bacteria’, non-bacterial infections and mixed infections), while antibiotic DCT (vs. non-antibiotic DCT) only showed a significant effect in combination with the pathogen categories streptococci and ‘other Gram-positive bacteria’.
Ability of Black-Box Optimisation to Efficiently Perform Simulation Studies in Power Engineering
(2023)
In this study, the potential of the so-called black-box optimisation (BBO) to increase the efficiency of simulation studies in power engineering is evaluated. Three algorithms ("Multilevel Coordinate Search"(MCS) and "Stable Noisy Optimization by Branch and Fit"(SNOBFIT) by Huyer and Neumaier and "blackbox: A Procedure for Parallel Optimization of Expensive Black-box Functions"(blackbox) by Knysh and Korkolis) are implemented in MATLAB and compared for solving two use cases: the analysis of the maximum rotational speed of a gas turbine after a load rejection and the identification of transfer function parameters by measurements. The first use case has a high computational cost, whereas the second use case is computationally cheap. For each run of the algorithms, the accuracy of the found solution and the number of simulations or function evaluations needed to determine the optimum and the overall runtime are used to identify the potential of the algorithms in comparison to currently used methods. All methods provide solutions for potential optima that are at least 99.8% accurate compared to the reference methods. The number of evaluations of the objective functions differs significantly but cannot be directly compared as only the SNOBFIT algorithm does stop when the found solution does not improve further, whereas the other algorithms use a predefined number of function evaluations. Therefore, SNOBFIT has the shortest runtime for both examples. For computationally expensive simulations, it is shown that parallelisation of the function evaluations (SNOBFIT and blackbox) and quantisation of the input variables (SNOBFIT) are essential for the algorithmic performance. For the gas turbine overspeed analysis, only SNOBFIT can compete with the reference procedure concerning the runtime. Further studies will have to investigate whether the quantisation of input variables can be applied to other algorithms and whether the BBO algorithms can outperform the reference methods for problems with a higher dimensionality.
Catalogs of competency-based learning objectives (CLO) were introduced and promoted as a prerequisite for high-quality, systematic curriculum development. While this is common in medicine, the consistent use of CLO is not yet well established in epidemiology, biometry, medical informatics, biomedical informatics, and nursing informatics especially in Germany. This paper aims to identify underlying obstacles and give recommendations in order to promote the dissemination of CLO for curricular development in health data and information sciences. To determine these obstacles and recommendations a public online expert workshop was organized. This paper summarizes the findings.
The trend towards the use of Ethernet in automation networks is ongoing. Due to its high flexibility, speed, and bandwidth, Ethernet nowadays is not only widely used in homes and offices worldwide but finding its way into industrial applications. Especially in automation processes, where many field devices send data in relative short time spans, the requirements for a safe and fast data transfer are high. This makes the use of industrial Ethernet essential. A new hardware-layer, specifically tailored for industrial applications, has been introduced in the form of Ethernet-APL (‘Advanced Physical Layer’). Ethernet-APL is based on the Ethernet standard and implements a two-wire Ethernet-based communication for field devices and provides data and power over a two-wire cable. The operation in areas with potentially explosive atmosphere is also possible. This enables a modular, fast, and transparent Ethernet network structure throughout the entire plant. However, by integrating Ethernet-APL into the field, industrial networks in the future will face the challenge of operating at varying datarates at different locations in the network, resulting in a ‘mixed link speed’ network. This can lead to limitations in packet-throughput and consequently to potential packet loss of system relevant data, which must be avoided. Therefore, the purpose of this thesis is to investigate the potential of packet loss in ‘mixed link speed’ networks.
One of the main concerns of this publication is to furnish a more rational basis for discussing bioplastics and use fact-based arguments in the public discourse. Furthermore, “Biopolymers – facts and statistics” aims to provide specific, qualified answers easily and quickly for decision-makers in particular from public administration and the industrial sector. Therefore, this publication is made up like a set of rules and standards and largely foregoes textual detail. It offers extensive market-relevant and technical facts presented in graphs and charts, which means that the information is much easier to grasp. The reader can expect comparative market figures for various materials, regions, applications, process routes, agricultural land use, water use or resource consumption, production capacities, geographic distribution, etc.
To learn a subject, the acquisition of the associated technical language is important.
Despite this widely accepted importance of learning the technical language, hardly any studies are published that describe the characteristics of most technical languages that students are supposed to learn. This might largely be due to the absence of specialized text corpora to study such languages at lexical, syntactical and textual level. In the present paper we describe a corpus of German physics text that can be used to study the language used in physics. A large and a small variant are compiled. The small version of the corpus consists of 5.3 Million words and is available on request.
To effectively prevent and control bovine mastitis, farmers and their advisors need to take infection pathways and durations into account. Still, studies exploring both aspects through molecular epidemiology with sampling of entire dairy cow herds over longer periods are scarce. Therefore, quarter foremilk samples were collected at 14-d intervals from all lactating dairy cows (n = 263) over 18 wk in one commercial dairy herd. Quarters were considered infected with Staphylococcus aureus, Streptococcus uberis, or Streptococcus dysgalactiae when ≥100 cfu/mL of the respective pathogen was detected, or with Staphylococcus epidermidis or Staphylococcus haemolyticus when ≥500 cfu/mL of the respective pathogen was detected. All isolates of the mentioned species underwent randomly amplified polymorphic DNA (RAPD)-PCR to explore strain diversity and to distinguish ongoing from new infections. Survival analysis was used to estimate infection durations. Five different strains of Staph. aureus were isolated, and the most prevalent strain caused more than 80% of all Staph. aureus infections (n = 46). In contrast, 46 Staph. epidermidis and 69 Staph. haemolyticus strains were isolated, and none of these caused infections in more than 2 different quarters. The 3 most dominant strains of Strep. dysgalactiae (7 strains) and Strep. uberis (18 strains) caused 81% of 33 and 49% of 37 infections in total, respectively. The estimated median infection duration for Staph. aureus was 80 d, and that for Staph. epidermidis and Staph. haemolyticus was 28 and 22 d, respectively. The probability of remaining infected with Strep. dysgalactiae or Strep. uberis for more than 84 and 70 d was 58.7 and 53.5%, respectively. Staphylococcus epidermidis and Staph. haemolyticus were not transmitted contagiously and the average infection durations were short, which brings into question whether antimicrobial treatment of intramammary infections with these organisms is justified. In contrast, infections with the other 3 pathogens lasted longer and largely originated from contagious transmission.
The shift towards RES introduces challenges related to power system stability due to the characteristics of inverter-based resources (IBRs) and the intermittent nature of renewable resources. This paper addresses these challenges by conducting comprehensive time and frequency simulations on the IEEE two-area benchmark power system with detailed type 4 wind turbine generators (WTGs), including turbines, generators, converters, filters, and controllers. The simulations analyse small-signal and transient stability, considering variations in active and reactive power, short-circuit events, and wind variations. Metrics such as rate of change of frequency (RoCoF), frequency nadir, percentage of frequency variation, and probability density function (PDF) are used to evaluate the system performance. The findings emphasise the importance of including detailed models of RES in stability analyses and demonstrate the impact of RES penetration on power system dynamics. This study contributes to a deeper understanding of RES integration challenges and provides insights for ensuring the reliable and secure operation of power systems in the presence of high levels of RES penetration.
Incorporation and Deposition of Spin Crossover Materials into and onto Electrospun Nanofibers
(2023)
We synthesized iron(II)-triazole spin crossover compounds of the type [Fe(atrz)3]X2 and incorporated and deposited them on electrospun polymer nanofibers. For this, we used two separate electrospinning methods with the goal of obtaining polymer complex composites with intact switching properties. In view of possible applications, we chose iron(II)-triazole-complexes that are known to exhibit spin crossover close to ambient temperature. Therefore, we used the complexes [Fe(atrz)3]Cl2 and [Fe(atrz)3](2ns)2 (2ns = 2-Naphthalenesulfonate) and deposited those on fibers of polymethylmethacrylate (PMMA) and incorporated them into core–shell-like PMMA fiber structures. These core–shell structures showed to be inert to outer environmental influences, such as droplets of water, which we purposely cast on the fiber structure, and it did not rinse away the used complex. We analyzed both the complexes and the composites with IR-, UV/Vis, Mössbauer spectroscopy, SQUID magnetometry, as well as SEM and EDX imaging. The analysis via UV/Vis spectroscopy, Mössbauer spectroscopy, and temperature-dependent magnetic measurements with the SQUID magnetometer showed that the spin crossover properties were maintained and were not changed after the electrospinning processes.
To design cost-effective prevention strategies against mastitis in dairy cow farms, knowledge about infection pathways of causative pathogens is necessary. Therefore, we investigated the reservoirs of bacterial strains causing intramammary infections in one dairy cow herd. Quarter foremilk samples (n = 8056) and milking- and housing-related samples (n = 251; from drinking troughs, bedding material, walking areas, cow brushes, fly traps, milking liners, and milker gloves), were collected and examined using culture-based methods. Species were identified with MALDI-TOF MS, and selected Staphylococcus and Streptococcus spp. typed with randomly amplified polymorphic DNA-PCR. Staphylococci were isolated from all and streptococci from most investigated locations. However, only for Staphylococcus aureus, matching strain types (n = 2) were isolated from milk and milking-related samples (milking liners and milker gloves). Staphylococcus epidermidis and Staphylococcus haemolyticus showed a large genetic diversity without any matches of strain types from milk and other samples. Streptococcus uberis was the only Streptococcus spp. isolated from milk and milking- or housing-related samples. However, no matching strains were found. This study underlines the importance of measures preventing the spread of Staphylococcus aureus between quarters during milking.
Appropriate data models are essential for the systematic collection, aggregation, and integration of health data and for subsequent analysis. However, recommendations for modeling health data are often not publicly available within specific projects. Therefore, the project Zukunftslabor Gesundheit investigates recommendations for modeling. Expert interviews with five experts were conducted and analyzed using qualitative content analysis. Based on the condensed categories “governance”, “modeling” and “standards”, the project team generated eight hypotheses for recommendations on health data modeling. In addition, relevant framework conditions such as different roles, international cooperation, education/training and political influence were identified. Although emerging from interviewing a small convenience sample of experts, the results help to plan more extensive data collections and to create recommendations for health data modeling.
In the last years generative models have gained large public attention due to their high level of quality in generated images. In short, generative models learn a distribution from a finite number of samples and are able then to generate infinite other samples. This can be applied to image data. In the past generative models have not been able to generate realistic images, but nowadays the results are almost indistinguishable from real images.
This work provides a comparative study of three generative models: Variational Autoencoder (VAE), Generative Adversarial Network (GAN) and Diffusion Models (DM). The goal is not to provide a definitive ranking indicating which one of them is the best, but to qualitatively and where possible quantitively decide which model is good with respect to a given criterion. Such criteria include realism, generalization and diversity, sampling, training difficulty, parameter efficiency, interpolating and inpainting capabilities, semantic editing as well as implementation difficulty. After a brief introduction of how each model works on the inside, they are compared against each other. The provided images help to see the differences among the models with respect to each criterion.
To give a short outlook on the results of the comparison of the three models, DMs generate most realistic images. They seem to generalize best and have a high variation among the generated images. However, they are based on an iterative process, which makes them the slowest of the three models in terms of sample generation time. On the other hand, GANs and VAEs generate their samples using one single forward-pass. The images generated by GANs are comparable to the DM and the images from VAEs are blurry, which makes them less desirable in comparison to GANs or DMs. However, both the VAE and the GAN, stand out from the DMs with respect to the interpolations and semantic editing, as they have a latent space, which makes space-walks possible and the changes are not as chaotic as in the case of DMs. Furthermore, concept-vectors can be found, which transform a given image along a given feature while leaving other features and structures mostly unchanged, which is difficult to archive with DMs.
Mixed-integer NMPC for real-time supervisory energy management control in residential buildings
(2023)
In recent years, building energy supply and distribution systems have become more complex, with an increasing number of energy generators, stores, flows, and possible combinations of operating modes. This poses challenges for supervisory control, especially when balancing the conflicting goals of maximizing comfort while minimizing costs and emissions to contribute to global climate protection objectives. Mixed-integer nonlinear model predictive control is a promising approach for intelligent real-time control that is able to properly address the specific characteristics and restrictions of building energy systems. We present a strategy that utilizes a decomposition approach, combining partial outer convexification with the Switch-Cost Aware Rounding procedure to handle switching behavior and operating time constraints of building components in real-time. The efficacy is demonstrated through practical applications in a single-family home with a combined heat and power unit and in a multi-family apartment complex with 18 residential units. Simulation studies show high correspondence to globally optimal solutions with significant cost savings potential of around 19%.
Purpose
This study aims to determine the intention to use hospital report cards (HRCs) for hospital referral purposes in the presence or absence of patient-reported outcomes (PROs) as well as to explore the relevance of publicly available hospital performance information from the perspective of referring physicians.
Methods
We identified the most relevant information for hospital referral purposes based on a literature review and qualitative research. Primary survey data were collected (May–June 2021) on a sample of 591 referring orthopedists in Germany and analyzed using structural equation modeling. Participating orthopedists were recruited using a sequential mixed-mode strategy and randomly allocated to work with HRCs in the presence (intervention) or absence (control) of PROs.
Results
Overall, 420 orthopedists (mean age 53.48, SD 8.04) were included in the analysis. The presence of PROs on HRCs was not associated with an increased intention to use HRCs (p = 0.316). Performance expectancy was shown to be the most important determinant for using HRCs (path coefficient: 0.387, p < .001). However, referring physicians have doubts as to whether HRCs can help them. We identified “complication rate” and “the number of cases treated” as most important for the hospital referral decision making; PROs were rated slightly less important.
Conclusions
This study underpins the purpose of HRCs, namely to support referring physicians in searching for a hospital. Nevertheless, only a minority would support the use of HRCs for the next hospital search in its current form. We showed that presenting relevant information on HRCs did not increase their use intention.
As a result of a research semester in the summer of 2022, a bibliography on multimodality in technical communication (TC) is presented. Given that TC primarily involves the development of instructional information, this bibliography holds relevance for anyone interested in the use of multimodality in the communication of procedural knowledge. The bibliography is publicly accessible as Zotero group library (https://bit.ly/multimodality_in_tc) and can be used and expanded.
After a description of the objectives and target group, the five disciplines from which the publications in the bibliography originate are presented. This is followed by information on the structure and search options of the Zotero group library, which are intended to support the search for publications on the respective research interest. The article concludes with some suggestions for collaborative efforts aimed at further enhancing and expanding the bibliography.
The author actively maintains the group library. Individuals seeking to contribute publications to the group library will receive the appropriate access rights from the author (claudia.villiger@hs-hannover.de). The author aspires to foster collaboration among researchers from diverse fields through this bibliography.
During the Corona-Pandemic, information (e.g. from the analysis of balance sheets and payment behavior) traditionally used for corporate credit risk analysis became less valuable because it represents only past circumstances. Therefore, the use of currently published data from social media platforms, which have shown to contain valuable information regarding the financial stability of companies, should be evaluated. In this data e. g. additional information from disappointed employees or customers can be present. In order to analyze in how far this data can improve the information base for corporate credit risk assessment, Twitter data regarding the ten greatest insolvencies of German companies in 2020 and solvent counterparts is analyzed in this paper. The results from t-tests show, that sentiment before the insolvencies is significantly worse than in the comparison group which is in alignment with previously conducted research endeavors. Furthermore, companies can be classified as prospectively solvent or insolvent with up to 70% accuracy by applying the k-nearest-neighbor algorithm to monthly aggregated sentiment scores. No significant differences in the number of Tweets for both groups can be proven, which is in contrast to findings from studies which were conducted before the Corona-Pandemic. The results can be utilized by practitioners and scientists in order to improve decision support systems in the domain of corporate credit risk analysis. From a scientific point of view, the results show, that the information asymmetry between lenders and borrowers in credit relationships, which are principals and agents according to the principal-agent-theory, can be reduced based on user generated content from social media platforms. In future studies, it should be evaluated in how far the data can be integrated in established processes for credit decision making. Furthermore, additional social media platforms as well as samples of companies should be analyzed. Lastly, the authenticity of user generated contend should be taken into account in order to ensure, that credit decisions rely on truthful information only.
PROFINET Security: A Look on Selected Concepts for Secure Communication in the Automation Domain
(2023)
We provide a brief overview of the cryptographic security extensions for PROFINET, as defined and specified by PROFIBUS & PROFINET International (PI). These come in three hierarchically defined Security Classes, called Security Class 1, 2 and 3. Security Class 1 provides basic security improvements with moderate implementation impact on PROFINET components. Security Classes 2 and 3, in contrast, introduce an integrated cryptographic protection of PROFINET communication. We first highlight and discuss the security features that the PROFINET specification offers for future PROFINET products. Then, as our main focus, we take a closer look at some of the technical challenges that were faced during the conceptualization and design of Security Class 2 and 3 features. In particular, we elaborate on how secure application relations between PROFINET components are established and how a disruption-free availability of a secure communication channel is guaranteed despite the need to refresh cryptographic keys regularly. The authors are members of the PI Working Group CB/PG10 Security.
In this paper we describe the selection of a modern build automation tool for an industry research partner of ours, namely an insurance company. Build automation has become increasingly important over the years. Today, build automation became one of the central concepts in topics such as cloud native development based on microservices and DevOps. Since more and more products for build automation have entered the market and existing tools have changed their functional scope, there is nowadays a large number of tools on the market that differ greatly in their functional scope. Based on requirements from our partner company, a build server analysis was conducted. This paper presents our analysis requirements, a detailed look at one of the examined tools and a summarizes our comparison of all three tools from our final comparison round.
On November 30th, 2022, OpenAI released the large language model ChatGPT, an extension of GPT-3. The AI chatbot provides real-time communication in response to users’ requests. The quality of ChatGPT’s natural speaking answers marks a major shift in how we will use AI-generated information in our day-to-day lives. For a software engineering student, the use cases for ChatGPT are manifold: assessment preparation, translation, and creation of specified source code, to name a few. It can even handle more complex aspects of scientific writing, such as summarizing literature and paraphrasing text. Hence, this position paper addresses the need for discussion of potential approaches for integrating ChatGPT into higher education. Therefore, we focus on articles that address the effects of ChatGPT on higher education in the areas of software engineering and scientific writing. As ChatGPT was only recently released, there have been no peer-reviewed articles on the subject. Thus, we performed a structured grey literature review using Google Scholar to identify preprints of primary studies. In total, five out of 55 preprints are used for our analysis. Furthermore, we held informal discussions and talks with other lecturers and researchers and took into account the authors’ test results from using ChatGPT. We present five challenges and three opportunities for the higher education context that emerge from the release of ChatGPT. The main contribution of this paper is a proposal for how to integrate ChatGPT into higher education in four main areas.
Background: In Germany, hospice and palliative care is well covered through inpatient, outpatient, and home-based care services. It is unknown if, and to what extent, there is a need for additional day care services to meet the specific needs of patients and caregivers.
Methods: Two day hospices and two palliative day care clinics were selected. In the first step, two managers from each facility (n = 8) were interviewed by telephone, using a semi-structured interview guide. In the second step, four focus groups were conducted, each with three to seven representatives of hospice and palliative care from the facilities’ hospice and palliative care networks. Interviews and focus groups were audio recorded, transcribed verbatim and analyzed using qualitative content analysis.
Results: The interviewed experts perceived day care services as providing additional patient and caregiver benefits. Specifically, the services were perceived to meet patient needs for social interaction and bundled treatments, especially for patients who did not fit into inpatient settings (due to, e.g., their young age or a lack of desire for inpatient admission). The services were also perceived to meet caregiver needs for support, providing short-term relief for the home care situation.
Conclusions: The results suggest that inpatient, outpatient, and home-based hospice and palliative care services do not meet the palliative care needs of all patients. Although the population that is most likely to benefit from day care services is assumed to be relatively small, such services may meet the needs of certain patient groups more effectively than other forms of care.
Purpose: The calculation of aggregated composite measures is a widely used strategy to reduce the amount of data on hospital report cards. Therefore, this study aims to elicit and compare preferences of both patients as well as referring physicians regarding publicly available hospital quality information.
Methods: Based on systematic literature reviews as well as qualitative analysis, two discrete choice experiments (DCEs) were applied to elicit patients’ and referring physicians’ preferences. The DCEs were conducted using a fractional factorial design. Statistical data analysis was performed using multinomial logit models.
Results: Apart from five identical attributes, one specific attribute was identified for each study group, respectively. Overall, 322 patients (mean age 68.99) and 187 referring physicians (mean age 53.60) were included. Our models displayed significant coefficients for all attributes (p < 0.001 each). Among patients, “Postoperative complication rate” (20.6%; level range of 1.164) was rated highest, followed by “Mobility at hospital discharge” (19.9%; level range of 1.127), and ‘‘The number of cases treated” (18.5%; level range of 1.045). In contrast, referring physicians valued most the ‘‘One-year revision surgery rate’’ (30.4%; level range of 1.989), followed by “The number of cases treated” (21.0%; level range of 1.372), and “Postoperative complication rate” (17.2%; level range of 1.123).
Conclusion: We determined considerable differences between both study groups when calculating the relative value of publicly available hospital quality information. This may have an impact when calculating aggregated composite measures based on consumer-based weighting.
The PROFINET protocol has been extended in the current version to include security functions. This allows flexible network architectures with the consideration of OT security requirements to be designed for PROFINET, which were not possible due to the network segmentation previously required. In addition to the manufacturers of the protocol stacks, component manufacturers are also required to provide a secure implementation in their devices. The necessary measures go beyond the use of a secure protocol stack. Using the example of an Ethernet-APL transmitter with PROFINET communication, this article shows which technical and organizational conditions will have to be considered by PROFINET device manufacturers in the future.
This paper aims to provide a structured overview of four open, participatory formats that are particularly applicable in inquiry-based teaching and learning contexts: hackathons, book sprints, barcamps, and learning circles. Using examples, mostly from the work and experience context of the Open Science Lab at TIB Hannover, we address concrete processes, working methods, possible outcomes and challenges.
The compilation offers an introduction to the topic and is intended to provide tools for testing in practice.
On November 30th, 2022, OpenAI released the large language model ChatGPT, an extension of GPT-3. The AI chatbot provides real-time communication in response to users’ requests. The quality of ChatGPT’s natural speaking answers marks a major shift in how we will use AI-generated information in our day-to-day lives. For a software engineering student, the use cases for ChatGPT are manifold: assessment preparation, translation, and creation of specified source code, to name a few. It can even handle more complex aspects of scientific writing, such as summarizing literature and paraphrasing text. Hence, this position paper addresses the need for discussion of potential approaches for integrating ChatGPT into higher education. Therefore, we focus on articles that address the effects of ChatGPT on higher education in the areas of software engineering and scientific writing. As ChatGPT was only recently released, there have been no peer-reviewed articles on the subject. Thus, we performed a structured grey literature review using Google Scholar to identify preprints of primary studies. In total, five out of 55 preprints are used for our analysis. Furthermore, we held informal discussions and talks with other lecturers and researchers and took into account the authors’ test results from using ChatGPT. We present five challenges and three opportunities for the higher education context that emerge from the release of ChatGPT. The main contribution of this paper is a proposal for how to integrate ChatGPT into higher education in four main areas.
In this poster we present the ongoing development of an integrated free and open source toolchain for semantic annotation of digitised cultural heritage. The toolchain development involves the specification of a common data model that aims to increase interoperability across diverse datasets and to enable new collaborative research approaches.
Pathologists need to identify abnormal changes in tissue. With the developing digitalization, the used tissue slides are stored digitally. This enables pathologists to annotate the region of interest with the support of software tools. PathoLearn is a web-based learning platform explicitly developed for the teacher-student scenario, where the goal is that students learn to identify potential abnormal changes. Artificial intelligence (AI) and machine learning (ML) have become very important in medicine. Many health sectors already utilize AI and ML. This will only increase in the future, also in the field of pathology. Therefore, it is important to teach students the fundamentals and concepts of AI and ML early in their studies. Additionally, creating and training AI generally requires knowledge of programming and technical details. This thesis evaluates how this boundary can be overcome by comparing existing end-to-end AI platforms and teaching tools for AI. It was shown that a visual programming editor offers a fitting abstraction for creating neural networks without programming. This was extended with real-time collaboration to enable students to work in groups. Additionally, an automatic training feature was implemented, removing the necessity to know technical details about training neural networks.
Recent research efforts have highlighted the potential of hybrid composites in the context of additive manufacturing. The use of hybrid composites can lead to an enhanced adaptability of the mechanical properties to the specific loading case. Furthermore, the hybridization of multiple fiber materials can result in positive hybrid effects such as increased stiffness or strength. In contrast to the literature, where only the interply and intrayarn approach has been experimentally validated, this study presents a new intraply approach, which is experimentally and numerically investigated. Three different types of tensile specimens were tested. The non-hybrid tensile specimens were reinforced with contour-based fiber strands of carbon and glass. In addition, hybrid tensile specimens were manufactured using an intraply approach with alternating carbon and glass fiber strands in a layer plane. In addition to experimental testing, a finite element model was developed to better understand the failure modes of the hybrid and non-hybrid specimens. The failure was estimated using the Hashin and Tsai–Wu failure criteria. The specimens showed similar strengths but greatly different stiffnesses based on the experimental results. The hybrid specimens demonstrated a significant positive hybrid effect in terms of stiffness. Using FEA, the failure load and fracture locations of the specimens were determined with good accuracy. Microstructural investigations of the fracture surfaces showed notable evidence of delamination between the different fiber strands of the hybrid specimens. In addition to delamination, strong debonding was particularly evident in all specimen types.
We present an approach towards a data acquisition system for digital twins that uses a 5G net- work for data transmission and localization. The current hardware setup, which utilizes stereo vision and LiDAR for 3D mapping, is explained together with two recorded point cloud data sets. Furthermore, a resulting digital twin comprised of voxelized point cloud data is shown. Ideas for future applications and challenges regarding the system are discussed and an outlook on further development is given.
Autonomous and integrated passenger and freight transport (APFIT) is a promising approach to tackle both, traffic and last-mile-related issues such as environmental emissions, social and spatial conflicts or operational inefficiencies. By conducting an agent-based simulation, we shed light on this widely unexplored research topic and provide first indications regarding influential target figures of such a system in the rural area of Sarstedt, Germany. Our results show that larger fleets entail inefficiencies due to suboptimal utilization of monetary and material resources and increase traffic volume while higher amounts of unused vehicles may exacerbate spatial conflicts. Nevertheless, to fit the given demand within our study area, a comparatively large fleet of about 25 vehicles is necessary to provide reliable service, assuming maximum passenger waiting times of six minutes to the expense of higher standby times, rebalancing effort, and higher costs for vehicle acquisition and maintenance.
The miniaturized Mössbauer-spectrometer (MIMOS II), originally devised by Göstar Klingelhöfer, is further developed by the Renz group at the Leibniz University Hanover in cooperation with the Hanover University of Applied Sciences and Arts. A new processing unit with a two-dimensional (2D) data acquisition was developed by M. Jahns. The advantage of this data acquisition is that no thresholds need to be set before the measurement. The energy of each photon is determined and stored with the velocity of the drive. After the measurement, the relevant area can be selected for the Mössbauer spectrum. Now we have expanded the evaluation unit with a power supply for a MIMOS drive and a MIMOS PIN detector. So we have a very compact MIMOS transmissions measurement setup. With this setup it is possible to process the signals of two detectors serially. Currently we are working on a parallel signal processing.
Harmonisation of German Health Care Data Using the OMOP Common Data Model – A Practice Report
(2023)
Data harmonization is an important step in large-scale data analysis and for generating evidence on real world data in healthcare. With the OMOP common data model, a relevant instrument for data harmonization is available that is being promoted by different networks and communities. At the Hannover Medical School (MHH) in Germany, an Enterprise Clinical Research Data Warehouse (ECRDW) is established and harmonization of that data source is the focus of this work. We present MHH’s first implementation of the OMOP common data model on top of the ECRDW data source and demonstrate the challenges concerning the mapping of German healthcare terminologies to a standardized format.
Monitoring of clinical trials is a fundamental process required by regulatory agencies. It assures the compliance of a center to the required regulations and the trial protocol. Traditionally, monitoring teams relied on extensive on-site visits and source data verification. However, this is costly, and the outcome is limited. Thus, central statistical monitoring (CSM) is an additional approach recently embraced by the International Council for Harmonisation (ICH) to detect problematic or erroneous data by using visualizations and statistical control measures. Existing implementations have been primarily focused on detecting inlier and outlier data. Other approaches include principal component analysis and distribution of the data. Here we focus on the utilization of comparisons of centers to the Grand mean for different model types and assumptions for common data types, such as binomial, ordinal, and continuous response variables. We implement the usage of multiple comparisons of single centers to the Grand mean of all centers. This approach is also available for various non-normal data types that are abundant in clinical trials. Further, using confidence intervals, an assessment of equivalence to the Grand mean can be applied. In a Monte Carlo simulation study, the applied statistical approaches have been investigated for their ability to control type I error and the assessment of their respective power for balanced and unbalanced designs which are common in registry data and clinical trials. Data from the German Multiple Sclerosis Registry (GMSR) including proportions of missing data, adverse events and disease severity scores were used to verify the results on Real-World-Data (RWD).
In this paper a new rotor position observer for permanent magnet synchronous machines (PMSM) based on an Extended-Kalman-Filter (EKF) is presented. With this method, just one single EKF is sufficent to evaluate the position information from electromotive force (EMF) and anisotropy. Thus, the PMSM can be controlled for the entire speed range without a position sensor and without the need to switch or synchronize between different observers. The approach covers online estimation of permanent magnetic field and mechanical load. The resulting EKF-based rotor position estimator is embedded in the existing cascaded control concept of the PMSM without need of additional angle trackers or signal filters. The experimental validation for the position sensorless control shows optimized dynamic behaviour.
There are many aspects of code quality, some of which are difficult to capture or to measure. Despite the importance of software quality, there is a lack of commonly accepted measures or indicators for code quality that can be linked to quality attributes. We investigate software developers’ perceptions of source code quality and the practices they recommend to achieve these qualities. We analyze data from semi-structured interviews with 34 professional software developers, programming teachers and students from Europe and the U.S. For the interviews, participants were asked to bring code examples to exemplify what they consider good and bad code, respectively. Readability and structure were used most commonly as defining properties for quality code. Together with documentation, they were also suggested as the most common target properties for quality improvement. When discussing actual code, developers focused on structure, comprehensibility and readability as quality properties. When analyzing relationships between properties, the most commonly talked about target property was comprehensibility. Documentation, structure and readability were named most frequently as source properties to achieve good comprehensibility. Some of the most important source code properties contributing to code quality as perceived by developers lack clear definitions and are difficult to capture. More research is therefore necessary to measure the structure, comprehensibility and readability of code in ways that matter for developers and to relate these measures of code structure, comprehensibility and readability to common software quality attributes.
The aim of this cross-sectional study was to investigate associated factors of the severity of clinical mastitis (CM). Milk samples of 249 cases of CM were microbiologically examined, of which 27.2% were mild, 38.5% moderate, and 34.3% severe mastitis. The samples were incubated aerobically and anaerobically to investigate the role of aerobic and anaerobic microorganisms. In addition, the pathogen shedding was quantitatively examined, and animal individual data, outside temperature and relative humidity, were collected to determine associated factors for the severity of CM. The pathogen isolated the most was Escherichia coli (35.2%), followed by Streptococcus spp. (16.4%). Non-aureus staphylococci (NaS) (15.4%) and other pathogens (e.g., Staphylococcus aureus, coryneforms) (15.4%) were the pathogens that were isolated the most for mild mastitis. Moderate mastitis was mostly caused by E. coli (38%). E. coli was also the most common pathogen in severe mastitis (50.6%), followed by Streptococcus spp. (16.4%), and Klebsiella spp. (10.3%). Obligate anaerobes (Clostridium spp.) were isolated in one case (0.4%) of moderate mastitis. The mortality rate (deceased or culled due to the mastitis in the following two weeks) was 34.5% for severe mastitis, 21.7% for moderate mastitis, and 4.4% for mild mastitis. The overall mortality rate of CM was 21.1%. The pathogen shedding (back logarithmized) was highest for severe mastitis (55,000 cfu/mL) and E. coli (91,200 cfu/mL). High pathogen shedding, low previous somatic cell count (SCC) before mastitis, high outside temperature, and high humidity were associated with severe courses of mastitis.
Economic and political/governmental infrastructural factors are major contributors to the economic development/growth of all sectors of a country, such as in the area of healthcare systems and clinical research, including the pharmaceutical industry. But what is the interaction between economic, and political/governmental infrastructural factors and the development of healthcare systems, especially, the performance of the pharmaceutical industry? Information from selected articles of a literature search of PubMed and by using Google Advanced Search led to the generation of five categories of infrastructural factors, and were filled with data from 41 African Countries using the World Health Organization data repository. Median changes over time were given and tested by Wilcoxon signed-rank test and Friedman test, respectively. Analysis of factors related to availability of healthcare facilities showed that physicians and pharmacies were significant increased, with insignificantly decreased number of hospital beds. Healthcare Financing by the Government showed notable differences. Private health spending decreased significantly unlike Gross National Income. Analysis of infrastructural factors showed that stable supply of electricity and the associated use of the Internet improved significantly. The low level of data on the expansion of paved road networks suggests less developed medical services in remote rural areas. Healthcare systems in African countries improved over the last two decades, but differences between the individual countries still prevail and some of the countries cannot yet offer an attractive sales market for the products of pharmaceutical companies.
Background:
Many patients with cardiovascular disease also show a high comorbidity of mental disorders, especially such as anxiety and depression. This is, in turn, associated with a decrease in the quality of life. Psychocardiological treatment options are currently limited. Hence, there is a need for novel and accessible psychological help. Recently, we demonstrated that a brief face-to-face metacognitive therapy (MCT) based intervention is promising in treating anxiety and depression. Here, we aim to translate the face-to-face approach into digital application and explore the feasibility of this approach.
Methods:
We translated a validated brief psychocardiological intervention into a novel non-blended web app. The data of 18 patients suffering from various cardiac conditions but without diagnosed mental illness were analyzed after using the web app over a two-week period in a feasibility trial. The aim was whether a nonblended web app based MCT approach is feasible in the group of cardiovascular patients with cardiovascular disease.
Results:
Overall, patients were able to use the web app and rated it as satisfactory and beneficial. In addition, there was first indication that using the app improved the cardiac patients’ subjectively perceived health and reduced their anxiety. Therefore, the approach seems feasible for a future randomized controlled trial.
Conclusion:
Applying a metacognitive-based brief intervention via a nonblended web app seems to show good acceptance and feasibility in a small target group of patients with CVD. Future studies should further develop, improve and validate digital psychotherapy approaches, especially in patient groups with a lack of access to standard psychotherapeutic care.
Purpose: Radiology reports mostly contain free-text, which makes it challenging to obtain structured data. Natural language processing (NLP) techniques transform free-text reports into machine-readable document vectors that are important for creating reliable, scalable methods for data analysis. The aim of this study is to classify unstructured radiograph reports according to fractures of the distal fibula and to find the best text mining method.
Materials & Methods: We established a novel German language report dataset: a designated search engine was used to identify radiographs of the ankle and the reports were manually labeled according to fractures of the distal fibula. This data was used to establish a machine learning pipeline, which implemented the text representation methods bag-of-words (BOW), term frequency-inverse document frequency (TF-IDF), principal component analysis (PCA), non-negative matrix factorization (NMF), latent Dirichlet allocation (LDA), and document embedding (doc2vec). The extracted document vectors were used to train neural networks (NN), support vector machines (SVM), and logistic regression (LR) to recognize distal fibula fractures. The results were compared via cross-tabulations of the accuracy (acc) and area under the curve (AUC).
Results: In total, 3268 radiograph reports were included, of which 1076 described a fracture of the distal fibula. Comparison of the text representation methods showed that BOW achieved the best results (AUC = 0.98; acc = 0.97), followed by TF-IDF (AUC = 0.97; acc = 0.96), NMF (AUC = 0.93; acc = 0.92), PCA (AUC = 0.92; acc = 0.9), LDA (AUC = 0.91; acc = 0.89) and doc2vec (AUC = 0.9; acc = 0.88). When comparing the different classifiers, NN (AUC = 0,91) proved to be superior to SVM (AUC = 0,87) and LR (AUC = 0,85).
Conclusion: An automated classification of unstructured reports of radiographs of the ankle can reliably detect findings of fractures of the distal fibula. A particularly suitable feature extraction method is the BOW model.
Key Points:
- The aim was to classify unstructured radiograph reports according to distal fibula fractures.
- Our automated classification system can reliably detect fractures of the distal fibula.
- A particularly suitable feature extraction method is the BOW model.
Background: Autism Spectrum Disorder (ASD) is characterized by impairments in social communication, limited repetitive behaviors, impaired language development, and interest or activity patterns, which include a group complex neurodevelopmental syndrome with diverse phenotypes that reveal considerable etiological and clinical heterogeneity and are also considered one of the most heritable disorders (over 90%). Genetic, epigenetic, and environmental factors play a role in the development of ASD.
Aim: This study was designed to investigate the extent of DNA damage in parents of autistic children by treating peripheral blood mononuclear cells (PBMCs) with bleomycin and hydrogen peroxide (H2O2).
Methods: Peripheral blood mononuclear cells (PBMCs) were isolated by the Ficoll method and treated with a specific concentration of bleomycin and H2O2 for 30 min and 5 min, respectively. Then, the degree of DNA damage was analyzed by the alkaline comet assay or single cell gel electrophoresis (SCGE), an effective way to measure DNA fragmentation in eukaryotic cells.
Results: Our findings revealed that there is a significant difference in the increase of DNA damage in parents with affected children compared to the control group, which can indicate the inability of the DNA molecule repair system. Furthermore, our study showed a significant association between fathers’ occupational difficulties (exposed to the influence of environmental factors), as well as family marriage, and suffering from ASD in offspring.
Conclusion: Our results suggested that the influence of environmental factors on parents of autistic children may affect the development of autistic disorder in their offspring. Subsequently, based on our results, investigating the effect of environmental factors on the amount of DNA damage in parents with affected children requires more studies.
Conventional fluorescent tubes are increasingly being replaced with innovative light-emitting diodes (LEDs) for lighting poultry houses. However, little is known about whether the flicker frequencies of LED luminaires are potential stressors in poultry husbandry. The term “light flicker” describes the fluctuations in the brightness of an electrically operated light source caused by the design and/or control of the light source. In this context, the critical flicker frequency (CFF) characterizes the frequency at which a sequence of light flashes is perceived as continuous light. It is known that CFF in birds is higher than that in humans and that light flicker can affect behavioral patterns and stress levels in several bird species. As there is a lack of knowledge about the impact of flicker frequency on fattening turkeys, this study aimed to investigate the effects of flicker frequency on the behavior, performance, and stress response in male turkeys. In 3 trials, a total of 1,646 male day-old turkey poults of the strain B.U.T. 6 with intact beaks were reared for 20 wk in 12 barn compartments of 18 m² each. Each barn compartment was illuminated using 2 full-spectrum LED lamps. Flicker frequencies of 165 Hz, 500 Hz, and 16 kHz were set in the luminaires to illuminate the compartments. Analyses of feather corticosterone concentration were performed on fully grown third-generation primaries (P 3) of 5 turkeys from each compartment. No significant differences were found in the development of live weight, feed consumption, or prevalence of injured or killed turkeys by conspecifics reared under the above flicker frequencies. The flicker frequencies also did not significantly influence feather corticosterone concentrations in the primaries of the turkeys. In conclusion, the present results indicate that flicker frequencies of 165 Hz or higher have no detrimental effect on growth performance, injurious pecking, or endocrine stress response in male turkeys and, thus, may be suitable for use as animal-friendly lighting.
Chronic kidney disease is one of the main causes of mortality worldwide. It affects more than 800 million patients globally, accounting for approximately 10% of the general population. The significant burden of the disease prompts healthcare systems to implement adequate preventive and therapeutic measures. This systematic review and meta-analysis aimed to provide a concise summary of the findings published in the existing body of research about the influence that mobile health technology has on the outcomes of patients with the disease. A comprehensive systematic literature review was conducted from inception until March 1st, 2023. This systematic review and meta-analysis included all clinical trials that compared the efficacy of mobile app-based educational programs to that of more conventional educational treatment for the patients. Eleven papers were included in the current analysis, representing 759 CKD patients. 381 patients were randomly assigned to use the mobile apps, while 378 individuals were assigned to the control group. The mean systolic blood pressure was considerably lower in the mobile app group (MD -4.86; 95%-9.60, -0.13; p=0.04). Meanwhile, the mean level of satisfaction among patients who used the mobile app was considerably greater (MD 0.75; 95% CI 0.03, 1.46; p=0.04). Additionally, the mean self-management scores in the mobile app groups were significantly higher (SMD 0.534; 95% CI 0.201, 0.867; p=0.002). Mobile health applications are potentially valuable interventions for patients. This technology improved the self-management of the disease, reducing the mean levels of systolic blood pressure with a high degree of patient satisfaction.
Social comparison theories suggest that ingroups are strengthened whenever important outgroups are weakened (e.g., by losing status or power). It follows that ingroups have little reason to help outgroups facing an existential threat. We challenge this notion by showing that ingroups can also be weakened when relevant comparison outgroups are weakened, which can motivate ingroups to strategically offer help to ensure the outgroups' survival as a highly relevant comparison target. In three preregistered studies, we showed that an existential threat to an outgroup with high (vs. low) identity relevance affected strategic outgroup helping via two opposing mechanisms. The potential demise of a highly relevant outgroup increased participants’ perceptions of ingroup identity threat, which was positively related to helping. At the same time, the outgroup’s misery evoked schadenfreude, which was negatively related to helping. Our research exemplifies a group's secret desire for strong outgroups by underlining their importance for identity formation.
Background: The globalization of clinical research should also benefit the population in developing markets. In this context, the approval of tested medicines and the associated expansion of medical care beyond clinical studies would be desirable as a possible long-term benefit.
Objectives: This study was designed to compare the development of the number of clinical trials with the number of marketing authorizations of medicines on the African continent. To contrast these 2 parameters, the data were analyzed using the model of an ecological study.
Methods: To reflect the broad spectrum of African developing countries with diverse levels of development, the data collection was based on 2 geographically selected sample countries each from Central, North, East, West, and Southern Africa. Based on the ClinicalTrials.gov registry, the first step was to collect trends data on the development of the clinical trials in the 10 selected countries of the country list of the African Region published by the World Health Organization for the period 2015 to 2018. Subsequently, data on the current number of marketing authorizations of medicines in the selected sample countries were identified using the online registries of the national authorities. The data were utilized in comparative analyses.
Results: Eight out of 10 model countries showed an increase in the number of clinical trials, with the exceptions of Cameroon and Libya, which showed an overall decline in research activity over the entire time. In direct comparison with drug registrations, the numbers indicate a similar development. The only exception here is Nigeria, a country with a solid performance in clinical research and yet a decrease in medicine registrations since 2015.
Conclusions: The expected increase in the development of clinical research as result of the globalization trend can basically be observed in most of the model countries. However, this increase does not guarantee an improvement in the number of medicine registrations. Although this is evident in some of the selected model countries, it cannot be projected to the entire African region. This may be linked to the diverse development of the individual countries due to the different political situations and the varying degrees of clinical research infrastructure.
Legal documents often have a complex layout with many different headings, headers and footers, side notes, etc. For the further processing, it is important to extract these individual components correctly from a legally binding document, for example a signed PDF. A common approach to do so is to classify each (text) region of a page using its geometric and textual features. This approach works well, when the training and test data have a similar structure and when the documents of a collection to be analyzed have a rather uniform layout. We show that the use of global page properties can improve the accuracy of text element classification: we first classify each page into one of three layout types. After that, we can train a classifier for each of the three page types and thereby improve the accuracy on a manually annotated collection of 70 legal documents consisting of 20,938 text elements. When we split by page type, we achieve an improvement from 0.95 to 0.98 for single-column pages with left marginalia and from 0.95 to 0.96 for double-column pages. We developed our own feature-based method for page layout detection, which we benchmark against a standard implementation of a CNN image classifier. The approach presented here is based on corpus of freely available German contracts and general terms and conditions.
Both the corpus and all manual annotations are made freely available. The method is language agnostic.
Family risks are known to be detrimental to children’s attachment development. This study investigated whether parental sensitivity plays different roles in early attachment development in the context of risk: Sensitivity was hypothesized to mediate risk effects on attachment, as well as a moderator that shapes the relation between risk and attachment. Multiple family risks, parental sensitivity (defined as responsivity and supportive presence), and children’s attachment security of 197 infants and toddlers (Mage = 15.25 months) and their caregivers were assessed in a prospective study with a cohort-sequential-design in Germany. Caregivers’ sensitivity served as a mediator of risk effects on attachment as well as a moderator that buffers adverse consequences of risk. Early sensitivity might be relevant in setting the stage for attachment development supporting resilience.
Companies worldwide have enabled their employees to work remotely as a consequence of the Covid 19 pandemic. Software development is a human-centered discipline and thrives on teamwork. Agile methods are focusing on several social aspects of software development. Software development teams in Germany were mainly co-located before the pandemic. This paper aims to validate the findings of existing studies by expanding on an existing multiple-case study. Therefore, we collected data by conducting semi-structured interviews, observing agile practices, and viewing project documents in three cases. Based on the results, we can confirm the following findings: 1) The teams rapidly adapted the agile practices and roles, 2) communication is more objective within the teams, 3) decreased social exchange between team members, 4) the expectation of a combined approach of remote and onsite work after the pandemic, 5) stable or increased (perceived) performance and 6) stable or increased well-being of team members.
AlphaGo’s victory against Lee Sedol in the game of Go has been a milestone in artificial intelligence. After this success, the team behind the program further refined the architecture and applied it to many other games such as chess or shogi. In the following thesis, we try to apply the theory behind AlphaGo and its successor AlphaZero to the game of Abalone. Due to limitations in computational resources, we could not replicate the same exceptional performance.
Network convergence is an increasing trend in the automation domain. More and more plant owners strive for a unification of networks in their plants. This yields a seamless network structure, simplified supervision, and reduced training effort for the personnel, as only one unified network technology needs to be handled. Ethernet-APL is one piece of the puzzle for such a converged network, supporting various real time protocols like PROFINET, EtherNet, HART-IP as well as the middleware protocol OPC UA. This paper gives an overview on the impact of Ethernet-APL field devices to OT security and proposes how to ensure OT security for them.
Background
Systematic reviews demonstrated that gait variables are the most reliable predictors of future falls, yet are rarely included in fall screening tools. Thus, most tools have higher specificity than sensitivity, hence may be misleading/detrimental to care. Therefore, this study aimed to determine the validity, and reliability of the velocity field diagram (VFD -a gait analytical tool), and the Timed-up-and-go test (TUG)-commonly used in Nigeria as fall screening tools, compared to a gold standard (known fallers) among community-dwelling older adults.
Method
This is a cross-sectional observational study of 500 older adults (280 fallers and 220 non-fallers), recruited by convenience sampling technique at community health fora on fall prevention. Participants completed a 7-m distance with the number of steps and time it took determined and used to compute the stride length, stride frequency, and velocity, which regression lines formed the VFD. TUG test was simultaneously conducted to discriminate fallers from non-fallers. The cut-off points for falls were: TUG times ≥ 13.5 s; VFD’s intersection point of the stride frequency, and velocity regression lines (E1) ≥ 3.5velots. The receiver operating characteristic (ROC) area under the curves (AUC) was used to explore the ability of the E1 ≥ 3.5velots to discriminate between fallers and non-fallers. The VFD’s and TUG’s sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were determined. Alpha was set at p < 0.05.
Results
The VFD versus TUG sensitivity, specificity, PPV and NPV were 71%, 27%, 55%, and 42%, versus 39%, 59%, 55%, and 43%, respectively. The ROC’s AUC were 0.74(95%CI:0.597,0.882, p = 0.001) for the VFD. The optimal categorizations for discrimination between fallers/non-fallers were ≥ 3.78 versus ≤ 3.78 for VFD (fallers versus non-fallers prevalence is 60.71% versus 95.45%, respectively), with a classification accuracy or prediction rate of 0.76 unlike TUG with AUC = 0.53 (95% CI:0.353,0.700, p = 0.762), and a classification accuracy of 0.68, and optimal characterization of ≥ 12.81 s versus ≤ 12.81 (fallers and non-fallers prevalence = 92.86% versus 36.36%, respectively).
Conclusion
The VFD demonstrated a fair discriminatory power and greater reliability in identifying fallers than the TUG, and therefore, could replace the TUG as a primary tool in screening those at risk of falls.
Social skills are essential for a successful understanding of agile methods in software development. Several studies highlight the opportunities and advantages of integrating real-world projects and problems while collaborating with companies into higher education using agile methods. This integration comes with several opportunities and advantages for both the students and the company. The students are able to interact with real-world software development teams, analyze and understand their challenges and identify possible measures to tackle them. However, the integration of real-world problems and companies is complex and may come with a high effort in terms of coordination and preparation of the course. The challenges related to the interaction and communication with students are increased by virtual distance teaching during the Covid-19 pandemic as direct contact with students is missing. Also, we do not know how problem-based learning in virtual distance teaching is valued by the students. This paper presents our adapted eduScrum approach and learning outcome of integrating experiments with real-world software development teams from two companies into a Master of Science course organized in virtual distance teaching. The evaluation shows that students value analyzing real-world problems using agile methods. They highlight the interaction with real-world software development teams. Also, the students appreciate the organization of the course using an iterative approach with eduScrum. Based on our findings, we present four recommendations for the integration of agile methods and real world problems into higher education in virtual distance teaching settings. The results of our paper contribute to the practitioner and researcher/lecturer community, as we provide valuable insights how to fill the gap between practice and higher education in virtual distance settings.
The aim of the podcast Digitization of Medicine is to interest a broader audience and, in particular, young women, in research and work in the field of medical informatics. This article presents the usage figures and discusses their significance for further research on the success of science communication. By 24/02/2022, a total of 24,351 downloads had been made. There were slightly more female than male listeners, and they tended to be younger. Despite the importance podcast are gaining for science communication, little is known about the respective user group and further research is needed. In this context, this paper aims to help make the effectiveness of podcasts comparable.
Acute stroke care is a time-critical process. Improving communication
and documentation process may support a positive effect on medical outcome. To achieve this goal, a new system using a mobile application has been integrated into existing infrastructure at Hannover Medical School (MHH). Within a pilot project, this system has been brought into clinical daily routine in February 2022. Insights generated may support further applications in clinical use-cases.
Data and Information Science: Book of Abstracts at BOBCATSSS 2022 Hybrid Conference, 23rd - 25th of May 2022, Debrecen.
This year marks the 30th anniversary of the BOBCATSSS. The BOBCATSSS is an international, annual symposium designed for librarians and information professionals in a rapidly changing environment. Over the past 30 years, the conference has included exciting topics, great venues, interested guests and engaging presenters.
This year we would like to introduce the topics of the many papers presented in the Book of Abstracts for the first time in presence at the University of Debrecen and hybrid. The Book of Abstracts provides an overview of all presentations given at BOBCATSSS. Presentations are listed in alphabetical order by title and include speeches, Pecha Kuchas, posters and workshops.
The theme of BOBCATSSS is Data and Information Science. Data and information are the basis for decisions and processes in business, politics and science. Particularly important in the current era of digital transformation. This is exactly where this year's subthemes come in. They deal with data science, openness as well as institutional roles.
Recent developments in the field of deep learning have shown promising advances for a wide range of historically difficult computer vision problems. Using advanced deep learning techniques, researchers manage to perform high-quality single-image super-resolution, i.e., increasing the resolution of a given image without major losses in image quality, usually encountered when using traditional approaches such as standard interpolation. This thesis examines the process of deep learning super-resolution using convolutional neural networks and investigates whether the same deep learning models can be used to increase OCR results for low-quality text images.
In this paper we investigate how concreteness and abstractness are represented in word embedding spaces. We use data for English and German, and show that concreteness and abstractness can be determined independently and turn out to be completely opposite directions in the embedding space. Various methods can be used to determine the direction of concreteness, always resulting in roughly the same vector. Though concreteness is a central aspect of the meaning of words and can be detected clearly in embedding spaces, it seems not as easy to subtract or add concreteness to words to obtain other words or word senses like e.g. can be done with a semantic property like gender.
Visual effects and elements in video games and interactive virtual environments can be applied to transfer (or delegate) non-visual perceptions (e.g. proprioception, presence, pain) to players and users, thus increasing perceptual diversity via the visual modality. Such elements or efects are referred to as visual delegates (VDs). Current fndings on the experiences that VDs can elicit relate to specifc VDs, not to VDs in general. Deductive and comprehensive VD evaluation frameworks are lacking. We analyzed VDs in video games to generalize VDs in terms of their visual properties. We conducted a systematic paper analysis to explore player and user experiences observed in association with specifc VDs in user studies. We conducted semi-structured interviews with expert players to determine their preferences and the impact of VD properties. The resulting VD framework (VD-frame) contributes to a more strategic approach to identifying the impact of VDs on player and user experiences.
To optimise udder health at the herd level, identifying incurable mastitis cases as well as providing an adequate therapy and culling strategy are necessary. Cows with clinical mastitis should be administered antibiotic medication if it is most likely to improve mammary cure. The somatic cell count (SCC) in milk of the monthly implemented Dairy Herd Improvement (DHI) test represents the most important tool to decide whether a cow has a promising mammary cure rate. Differential cell count (DCC) facilitates the specification of the immunological ability of defence, for example by characterising leukocyte subpopulations or cell viability. The aim of this study was to assess the DCC and cell viability in DHI milk samples regarding the cytological (CC) and bacteriological cure (BC) of the udder within a longitudinal study, thereby gaining a predictive evaluation of whether a clinical mastitis benefits from an antibiotic treatment or not. The cows enrolled in this study had an SCC above 200,000 cells/mL in the previous DHI test. Study 1 assessed the CC by reference to the SCC of two consecutive DHI tests and included 1010 milk samples: 28.4% of the mammary glands were classified as cytologically cured and 71.6% as uncured. The final mixed logistic regression model identified the total number of non-vital cells as a significant factor associated with CC. An increasing amount of non-vital cells was related to a lower individual ability for CC. Cows which were in the first or second lactation possessed a higher probability of CC than cows having a lactation number above two. If animals developed a clinical mastitis after flow cytometric investigation, the BC was examined in study 2 by analysing quarter foremilk samples microbiologically. Taking 48 milk samples, 81.3% of the mammary glands were classified as bacteriologically cured and 18.7% as uncured. The percentage of total non-vital cells tended to be lower for cows which were cured, but no significance could be observed. This study revealed that the investigation of the proportion of non-vital cells in DHI milk samples can enhance the prognosis of whether an antibiotic treatment of clinical mastitis might be promising or not. Prospectively, this tool may be integrated in the DHI tests to facilitate the decision between therapy or culling.
Severe mastitis can lead to considerable disturbances in the cows’ general condition and even to septicemia and death. The aim of this cross-sectional study was to identify factors associated with the severity of the clinical expression of mastitis. Streptococcus (Str.) uberis (29.9%) was the most frequently isolated pathogen, followed by coliform bacteria (22.3%). The majority of all mastitis cases (n = 854) in this study were either mild or moderate, but 21.1% were severe. It can be deduced that the combination of coliform pathogens and increasing pathogen shedding of these showed associations with severe mastitis. Furthermore, animal-related factors associated with severe disease progression were stages of lactation, and previous diseases in the period prior to the mastitis episode. Cows in early lactation had more severe mastitis. Ketosis and uterine diseases in temporal relation to the mastitis were associated with more severe mastitis in the diseased cows. Hypocalcemia was significantly associated with milder mastitis. As another factor, treatment with corticosteroids within two weeks before mastitis was associated with higher severity of mastitis. Knowledge of these risk factors may provide the basis for randomized controlled trials of the exact influence of these on the severity of mastitis.
Background: One of the major challenges in pediatric intensive care is the detection of life-threatening health conditions under acute time constraints and performance pressure. This includes the assessment of pediatric organ dysfunction (OD) that demands extraordinary clinical expertise and the clinician’s ability to derive a decision based on multiple information and data sources. Clinical decision support systems (CDSS) offer a solution to support medical staff in stressful routine work. Simultaneously, detection of OD by using computerized decision support approaches has been scarcely investigated, especially not in pediatrics.
Objectives: The aim of the study is to enhance an existing, interoperable, and rulebased CDSS prototype for tracing the progression of sepsis in critically ill children by augmenting it with the capability to detect SIRS/sepsis-associated hematologic OD, and to determine its diagnostic accuracy.
Methods: We reproduced an interoperable CDSS approach previously introduced by our working group: (1) a knowledge model was designed by following the commonKADS methodology, (2) routine care data was semantically standardized and harmonized using openEHR as clinical information standard, (3) rules were formulated and implemented in a business rule management system. Data from a prospective diagnostic study, including 168 patients, was used to estimate the diagnostic accuracy of the rule-based CDSS using the clinicians’ diagnoses as reference
Coaxial Laser wire Direct Energy Deposition (L-DED) promises a direction-independent buildup due to a centric supply of the welding material. To fabricate Functionally Graded Materials (FGMs), a processing head was designed that is capable of supplying two wire materials into the processing zone. This study investigates the direction dependency of welding seams produced by two 1.4718 metal wires with a diameter of 0.8 mm in a coaxial laser setup using three separately controllable single laser beams with a maximum combined laser power of 660 W. The welding wires are supplied simultaneously to the laser spot under an incidence angle of 3.5° to the middle axis of the processing head. The seam geometry is investigated using a confocal laserscanning-microscope. A comparison of the height, width and macroscopic seam geometry reveals the influence of the welding direction on the seam geometry and quality in Laser Double wire Direct Energy Deposition (LD-DED).
Background: Upsurge in cardiopulmonary dysfunctions in Enugu, Nigeria, involved mainly cement workers, automobile spray painters, woodworkers, and Cleaners and was worsened in the dry season, suggesting the need for an occupation-specific characterization of the disease features and seasonal evaluation of air quality for prevention and management.
Methods: We conducted a randomized cross-sectional study of eighty consenting participants (in Achara Layout, Enugu), comprising 20 cement workers (39.50 ± 14.95 years), 20 automobile spray painters (40.75 ± 9.85 years), 20 woodworkers (52.20 ± 9.77 years), and 20 cleaners (42.30 ± 9.06 years). The air quality, some haematological (fibrinogen-Fc, and C-reactive protein-CRP), and cardiopulmonary parameters were measured and analyzed using ANCOVA, at p < 0.05.
Results: The dry season particulate matter (PM) in ambient air exceeded the WHO standards in the New layout [PM10 = 541.17 ± 258.72 µg/m3; PM2.5 = 72.92 ± 25.81 µg/m3] and the University campus [PM10 = 244 ± 74.79 µg/m3; PM2.5 = 30.33 ± 16.10 µg/m3], but the former was twice higher. The PM differed significantly (p < 0.05) across the sites. Forced expiratory volume at the first second (FEV1) (F = 6.128; p = 0.001), and Peak expiratory flow rate (PEFR) (F = 5.523; p = 0.002), differed significantly across the groups. FEV1/FVC% was < 70% in cement workers (55.33%) and woodworkers (61.79%), unlike, automobile spray painters (72.22%) and cleaners (70.66%). FEV1 and work duration were significantly and negatively related in cement workers (r = -0.46; r2 = 0.2116; p = 0.041 one-tailed). CRP (normal range ≤ 3.0 mg/L) and Fc (normal range—1.5–3.0 g/L) varied in cement workers (3.32 ± 0.93 mg/L versus 3.01 ± 0.85 g/L), automobile spray painters (2.90 ± 1.19 mg/L versus 2.54 ± 0.99 mg/L), woodworkers (2.79 ± 1.10 mg/L versus 2.37 ± 0.92 g/L) and cleaners (3.06 ± 0.82 mg/L versus 2.54 ± 0.70 g/L).
Conclusion(s): Poor air quality was evident at the study sites, especially in the dry season. Cement workers and automobile spray painters showed significant risks of obstructive pulmonary diseases while woodworkers had restrictive lung diseases. Cement workers and cleaners recorded the highest risk of coronary heart disease (CRP ≥ 3.0 mg/L). The similarity in Fc and CRP trends suggests a role for the inflammation-sensitive proteins in the determination of cardiovascular risk in cement workers and cleaners. Therefore, there are occupation-specific disease endpoints of public health concern that likewise warrant specific preventive and management approaches among the workers.
As part of the European Network for Optimization of Veterinary Antimicrobial Treatment (ENOVAT), a webinar on the topic “Mastitis Treatment in Lactation” was held, in which eight mastitis experts from different European countries (Spain, The Netherlands, Estonia, Ireland, Poland, Finland, Germany, and Italy) presented their treatment approaches for clinical mastitis in lactation. The aim of this study was to compare the therapeutic approaches to identify commonalities and differences. In all eight participating countries, the decision to start treatment is usually made by the veterinarians, while the farm personnel are responsible for treatment administration. Antibiotic treatment is then typically administered intramammarily. The treatment duration often depends on the label instructions and is frequently extended if Staphylococcus aureus or Streptococcus uberis is involved. Administering supportive therapy, especially non-steroidal anti-inflammatory drugs (NSAIDs) is an established practice in all countries. Penicillin is the first-choice drug for the treatment of mastitis in an increasing number of countries. The use of critically important antimicrobials (CIAs) such as quinolones and third- and fourth-generation cephalosporins is at a low level in Finland and The Netherlands. In Estonia, Germany, Italy, and Spain, the use of CIAs is declining and is only allowed if milk samples are analyzed in advance following the legal framework. Systems for monitoring antibiotic use are being introduced in more and more countries. This exchange of different views will help the European countries to move towards a common high standard of antimicrobial stewardship in veterinary medicine.
Catholic Ownership, Physician Leadership and Operational Strategies: Evidence from German Hospitals
(2022)
Previous research has revealed that Catholic hospitals are more likely follow a strategy of horizontal diversification and maximization of the number of patients treated, whereas Protestant hospitals follow a strategy of horizontal specialization and focus on vertical differentiation. However, there is no empirical evidence pertaining to this mechanism. We conduct an empirical study in a German setting and argue that physician leadership mediates the relationship between ownership and operational strategies. The study includes the construction of a model combining data from a survey and publicly available information derived from the annual quality reports of German hospitals. Our results show that Catholic hospitals opt for leadership structures that ensure operational strategies in line with their general values, i.e., operational strategies of maximizing volume throughout the overall hospital. They prefer part-time positions for chief medical officers, as chief medical officers are identified to foster strategies of maximizing the overall number of patients treated. Hospital owners should be aware that the implementation of part-time and full-time leadership roles can help to support their strategies. Thus, our results provide insights into the relationship between leadership structures at the top of an organization, on the one hand, and strategic choices, on the other.
We performed classical molecular dynamics simulations to model the scattering process of nitric oxide, NO, off graphene supported on gold. This is motivated by our desire to probe the energy transfer in collisions with graphene. Since many of these collision systems comprising of graphene and small molecules have been shown to scatter non-reactively, classical molecular dynamics appear to describe such systems sufficiently. We directed thousands of trajectories of NO molecules onto graphene along the surface normal, while varying impact position, but also speed, orientation, and rotational excitation of the nitric oxide, and compare the results with experimental data. While experiment and theory do not match quantitatively, we observe agreement that the relative amount of kineti cenergy lost during the collision increases with increasing initial kinetic energy of the NO. Furthermore, while at higher collision energies, all NO molecules lose some energy, and the vast majority of NO is scattered back, in contrast at low impact energies, the fraction of those nitric oxide molecules that are trapped at the surface increases, and some NO molecules even gain some kinetic energy during the collision process. The collision energy seems to preferentially go into the collective motion of the carbon atoms in the graphene sheet.
Milk concentrates are used in the manufacturing of dairy products such as yogurt and cheese or are processed into milk powder. Processes for the nonthermal separation of water and valuable milk ingredients are becoming increasingly widespread at farm level. The technical barriers to using farm-manufactured milk concentrate in dairies are minimal, hence the suspicion that the practice of on-farm raw milk concentration is still fairly uncommon for economic reasons. This study, therefore, set out to investigate farmers’ potential willingness to adopt a raw milk concentration plant. The empirical analysis was based on discrete choice experiments with 75 German dairy farmers to identify preferences and the possible adoption of on-farm raw milk concentration. The results showed that, in particular, farmers who deemed the current milk price to be insufficient viewed on-farm concentration using membrane technology as an option for diversifying their milk sales. We found no indication that adoption would be impeded by a lack of trustworthy information on milk processing technologies or capital.
Nowadays, problems related with solid waste management become a challenge for most countries due to the rising generation of waste, related environmental issues, and associated costs of produced wastes. Effective waste management systems at different geographic levels require accurate forecasting of future waste generation. In this work, we investigate how open-access data, such as provided from the Organisation for Economic Co-operation and Development (OECD), can be used for the analysis of waste data. The main idea of this study is finding the links between socioeconomic and demographic variables that determine the amounts of types of solid wastes produced by countries. This would make it possible to accurately predict at the country level the waste production and determine the requirements for the development of effective waste management strategies. In particular, we use several machine learning data regression (Support Vector, Gradient Boosting, and Random Forest) and clustering models (k-means) to respectively predict waste production for OECD countries along years and also to perform clustering among these countries according to similar characteristics. The main contributions of our work are: (1) waste analysis at the OECD country-level to compare and cluster countries according to similar waste features predicted; (2) the detection of most relevant features for prediction models; and (3) the comparison between several regression models with respect to accuracy in predictions. Coefficient of determination (R2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE), respectively, are used as indices of the efficiency of the developed models. Our experiments have shown that some data pre-processings on the OECD data are an essential stage required in the analysis; that Random Forest Regressor (RFR) produced the best prediction results over the dataset; and that these results are highly influenced by the quality of available socio-economic data. In particular, the RFR model exhibited the highest accuracy in predictions for most waste types. For example, for “municipal” waste, it produced, respectively, R2 = 1 and MAPE = 4.31 global error values for the test set; and for “household” waste, it, respectively, produced R2 = 1 and MAPE = 3.03. Our results indicate that the considered models (and specially RFR) all are effective in predicting the amount of produced wastes derived from input data for the considered countries.
Growing up in high-risk environments is detrimental to children’s development of attachment security. Parenting behavior is hypothesized to be the mechanism through which risks exert their influence. However, risk influences can vary between individuals by gender. Aim of this study was to explore specific pathways of family risk on early attachment security and additionally examine the transmission via parenting behavior. The sample consisted of 197 children and their primary caregivers. Children’s age ranged between 10 and 21 months (M = 15.25, SD = 3.59). Data assessment included 21 distal and proximal family risk factors, children’s attachment security, and parental responsivity and supportive presence. Whereas distal risk factors had an adverse effect only on girls’ attachment security, proximal risks negatively affected only boys’ attachment security. Additionally, patterns of risk factors occurring in our sample were analyzed using an exploratory principal component analysis. Regardless of the child’s gender, a low socio-economic status was negatively related to attachment security of all children. Migration and crowding and a high emotional load of the primary caregiver both negatively predicted girls’ but not boys’ attachment security. However, the attachment security of boys was affected by a negative family climate. Most of the adverse risk effects on attachment security were mediated by parental responsivity and supportive presence so that the transmission of risk occurs through parenting behavior. Results revealed a different susceptibility of family risks for girls and boys. The consideration of a gender-sensitive approach in developmental psychopathology and interventions of developmental child welfare services is recommended.
In a cross-sectional study, impact of management in dairy farms on calf mortality rates and prevalence of rotavirus and Cryptosporidium parvum in feces of calves was investigated. Sixty-two commercial dairy herds in Mecklenburg-Western Pomerania, Germany, were stratified selected in 2019. We performed in-person interviews and fecal specimens in samples of all-female calves of age 7 up to 21 days. Management data were documented on farm level. A Multiscreen Ag-ELISA was performed to determine rotavirus and Cryptosporidium parvum. Associations between two calf mortality rates, detection of C. parvum and rotavirus, and predictors were examined with GLM models. In farms with routine vaccination against respiratory diseases, 31-days mortality rate was 4.2% +/-1.26 compared to 7.6% +/-0.97 (p = 0.040) on non-vaccinating farms. Six-months mortality was lower in farms that continued feeding milk to calves during periods of diarrhea compared to farms that did not (6.9% +/-0.8 vs. 12.4% +/-2.3). In case of a routine shifting of calves from the calving box into calf boxes less C. parvum was detected compared to an individual moving of calves (33.3% +/-2.6 vs. 19.6% +/-5.3; p = 0.024). Our model confirms a positive association between occurrence of aqueous feces and frequency of detection of C. parvum (45.4% +/-23.6 vs. 21.4% +/-18.7; p < 0.001). Frequency of detection of rotavirus was lower in farms that reported a defined amount of applicated colostrum per calf than in farms that presented a range of colostrum instead of a defined amount. This study indicates the potential for mitigation of risk factors for mortality in calves.
In 2020, the world changed due to the Covid 19 pandemic. Containment measures to reduce the spread of the virus were planned and implemented by many countries and companies. Worldwide, companies sent their employees to work from home. This change has led to significant challenges in teams that were co-located before the pandemic. Agile software development teams were affected by this switch, as agile methods focus on communication and collaboration. Research results have already been published on the challenges of switching to remote work and the effects on agile software development teams. This article presents a systematic literature review. We identified 12 relevant papers for our studies and analyzed them on detail. The results provide an overview how agile software development teams reacted to the switch to remote work, e.g., which agile practices they adapted. We also gained insights on the changes of the performance of agile software development teams and social effects on agile software development teams during the pandemic.
Complex Event Processing (CEP) is a modern software technology for the dynamic analysis of continuous data streams. CEP is able of searching extremely large data streams in real time for the presence of event patterns. So far, specifying event patterns of CEP rules is still a manual task based on the expertise of domain experts. This paper presents a novel batinspired swarm algorithm for automatically mining CEP rule patterns that express the relevant causal and temporal relations hidden in data streams. The basic suitability and performance of the approach is proven by extensive evaluation with both synthetically generated data and real data from the traffic domain.
Electrospinning with consequent thermal treatment consists in a carbon fiber production method that spins a polymer solution to create fibers with diameters around a few hundred nanometers. The thermal treatments are used for the cyclization and then carbonization of the material at 1700 °C for one hour. The unique structure of micro- and nano-carbon fibers makes them a promising material for various applications ranging from future battery designs to filtration. This work investigated the possibility of using milled gasification biochar, derived from a 20 kW fixed-bed gasifier fueled with vine pruning pellets, as an addictive in the preparation of electrospinning solutions. This study outlined that solvent cleaning and the consequent wet-milling and 32 µm sifting are fundamental passages for biochar preparation. Four different polyacrylonitrile-biochar shares were tested ranging from pure polymer to 50–50% solutions. The resulting fibers were analyzed via scanning electron microscopy, and energy-dispersive X-ray and infrared spectroscopy. Results from the morphological analysis showed that biochar grains dispersed themselves well among the fiber mat in all the proposed shares. All the tested solutions, once carbonized, exceeded 97%wt. of carbon content. At higher carbonization temperatures, the inorganic compounds naturally showing in biochar such as potassium and calcium disappeared, resulting in an almost carbon-pure fiber matrix with biochar grains in between.
In the context of the ongoing digitization of interdisciplinary subjects, the need for digital literacy is increasing in all areas of everyday life. Furthermore, communication between science and society is facing new challenges, not least since the COVID-19 pandemic. In order to deal with these challenges and to provide target-oriented online teaching, new educational concepts for the transfer of knowledge to society are necessary. In the transfer project “Zukunftslabor Gesundheit” (ZLG), a didactic concept for the creation of E-Learning classes was developed. A key factor for the didactic concept is addressing heterogeneous target groups to reach the broadest possible spectrum of participants. The concept has already been used for the creation of the first ZLG E-Learning courses. This article outlines the central elements of the developed didactic concept and addresses the creation of the ZLG courses. The courses created so far appeal to different target groups and convey diverse types of knowledge at different levels of difficulty.
Dramatic increases in the number of cyber security attacks and breaches toward businesses and organizations have been experienced in recent years. The negative impacts of these breaches not only cause the stealing and compromising of sensitive information, malfunctioning of network devices, disruption of everyday operations, financial damage to the attacked business or organization itself, but also may navigate to peer businesses/organizations in the same industry. Therefore, prevention and early detection of these attacks play a significant role in the continuity of operations in IT-dependent organizations. At the same time detection of various types of attacks has become extremely difficult as attacks get more sophisticated, distributed and enabled by Artificial Intelligence (AI). Detection and handling of these attacks require sophisticated intrusion detection systems which run on powerful hardware and are administered by highly experienced security staff. Yet, these resources are costly to employ, especially for small and medium-sized enterprises (SMEs). To address these issues, we developed an architecture -within the GLACIER project- that can be realized as an in-house operated Security Information Event Management (SIEM) system for SMEs. It is affordable for SMEs as it is solely based on free and open-source components and thus does not require any licensing fees. Moreover, it is a Self-Contained System (SCS) and does not require too much management effort. It requires short configuration and learning phases after which it can be self-contained as long as the monitored infrastructure is stable (apart from a reaction to the generated alerts which may be outsourced to a service provider in SMEs, if necessary). Another main benefit of this system is to supply data to advanced detection algorithms, such as multidimensional analysis algorithms, in addition to traditional SIEMspecific tasks like data collection, normalization, enrichment, and storage. It supports the application of novel methods to detect security-related anomalies. The most distinct feature of this system that differentiates it from similar solutions in the market is its user feedback capability. Detected anomalies are displayed in a Graphical User Interface (GUI) to the security staff who are allowed to give feedback for anomalies. Subsequently, this feedback is utilized to fine-tune the anomaly detection algorithm. In addition, this GUI also provides access to network actors for quick incident responses. The system in general is suitable for both Information Technology (IT) and Operational Technology (OT) environments, while the detection algorithm must be specifically trained for each of these environments individually.
Corynebacterium spp. are frequently detected in bovine quarter milk samples, yet their impact on udder health has not been determined completely. In this longitudinal study, we collected quarter milk samples from a dairy herd of approximately 200 cows, ten times at 14 d intervals. Bacteriologically, Catalase-positive and Gram-positive rods were detected in 22.7% of the samples. For further species diagnosis, colonies were analyzed by MALDITOF MS. Corynebacterium bovis, C. amycolatum, C. xerosis and 10 other Corynebacterium spp. were detected. The three aforementioned species accounted for 88.4%, 8.65% and 0.94% of all cultured Corynebacterium spp., respectively. For further evaluation of infection dynamics, the following three infection definitions were applied: A (2/3 consecutive samples positive for the same species), B (≥1000 cfu/mL in one sample), C (isolated from a clinical mastitis case). Infections according to definition B occurred most frequently and clinical mastitis with Corynebacterium spp. occurred once during sampling. Life tables were used to determine the duration of infection. According to infection definition A, infection durations of 111 d and 98 d were obtained for C. bovis and C. amycolatum, respectively. Exemplarily, longer lasting infections were examined for their strain diversity by RAPD PCR. A low strain diversity was found in the individual quarters that indicates a longer colonization of the udder parenchyma by C. bovis and C. amycolatum.