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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.
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.
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.
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).
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.
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.
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.
Das Gesundheitsdatennutzungsgesetz – Potenzial für eine bessere Forschung und Gesundheitsversorgung
(2023)
Im Koalitionsvertrag der Ampel-Koalition wird für die laufende Legislaturperiode ein Gesundheitsdatennutzungsgesetz (GDNG) angekündigt. Dieses Gesetz soll „zu einer besseren wissenschaftlichen Nutzung in Einklang mit der DSGVO“ führen. Bekanntermaßen steht unser Gesundheitssystem vor großen Herausforderungen (Demografie, Digitalisierung, Fachkräftemangel, Klimakrise, regionale Unterschiede, etc.) und ist jetzt schon das teuerste in Europa bei mittelmäßiger Leistung. Diese Herausforderungen können effizienter und evidenzgeleitet bewältigt werden, wenn wie im geplanten GDNG angedacht, die Datenressourcen für die Evaluierung und Weiterentwicklung des Gesundheitssystems und der Gesundheitsversorgung optimal genutzt werden. In den folgenden Ausführungen werden aus Sicht von Versorgungsforscher*innen Voraussetzungen und Desiderata für eine optimale Ausgestaltung des Gesetzes formuliert. Das Papier wurde durch das Deutsche Netzwerk Versorgungsforschung (DNVF) und die Arbeitsgruppe Erhebung und Nutzung von Sekundärdaten (AGENS) der Deutschen Gesellschaft für Sozialmedizin und Prävention (DGSMP) und der Deutschen Gesellschaft für Epidemiologie (DGEpi) erstellt und wird von den unterzeichnenden Fachgesellschaften getragen. Das vorliegende Positionspapier und die hier aufgestellten Forderungen sind vor der Veröffentlichung und damit in Unkenntnis des Referentenentwurfs zum GDNG formuliert worden.
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.
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’.