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FID Civil Engineering, Architecture and Urbanism digital - A platform for science (BAUdigital)
(2022)
University Library Braunschweig (UB Braunschweig), University and State Library Darmstadt (ULB Darmstadt), TIB – Leibniz Information Centre for Technology and Natural Sciences and the Fraunhofer Information Centre for Planning and Building (Fraunhofer IRB) are jointly establishing a specialised information service (FID, "Fachinformationsdienst") for the disciplines of civil engineering, architecture and urbanism. The FID BAUdigital, which is funded by the German Research Foundation (DFG, "Deutsche Forschungsgemeinschaft"), will provide researchers working on digital design, planning and production methods in construction engineering with a joint information, networking and data exchange platform and support them with innovative services for documentation, archiving and publication in their data-based research.
High-performance firms typically have two features in common: (i) they produce in more than one country and (ii) they produce more than one product. In this paper, we analyze the internationalization strategies of multi-product firms. Guided by several new stylized facts, we develop a theoretical model to determine optimal modes of market access at the firm–product level. We find that the most productive firmssell core varieties via foreign direct investment and export products with intermediate productivity. Shocks to trade costs and technology affect the endogenous decision to export or produce abroad at the product-level and, in turn, the relative productivity between parents and affiliates.
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.
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
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.
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.
We present a methodology based on mixed-integer nonlinear model predictive control for a real-time building energy management system in application to a single-family house with a combined heat and power (CHP) unit. The developed strategy successfully deals with the switching behavior of the system components as well as minimum admissible operating time constraints by use of a special switch-cost-aware rounding procedure. The quality of the presented solution is evaluated in comparison to the globally optimal dynamic programming method and conventional rule-based control strategy. Based on a real-world scenario, we show that our approach is more than real-time capable while maintaining high correspondence with the globally optimal solution. We achieve an average optimality gap of 2.5% compared to 20% for a conventional control approach, and are faster and more scalable than a dynamic programming approach.
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.
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.
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.