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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.
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.
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.