Zum Einfluß geburtshilflich-perinatologischer Maßnahmen auf die Mortalität und Frühmorbidität von Frühgeborenen der Gewichtsklasse 500 bis 1500 Gramm = Effect of obstetric-perinatal measures on mortality and early morbidity of premature infants weighing 500 to 1,500 grams

In a retrospective analysis of perinatal influencing factors in 186 premature newborns of the Department of Gynaecology of the University of Erlangen covering the period from 1982-1987 with birth weights between 500 and 1500 grams, the mortality and early morbidity were analysed, as characterised by cerebral haemorrhages, respiratory distress syndrome and infections insofar, as they had been connected with the obstetrical approach and paediatric intensive-care treatment, 45 infants born in 1982/83 were compared with 141 infants, who had been subjected to a different treatment approach during 1984 to 1987. During the second period, there was a marked drop both in mortality and in the incidence of asphyxia-induced severe cerebral haemorrhage and of the respiratory distress syndrome. A shortened latency period after premature rupture of the amnion, and a more pronounced presence of a neonatologically experienced team of paediatricians were found to be significant obstetric liberal influencing factors in determining the need to perform Caesarean section. The triplication of the frequency of Caesarean section observed resulted in a 50% reduction in perinatal mortality and morbidity. Infants with pelvic presentation benefited most from the more liberal performance of Caesarean section, as did infants with vertex presentation. Shortening of the latency phase in premature rupture resulted in a marked reduction in infection morbidity and mortality. Therefore we conclude, that the frequently practised procrastination with the aim to await an improvement in lung maturity should be replaced by a more active obstetric management, avoiding both infection and birth trauma. Obstetric decisions should be based rather on prenatal estimation of weight than on the calculated gestational age. At present, the lowest birth weight associated with the expectation of a healthy life is considered to be 750 grams

Designing and building a cost-efficient survey drone

In this paper, we present a workflow how to design and implement a low-cost survey drone that meets the quality requirements of a much higher cost drone system. The technical specifications of available components and our design boundaries were applied in eCalc RC - xcopterCalc calculator in which the optimal setup was found by simulation. The main boundaries of design were derived from safety, operation time and payload capacity. Pixhawk 2 FCU, which is based on ArduPilot open source platform, was selected to handle autopilot and control functionalities. In addition, the system included a camera and a gimbal. The camera was controlled by FCU, which allows to geotag images using the on-board GPS data. The assembled survey drone was tested in a real survey mission. We successfully managed to complete a 13 minutes survey mission in mild wind conditions. According to simulation, the expected flight time range was between 9 and 15 minutes. In addition, simulation provided useful information on how the drone worksunder certain conditions such as working in extreme temperatures or high elevation locations as well as under heavy payloads. Even though our example was a survey drone, it is possible to use the same principles to design and implement a drone suitable for other tasks.Peer reviewe

Terrestrial laser scanning data combined with 3D hydrological modeling decipher the role of tillage in field water balance and runoff generation

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Approaches for Mapping Night-Time Road Environment Lighting Conditions

The integration of the 3D measurement techniques with luminance imaging has increased the potential for mapping night-time road lighting conditions. In this study, we present selected static and mobile approaches for the purpose. The measurement methods include conventional 2D imaging luminance photometry and the integration of the luminance imaging with terrestrial and mobile laser scanning. In addition, we present our initial experiences with performing integrated luminance mapping and photogrammetric reconstruction from drone imagery. All of the presented methods require that the camera is calibrated with a reference luminance source. Our results show the results of luminance calibration and feasibility of 3D luminance point clouds for evaluating road surface luminances. In addition, we discuss the other potential applications, limitations and future research.Peer reviewe

Unexpected measurement results of 94 GHz lens antenna in short far-field conditions

International audienc

EXPERIENCES from the PROJECT COURSE in GEOINFORMATICS

The aim of this paper is to share our experiences and thoughts about a project course in geoinformatics. The course has been organised annually since 2017. We hope that this article provides ideas about when new project-based courses are designed or existing ones are renewed. We wanted to increase students' motivation by providing assignments from companies or other organisations as well as cooperation with them. Working with real clients makes the project work much more interesting than projects without a real-life connection. We provide topics from various fields of geoinformatics, such as geoinformation technology, geodesy, photogrammetry, laser scanning and remote sensing. The students worked in small groups that were supported by an advisor and a facilitator. The advisor helps with substance and the facilitator assists with reflection and improving working process, i.e. not only to complete the task but also to learn about capabilities for project work, self-directive teamwork and learning to learn (metalearning). To sum up, during the course students increase their knowledge and expertise on geoinformatics, learn skills for client-centered project work and learn how to support their learning through self- and peer-reflection. In other words, the course aims to develop skills that are useful throughout the students' forthcoming careers.Peer reviewe

Luminance-corrected 3D point clouds for road and street environments

A novel approach to evaluating night-time road and street environment lighting conditions through 3D point clouds is presented. The combination of luminance imaging and 3D point cloud acquired with a terrestrial laser scanner was used for analyzing 3D luminance on the road surface. A calculation of the luminance (cd/m2) was based on the RGB output values of a Nikon D800E digital still camera. The camera was calibrated with a reference luminance source. The relative orientation between the luminance images and intensity image of the 3D point cloud was solved in order to integrate the data sets into the same coordinate system. As a result, the 3D model of road environment luminance is illustrated and the ability to exploit the method for evaluating the luminance distribution on the road surface is presented. Furthermore, the limitations and future prospects of the methodology are addressed. The method provides promising results for studying road lighting conditions in future lighting optimizations. The paper presents the methodology and its experimental application on a road section which consists of five luminaires installed on one side of a two-lane road in Otaniemi, Espoo, Finland.Peer reviewe

Utilising Simulated Tree Data to Train Supervised Classifiers

The aim of our research was to examine whether simulated forest data can be utilized for training supervised classifiers. We included two classifiers namely the random forest classifier and the novel convolutional neural network classifier that utilizes feature images. We simulated tree parameters and created a feature vector for each tree. The original feature vector was utilised with random forest classifier. However, these feature vectors were also converted into feature images suitable for input into a YOLO (You Only Look Once) convolutional neural network classifier. The selected features were red colour, green colour, near-infrared colour, tree height divided by canopy diameter, and NDVI. The random forest classifier and convolutional neural network classifier performed similarly both with simulated data and field-measured reference data. As a result, both methods were able to identify correctly 97.5 % of the field-measured reference trees. Simulated data allows much larger training data than what could be feasible from field measurements

XXIV ISPRS Congress “Imaging today, foreseeing tomorrow”, Commission II

The aim of our research was to examine whether simulated forest data can be utilized for training supervised classifiers. We included two classifiers namely the random forest classifier and the novel convolutional neural network classifier that utilizes feature images. We simulated tree parameters and created a feature vector for each tree. The original feature vector was utilised with random forest classifier. However, these feature vectors were also converted into feature images suitable for input into a YOLO (You Only Look Once) convolutional neural network classifier. The selected features were red colour, green colour, near-infrared colour, tree height divided by canopy diameter, and NDVI. The random forest classifier and convolutional neural network classifier performed similarly both with simulated data and field-measured reference data. As a result, both methods were able to identify correctly 97.5 % of the field-measured reference trees. Simulated data allows much larger training data than what could be feasible from field measurements