Wertorientierungen bei Kindern zu Beginn der Schulzeit. Eine empirische Untersuchung zu Strukturen, Entwicklungen und familiären Determinanten.

Die Untersuchung der Entstehung von Wertorientierungen bei Kindern stellt in der soziologischen Forschung ein Nischenthema dar. In der Sozial- und Persönlichkeitspsychologie existiert zu diesem Gebiet dagegen ein dynamisches Forschungsfeld. Allerdings zeigt die psychologische Werteforschung zwei Mankos. Die Wertestrukturen von Kindern werden hier querschnittlich untersucht, was keine Erkenntnisse über ihre Entwicklung im Zeitverlauf zulässt. Dazu vernachlässigt die psychologische Forschung die soziale Bedingtheit der Werteentstehung bei Kindern. Die vorliegende Arbeit schließt diese Forschungslücken und versucht somit eine soziologische Wiedereinbettung der Werteforschung in der Kindheit. Hierfür werden in einer Stichprobe von 249 Kindern in der ersten und zweiten Klasse Wertepräferenzen über Selbstauskunftsverfahren erhoben. In einem ersten Schritt werden über explorative Analysen die vorliegenden Wertestrukturen identifiziert. Über Strukturgleichungsmodelle wird in einem nächsten Schritt die Entwicklung der Wertestruktur von der ersten zur zweite Klasse längsschnittlich untersucht. Im letzten Teil der Arbeit wird der Einfluss familiärer Struktur- und Prozessmerkmale auf die Wertorientierungen und Wertestruktur der Kinder überprüft. Die Ergebnisse der Analysen zeigen bereits in der ersten Klasse eine plausible Wertestruktur. Sie beruht auf einer Grunddifferenzierung in individualistische und kollektivistische Werte. Während sich in der ersten Klasse nur für den kollektivistischen Wertebereich Substrukturen nachweisen lassen, zeigen sich in der zweiten Klasse auch im Bereich der individualistischen Werte Ausdifferenzierungen. Unter den familiären Bedingungen lassen sich besonders für den sozioökonomischen Status der Eltern, ihre religiösen Wertorientierungen wie auch ihren Erziehungsstil Einflüsse auf die Wertorientierungen und Wertestruktur der Kinder nachweisen. Eine Erklärung der Struktur von Wertorientierungen bei Kindern, ihrer Entwicklung und der familiären Einflüsse bezieht theoretische Ansätze aus der Bedürfnisforschung, der Entwicklungspsychologie und der Sozialisationsforschung mit ein

Advanced Methods for Kiln-Shell Monitoring to Optimize the Waelz Process for Zinc Recycling

The recycling of zinc in the Waelz process is an important part of the efficient use of resources in the steel processing cycle. The pyro-metallurgical processing of zinc-containing wastes takes place in a Waelz rotary kiln. Various measured variables are available to monitor the process. The temperature of the kiln-shell is analyzed by an infrared kiln-shell-scanner. In this paper, methods are presented which eliminate external weather-related disturbances on the temperature measured by the kiln-shell-scanner using a weather model and which extend the monitoring of the regularly necessary melting process to remove accretions. For this purpose, an adapted sigmoid estimation is introduced for the melting process, which provides new information about the current process status and a forecast of the further development of the melting process. As an assistance system for the plant operator, this enables an efficient execution of the melting process and reduces downtimes

Evaluation of Deep Learning-Based Segmentation Methods for Industrial Burner Flames

The energetic usage of fuels from renewable sources or waste material is associated with controlled combustion processes with industrial burner equipment. For the observation of such processes, camera systems are increasingly being used. With additional completion by an appropriate image processing system, camera observation of controlled combustion can be used for closed-loop process control giving leverage for optimization and more efficient usage of fuels. A key element of a camera-based control system is the robust segmentation of each burners flame. However, flame instance segmentation in an industrial environment imposes specific problems for image processing, such as overlapping flames, blurry object borders, occlusion, and irregular image content. In this research, we investigate the capability of a deep learning approach for the instance segmentation of industrial burner flames based on example image data from a special waste incineration plant. We evaluate the segmentation quality and robustness in challenging situations with several convolutional neural networks and demonstrate that a deep learning-based approach is capable of producing satisfying results for instance segmentation in an industrial environment

Education from the crib on: The potential of the Newborn Cohort of the German National Educational Panel Study

The Newborn Cohort of the German National Educational Panel Study provides longitudinal data for about 3,500 newborns and their families. Starting in 2012, nine annual waves have been published until 2022. The data sets include information on domain-specific and general competencies from standardized tasks/tests with children, observational data on semi-standardized parent-child-interaction, as well as data from parent’s and educational institution’s interviews/questionnaires. The data is accessible via the Research Data Center of the LIfBi and comprehensively documented (English, German) to be used, e.g., in research on child development, educational trajectories, as well as on facets of longitudinally assessed learning environments

Two- and Three-Dimensional Benchmarks for Particle Detection from an Industrial Rotary Kiln Combustion Chamber Based on Light-Field-Camera Recording

This paper describes a benchmark dataset for the detection of fuel particles in 2D and 3D image data in a rotary kiln combustion chamber. The specific challenges of detecting the small particles under demanding environmental conditions allows for the performance of existing and new particle detection techniques to be evaluated. The data set includes a classification of burning and non-burning particles, which can be in the air but also on the rotary kiln wall. The light-field camera used for data generation offers the potential to develop and objectively evaluate new advanced particle detection methods due to the additional 3D information. Besides explanations of the data set and the contained ground truth, an evaluation procedure of the particle detection based on the ground truth and results for an own particle detection procedure for the data set are presented

Investigations of Ratio-Based Integrated Influence Lines as Features for Bridge-Damage Detection

Prestressed concrete bridges built between 1960 and 1990 no longer meet today’s requirements due to loads and increasing mileage of higher loads that have increased since the bridges were designed. Prestressed concrete bridges are representative of Germany’s existing bridges. In order to deal with the large number of ageing bridges, recalculations and measurements for control as well as bridge monitoring are an important means of support. For both, it is important to find features that are damage-sensitive as well as robust against measurement noise, vehicle parameters (dynamics, geometry, weight, etc.) and environmental influences (temperature, wind, etc.). In this paper, we present features for damage detection based on the influence line, which are investigated with respect to the above requirements by using the analytical solution of the Euler–Bernoulli beam and more complex numerical bridge simulations. In this context, we restrict ourselves to the damage caused by bending stress. The features are calculated on the basis of single vehicle crossings over the bridge for the strain in the longitudinal direction as well as for the deflection of the bridge at different sensor positions. The ratio-based features are compared with raw data and natural frequencies in a classification. Additionally, the sensor positioning is considered. The investigations shows that the ratio-based integrated influence lines are equivalent to or better than the modal parameters, especially when noise and temperature changes are taken into account

Comparison of the Depth Accuracy of a Plenoptic Camera and a Stereo Camera System in Spatially Tracking Single Refuse-derived Fuel Particles in a Drop Shaft

With the development of depth cameras in the last decades, several cameras are able to acquire 3D information of the captured scenes, such as plenoptic camera and stereo camera system. Because of the differences in principle and construction of various depth cameras, different cameras own particular advantages and disadvantages. Therefore, a comprehensive and detailed comparison of different cameras is essential to select the right camera for the application. Our research compared the depth accuracy and stability of a stereo camera system and a plenoptic camera by monitoring the settling processes of various refuse-derived fuel particles in a drop shaft. The particles are detected at first using detection approaches, and the particle detections are subsequently associated in accordance with data association algorithms. The spatial particle trajectories are obtained by the tracking-by-detection approach, based on which the performances of the cameras are evaluated

3D Refuse-derived Fuel Particle Tracking-by-Detection Using a Plenoptic Camera System

Multiple particle tracking-by-detection is a widely investigated issue in image processing. The paper presents approaches to detecting and tracking various refuse-derived fuel particles in a industrial environment using a plenoptic camera system, which is able to yield 2D gray value information and 3D point clouds with noticeable fluctuations. The presented approaches, including an innovative combined detection method and a post-processing framework for multiple particle tracking, aim at making the most of the acquired 2D and 3D information to deal with the fluctuations of the measuring system. The proposed novel detection method fuses the captured 2D gray value information and 3D point clouds, which is superior to applying single information. Subsequently, the particles are tracked by the linear Kalman filter and 2.5D global nearest neighbor (GNN) and joint probabilistic data association (JPDA) approach, respectively. As a result of several inaccurate detection results caused by the measuring system, the initial tracking results contain faulty and incomplete tracklets that entail a post-processing process. The developed post-processing approach based merely on particle motion similarity benefits a precise tracking performance by eliminating faulty tracklets, deleting outliers, connecting tracklets, and fusing trajectories. The proposed approaches are quantitatively assessed with manuelly labeled ground truth datasets to prove their availability and adequacy as well. The presented combined detection method provides the highest F 1 -score, and the proposed post-processing framework enhances the tracking performance significantly with regard to several recommended evaluation indices