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Nitrogen (N) pollution of groundwater bodies is often a result of high livestock densities combined with use of mineral N fertilisers in Northwest Germany, specifically in combination with sandy soils and high amounts of precipitation. Organic agriculture is discussed as an alternative management practice reducing nitrogen losses due to area-based livestock densities and waiving of mineral N fertilisers. A field trial with integrated ceramic suction cups over three years showed potential for reduced N loads under conventional management specifically with organic fertilisation. Now, the field trial is under transition into organic farming with promising additional benefits for drinking water quality and the great potential to develop optimised N management strategies.
Studies on nutrition have historically concentrated on food-shortages and over-nutrition. The physiological states of feeling hungry or being satiated and its dynamics in food choices, dietary patterns, and nutritional behavior, have not been the focus of many studies. Currently, visual analytic using easy-to-use tooling offers applicability in a wide-range of disciplines. In this interdisciplinary pilot-study we tested a novel visual analytic software to assess dietary patterns and food choices for greater understanding of nutritional behavior when hungry and when satiated. We developed software toolchain and tested the hypotheses that there is no difference between visual search patterns of dishes 1) when hungry and when satiated and 2) in being vegetarian and non-vegetarian. Results indicate that food choices can be deviant from dietary patterns but correlate slightly with dish-gazing. Further, scene perception probably could vary between being hungry and satiated. Understanding t he complicated relationship between scene perception and nutritional behavioral patterns and scaling up this pilot-study to a full-study using our introduced software approaches is indispensable.
The central aim of the investigation at hand is to deal with the problem areas of Human Resource Management, which arise by demographic changes and migration. The paper focuses on mutual relationships. Managers and human resource managers are considered as multipliers. Older employees, migrants and women are important potential. Therefore, following research questions have been investigated: Which competences are necessary to promote to recognise the potential of migrants correctly and to promote them? Do the multipliers have to be more sensitized for the issue diversity? Do they have to develop specific competences to make the system more permeable and to make the entry and promotion of migrants possible? Which competences should be promoted to increase the sensitivity for diversity? The questions were examined by a qualitative investigation to develop hypotheses for a quantitative study. Overall, 30 interviews with managers, human resource managers and diversity representatives of the large DAX companies were conducted. Furthermore, 17 employees with immigration background and 15 employees without an immigration background were interviewed. The data was transcribed and analysed by the qualitative content analysis according to Mayring (2010). Comparative analyses were made with single items with Likert Scales. The investigation of managers and employees is a highly diversified issue. Therefore, the main focus of the project lays on the problem areas, conflicts and competences of human resources managers in demographic-sensitive personnel management. In comparison, employees with and without an immigration background were asked. The results show an interesting field of tension between self-perception and perception of others and the assessment of the relevance of diversity attitudes and measures, competences and their implementation. Furthermore, a contrary perception regarding strains and stresses of person with and without immigration background is determined, which is developed in the consequences of migrations stress and experiences of discrimination. The results indicate the need of promotion of competences, especially regarding intercultural competence. A critical analysis of the results will be presented.
To assess the effect of intercropping on malting quality a field trial with spring barley (Hordeum vulgare) and legume (pea) as well as non-legume (camelina and linseed) intercrops in two additive seeding ratios as well as sole cops was established in 2017 at the organic experimental station of University of Applied Sciences Osnabrück in North-Western Germany. Two tested malting barley cultivars (cv. Marthe and cv. Odilia) showed different performance, but all variants achieved brewing quality. Results after two years indicate that linseed and camelina were able to limit protein content. For best land-use efficiency of malting barley production intercropping with linseed showed best results. Mixed intercropping can help to promote internal efficiency loops and is therefore a promising sustainable intensification strategy for more resilient future crop production under changing climate conditions.
Process modeling languages help to define and execute processes and workflows. The Business Process Model and Notation (BPMN) 2.0 is used for business processes in commercial areas such as banks, shops, production and supply industry. Due to its flexible notation, BPMN is increasingly being used in non-traditional business process domains like Internet of Things (IoT) and agriculture. However, BPMN does not fit well to scenarios taking place in environments featuring limited, delayed, intermittent or broken connectivity. Communication just exists for BPMN - characteristics of message transfers, their priorities and connectivity parameters are not part of the model. No backup mechanism for communication issues exists, resulting in error-prone and failing processes. This paper introduces resilient BPMN (rBPMN), a valid BPMN extension for process modeling in unreliable communication environments. The meta model addition of opportunistic message flows with Quality of Service (QoS) parameters and connectivity characteristics allows to verify and enhance process robustness at design time. Modeling of explicit or implicit, decision-based alternatives ensures optimal process operation even when connectivity issues occur. In case of no connectivity, locally moved functionality guarantees stable process operation. Evaluation using an agricultural slurry application showed significant robustness enhancements and prevented process failures due to communication issues.
Aims: This study examines the relationship between the time, students spent abroad, personality traits and circumstances during this time with the student’s intercultural competence and integration performance in the target culture. Design and sample: The study had a correlative cross-sectional design. 202 academic subjects were surveyed. The average age was 22 years. There was one measuring time, to which 58 % of the participants stated that they have had a stay abroad. Measurements: Metacognitive, cognitive, motivational and behavioural intercultural competence were measured with the Cultural Intelligence Scale. The personality traits involvement, discipline, social competence, cooperation, dominance and stability were captured with the "Bochum inventory for job-related personality description-6F". Work-related attitudes as patterns of behaviour and experience were measured using the "Work-related Behaviour and Experiencing Pattern 44" (Geman: Arbeitsbezogene Verhaltens- und Erlebensmuster; AVEM). In addition, the demographic factors and characteristics of stays abroad as well as the integration into the target culture based on the Sociocultural Adaption Scale were examined. The data was tested for relationships and differences by tests for mean differences, variance and regression analyses. Results: There was a positive correlation between duration and cognitive, motivational and behavioural intercultural competence. The motivational competence is higher in subjects who have no risk pattern in the AVEM. The different types of competence influence each other at diverse times. Moreover, the suggested structural equation model could be confirmed. This showed the effect of the AVEM pattern on intercultural competence, moderated by the stay abroad and the social competence.
This paper presents an optimized algorithm for estimating static and dynamic gait parameters. We use a marker- and contact-less motion capture system that identifies 20 joints of a person walking along a corridor.
Based on the proposed gait cycle detection basic metrics as walking frequency, step/stride length, and support phases are estimated automatically. Applying a rigid body model, we are capable to calculate static and dynamic gait stability metrics. We conclude with initial results of a clinical study evaluating orthopaedic technical support.
In this paper, we evaluate the application of Bayesian Optimization (BO) to discrete event simulation (DES) models. In a first step, we create a simple model, for which we know the optimal set of parameter values in advance. We implement the model in SimPy, a framework for DES written in Python. We then interpret the simulation model as a black box function subject to optimization. We show that it is possible to find the optimal set of parameter values using the open source library GPyOpt. To enhance our evaluation, we create a second and more complex model. To better handle the complexity of the model, and to add a visual component, we build the second model in Simio, a commercial off-the-shelf simulation modeling tool. To apply BO to a model in Simio, we use the Simio API to write an extension for optimization plug-ins. This extension encapsulates the logic of the BO algorithm, which we deployed as a web service in the cloud.
The fact that simulation models are black box functions with regard to their behavior and the influence of their input parameters makes them an apparent candidate for Bayesian Optimization (BO). Simulation models are multivariable and stochastic, and their behavior is to a large extent unpredictable. In particular, we do not know for sure which input parameters to adjust to maximize (or minimize) the model’s outcome. In addition, the complex models can take a substantial amount of time to run.
Bayesian Optimization is a sequential and self-learning algorithm to optimize black box functions similar to as we find them in simulation models: they contain a set of parameters for which we want to identify the optimal set, they are expensive to evaluate, and they exhibit stochastic noise. BO has proven to efficiently optimize black box functions from varius disciplines. Among those, and most notably, it is successfully applied in machine learning algorithms to optimize hyperparameters.