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Hintergrund
Negatives Geburtserleben steht im Verdacht mit nachfolgender Sterilität, einem längeren Abstand zum nächsten Kind, sowie dem Wunsch nach einer primären Sectio bei einer weiteren Schwangerschaft assoziiert zu sein. Die derzeitige Datenlage ist kontrovers.
Methodisches Vorgehen
Das Design entspricht einer explorativen, qualitativen Querschnittsstudie. Dazu wurden zehn leitfadengestützte Interviews nach dem Prinzip des Problemzentrierten Interviews durchgeführt. Eingeschlossen wurden Primiparae, die vor dem 11.03.2020 (vor den Einschränkungen der Covid-19-Pandemie) geboren haben. Die Interviews wurden nach der inhaltlich strukturierenden qualitativen Inhaltsanalyse nach Kuckartz analysiert.
Ergebnisse
Insbesondere Frauen mit negativem Geburtserleben tendieren zu einer abgeschwächten Bereitschaft für eine weitere Schwangerschaft, dazu keine weitere Schwangerschaft zu planen sowie einen längeren Abstand zu einer weiteren Schwangerschaft zu wahren. Nur in Einzelfällen kann bei Frauen mit positiver Geburtserfahrung eine verstärkte Bereitschaft für eine weitere Schwangerschaft festgestellt werden. Die Entscheidung bezüglich der Planung einer Schwangerschaft ist multifaktoriell und sowohl vom Geburtserleben als auch von persönlichen, medizinischen, familiären und finanziellen Aspekten abhängig.
Diskussion
Besonders das Auftreten medizinischer Besonderheiten, das negative Erleben des Geburtsmodus, der peripartalen Schmerzen sowie der Betreuung und Versorgung scheinen Einflussfaktoren zu sein, welche die Bereitschaft für die Planung einer weiteren Schwangerschaft abschwächen. Es besteht weiterer Forschungsbedarf durch quantitative Arbeiten und in der Begriffsdefinition von Geburtserleben.
Fazit
Das individuelle Geburtserleben von Primiparae kann einen Einfluss auf die Planung einer weiteren Schwangerschaft haben.
Background
Spinach is a nitrogen (N) demanding crop with a weekly N uptake of up to 60 kg ha–1. Consequently, a high N supply is required, which can temporarily lead to high quantities of nitrate (NO3–) being at risk of leaching.
Aims
The objective of this study was to develop a N fertilization approach to reduce the risk of NO3– leaching in field-grown spinach production without adversely affecting crop yield and quality at an early and late harvest stage.
Methods
Ten fertilization trials were conducted to compare different base fertilization rates and splits of top dressings. For top dressings, granulated fertilizers or foliar sprays were used. In a further treatment, N supply was reduced by withholding the second top dressing of 50–70 kg ha−1.
Results
Nitrate concentration at risk of leaching was considerably reduced by decreasing the base fertilizer rate as well as by splitting the top dressing. However, at an early harvest stage, total aboveground dry mass was reduced by, on average, 6% by these measures across all seasons. In contrast, at a later harvest stage, spinach was less affected by the fertilizer schedule. Urea foliar sprays proved to be insufficient in promoting plant growth and caused leaf necrosis. A reduced N supply led to impaired plant growth and yellowish leaves in both spring and winter.
Conclusions
Base N fertilization of spinach is only required in spring, but not in other seasons. Despite slight yield reduction, the top dressing should be split to reduce the risk of NO3− leaching after an early harvest.
In open-field vegetable production, high quantities of soil mineral nitrogen (Nmin) and N-rich crop residues often remain in the field at harvest. After the harvest of crops in autumn, this N can lead to considerable nitrate (NO3−) losses during the subsequent winter leaching period. In four field trials, different tillage depths (3–4, 10, 30 cm) and dates (early autumn, late autumn, early spring) were investigated to reduce N losses after growing spinach in the autumn. In a further treatment, the nitrification inhibitor 3,4-Dimethylpyrazole phosphate (DMPP) was directly applied to the crop residues. Potential N losses were calculated by a balance sheet approach based on Nmin concentration (0–90 cm), measured N mineralization and N uptake by catch crops. By postponing the tillage date from early to late autumn or spring, resprouting spinach stubbles acted as a catch crop, reducing N losses by up to 61 kg ha−1. However, if the spinach biomass collapsed, the N losses increased by up to 33 kg ha−1 even without tillage. The application of DMPP as well as the tillage depth were less effective. Overall, postponing tillage to spring seems to be the most promising approach for reducing N losses during the off-season.
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.
Design knowledge on smart glasses-based systems is scarce. Utilizing literature analysis on software development publications, insights from the design and implementation of four smart glasses-based systems and expert interviews, we elicited 16 design principles to provide guidance in the development of future service support systems. Heuristic Theorizing is an abductive Design Science Research method, hitherto far too little known or little noticed, which was applied to conduct the research. We contribute to theory and practice with applicable design principles to support the development of smart glasses-based systems. Phenomena known to have an impact on the adoption of smart glasses are addressed by these design principles.
Warum sind Erziehungshilfen ein verbreiteter Teil der Schulorganisation? Was unterscheidet, verbindet aber auch die schulische und außerschulische Erziehungshilfe? Und wie könnte eine bedarfsgerechte, die individuelle Förderung der jungen Menschen unterstützende, Familien stärkende und die sozialräumliche Vernetzung voranbringende Kooperation zwischen Schulen und Erziehungshilfe aussehen? Diesen Fragen widmet sich dieses Buch ausgehend von lebensweltlichen Perspektiven junger Menschen in einer sehr grundlegenden Weise. Dabei werden nicht nur die Anlässe, Erscheinungsformen und Bedingungen der Arbeitsbündnisse von Schule und Erziehungshilfe erörtert, sondern auch ein neu gewichtetes und innovative Gestaltungsmöglichkeiten eröffnendes Verständnis von Schule und Erziehungshilfe.
The energy transition involves various challenges. One key aspect is the decentralization of power generation, which requires new actors. In order to integrate these into the system in the best possible way, there are various approaches e.g. in cooperation in citizens' initiatives or cooperatives (Dorniok, 2016).
Cooperation in general can enable the implementation of certain business models or can increase profitability by the exploitation of economies of scale (Skovsgaard & Jacobsen, 2017; Theurl, 2010). Synergy effects result from the utilization of know-how, different technologies or resources of the partners involved to complement the own competencies and services (Eggers & Engelbrecht, 2005; Sander, 2009). Cooperation exists in various industries and enable the participating companies to compensate their size-related resource deficits (Glaister & Buckley, 1996; Todeva & Knoke, 2005). This creates the opportunity to develop innovations, open up new markets, exploit newly created economies of scale and share costs and risks (Franco & Haase, 2015). In agriculture, cooperation in the form of cooperatives have been of essential importance for a long time, especially with the aim of exploiting synergy effects (Bareille et al., 2017). In the field of renewable energy development, cooperation in form of citizen cooperatives make a significant contribution to the participation of citizens in political, social and financial aspects of the energy transition (Huybrechts & Mertens, 2014). Energy cooperatives are frequently discussed as a potential actor in the energy transition and are increasingly being established to advance the common interests of stakeholders. For example, the joint operation of decentralized power generation plants can involve new actors in the energy transition through regional cooperation (Walk, 2014).
Existing biogas plants in Germany need new business models after the 20-year Renewable Energy Sources Act feed-in tariff expires. For continued operation, a business model innovation is needed, which can be realized based on the different technical utilization pathways. Cooperation can have a significant impact on the profitability of the different business models, especially by exploiting synergy effects (Karlsson et al., 2019). In addition, cooperation can help to ensure that existing plants continue to operate at all.
Currently, the most widespread use of biogas in Germany is in the coupled generation of electricity and heat. Additionally, there is the possibility of upgrading biogas to biomethane or biogenic hydrogen path (Mertins & Wawer, 2022).
Different options for cooperative business models that exist in the biogas utilization pathways are presented. The focus is on explaining the advantages of a joint approach compared to single-farm business models and identifying the relevant actors. Subsequently, drivers and barriers for the different cooperative business models are identified and classified based on 20 semi-structured interviews with plant operators in the administrative district of Osnabrück. The aim is to identify drivers and barriers for cooperative post-EEG operation. As a result, political instruments are to be found that make it possible to involve relevant actors and thus stimulate the best possible continued operation from the point of view of the energy system. The results are structured according to the PESTEL analysis. This assigns drivers and barriers to the categories political, economic, sociocultural, technological, ecological and legal (Kaufmann, 2021). The analysis of the interviews is supplemented and validated by a literature review.
Drivers and barriers for cooperative business models are manifold and can vary mainly depending on the plant and the operator.
Drivers
• Political
o Promotion of renewable energies: reduce dependence on fossil (Russian) fuels
• Economic
o Expectation of synergies (information sharing, shared risk, economies of scale)
o Planning security (fixed supply or purchase contracts)
o Access to new markets (not accessible by single-farm business models)
o Cost savings by sharing infrastructure, technology
o Positive return expectation
• Sociocultural
o Motivating, innovative environment
o Lowers barriers to participation in new markets
o Target-oriented partnerships
o Better use of capacities and strengths
o Strengthening regional value creation
• Technological
o Economies of scale (efficiency)
o Available, mature technology
o Storable, transportable gas
o Well-developed infrastructure
• Ecological
o Increase in plant efficiency
o Reduction of greenhouse gas emissions
o Promotion of the circular economy by utilization of organic waste and agricultural residues
o Improving soil quality (fermentation residues as fertilizer)
Barriers
• Political
o Competition to other renewable energies
• Economic
o Uncertainty about future development of energy markets
o Disagreements between the cooperation partners
o Lack of flexibility due to longer-term contractual obligations
o Allocation of profits
• Sociocultural
o Cooperation with current competitor
o Cultural differences and lack of trust
o Acceptance by the general public (e.g. overproduction of maize)
• Technological
o Different technology that is difficult to combine
o Data protection
• Ecological
o Competition for agricultural land
o Use of monocultures
o Emissions from plant
o Pollution from transport
• Legal
o Legal requirements and regulations
o Unfavorable regulatory environment, e.g. long permitting process
One finding is that uncertainty is a major barrier for plant operators. This includes uncertainty about regulatory frameworks and political requirements, as well as about the general development of the energy markets. In addition, social factors such as lack of reliability and disagreement about revenue sharing are a potential barrier. A key driver for the implementation of cooperative business models is the expectation of synergy effects. In addition, operators are driven by a positive expectation of returns and the responsibility for securing the energy supply in times of crisis.
The drivers identified can now be used to develop strategies to advance cooperative business models. In particular, synergy effects should be exploited so that operators can benefit from cooperation. The advantages can also be highlighted and communicated to increase acceptance among the general public. Another important step is to reduce the barriers discussed above. In order to reduce social barriers in particular, it may be advisable to include an external partner in the cooperation, such as a municipal utility that operates an upgrading plant and concludes purchase agreements with the individual partners. In addition, it would be politically expedient to provide the operators with a clear framework for the future in order to reduce uncertainties. As a further aspect, knowledge transfer on new technologies and markets should take place.
Artificial intelligence (AI) and human-machine interaction (HMI) are two keywords that usually do not fit embedded applications. Within the steps needed before applying AI to solve a specific task, HMI is usually missing during the AI architecture design and the training of an AI model. The human-in-the-loop concept is prevalent in all other steps of developing AI, from data analysis via data selection and cleaning to performance evaluation. During AI architecture design, HMI can immediately highlight unproductive layers of the architecture so that lightweight network architecture for embedded applications can be created easily. We show that by using this HMI, users can instantly distinguish which AI architecture should be trained and evaluated first since a high accuracy on the task could be expected. This approach reduces the resources needed for AI development by avoiding training and evaluating AI architectures with unproductive layers and leads to lightweight AI architectures. These resulting lightweight AI architectures will enable HMI while running the AI on an edge device. By enabling HMI during an AI uses inference, we will introduce the AI-in-the-loop concept that combines AI's and humans' strengths. In our AI-in-the-loop approach, the AI remains the working horse and primarily solves the task. If the AI is unsure whether its inference solves the task correctly, it asks the user to use an appropriate HMI. Consequently, AI will become available in many applications soon since HMI will make AI more reliable and explainable.