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- To strengthen its expanding role in global health, the German government is currently preparing a new global health strategy, to be published in 2019.
- As social, political and economic determinants are highly relevant for population health, the German government will need to increase coherence in order to promote its emphasis on creating equal opportunities and reducing inequalities in and between countries.
- For further strengthening its commitment to universal health coverage, for promoting decent work and healthy labour conditions, and for enforcing the right to health, the German government will have to stress the mandatory role of the public sector for global health
The 1st Jordanian Conference on Logistics in the Mashreq Region was organized within the framework of the research project “JOINOLOG”, funded by the German Ministry of Education and Science.
The project’s conclusion and introduction to an audience of peers was the 1st Jordanian Conference on Logistics in the Mashreq Region (JCLM1), conducted on November 14th and 15th, 2023. These are the proceedings of this final event. They consolidate the shared efforts of all participants and speakers in JCLM1. The collection of these scientific results aims to promote logistical sciences and its transfer into application, which is reflected by their multidimensional presentation in this document.
Background: In 2021, a significant proportion of adult deaths in Germany, comprising of 447,473 individuals, occurred within hospital settings, representing nearly half of the total deaths in the country, which numbered 1,023,687 (statistika 2021). Consequently, nurses play a pivotal role as primary caregivers for this patient group, necessitating comprehensive education to address their specific needs. Existing literature suggests that nursing students often lack the adequate preparation to provide care for this group, with factors such as insufficient theoretical knowledge and suboptimal mentoring during clinical placements (Bloomfield et al. 2015; Gillan et al. 2014; Leighton 2009, Leighton/Dubas 2009). The use of simulation has proven effective in bridging the theory-practice gap, particularly in the context of End-of-Life Care. The objective of this study was to assess nursing students' perceptions of the use of simulation in learning about End-of-Life Care (Gillan et al. 2014; Moreland et al. 2012).
Methods: Over a three-year period, three cohorts of third-year nursing students at Fulda University of Applied Sciences engaged in simulated experiences involving a dying patient and one or more family members. The authors created three different scenarios in which the students had to perform oral care, break bad news to family members and administer palliative pain medication. During the simulations, the family member(s) confronted the students with questions concerning spiritual care and improving the quality of life at this stage. This project utilized a qualitative design. After the simulation and debriefing sessions, semi-structured interviews and group discussion were conducted. After transcribing, the interviews were analyzed using open and axial coding, after the Glaser and Strauss approach to Grounded Theory.
Results: The process of theoretical coding yielded five results: Simulation revealed to be a good tool to learn about End-of-Life Care (1), simulation focused on communication (2), the importance of spiritual care (3), the aspect of realism (4) and a lack of theoretical knowledge (5).
Conclusion: Simulation-based learning seems to be a valuable tool in the teaching of End-of-Life-Care especially with a focus on communication.
Abstract
Forest-based carbon credits are crucial in most Emissions Trading Schemes as they offer a cost-efficient means of offsetting hard-to-abate emissions. To date, this has not been the case in the European Union Emissions Trading Scheme (EU ETS). However with the Paris Agreement rulebook now finalized, there could be an opportunity to revive this flexibility mechanism in European climate policy. Based on 24 expert interviews, we examined the forest potential within the EU ETS across short, medium, and long-term time frames. We found that the compliance system will remain blocked until 2030, but there is a greater likelihood of transitioning towards the inclusion of forest-based removals and reductions in the long term. Although forestry projects have faced significant reluctance in the EU, there is unanimous agreement on the importance of both technological solutions and such initiatives for climate protection. To fully leverage the potential of forest activity in the future, it will be necessary to adopt different methods and tools (e.g., liability regimes), stricter legislation on socio-economic factors (e.g., land use rights), overcoming implementation hurdles (e.g., do not compromise deterrence through mitigation), and maintaining an open political stance. This study provides a comprehensive perspective on the barriers and potentials of forestry projects within the compliance system of the EU which is essential to be addressed when re-opening the discussion on future eligibility. The implication of the findings suggest an immediate start to adopt to the barriers for carbon credit readiness in the next phase of the EU ETS beginning of 2030.
Abstract:
There is still little experience in Germany in employing peers in social psychiatric institutions and services. Based on the European Leonardo da Vinci project „Experienced Involvement" from 2005-2007 pioneering work took long to broaden ist influence.
The presented work focused on the employment situation of ExIn recovery accompaniments in Germany and used a mixed methods design for this. On one hand a complete survey with a questonaire was used. This focused on the type and scope of Experienced Involvement as well as fields of application of ExIn recovery support and asked for reasons for non-employment and potential perspectives for future engagement. To find out about the subjective perspectives qualitative research methodes were used. This started with the implementation of focus groups to bring in the perspective of prospective ExIn recovery accompaniments. Further on guideline-based interviews were conducted with ExIn recovery accompaniments and their teammates on the experience of professional action, the conditions for this and the effects on the services and themselves.
A framework for current signal based bearing fault detection of permanent magnet synchronous motors
(2023)
Permanently excited synchronous motors are the driving components in countless systems and applications. The most common cause of motor failures are the bearings. Data-driven approaches have been used for predictive defect detections since many years, to prevent motors from an unexpected breakdown. In this way, downtime costs can be reduced and maintenance intervals based on actual wear can be realized.
Existing approaches are usually based on structure-borne sound sensors that have to be attached externally to the motors. The resulting costs reduce the economic attractiveness and scalability of the solution. Therefore, the focus of this dissertation is on fault detection based on internal motor current signals. Hurdles, arising from the choice of this signal sources, are to be tackled by the developed fault detection framework. By this, an adequate alternative to the use of external sensors is achieved. The core of the framework is the development of a fault detection pipeline, which is to be applicable under expected conditions of real-world applications.
The main pillars are data transformation methods derived from expert knowledge of different domains. These are concatenated and parameterized in an automated manner to reduce the human induced bias on the solution generation process.
Starting with a review of the state of research, existing research gaps are identified. From this, the research hypothesis and concrete research questions are derived and the general relevance of research is motivated. Subsequently, a conceptual description of the developed framework is given. In contrast to related work, the proposed approach focuses on the abstraction of the motors operating parameters from the pipeline hyperparameters uniquely at training time. This makes reparameterizations in the course of varied motor parameters obsolete, which increases the robustness with respect to real-world use cases.
The data used for the validation of the framework was acquired under real-world operating conditions to enable extensive stress tests of the developed pipelines. The results confirm the suitability of the framework in terms of general current based bearing fault detection as well as the intended use cases, regarding the working condition transfers.
We explore whether the integration of carbon offsets into investment portfolios improves perfor-mance. Our results show that investment strategies that include such offsets achieve higher Sharpe Ratios than the diversified benchmark portfolios. The efficient frontier of optimal portfolio choices is shifted upwards as a result of including compliance and voluntary carbon offsets in the portfolio. Our results also show that while diversified portfolios may benefit from carbon offsets integration, voluntary carbon offsets are significantly more sensitive to exogenous shocks than compliance carbon allowances. All these results are novel and may encourage investors to invest in such sustainable asset classes.
In our paper we investigate the role of civil society organisations (CSOs) in the provision of services and in forming advocacy coalitions for illegalized migrants in Bern and Vienna. We analyse the variety of CSOs which actively challenge policies of exclusion at the urban level. We examine the political and social practices of CSOs in local welfare arrangements and their organizational structures, the way they build up solidarity relations, networks and alliances, and their relations to municipality and urban authorities. By focusing on varieties of practices and strategies of CSOs, we shed light on civil society’s crucial role concerning the construction of urban infrastructure of solidarity and aim to show how local arrangements for illegalized migrants are co-produced and negotiated by a variety of actors within urban settings.
Currently, process control in automation technology is mostly regulated by fixed process parameters as a compromise between several identically constructed systems or by plant operators, who are often guided by intuition based on decades of experience. Some operators are not able to pass on their knowledge to the next generation due to societal developments, e.g. academization or increased desire for self-actualization. In contrast, the vision of Smart Factories includes intelligent machining processes that should ultimately lead to self-optimization and adaptation to uncontrollable variables. To consistently implement this vision of self-optimizing machines, a defined quality criterion must be automatically monitored and act as a feedback for continual, autonomous and safe optimization. The term safe refers to the compliance with process quality standards, which must always be maintained. In a very conservative branch such as automation technology, no risks whatsoever are allowed through random experiments for data generation in production operations, since, for example, an unscheduled downtime leads to serious financial losses. Furthermore, machine-driven decisions may at no time pose a threat. Thus, decisions under uncertainty may only be taken where the amount of uncertainty can be considered uncritical. Additionally, industrial applications require a guaranteed real-time capability in terms of reaction to ensure that the actions can be taken in time whenever needed. Since economic aspects are often crucial for decisions in industry, necessary experiments under laboratory conditions, for example, should also be as avoidable as possible, while the effort required for integration into a field application should be as simple as possible.
The aim of this work is the scientific investigation of the integration of learning feedback
for intelligent decision making in the control of industrial processes. The successful integration enables data-driven process optimization. To get closer to the vision of self-optimizing machines, safe optimization methods for industrial applications on the process level are investigated and developed. Here, considering the given restrictions of the automation industry is critical. This work addresses several fields including technical, algorithmic and conceptual aspects. The algorithmic refinements are essential for enabling a wider use of safe optimization for industrial applications. They allow, e.g., the automatic handling of the majority of hyper-parameters and the solution of complex problems by increased computational efficiency. Furthermore, the trade-off between exploration and exploitation of safe optimization in high-dimensional spaces is improved. To account for changeable states perceived via sensor data, contextual Bayesian optimization is modified so that safety requirements are met and real-time capability is satisfied. A software application for industrial safe optimization is implemented within a real-time capable control to be able to interact with other software modules to reach an intelligent decision. Further contributions cover recommendations regarding technical requirements with focus on edge control devices and the conceptual inclusion of machine learning to industrial process control.
To emphasize the application relevance and feasibility of the presented concepts, real world lighthouse projects are realized in the course of this work, indented to reduce skepticism and thus initiate the breakthrough of self-optimizing machines.
Climate change is a global challenge, with estimated mitigation costs ranging from $1.6 to $3.8 trillion per year. As a pioneer in climate action, the European Union has the most exten-sive emissions trading system worldwide (90% of the global value of $759 billion in 2021). In this paper, we review the European Union's climate strategy, emphasizing the EU Emissions Trading System (EU ETS) development, and the role of tropical forest carbon credits for off-setting. We argue that the European Union continues to leave a significant potential of trop-ical forests as natural carbon sinks unattended. In contrast, we reveal that the regulators can learn from the experiences made in the past and the finalization of the rulebook for Article 6 of the Paris Agreement. We present a proposal on changes to the EU ETS regulation by con-verting the European Commission's proposal to increase the linear reduction factor from 2.2% to 4.2% to the eligibility of forest carbon credits, resulting in additional funding poten-tial for forestry projects to increase necessary carbon sinks. Simultaneously, allowing flexibil-ity of investing to a limited extent in neutralization projects mitigates the risk of overstress-ing regulated companies to reach the emission reduction targets.