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Free Trade versus Democracy and Social Standards in the European Union: Trade-Offs or Trilemma?
(2019)
This article aims at conceptualising, in analytical as well as normative-theoretical terms, the tensions between free trade, democratic and social standards, and national sovereignty that are named in Dani Rodrik´s “globalisation trilemma” for the case of the European Union (EU). It is argued that the trilemma concept is much more fitting to the EU than a simple trade-off concept. This model offers a conceptual path to both analysing existing tensions and thinking of resolving them: a) the EU has, indeed, been intervening into national democracies and national sovereignty as its legislation is superior to national legislation; b) EU legislation and judgements of the Court of Justice of the EU have been reducing national social standards; c) executives and numerous new institutions and agencies with indirect legitimation have taken over competencies that formerly lay in the domain of national directly legitimated legislatives; and d) these negative effects relate to the EU’s giving preference to the liberalisation of free trade of capital, goods and services over democracy, social standards, and national sovereignty. Against the framework of the globalisation trilemma, analysis is combined with normative-theoretical judgements on the quality democracy of the setting that has been found and a conceptual discussion. The article concludes by discussing the perspectives of the setting examined and the possible paths to solutions, arguing that in order to keep a high level of economic integration, democracy, and social standards in the EU, national sovereignty needs to give way.
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.
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.
The aim of this paper is to examine the causes of food waste and potential prevention strategies from a grocery retail store owner’s perspective. We therefore conducted a case study in a German region through semi-structured expert interviews with grocery retail store owners. From the collected responses, we applied a qualitative content analysis. The results indicated that store owners try to avoid food waste as this incurs a financial loss for them that directly affects them personally, as opposed to store managers of supermarket chains who receive a fixed salary. The main causes of food waste in the grocery retail stores in the region surveyed are expiration dates, spoilage, consumer purchasing behavior, and over-ordering of food products. The most appropriate food waste prevention strategies developed by store owners are those based on store owners’ experience and their own management style, such as the optimization of sales and management strategies, including precise planning, accurate ordering, and timely price reductions on soon-to-be-expiring food products. The redistribution of food surpluses as donations to food banks, employees, and as animal feed further helps to reduce the amount of food waste, but not the financial loss. This study enhances the literature by revealing that grocery retail store owners have the ability and are willing to successfully implement and enforce food prevention strategies in their stores.
To date, studies on individual and organizational health literacy (OHL) in facilities for people with disabilities are scarce. Thus, the aims of this study are (1) to adapt an existing instrument for measuring organizational health literacy (OHL), namely, the “Health literate health care organization scale” (HLHO-10), to the context of facilities for people with disabilities, (2) to quantitatively examine characteristics of OHL, and (3) to qualitatively assess the definition and role of OHL by interviewing managers and skilled staff. An online study in Germany with N = 130 managers and skilled staff in facilities for people with disabilities was conducted, using the adapted HLHO-10 questionnaire. Univariate analyses were applied. Qualitative content analysis was used to investigate interview data from N = 8 managers and skilled staff from N = 8 facilities for people with disabilities in Hesse, Germany. Quantitative results revealed that respondents reported a below-average level in HLHO-10, with the lowest level found in the attribute of participative development of health information. The qualitative findings showed a clear need for improved navigation to and in facilities. The quantitative and qualitative findings are mainly consistent. Future research and measures should focus on facilities for people with disabilities in order to strengthen the development of and access to target-group-specific health information, as well as to establish a health-literate working and living environment.
Abstract knowledge is deeply grounded in many computer-based applications. An important research area of Artificial Intelligence (AI) deals with the automatic derivation of knowledge from data. Machine learning offers the according algorithms. One area of research focuses on the development of biologically inspired learning algorithms. The respective machine learning methods are based on neurological concepts so that they can systematically derive knowledge from data and store it. One type of machine learning algorithms that can be categorized as "deep learning" model is referred to as Deep Neural Networks (DNNs). DNNs consist of multiple artificial neurons arranged in layers that are trained by using the backpropagation algorithm. These deep learning methods exhibit amazing capabilities for inferring and storing complex knowledge from high-dimensional data.
However, DNNs are affected by a problem that prevents new knowledge from being added to an existing base. The ability to continuously accumulate knowledge is an important factor that contributed to evolution and is therefore a prerequisite for the development of strong AIs. The so-called "catastrophic forgetting" (CF) effect causes DNNs to immediately loose already derived knowledge after a few training iterations on a new data distribution. Only an energetically expensive retraining with the joint data distribution of past and new data enables the abstraction of the entire new set of knowledge. In order to counteract the effect, various techniques have been and are still being developed with the goal to mitigate or even solve the CF problem. These published CF avoidance studies usually imply the effectiveness of their approaches for various continual learning tasks.
This dissertation is set in the context of continual machine learning with deep learning methods. The first part deals with the development of an application-oriented real-world evaluation protocol which can be used to investigate different machine learning models with regard to the suppression of the CF effect. In the second part, a comprehensive study indicates that under the application-oriented requirements none of the investigated models can exhibit satisfactory continual learning results. In the third part, a novel deep learning model is presented which is referred to as Deep Convolutional Gaussian Mixture Models (DCGMMs). DCGMMs build upon the unsupervised approach of Gaussian Mixture Models (GMMs). GMMs cannot be considered as deep learning method and they have to be initialized in a data-driven manner before training. These aspects limit the use of GMMs in continual learning scenarios.
The training procedure proposed in this work enables the training of GMMs by using Stochastic Gradient Descent (SGD) (as applied to DNNs). The integrated annealing scheme solves the problem of a data-driven initialization, which has been a prerequisite for GMM training. It is experimentally proven that the novel training method enables equivalent results compared to conventional methods without iterating their disadvantages. Another innovation is the arrangement of GMMs in form of layers, which is similar to DNNs. The transformation of GMMs into layers enables the combination with existing layer types and thus the construction of deep architectures, which can derive more complex knowledge with less resources.
In the final part of this work, the DCGMM model is examined with regard to its continual learning capabilities. In this context, a replay approach referred to as Gaussian Mixture Replay (GMR) is introduced. GMR describes the generation and replay of data samples by utilizing the DCGMM functionalities. Comparisons with existing CF avoidance models show that similar continual learning results can be achieved by using GMR under application-oriented conditions. All in all, the presented work implies that the identified application-oriented requirements are still an open issue with respect to "applied" continual learning research approaches. In addition, the novel deep learning model provides an interesting starting point for many other research areas.
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.
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.
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.
Abstract
Over the course of the European Sovereign Debt Crisis members of the euro area have put up significant resources to stabilize the financial situation of a few fellow member states. In Germany, this support is subject to a controversial discussion. One aspect in that is the extent of support provided. Using the financial assistance provided to Greece as an example, this paper sheds some light on the financial burden for Germany in comparison to other member states of the euro area, especially Estonia, Latvia and Lithuania. This implies not only an interesting comparison of strains between large and small economies but also between original and later euro area members.
Keywords: euro area, debt crisis, exposure, Greece, Baltic states, Germany