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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.
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.
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.
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:
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.
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 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.
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.
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 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
We present the first comprehensive and systematic review on the structurally diverse toco-chromanols and -chromenols found in photosynthetic organisms, including marine organisms, and as metabolic intermediates in animals. The focus of this work is on the structural diversity of chromanols and chromenols that result from various side chain modifications. We describe more than 230 structures that derive from a 6-hydroxy-chromanol- and 6-hydroxy-chromenol core, respectively, and comprise di-, sesqui-, mono- and hemiterpenes. We assort the compounds into a structure–activity relationship with special emphasis on anti-inflammatory and anti-carcinogenic activities of the congeners. This review covers the literature published from 1970 to 2017.
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
Good governance and redistribution in health financing : Pro-poor effects and general challenges
(2017)
Good governance has increasingly attained priority in international cooperation and health-system performance. Governance refers to all steering activities by public entities to influence the behaviour and activities of stakeholders involved. In the health sector, governance refers to a wide range of functions related to guidance and rule-making carried out by governments or other public decision-makers. More specifically, governance in the health-financing system applies to two different aspects: in addition to the approaches, strategies and policies determining how financial flows are implemented, managed and supervised according to rules- or outcome-based indicators, health-financing governance encompasses the question of how far resource generation, pooling and allocation are organised in an equitable, fair and sustainable manner. Individual and collective financial sustainability, burden sharing and social coherence or solidarity are essential parts of health-financing governance and depend deeply on societal priorities and values. Fairness of financing, transparent risk pooling and accountable purchasing of health services are intrinsic elements of governance in health financing and critical for achieving universal health coverage. The government is ultimately responsible for implementing an appropriate framework for a transparent, accountable and reliable health-financing system, for ensuring that the intermediate institutions can perform their functions, for executing effective and powerful supervision, and for providing civil society with the means to demand transparency and good financial governance.
Health-financing indicators show the system’s ability to effectively mobilise and allocate resources, implement social protection and pooling schemes, and distribute the financial burden of care equitably. Essentially two groups of indicators exist for assessing governance in the health financing system: rules-based approaches consider the existence of appropriate policies, strategies, and codified approaches for governance; outcome-based indicators measure whether rules and procedures are effectively implemented or enforced and health-financing targets achieved.
The aim of this project is to prepare a nutrition guidebook for early childhood active stakeholders that are applicable across Europe and Turkey. The developed nutrition guidebook is the result of two-year collaboration between academics from different professions (nutritionists, home economists, paediatricians, education scientists, health psychologists) across five countries.
The following article is targeted at both trainers and managers in educational organisations. It aims to provide support to those who would like to implement e-learning and new teaching methods in their organisations and who have a) already experienced resistance to such a change management process, or b)expect potential resistance. The article intends to help this target group by providing an understanding of the role of new media in the process of changing a “learning culture” in an educational organisation. Furthermore it introduces an approach to change management in educational settings which attempts to change resistance from a hindering to a productive element of change processes.
Based on a first video conference: small and middle sized companies in Russia and Germany: A comparative view organized by University "Interregional Institute of Economics and Law", Saint Petersburg/Russia and the University of Applied Sciences – Faculty of Business, Fulda/Germany held on 20th May 2010. Both institutes decided to organize a follow-up conference on January 27th 2011. Again the focus was to compare both markets for international operating companies in reasons for going abroad. The following papers are the outcome of this conference and were presented on the one hand by Fulda master students and on the other hand by Master Students from Saint Petersburg. The overall focus was again a comparative work from a company point of view. Hereby the main research question was to present different case studies based on a heterogeneous group of German-based companies in terms of size and branches. Success and failure in international management activities are discussed on an empirical and statistical basis. Furthermore the students from both institutes learnt also some practical matters like for example how can a foreign company establish its legal presence in Russia?
CHANCE is a project funded by the EU-programme GRUNDTVIG/ “Lifelong Learning Programme” conducted from December 2007 to November 2009. Partners from the participating countries presented their individual project results at the 2nd international meeting on June 12th 2009, in Fulda, Germany. CHANCE describes new pathways to enhance and support people in the long term to be well-informed and to take responsibility for their own health. The focus of the project was based on the following questions: - What resources are offered by the community to live healthy or healthier and what are the barriers that need to be resolved? - Are there cultural differences in health behaviours and in the perception of health information? - What health information is perceived in general and by whom? - What information and health interventions are required? CHANCE shows how people in different European cities and communities live, perceive information with regard to health and process it. The inhabitants of the communities were motivated to participate actively in the improvement of local interventions with regard to consumer education in health. The community approach aims to reach socially, culturally or economically disadvantaged groups such as elderly people, migrants and single parents.
This article guides you through the development of a successful moderated and collaborative e-learning course on the basis of an e-learning pattern template. The created patterns are a blueprint of the learning activity which could be implemented by using different web-based communication tools. The “e-learning pattern template” takes the special context of online-courses (compared to face-to-face teaching) into account, with a development focus on the participants’ motivation.
Can Buddhism be called a stronghold of free thinking? What relevance might Buddhism have for social developments in the twenty-first century, and where will it position itself in these processes? Free thinking has been emphasized and celebrated as an outstanding accomplishment of the human mind. This anthology might inspire the reader to look at some questions of global concern from a new angle and provide a stimulus for developing a freethinking attitude. It is the outcome of international and even transcontinental cooperation involving expert authors from Asia, Australia, Europe and the U.S.A. Contributions have been made by Bhikkhu Anâlayo, Karl-Heinz Brodbeck, Ashby Butnor, Silja Graupe, Guang Xing, Barbara Kameniar, Sallie B. King, and Charles S. Prebish.