Helmut Schmidt University

Activist burnout is a common threat to activists’ personal sustainability and to a movement's effectiveness. Compared to related fields such as humanitarian aid or social work we know relatively little about mental health risks in activists or how a specific activist environment may contribute to mental health outcomes. This study examines the case of the No Borders movement in Europe, a grassroots movement fighting for migrant rights. The movement's groups are highly diverse in terms of nationality, ethnicity, culture, and religion because they are composed of refugees, migrants, and local populations. Following the vulnerability-stress-model, the article asks: which specific stressors occur in the No Borders movement? The analysis is exploratory and based on ethnographic research and qualitative interviews ( N = 26). Situational Analysis (SitA) shows that: a) activists have to navigate a complex environment in which radical grassroots activism meets humanitarian emergencies, and b) in dealing with diversity and intergroup conflicts they are under pressure to live up to their political ideals. These insights led to the identification of three stressors: prefigurative betrayal, inadequate expectations, and split of life-worlds. Understanding these stressors can contribute to informing preventive measures in No Borders and in other migrant or antiracist movements.

Forensic entomology can help estimate the postmortem interval in criminal investigations. In particular, forensically important fly species that can be found on a body and in its environment at various times after death provide valuable information. However, the current method for identifying fly species is labor intensive, expensive, and may become more serious in view of a shortage of specialists. In this paper, we propose the use of computer vision and deep learning to classify adult flies according to three different families, Calliphoridae, Sarcophagidae, Rhiniidae, and their corresponding genera Chrysomya, Lucilia, Sarcophaga, Rhiniinae, and Stomorhina, which can lead to efficient and accurate estimation of time of death, for example, with the use of camera-equipped drones. The development of such a deep learning model for adult flies may be particularly useful in crisis situations, such as natural disasters and wars, when people disappear. In these cases drones can be used for searching large areas. In this study, two models were evaluated using transfer learning with MobileNetV3-Large and VGG19. Both models achieved a very high accuracy of 99.39% and 99.79%. In terms of inference time, the MobileNetV3-Large model was faster with an average time per step of 1.036 seconds than the VGG19 model, which took 2.066 seconds per step. Overall, the results highlight the potential of deep learning models for the classification of fly species in forensic entomology and search and rescue operations.

The harmonic grid impedance is a crucial system property utilized in various applications. However, traditional simulation-based methods are often limited by the lack of access to essential information, such as equipment characteristics, load behaviors, and grid topologies. In response, measurement-based black-box techniques have been developed, with initial commercial solutions available for low-voltage applications. Unfortunately, for the medium-voltage level, only a limited number of prototypes exist. Unlike other techniques, which require large, immobile setups, the measurement system described in Section II is the first of its kind to be successfully applied in several research projects at various grid connection points. This paper presents, for the first time, real-world grid impedance measurements collected from multiple sites over extended time periods on the medium-voltage level. In addition to the measurement data, this paper provides comprehensive metadata for each measurement site, which is presented in Section III and the appendix, significantly enhancing the usability of these results for further research. The measurement results have been thoroughly analyzed, providing valuable insights into medium-voltage grid behavior. The evaluation of the measurement data in Section IV includes general observations comparing grid impedances at various connection points, as well as graphical and statistical evaluations of the time-dependent behavior of grid impedance. Special attention is given to resonance points, and the influence of renewable energy installations on grid impedance. Finally, the results are summarized and discussed in Section V.

To pave the way for a country’s development, actors in development cooperation rely on the establishment of a functioning nation-state—because only in this context can bilateral and legally binding agreements ultimately be concluded. If one tries to break out of this ethnocentric perspective, the question arises whether the idea of the developing nation-state is practically applicable everywhere. Can pacification and democratisation in the case of wars and conflicts be achieved from the outside? And if so, how can “failed states” occur? This article deals with the German approach to engagement in fragile states and highlights the importance of the SDGs in this endeavour of German state development cooperation.

This paper has examined the milestones of German state development cooperation since the 1960s and its role in the context of German foreign policy. Over time, development cooperation has evolved from humanitarian aid and infrastructure development in countries of the Global South to a more strategic orientation with long-term development policy goals. The reunification of Germany in the 1990s brought about comprehensive restructuring, including the integration of the new federal states and a stronger focus on regional cooperation. Particular attention is paid to the strategic goals, institutional changes, and priorities of German development cooperation. It now also includes the promotion of self-responsibility, good governance, and economic development. Present trends such as climate change, conflicts, and migration pose new challenges.

The German state development cooperation in the context of the Ukraine conflict is an increasingly relevant and multifaceted topic. The effects of the conflict on German development cooperation and the associated political and strategic decisions are examined. The challenges and opportunities that Germany faces in providing humanitarian aid, reconstruction and long-term development cooperation in Ukraine and the surrounding regions are analysed. Special attention is given to Germany’s diplomatic efforts to shape development cooperation with the aim of stabilising the region and promoting peace and security. The investigation is based on present political developments and the principles of German development cooperation in conflict and crisis areas. Finally, possible future scenarios are outlined, and recommendations for action for German development cooperation in the context of the Ukraine conflict are presented.

The question of the development policy impact of the Bundeswehr and a consideration of German and international security policy through the lens of development cooperation may still be unusual even after the experiences in Afghanistan. Traditionally, the Bundeswehr is a valued partner in cases of international disaster relief and humanitarian aid; the first international deployment of the Bundeswehr was the logistical support of emergency aid during the earthquake in Agadir in Algeria in 1960. Since then, much has changed, and this article takes a look at the recent history of the Bundeswehr in the field of ICM (international crisis management). In particular, the longest armed deployment in the history of the Bundeswehr in Afghanistan and the White Paper 2016 as a response to the growing security policy tasks are viewed from a development policy perspective.

At the centre of the discussion is the so-called Zeitenwende of 2022 as a response to Russia’s war of aggression against Ukraine. The effects of the “Zeitenwende” on the design of strategies and implications of German development policy are highlighted. Furthermore, the implementation of the development policy “design year” 2015 and the present development policy discussion are analysed. In particular, the resulting challenges and opportunities that arise at the actor level within the framework of German government development cooperation are discussed. Based on nine trend indicators, interfaces and theses for the perspectives of future German government development cooperation are formulated.

The focus is on the analysis of the first 31 years of German state development policy after the end of the East-West confrontation and the reunification of Germany. From a contemporary historical perspective, global political changes and the transformation processes of the ministerial self-understanding are evaluated. In addition, key interfaces to other policy areas, such as security policy after 11 September 2001, and the global role of binding development goals such as the MDGs up to the SDGs and the global changes caused by the COVID-19 pandemic are in focus.

Alarm flood classification (AFC) methods are crucial in assisting human operators to identify and mitigate the overwhelming occurrences of alarm floods in industrial process plants, a challenge exacerbated by the complexity and data-intensive nature of modern process control systems. These alarm floods can significantly impair situational awareness and hinder decision-making. Existing AFC methods face difficulties in dealing with the inherent ambiguity in alarm sequences and the task of identifying novel, previously unobserved alarm floods. As a result, they often fail to accurately classify alarm floods. Addressing these significant limitations, this paper introduces a novel three-tier AFC method that uses alarm time series as input. In the transformation stage, alarm floods are subjected to an ensemble of convolutional kernel-based transformations (MultiRocket) to extract their characteristic dynamic properties, which are then fed into the classification stage, where a linear ridge regression classifier ensemble is used to identify recurring alarm floods. In the final novelty detection stage, the local outlier probability (LoOP) is used to determine a confidence measure of whether the classified alarm flood truly belongs to a known or previously unobserved class. Our method has been thoroughly validated using a publicly available dataset based on the Tennessee-Eastman process. The results show that our method outperforms two naive baselines and four existing AFC methods from the literature in terms of overall classification performance as well as the ability to optimize the balance between accurately identifying alarm floods from known classes and detecting alarm flood classes that have not been observed before.

In cold spray, successful bonding occurs when particle impact velocities exceed the critical velocity. The description of the critical velocity includes temperature upon impact and material properties, relying on tabulated data of bulk material. However, rapid solidification of powder particles during gas atomization results in higher strengths than reached by respective bulk materials, causing an underestimation of the critical velocity. Thus, a readjustment of the semiempirical calibration constants can supply a more accurate prediction of the requested spray conditions for bonding. Using copper and aluminum as examples, experimentally determined particle strengths for various particle sizes were 43% and 81% higher than those of the corresponding soft bulk materials. Cold spraying was performed over a wide range of parameter sets, achieving deposition efficiencies (DE) ranging from 2% to 98%. DEs were plotted as a function of particle impact velocities and temperatures, as calculated by a fluid dynamic approach. By using DEs of 50%, the critical velocities of the different powders and the corresponding semiempirical constants were determined. The results reveal material-dependent differences in the mechanical pre-factor. This allows a more precise description of individual influences by particle strengths on critical velocities and enhances the understanding and prediction of coating properties.

The assessment of individual students is not only crucial in the school setting but also at the core of educational research. Although classical test theory focuses on maximizing insights from student responses, the Bayesian perspective incorporates the assessor's prior belief, thereby enriching assessment with knowledge gained from previous interactions with the student or with similar students. We propose and illustrate a formal Bayesian approach that not only allows to form a stronger belief about a student's competency but also offers a more accurate assessment than classical test theory. In addition, we propose a straightforward method for gauging prior beliefs using two specific items and point to the possibility to integrate additional information.

Background Mechanical thrombectomy (MT) is an established therapy for acute ischemic stroke (AIS), but recanalization is not always achieved. Common reasons are inadequate removal at the thrombus site and difficulties with the access route. In order to identify risk factors for MT failure we conducted a retrospective study on a high-volume comprehensive stroke center. Methods Evaluation of 552 thrombectomies (2019-23; anterior and posterior circulation, direct aspiration +/- stent retriever [ SR]). MT failures (= modified Thrombolysis in Cerebral Infarction score 0 or 1) were analyzed for age, sex, pre- and post-MT modified Rankin Scale, bridging intravenous thrombolysis (IVT), occlusion site (anterior / posterior circulation, proximal / distal), the Kaesmacher classification and time trend results. Results MT failure occurred in 56 patients (10.1%; median age 76; 53.6% female). Nineteen (33.9%) patients received IVT ( p = 0.326). Logistic regression analysis did not show a significant association of age, sex or occlusion site with MT failure ( p = 0.165, p = 0.738, p = 0.838). Distal MT generally demonstrated lower success rates ( p < 0.01). According to the Kaesmacher classification SR failure was the most frequent cause of MT failure (category 2B: 48%, p < 0.001). Time trend analysis suggests improving recanalization rates in the further course (4 times in year-on-year comparison; p < 0.01). Conclusion MT failure occurs in AIS treatment, even in high-volume centers and occurs more frequently in distal occlusions. Improvements in device technology, particularly SR, and ongoing refinements in access route selection offer the prospect of better outcomes in the future.

Close Quarters Battle (CQB) is an operational approach in confined spaces gaining increasing significance in urban combat missions. Due to its high psychophysiological demands, the CQB ability is an essential selection criterion for special forces. Until now, there has been no research on predictors of CQB capability. This study examined the influence of the Big Five personality traits, self-esteem, resilience, attentional ability, 2D:4D digit ratio, and mindfulness on the CQB performance. The German sample comprised a total of n = 45 individuals (n = 29police special forces; n = 16 unspecialized soldiers) who conducted psychometrics and a CQB test consisting of three scenarios. In these scenarios, two independent experts evaluated tactical behavior, weapon handling, gaze behavior, response time, and failures using a standardized behavioral observation instrument based on video recordings (external cameras and mobile eye-tracking). The results revealed that only extraversion predicted the CQB performance (β = -.40, p = .035). However, the mean 2D:4D ratio was strongly associated with gaze behavior (r = .45, p = .007), tactical behavior (r = .41, p = .019), and attentional ability (p = .57, p < .001). Surprisingly, the findings indicate that CQB, as a high-risk and analytical task, is better performed by introverted personnel.

Soil plugging in open-ended piles is likely to occur in dense sandy soils which has been the subject of numerous scientific investigations. In contrast, the plugging behaviour in clayey soils is relatively unknown. In this paper an analytical approach to determine the soil plug resistance and its development during jacking in clayey soils is proposed. The new approach was developed using numerical simulations and was validated by back-calculating a field test from the literature. A total stress approach for the numerical simulations was chosen. The values calculated with the analytical formulas and the numerical solutions as well as the measurements from field tests are in very good agreement, given the high complexity of the interactions between pile and soil. Both the plug resistance and the plug height can be calculated during the jacking process. Further research is required to refine the proposed approach for soil plug resistance evaluation, as the proposed method is believed to enhance the understanding of occurring mechanisms.

In this study, we develop a novel multi-fidelity deep learning approach that transforms low-fidelity solution maps into high-fidelity ones by incorporating parametric space information into an autoencoder architecture. This method’s integration of parametric space information significantly reduces the amount of training data needed to effectively predict high-fidelity solutions from low-fidelity ones. In this study, we examine a two-dimensional steady-state heat transfer analysis within a heterogeneous materials microstructure. The heat conductivity coefficients for two different materials are condensed from a 101 $\times$ 101 grid to smaller grids. We then solve the boundary value problem on the coarsest grid using a pre-trained physics-informed neural operator network known as Finite Operator Learning (FOL). The resulting low-fidelity solution is subsequently upscaled back to a 101 $\times$ 101 grid using a newly designed enhanced autoencoder. The novelty of the developed enhanced autoencoder lies in the concatenation of heat conductivity maps of different resolutions to the decoder segment in distinct steps. Hence the developed algorithm is named microstructure-embedded autoencoder (MEA). We compare the MEA outcomes with those from finite element methods, the standard U-Net, and an interpolation approach as an upscaling technique. Our analysis shows that MEA outperforms these methods in terms of computational efficiency and error on representative test cases. As a result, the MEA serves as a potential supplement to neural operator networks, effectively upscaling low-fidelity solutions to high-fidelity while preserving critical details often lost in traditional upscaling methods, such as sharp interfaces features lost in the context of interpolation approaches.